Air Taxi Readiness Index 2023Assessing the preparedness of 60 territories in the race for vertical t.
2022-12-30
21页
5星级
TRANSPORTATION AND LOGISTICSMARKET UPDATEQ3 2022Houlihan Lokey is a leading global investment bank w.
2022-12-29
24页
5星级
Published by Directorate of Statistics and EconomicsMinistry of Railways(Railway Board),Government of India,New DelhiINDIAN RAILWAYSANNUAL REPORT&ACCOUNTS 2020-21Bharat SarkarGovernment of IndiaRail MantralayaMinistry of Railways(Railway Board)(With highlights of the performance for 2021-22)BHARAT SARKARGOVERNMENT OF INDIARAIL MANTRALAYAMINISTRY OF RAILWAYS(RAILWAY BOARD)ANNUAL REPORT&ACCOUNTS2020-21(With highlights of the performance for 2021-22)INDIAN RAILWAYSContentsOrganization Structure2Welfare,Development and Empowerment of Women86Apex Management3ReviewProspects5Facilities to Divyangjan87Finance8Security91Freight Operation14Vigilance95Passenger Business19Promoting Hindi97Planning35North Eastern Region99Engineering37Public Relations104Railway Electrification42Railway Engineers Regiments(Territorial Army)106Signal and Telecom47Undertakings and other Organizations109Safety52Advisory Boards141Rolling Stock58Important Events142Material Management66Glossary147Research and Development68Summary of Audit Observations provided by C&AG and ATNs149Managing the Environment76Financial Statements and Operating Statistics157Personnel792INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21ZONAL RAILWAYS(OPEN LINE)GENERAL MANAGERS1.CENTRAL2.EASTERN3.EAST CENTRAL4.EAST COAST5.NORTHERN6.NORTH CENTRAL7.NORTH EASTERN8.NORTHEAST FRONTIER9.NORTH WESTERN10.SOUTHERN11.SOUTH CENTRAL12.SOUTH EASTERN13.SOUTH EAST CENTRAL14.SOUTH WESTERN15.WESTERN16.WEST CENTRAL17.METROGENERAL MANAGERSCHITTARANJAN LOCOMOTIVE WORKS,CHITTARANJAN BANARAS LOCOMOTIVE WORKS,VARANASIINTEGRAL COACH FACTORY,PERAMBURRAIL COACH FACTORY,KAPURTHALARAIL WHEEL FACTORY,YELAHANKAMODERN COACH FACTORY,RAE BARELICAO(R)*DIESEL LOCO MODERNIZATION WORKS,PATIALARAIL WHEEL PLANT,BELAGENERAL MANAGERSCENTRAL ORGANIZATION FOR RAILWAY ELECTRIFICATION,ALLAHABADNF RAILWAY(CONSTRUCTION),GUWAHATI CAO(R)*CENTRAL ORGANIZATION FOR MODERNISATION OF WORKSHOPS(COFMOW)DIRECTOR GENERALNATIONAL ACADEMY OF INDIAN RAILWAYS,VADODARADG&EX-OFFICIO GM RDSO,LUCKNOWBCLBSCLBWELCONCORDFCCILIRCONIRCTCIRFCKMRCLKRCLMRVCRCILRITESRVNLCRISRLDAPRODUCTION UNITSOTHER UNITSCPSE/CORP&AUTONOMOUS BODIES/AUTHORITIES*CHIEF ADMINISTRATIVE OFFICER(RAILWAYS)MINISTER OF RAILWAYSORGANIZATION STRUCTURE OF INDIAN RAILWAYSMEMBER(TRACTION&ROLLING STOCK)-M/T&RSMEMBER INFRASTRUCTURE-M/INFRAMEMBER OPERATIONS&BUSINESS DEVELOPMENT-M/O&BDMEMBER FINANCE-MF DIRECTOR GENERAL/RHS-DG/RHSDIRECTOR GENERAL/HR-DG/HRDIRECTOR GENERAL/RPF-DG/RPFDIRECTOR GENERAL/SAFETY-DG/SAFETYSECRETARY,RAILWAY BOARDMINISTER OF STATE FOR RAILWAYS(D)MINISTER OF STATE FOR RAILWAYS(J)CHAIRMAN&CEO,RAILWAY BOARD3INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21Apex ManagementMinister of Railways Ashwini VaishnawMinister of State for Railways(J)Darshana JardoshMinister of State for Railways(D)Raosaheb Patil DanveMembers,Railway BoardChairman&Chief Executive officer(CEO)V.K.TripathiMember(Finance)Naresh SalechaMember(Operation and Business Development)Sanjay Kumar MohantyMember(Traction&Rolling Stock)Sanjeev Mittal*Member(Infrastructure)Sanjeev MittalSecretary R.N.Singh*Directors-GeneralDG/RPF Sanjay ChanderDG/RHS VacantDG/HRVacantDG(Safety)Ravinder GuptaGeneral Managers,Zonal RailwaysCentral Anil K.LahotiEastern Arun AroraEast Central A.SharmaEast Coast Archana Joshi(L/A)*MetroManoj JoshiNorthern Ashutosh GangalNorth Central Pramod KumarNorth Eastern A.Sharma(L/A)*Northeast Frontier Anshul GuptaNorth Western Vijay SharmaSouthern John ThomasSouth Central Sanjeev Kishore(L/A)*South Eastern Archana JoshiSouth East Central Alok KumarSouth Western Sanjeev KishoreWestern Alok KansalWest Central Sudhir Kumar GuptaGeneral Managers,Production UnitsChittaranjan Locomotive WorksSatish K.KashyapDiesel Locomotive WorksAnjali GoyalIntegral Coach Factory A.K.AgarwalRail Wheel Factory Ajai Kumar DubeyRail Coach Factory,Kapurthala Ashesh AgarwalModern Coach Factory,Rae Bareli V.M.SrivastavaGeneral Managers,Construction UnitsNortheast Frontier Railway(Construction)Sunil SharmaCentral Organization for Railway Electrification Y.P.SinghDirector-GeneralNational Academy of Indian Railways S.P.S.ChauhanDirector-General and Ex-Officio General ManagerResearch,Designs and Standards Organization Sanjiv BhutaniChief Administrative Officers(Railways)Central Organization for Modernization of Workshops Vivek KumarDiesel Loco Modernization Works S.N.DubeyRail Wheel Plant,Bela Shubhranshu*Looking after.*Metro Railway,Kolkata.(As on 3rd January,2022)4INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-215INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21New Building of Railway Hospital Perambur,SRMagnificent Sout Eastern Railway Headquarters Building at Garden Reach KolkataReview-Prospects Results:2020-21Financial PerformanceThe year 2020-21 ended with an excess of earning over expenditure to the tune of 2,547.48 crore which was appropriated to Development Fund(1,547.45 crore)and Rashtriya Rail Sanraksha Kosh(RRSK)(1,000 crore)Freight Operation2019-202020-21Absolute Variation%age VariationRevenue Originating Tonnes(million)1208.411230.9422.531.86Revenue Net Tonne Kms.(billion)707.67719.7612.091.71Goods Earnings (in crore)1,11,472.30 1,15,738.384,266.083.83Excludes other goods earnings such as wharfage,demurrage etc.Passenger Business2019-202020-21Absolute Variation%age VariationNumber of Passengers carried(million)8,0861,250-6,836-84.54Passenger Kilometers(billion)1,051231-820-78.02Passenger Earnings(in crore)50,669.0915,248.49-33,420.60-65.96Engineering WorksDuring the year 2020-21 the following Engineering Works were accomplished:2020-21(in km.)Construction of New Lines286.31Conversion to Broad Gauge469.93Track Renewal4,363Electrification6,015 kms.of IRs route was electrified during 2020-21.Safety,Signal and TelecomDetails of consequential train accidents and train accidents per million train kilometres(an important index of Safety)during 2020-21 as compared to 2019-20 are given below:2019-202020-21Consequential Train Accidents*5421Train Accidents Per Million Train Kilometres0.050.03*excluding Konkan RailwayBirds Eye view of MCF,Rae Bareli6INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21CLW produces loco with aerodynamically design Tejas capable of 160 kmph,CLWA view of DMW WorkshopRedevelopment of Habibganj Station WCRFront view of Yognagri Rishikesh Station,NRFollowing steps were taken for improving passenger amenities:Provision2020-21(No.of Stations)Public Address System4,752Train Display Board1,147Coach Guidance System652Operating EfficiencySome important efficiency indices for 2020-21 compared to 2019-20 were as follows:Efficiency IndexBroad GaugeMetre Gauge2019-20 2020-212019-20 2020-21Net tonne kms.per wagon per day 7,0576,861-Speed(kmph)of all goods trains(all traction)23.643.2-Percentage of loaded to total wagon km 62.562.4-Net load per goods train(tonnes)1763*1738-Net tonne km per engine hour14,287*13,881-Passenger vehicle km per vehicle per day534185113*114*RevisedMaterials ManagementMaterials Management on IR is being progressively revamped with a view to reduce costs,storage,handling,insurance and dividend charges.Turn Over Ratio in terms of value of inventories to value of materials consumed was 13%(without fuel)and 12%(with fuel)during 2020-21 as compared to 15%(without fuel)and 10%(with fuel)during last year.The disposal of condemned Rolling Stock and scrap arising was monitored closely.Managing the EnvironmentEfforts are steadily being made to make Railway operations environment friendly and to bring down the adverse effects through adaptation of cleaner technologies,energy conservation measures,afforestation on vacant railway land,etc.Railway is utilizing wasteland and rooftops for setting up solar power plants.Human Resource DevelopmentA number of initiatives were taken to improve the quality of training programmes for railway employees in order to improve productivity.In this direction,National Rail&Transportation Institute(NRTI)has been set up as Indias first University focused on transport related education,multi disciplinary research&training in Vadodara,Gujarat.Industrial Relations and PersonnelAs on 31st March,2021,IR had 12,52,347 regular employees as against 12,54,386*as on 31st March,2020 a decrease by 2,039.Industrial Realation remained cordial during 2020-21.Productivity Linked Bonus equivalent to 78 days wages was paid to all non-gazetted employees(excluding RPF/RPSF personnel)for 2020-21.RPF/RPSF personnel belonging to Group C category were sanctioned an ad hoc bonus equivalent to 30 days wages.*revised7INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21Rail Soudha SWR Zonal Office HubballiRapid Antigen Test BLWSharamik Special Train,ECRIsolation Coaches,ECRStaff WelfareIRs welfare schemes cover a wide spectrum of activities,viz.,educational facilities and financial assistance to the children of Railway employees,handicraft centres for augmenting family income,financial assistance in sickness,subsidized housing and canteen facilities at work places and medical cover for employees and their families during service and after retirement.Performance:2021-22(1st January,2021-31st December,2021)Passenger Business*During January-December,2021,the number of originating passengers in IR was 3,143.59 million vis-vis 2,276.31 million during the corresponding period of last year,registering an increase of 867.28 million(38.10%).The passenger earning during this period was 35,628.57 crore showing an increase by 17,750.11 crore(99.28%)compared to the earnings during the corresponding period of the last year.Freight Operation*Loading of revenue-earning traffic for the year 2021(January-December,2021)compared to the corresponding period of last year,was as under:(Million tonnes)CommodityJan.to Dec.,2020Jan.to Dec.,2021Coal537.35634.55Raw material for steel plantsExcept Iron Ore 23.3628.88Pig iron and finished steel 55.8668.25Iron ore151.92171.67Cement111.20138.52Food grains60.3265.45Fertilizers55.5248.23P.O.L(Mineral Oil)43.2444.94Balance other goods151.43192.30Total revenue earning traffic1,190.201,392.79*Based on Statement of Gross Earning on originating basis.*Based on Statement 7-AEstimation for the period January-March 2022Freight OperationAnticipated freight volume likely to be carried by the Railways for the three months period from January to March 2022 is 371 million tonnes excluding KRCL and DFCCIL.Passenger Business The total no.of anticipated originating passengers for the three ensuing months from January to March,2022 will be 1,315.27 million.8INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21FinanceResults for 2020-21Gross Traffic Receipts of the Railways went down from 1,74,356.60 crore in 2019-20 to 1,40,570.52 crore in 2020-21.Total Working Expenses decreased from 1,71,319.21 crore in 2019-20 to 1,36,567.51 crore in 2020-21.After taking into account the miscellaneous transactions,the Net Revenue Receipts were 2,547.48 crore.There was no dividend payment during 2020-21 as per RCC recommendations,therefore the year ended with an excess of 2,547.48 crore which was appropriated to Development Fund(1,547.48)crore and Rashtriya Rail Sanraksha Kosh(RRSK)(1,000 crore).The Financial Results for 2020-21 compared to 2019-20 are summarized as below:(in crore)2019-202020-21VariationCapital Investment(excluding MTPs and Circular Railway,Udhampur-Baramula project and appropriation to SRSF)3,21,141.96#3,34,239.7813,097.82Investment from Capital Fund53,449.9153,449.91-Total3,74,591.87#3,87,689.6913,097.82Gross Traffic Receipts1,74,356.601,40,570.52(-)33,786.08Total Working Expenses1,71,319.211,36,567.51(-)34,751.70Net Traffic Receipts3,037.394,003.01965.62Miscellaneous Transactions(Net)(-)1,447.77(-)1,455.53(-)7.76Net Revenue Receipts1,589.622,547.48957.86Dividend Payable to General Revenues-Excess( )/Shortfall(-)1,589.622,547.48957.86Percentage of(a)Working Expenses to Gross Earnings 98.36.45%(-)0.91(b)Net Revenue to Capital Investment from Capital Fund0.420.660.24Capital Investment*(in paise)per NTKM479525( )46*Includes Investment from Capital Fund.#RevisedRevenueThe Gross Traffic Receipts went down by 33,786.08 crore(19.38%)over the previous year.The break-up in terms of major sources is given in Statement IA of Financial Statements.Working ExpensesThe total Working Expenses during 2020-21 were 1,36,567.51 crore a decrease of 34,751.70 crore over 2019.20.Grant-wise distribution of GROSS REVENUE AND WORKING EXPENSES(INCLUDING MISC.)GROSS REVENUE RECEIPTS TOTAL WORKING EXPENSES NET REVENUE(IN CRORE)3,774 2018-19 1,90,507 1,86,733 1,590 2019-20 1,74,695 1,73,105 2,547 2020-21 1,40,571 1,36,568 1,40,7841,38,2369INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21Working Expenses is given in Statements IB and IC of Financial Statements.Balance Sheet:A summary of the Balance Sheet as on March 31,2021 and variation over 2019-20 is as follows:(in crore)AssetsAs onMarch 31,2021Variation overPrevious yearBlock Assets6,70,725.7830,317.51Funds with Central Government:(i)Reserve funds(ii)Banking accounts6,893.9362,324.04 69,217.9725,784.45Sundry Debtors,etc.4,998.64348.68Cash in hand703.9899.20Total7,45,646.3756,549.84LiabilitesRepresented by:Capital Investment*4,06,883.88Investment financed from internal sources,etc.2,63,841.90Total6,70,725.7830,317.51Reserve Funds6,893.9332,624.58Banking Accounts:(i)Provident Fund40,292.15(ii)Misc.Deposits21,974.81(iii)F.Loans and Advances57.08Total62,324.04-6,840.12Sundry Creditors,etc.5,702.62447.89Total7,45,646.3756,549.86*Excludes 16,886.34 crore for MTPs,1,911.10 crore for Circular Railways,11,954.00 crore Appropriation to SRSF,45,000.00 crore Appropriation to Rashtriya Rail Sanraksha Kosh(RRSK)and 22,357.03 crore Appropriation to Railway Safety Fund(RSF).*Includes 56,617.40 crore of investment in DFCCIL and 16,026.70 crore of Udhampur-Srinagar-Baramula Project(National Project).Excludes TWFA:0.01 crore of DRF,0.03 crore of DF,0.00 crore of RSF and total 0.04 crore of TWFA.*Deferred Dividend LiabilityThis is a Contingent Liability and does not appear in the balance sheet.*Further,Railways has been exempted from payment of dividend since 2016-17.With merger of Railway Budget with Union Budget,the 97.29 98.36 2018-19 2019-20 2020-21 OPERATING RATIO(PERCENT)100.0 75.0 50.0 25.0 0(PERCENT)97.45 1,590 2,547 3,774 EXCESS/SHORTFALL(IN CRORES)4,000 3,000 2,000 0(In Crs.)EXCESS 1,000 2018-19 2019-20 2020-21 10INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21capital-at-charge of the Railway has been written off and consequently dividend liability also.Railway Capital FundIn pursuance of the recommendations of Railway Convention Committee(1991),as contained in their Second Report,Railway Capital Fund has come into operation from 1992-93.Appropriation to the Fund is from Revenue Surplus and it intends to finance expenditure on assets of capital nature.Investment made from the Fund upto 2020-21 was 53,449.91 crore.Reserve Fund BalancesAs per recommendations of Railway Convention Committee(1991),contained in their Second Report,the two existing funds,viz.Accident Compensation,Safety&Passenger Amenities Fund and Revenue Reserve Fund,have been restructured to accommodate expenditure on Safety and Passenger Amenity Works.Balance of the abolished Funds has,therefore,been merged with Development Fund.The position of the Funds as on March 31,2021 compared to March 31,2020 is as follows:(in crore)Name of the Fund Balance as on 1.4.2020Contribution to Fund during 2020-21 Withdrawls during 2020-21Balance as on 31.3.2021DRF*833.55423.72671.92585.35DF*519.291,573.151,075.891,016.54CF*400.3513.610413.96Pension Fund*(-)28,398.4680,100.9248,434.963,267.49RSF*509.190(-)2.90512.09DSF*214.737.300222.03RRSK190.731,000.00314.25876.48Total(-)25,730.6283,118.6850,494.126,893.95*Includes 3.94 crore under RCF,-3.94 crore under RRSK due to TWFA for the year 2020-21 and also includes interest under DRF 23.72 cores,DF 25.67 corer,CF 13.61 crore,Pension Fund-420.08 crore and DSF 7.30 crore.The total balance in the Reserve Funds as on March 31,2021 was 6,893.95 crore,representing a increase of 32,624.57 crore over the previous year.Cash FlowFinance generated through IRs internal resources provided 48,979.77 crore during the year 2020-21.The details of internal resource generation and utilisation of funds for financing the Plan outlay are shown in Statement IV of Financial Statements.11INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21Coal loading in Dhanbad Division,ECRFirst 12000HP WAG 12 Loco ECRIntroduction of Advance Technology High Horse Power Diesel Locomotive WDG6G RDSODuring 2020-21,the total Plan investment was 48,979.77 crore,(including MTPs,Circular Railways&National Projects).This was financed from Budgetary Support to the extent of 14,296.04 crore(inclusive of capital invested on MTPs&Circular Railways and Dividend free project Udhampur-Srinagar-Baramulla.The corresponding position during 2018-19 was that out of a total Plan investment was 28,666.88 crore(excluding MTPs,Circular Railways and National Project).This was financed from Budgetary Support to the extent of 26,302.14 crore(exclusive of capital invested on MTPs&Circular Railways amounting to 2,364.74 crore and 0.00 crore invested on Dividend free project Udhampur-Srinagar-Baramulla).The balance of the Plan investment was met from internal and extra-budgetary resources.During the year 2020-21,a decrease of the fund balance was to the tune of 32,624.57 crore to finance the Plan expenditure.Audit ObjectionsIR had a total of 1,464 Audit Notes Part I,759 Special Letters and 4,657 Audit Inspection Reports Part-I as on March 31,2021 as compared to 1,370,873 and 4,622 outstanding respectively at the end of March 31,2020.IR has a well-structured system of ensuring discussion and disposal of all audit objections,inspection reports,draft paras,etc.Tripartite meetings are held at various levels involving the Audit,Accounts and Executive Departments.The draft paras are also discussed at the highest levels between Railway Board and the Audit Department,and based on the replies given,many of them get closed.Summary of Audit Observations on the working of Ministry of Railway,as provided by C&AG for the year,is at page 149.Financial arrangement between the Railways and the Government.Like other Ministries/Departments of the Union,the Ministry of Railways is an integral part of the Union Finance/Budget.Broadly,the revenue expenditure of the Railway is expected to be met from the revenue receipts of the Railways.The excess of revenue receipts over the revenue expenditure is put into the Railway Reserve Funds like Development Fund,Capital Fund,Rashtriya Rail Sanraksha Kosh(RRSK)and Debt Service Fund mainly for being used as internal resources for Capex.The Capital expenditure of the Railways is met from the Gross Budgetary Support from the General Exchequer,Extra Budgetary Resources from the market and partnerships besides the internal resource generation.Investment by IRFCSince 1987-88,Indian Railway Finance Corporation Ltd.(IRFC),a PSU under the administrative control of the Ministry of Railways(MOR),has been mobilizing market borrowings to finance capital expenditure in the Railways.Market funds raised by IRFC constitute Extra-Budgetary Resources(EBR)for Railway Plan and are invested in rolling stock and projects which are leased by IRFC to MOR.With the addition of new rolling stock financed in 2020-21,the fleet 12INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21HABD Hot Axle Box Detector systems NFRAC EMU(Local Train)of assets leased by IRFC represent over 70%of all rolling stock in operation on Indian Railways.Details of new rolling stock taken on lease from IRFC in 2020-21 and cumulative investment from this source are as under:Category of assetsRolling stock taken on lease in 2020-21Rolling stock under lease at the end of 2020-21Nos.Value(in crore)Nos.Value(in crore)Elec.Locos89911,512.046,99866,449.14Diesel Locos1102,146.834,68247,689.85Total Locos1,00913,658.8811,6801,14,138.99Wagons10,1313,417.572,26,05448,579.28Coaches4,69711,484.6467,54889,331.77Track Machines&Cranes-81342.33Other Misc.Items-1,034.38Total 28,561.09 2,53,426.75On the rolling stock assets taken on lease from IRFC,Railways pay lease rentals semi-annually in advance,to enable IRFC to service the debt.Quantum of lease rentals paid by MOR in 2020-21 was 23,088.52 crore,of which 11,373.73 crore constituted capital component and 11,714.79 crore interest component.IRFC also provided funding to the tune of 2,078.49 crore in 2011-12 to meet capital expenditure in 90 doubling and 32 electrification projects in that year.The project assets to the extent funded are on lease from IRFC to MOR.MOR paid an amount of 297.48 crore as lease rentals in 2020-21(110.27 crore capital component and 187.21 crore interest component).IRFC has also been providing market funds to Rail Vikas Nigam Limited(RVNL)to finance bankable railway projects under implementation by them.The amount of funds made available to RVNL till end of 2020-21 is 7,165.08 crore,including 1,429.69 crore in 2020-21.Funds are provided by MOR to RVNL to meet RVNL s debt servicing obligations to IRFC(443.88 crore in 2020-21).A new source of funding viz.Extra-Budgetary Resources(Institutional Finance)or EBR-IF has been introduced from FY 2015-16.EBR-IF funds are long term funds which are being deployed to finance throughput enhancement projects of Railways like doubling and electrification projects,which are otherwise not adequately funded due to resource constraints.The cumulative EBR-IF funds made available by IRFC to Railways till end of 2020-21 is 1,23,073.67 crore,including 21,839.68 crore in 2020-21.In FY 2020-21,due to resource constraints expenditure which was previously charged to Capital/Railway Safety Fund works was financed through Extra Budgetary Resources(Special)as a one-time arrangement.An amount of 50,550.95 crore was provided by IRFC in FY 2020-21 for financing of various railway projects under EBR(Special).In NRCH New Delhi Hospital13INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21Selected Financial RatiosS.No.ItemUnit2019-202020-21(A)Financial Ratios1.Operating ratio%Age98.3697.452.Rate of return on Capital%Age0.420.663.Working ratio of IR%Age91.988.54.Operating ratio with subsidy(Cost recovery)%Age74.2266.55.Operating ratio for Coaching(passenger)and Goods(Freight)i.Goods%Age73.1183.20ii.Coaching%Age207.84454.686.Debt Servicing as percentage of OWE and as a percentage of Gross receipts.i.Debt servicing as percentage of OWE%Age13.917.6ii.Debt servicing as percentage of Gross Receipts%Age11.917.07.Capex to Revenue ratio Capex(from internal generation)/Revenue%Age1.01.5(B)Earning/Yield Ratios(Based on Apportion Earning)8.Passenger yield/PKMsIn Paise48.2265.979.Fright yield/NTKMsIn Paise157.52160.80Productivity indexi.Employee Productivity6,24,3155,90,282ii.Infrastructure Productivity61,93,41458,38,651(C)Asset Utilization10.Utilization of Assetsi.NTKMs per wagon per day-(BG)KMs7,0576,861ii.Wagon KMs per Wagon day-(BG)KMs188.7181.5iii.Wagon turn around-BGIn days5.305.43iv.Average Load per Wagon-BG Tonnes61.3068.8(D)Operating Indices11.Average speed of Goods Train (BG)All traction KM/hour23.643.212.Infective percentage of Rolling Stock (BG)i.Diesel Locos%Age8.199.77ii.Electric Locos%Age6.997.35iii.EMU Coaches%Age12.210.0iv.Passenger Carriages%Age6.146.17v.Other Coaching Vehicles%Age5.085.22vi.Wagons%Age3.074.5213.Specific Fuel Consumption(Consumption per 1000 GTKMs)(BG)i.Passenger service DieselKLs.3.593.31ii.Goods services DieselKLs.1.921.9214.Specific Energy Consumption(Consumption per 1000 GTKMs)(BG)i.Passenger service-ElectricityK.Wt.Hrs.18.415.6ii.Goods services-ElectricityK.Wt.Hrs.6.137.0915.Punctuality Index Punctuality(M/Exp.Trains)(BG)%age75.6994.1716.Accident per Million train Kilometers 0.050.03*Revised14INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21Freight OperationIn 2020-21,IR loaded 1,233.85 million tonnes of freight traffic of which 1,230.94 million tonnes was revenue-earning and 2.91 million tonnes of non-revenue earning and achieved total net tonne kilometers(NTKMs)of 720 billion as against 708 billion in 2019-20.The freight earnings went up from 1,13,487.89 crore in 2019-20 to 1,17,231.82 crore in 2020-21,registering an increase of 3.30%.Commodity wise loading of revenue-earning traffic in 2020-21 as compared to 2019-20 was as follows:CommodityTonnes carried(in millions)2019-202020-21Coal(i)for Steel Plants57.0752.95(ii)for Washeries0.130.34(iii)for Power House252.92218.11(iv)for public use 276.75270.42Total586.87541.82Raw material for steel plants except iron ore25.5724.90Pig iron and finished steel(i)from steel plants31.4332.92(ii)from other points21.7027.14Total53.1360.06Iron ore(i)for export17.4725.18(ii)for steel plants85.5584.67(iii)for other domestic users50.3549.28Total153.37159.13Cement110.10120.40Foodgrains37.5362.82Fertilizers51.3953.79Mineral oil(POL)44.6842.48Container Servicei)Domestic containers11.3112.61ii)EXIM containers49.7750.55Total61.0863.16Balance other goods84.69102.38Total Revenue earning traffic1,208.411,230.94A Picturesque view of Konkan Railway Route KRCLFreight trains loaded with iron ore and steel at Noamundi SERGoods transportation in order to ensure hassle free supply of essential commodities and food grains in any part of the country during the lockdown,NFR15INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21Revenue-earning freight traffic for major bulk commodities/commodity groups in 2020-21 compared with 2019-20 was as follows:S.No.Commodity/Commodity groupTonnes originating(in million)Net tonne kilometers(in million)Earnings$(in crore)2019-202020-212019-202020-212019-202020-211Coal586.87541.822,93,0512,39,39054,426.6849,578.452Raw material for steel plants except iron ore25.5724.9014,43813,6632,216.231,978.823Pig Iron&finished steel53.1360.0645,02949,1237,286.657,416.784Iron ore153.37159.1350,32062,52410,965.9012,661.435Cement110.10120.4063,93373,6058,744.829,713.676Foodgrains37.5362.8252,64180,6816,153.669,212.577Fertilizers51.3953.7947,16249,0115,807.655,826.028Mineral oil(POL)44.6842.2830,77429,9705,928.065,727.299Container Services61.0863.1656,68655,3312,553.955,113.6010Balance other goods84.69102.3853,63166,4647,388.708,509.7511Total revenue earning traffic1,208.411,230.947,07,6657,19,7621,11,472.30 1,15,738.38$Excludes other goods earnings such as wharfage,demurrage,etc.Trend of revenue-earning freight traffic for the last 3 years is as follows:2018-192019-202020-21Tonnes originating(million)1,221.481,208.411,230.94Net tonne kms.(million)7,38,5237,07,6657,19,762Average lead(kms.)605586585Goods earnings$(in crore)1,22,580.311,11,472.301,15,738.38$Excludes other goods earnings such as wharfage,demurrage,etc.Mini Kisan Rail carrying Milk,ECRInstitutional Complex for ICAR Pusa,New Delhi,IRCONCOMMODITY 2019-20 2020-21 PATTERN OF REVENUE-EARNING FREIGHT TRAFFIC(PERCENTAGE TO TOTAL)CEMENT IRON ORE COAL FOODGRAINS MINERAL OILS FERTILIZERS IRON AND STEEL OTHER GOODS 48.5712.693.103.709.114.254.4014.1841.417.117.444.359.046.666.3617.6348.829.845.525.327.845.216.5410.9144.0212.935.103.459.784.374.8815.4733.268.6911.214.1610.236.816.8218.8242.8410.947.964.958.395.036.4113.482018-19 2019-20 2020-21 2018-19 2019-20 2020-21 NET TONNE KILOMETRES(REVENUE-EARNING)TONNES ORIGINATING(REVENUE-EARNING)1,600.00 1,200.00 800.00 400.00 0(MILLION)1,221.48 1,208.41 1,230.94 2018-19 2019-20 2020-21 16INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21Freight Services Structure and RatesThere was no increase in freight in 2020-21.However,various initiatives were taken during this period which includes Short lead concession under which discount in freight at the rate of 50%,25%and 10%is granted to the traffic booked upto 0-50 KM,51-75 KM and 76-90 KM respectively except Coal&Coke and Iron ore traffic.Long lead concession for Coal&Coke traffic for distance 1400 KM 20%,for Iron&Steel for distance 1600 Km and 700 KM-1600 Km 20%and 15%,respectively;and for Iron Ore traffic for distance 1500 Km 20%.Distance based graded concession 20%to 25%for transportation of Clinker for lead of 1000 km,Premium Indent Scheme,discount for transportation of fly ash,Round Trip Tariff policy,Reduction in permissible carrying capacity(PCC)for loading of Pet Coke,Relaxation in distance for operation of Mini Rake,5%discount in haulage charges for loaded Containers and 25%discount in haulage charges of empty containers and empty flats,Roundtrip based charging for ultra short lead(50 Km)Container traffic etc.In addition certain initiatives have also been taken to deal with situation arisen due to Covid-19 which includes exemption from levy of ancillary charges namely Demurrage,Wharfage,Stacking,Detention and Ground Usage Charges from 24.03.2020 to 17.05.2020,non-levy of haulage charge for movement of empty containers and empty flat from 24.03.2020 to 08.05.2020&from 17.12.2020 to 31.12.2020 and exemption from levy of stabling charges in case of container traffic from 24.03.2020 to 31.03.2021.Freight Marketing1.Development of Private Terminals(PFT)through private investment:Private Freight Terminal(PFT)facilitates rapid development of a network of freight terminals with private investment.The focus of the policy is on providing efficient and cost effective logistics services with warehousing solution to end users.2.Procurement of rakes for freight traffic by inviting private investments:i.General Purpose Wagon Investment Scheme(GPWIS):To allow investment for procurement of General Purpose Wagons by End users,Public Sector Undertaking(PSUs),Port Owner,Logistics Providers and Mines Owners.The scheme permits eligible investors to invest in minimum of one rake of general purpose wagon in any of the desired circuit(s)to carry any commodity in these rakes.ii.Liberalized Special Freight Train Operators(LSFTO)Scheme:Liberalized Special Freight Train Operators(LSFTO)Scheme has been started with effect from 16.03.2020 by amalgamating erstwhile two schemes viz.Liberalized Wagon Investment Scheme(LWIS)and Special Freight Train Operator(SFTO)Scheme.The objective of the policy is to increase Railways share in transportation of non conventional traffic in high capacity and special purpose wagons to increase the commodity 2018-19 2019-20 2020-21 2018-19 2019-20 2020-21 NET TONNE KILOMETRES(REVENUE-EARNING)TONNES ORIGINATING(REVENUE-EARNING)160,000.0 120,000.0 80,000.0 40,000.0 0(In Crs.)GOODS EARNINGS 1,22,580.31 1,11,472.30 800,000 600,000 400,000 200,000 0(MILLION)719,762 707,665 738,523 2018-19 2019-20 2020-21 1,600.00 1,200.00 800.00 400.00 0(MILLION)1,221.48 1,208.41 1,230.94 AVERAGE LEAD AVERAGE DISTANCE OF MOVEMENT OF A TONNE OF GOODS(REVENUE-EARNING TRAFFIC)2008-09 2009-10 2010-11 700 525 350 175 0(Kms.)657 662 676 800 600 400 200 0(Kms.)AVERAGE LEAD AVERAGE DISTANCE OF MOVEMENT OF A TONNE OF GOODS(REVENUE-EARNING TRAFFIC)605 586 585 2018-19 2019-20 2020-21 1,15,738.38 NET TONNE KILOMETRES(REVENUE-EARNING)TONNES ORIGINATING(REVENUE-EARNING)160,000.0 120,000.0 80,000.0 40,000.0 0(In Crs.)GOODS EARNINGS 1,22,580.31 1,11,472.30 800,000 600,000 400,000 200,000 0(MILLION)719,762 707,665 738,523 2018-19 2019-20 2020-21 1,600.00 1,200.00 800.00 400.00 0(MILLION)1,221.48 1,208.41 1,230.94 AVERAGE LEAD AVERAGE DISTANCE OF MOVEMENT OF A TONNE OF GOODS(REVENUE-EARNING TRAFFIC)2008-09 2009-10 2010-11 700 525 350 175 0(Kms.)657 662 676 800 600 400 200 0(Kms.)AVERAGE LEAD AVERAGE DISTANCE OF MOVEMENT OF A TONNE OF GOODS(REVENUE-EARNING TRAFFIC)605 586 585 2018-19 2019-20 2020-21 1,15,738.38 2018-19 2019-20 2020-21 NET TONNE KILOMETRES(REVENUE-EARNING)TONNES ORIGINATING(REVENUE-EARNING)160,000.0 120,000.0 80,000.0 40,000.0 0(In Crs.)GOODS EARNINGS 1,22,580.31 1,11,472.30 800,000 600,000 400,000 200,000 0(MILLION)719,762 707,665 738,523 2018-19 2019-20 2020-21 1,600.00 1,200.00 800.00 400.00 0(MILLION)1,221.48 1,208.41 1,230.94 AVERAGE LEAD AVERAGE DISTANCE OF MOVEMENT OF A TONNE OF GOODS(REVENUE-EARNING TRAFFIC)2008-09 2009-10 2010-11 700 525 350 175 0(Kms.)657 662 676 800 600 400 200 0(Kms.)AVERAGE LEAD AVERAGE DISTANCE OF MOVEMENT OF A TONNE OF GOODS(REVENUE-EARNING TRAFFIC)605 586 585 2018-19 2019-20 2020-21 1,15,738.38 17INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21base of Rail Traffic.This policy provides an opportunity to logistics service providers or manufacturer to invest in wagons and use advantages of rail transport of selected commodity to create a win-win situation for railways and themselves.This also creates an avenue for end users to optimally utilize their rolling stock by transporting their commodities as well as commodities of third party.iii.Automobiles Freight Train Operator(AFTO)Scheme:Automobile Freight Train Operator Scheme permits procurement and operation of special purpose rakes by private parties for transportation of automobiles sector.iv.Wagon Leasing Scheme(WLS):The Scheme introduced the concept of leasing of railway wagons on Indian Railways.The scheme aims at induction of rakes of general purpose wagons,special purpose wagons and wagons for containers movements through PPP route.Wagon Leasing Companies can lease wagon under Automobiles Freight Train Operator(AFTO)scheme,General Purpose Wagon Investment Scheme(GPWIS),Liberalized Special Freight Train Operators(LSFTO)scheme,Automobiles Freight Train Operator(AFTO)scheme and also to Container Train Operators.Claims(A)Claim(2020-21)The number of claims registered by Railways was 7,391 during the year 2017-18,5,991 during 2018-19,5,760 in 2019-20 and 3,845 in 2020-21.(B)Claim(April-September,2021)The number of claims registered by Railways was 3,086 during the period of April-September 2018,3,281 during the period of April-September 2019,1,403 during the period of April-September,2020 and 1,683 during the period of April-September,2021.Measures initiated by Railways to prevent arising of claims are as under:-1.Monitoring of sick&detached wagons for proper connection and dispatch to its booked destination/rightful consignee.Wagon attachment/detachment details are recorded in FOIS module for this purpose.2.Booking/loading/unloading/delivery details of parcels/luggage are fed into the PMS for tracking of parcels to facilitate the status of booked consignments to the customers.3.At the time of booking,remarks on forwarding notes are obtained from the consignor or his authorized agent regarding defective packing conditions wherever necessary such as,Internal/external packing conditions not complied with.Liable for damage during transit etc.and the same remarks are reproduced on RR/PWB also.Improvement in Kuberpur Goods Shed Agra Division NCRDrone Camera Survellence,ECoRParcel Loading NR18INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-214.Sending copy of ORRS/PRRs to the destination station before arrival of the consignments,to facilitate connection and delivery of the consignments.5.Weighment of rakes through electronic in-motion-weighbridges and weighment of parcels at destination before delivery.6.Escorting of rakes.7.Instructions regarding Monsoon Precautions are issued to all concerned in advance every year in order to prevent damage of the consignment by wet.8.Proper closing,bolting and securing the doors of wagons.9.Use of dunnage to prevent pilferage of contents from bagged consignments.Use of Gunny Strips/Plastic sheets at door crevices to prevent seepage of water;and use of tarpaulins on open wagons.10.Arranging sufficient CCTVs in all major Parcel Offices.11.Adequate lighting facility with high mast at Goods Sheds.12.Regular counselling and training given to all staff and officers regarding latest rules,circulars and new technologies in respect of Claims matters.LED fittings at Station,KRCLNewly inaugurated Sankrail Goods Terminal Yard SER19INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21Passenger BusinessThe profile of passenger traffic in 2020-21 as compared to 2019-20 is outlined below:SuburbanNon-suburban2019-202020-212019-202020-21Passengers originating(millions)4,5979173,489333Passenger kilometers(millions)1,37,13030,0759,13,6082,01,051Average lead(kilometres)29.832.8261.9603.5Earnings(in crore)2,843.09589.3147,826.0114,659.18Average rate per passenger kilometer(paise)20.719.652.472.91The overall trend of passenger traffic in the last three years was as follows:Total suburban and Non-suburban2018-192019-202020-21Passenger earnings(in crore)51,066.65 50,669.0915,248.49Passenger journeys(millions)8,439 8,0861,250Passenger kilometres(millions)11,57,174 10,50,7382,31,126Average lead(kilometres)137.1 129.9184.8Fare StructureIndian Railways has revised the basic passenger fare w.e.f.01.01.2020 with following features:-01 paisa per kilometer increased in ordinary non-AC classes in Non-suburban.02 paisa per kilometer increased in Mail/Express Non-AC classes in Non-Suburban.04 paisa per kilometer increased in AC classes in Non-suburban.No increase in passenger fare of Suburban.No increase in season tickets(both suburban&Non-Suburban)Ticketless TravelDuring 2020-21,0.95 lakh checks were conducted against ticketless/irregular travel(including carriage of unbooked luggage).About 32.56 lakh cases of ticketless/irregular travel/unbooked luggage were detected and 152.25 crore were realized on this account.PunctualityThe cumulative punctuality for financial year 2020-21 over IR is 94.17%.This is an improvement of 18.48%as compared to corresponding period of last year.Unit RevenueThe average rate per passenger kilometer was 66 paise in 2020-21 2018-19 2019-20 2020-21 2018-19 2019-20 2020-21 8,439 10,000(MILLION)7,500 5,000 2,500 0 8,086 1,250 PASSENGER JOURNEYS(NUMBER)PASSENGER KILOMETRES 60,000 45,000 30,000 15,000 0(In Crs.)PASSENGER EARNINGS 51,066.65 15,248.49 50,669.09 2018-19 2019-20 2020-21 2018-19 2019-20 2020-21 8,439 10,000(MILLION)7,500 5,000 2,500 0 8,086 1,250 PASSENGER JOURNEYS(NUMBER)PASSENGER KILOMETRES 60,000 45,000 30,000 15,000 0(In Crs.)PASSENGER EARNINGS 51,066.65 15,248.49 50,669.09 231,126 1,200,000(MILLION)900,000 600,000 300,000 1,050,738 2018-19 2019-20 2020-21 0 1,157,174 2018-19 2019-20 2020-21 2018-19 2019-20 2020-21 8,439 10,000(MILLION)7,500 5,000 2,500 0 8,086 1,250 PASSENGER JOURNEYS(NUMBER)PASSENGER KILOMETRES 231,126 1,200,000(MILLION)900,000 600,000 300,000 1,050,738 2018-19 2019-20 2020-21 0 1,157,174 20INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21as against 48.2 paise in 2019-20.Average revenue for different classes,was as follows:Earnings per passenger kilometer(paise)Earnings per passenger journey(in)2019-202020-21 2019-202020-21Suburban(all classes)20.719.66.26.4Non-Suburban:AC Ist Class273.6350.91,612.62,449.1AC Sleeper166.0193.51,266.71,751.1AC 3-Tier129.4147.71,090.31,464.2Ist class83.7354.566.7189.5AC Chair Car164.4181.4512.4586.6Sleeper Class:(i)Mail/Express50.655.2 395.4527.4(ii)Ordinary48.4108.332.4-2.0Second Class:(i)Mail/Express32.738.4116.2188.4(ii)Ordinary23.848.716.535.9Total Non-suburban52.372.9137.1440.0During the year 2020-21,Indian Railways introduced new trains,extended the runs and increased the frequency of existing trains,as given below:Trains introducedTrain Runs extendedFrequency of trains increasedTotalNon-suburban503406 90Suburban04-04Total54340694(Trains in singles)Catering ServicesCatering services are provided to the travelling passengers in trains and at stations.Catering Policy-2017 mandates the service of meals in trains from the Base Kitchens owned,operated and managed by IRCTC.However,after COVID-19 pandemic,it was decided by Ministry of Railways to introduce the service of branded pre-cooked“Ready to Eat”(RTE)meals,in place of cooked food,to ensure quality and hygiene of on-board catering services.RTE meals are procured from reputed empanelled firms shortlisted on the basis of technical qualifications.These meals are provided through Pantry Cars(in 281 pairs of trains)and Train Side Vending(in pairs of 164 trains).Passengers travelling in the trains also have the facility to order food of their choice through e-Catering services which are available at 224 stations and an average of 9,697 meals are being served per day.Passengers can also purchase food items from Static Catering Units which include 583 Major Static Units(Food Plaza,Fast Food Units,Jan Ahaar,Refreshment Rooms and Automatic Vending Machines)and 9,129 Minor Static Units(all stalls and trolleys).In addition,there are 1,127 Water Vending Machines,Escalator with enhanced safety features Kolkata MetroAutomatic coach washing plant installed in washing line Agra NCR21INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21928 Multi Purpose Stalls,702 Bookstalls,44 Miscellaneous/Curio Stalls,03 exclusive Chemist Stalls and 01 Bookstall cum Chemist Corner to ensure availability of items of travelling needs of passengers.In view of the pandemic,a number of measures/initiatives have been taken to ensure the quality and hygiene of food being served to the passengers,which are as under:-In order to meet the basic needs of travelling passengers in Shramik Special trains,1.96 crore meals and 2.19 crore Packaged Drinking Water bottles were provided.About 35 lakh free meals were distributed jointly by Commercial Deptt.and IRCTC to the needy people at various locations across country during the nationwide lockdown.Service of only RTE Meals in trains.Sale of disposable bedroll kits/items,protective gears and hygiene items such as masks,sanitizers,gloves,etc.has been permitted through Multi Purpose Stalls.Rail TourismIndian Railway through Indian Railway Catering and Tourism Corporation Ltd(IRCTC)has been working towards promotion of Rail based Tourism through Rail Tour Packages,Budgeted tourist trains,Charter Business,etc.Now the horizon of tourism activities has been expanded to include Rail based tourism activities as well as Non-Rail based tourism products to succeed in the competitive market and to better synergies and offer better tourism products to people.The rail connectivity helps in reaching and promoting tourist destinations on pan India basis.IRCTC provides various types of tourism products ranging from budget to luxury class passengers.In addition,it is also engaged in construction of budget hotels at prime locations,providing information about various tourist destinations as well as tour packages being provided by IRCTC through its Tourism Information&Facilitation Centres,Executive Lounges at railway stations etc.The Financial Year 2020-21 was very tough for Tourism and Hospitality industry due to COVID-19 pandemic spread and restrictions imposed by Government of India.International&Domestic Travel was restricted to contain the COVID-19 spread.Hence,the travel and tourism sector got a set-back during the period.With the easing of restrictions,IRCTC has restarted the Rail tourism and has issued SOP for tourist trains to observe COVID-19 protocol in line with Govt.of India guidelines.The important train/coach services and various package tours are(i)Luxury Tourist Trains-Maharajas Express&Golden Chariot,Palace on wheels,Deccan Odyssey,(ii)Deluxe Trains-Buddhist Circuit Special Train&AC Deluxe Tourist Train,(iii)Budget Tourist Trains-Bharat Darshan/Aastha Circuit Tourist Trains&Pilgrim Special Tourist Train,(iv)Rail tour Packages,(v)and Online Charter Train/Coach/Saloon Car.1.Luxury Tourist Trainsa.Maharajas ExpressIRCTC is managing and operating the Maharajas Express Covid_19 vaccination centres are being operated in Railway hospitals at Prayagraj Kanpur Tundla Jhansi and Agra NCRExterior of the Vista dome tourist coach of Ahmedabad Kevadiya Janshatabdi Express SWR22INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21independently and successfully with the world recognition.Maharajas Express has been awarded as worlds leading Luxury Tourist Train for seven consecutive years i.e.2012 to 2018 as announced in World Travel Award ceremony.Awarded as“Luxury Hospitality and Life Style Awards”for consecutive two years i.e.2015&2016 as announced in Seven Star Luxury Award.The 23-carriage-long train houses four different types of accommodation viz.Deluxe Cabins,Junior Suite Cabins,Suites and Presidential Suite.All cabins come with en-suite bathrooms.The cabins are provided with adequate wardrobe space and luggage holds.The train houses a full kitchen onboard which serves a mix of International and Indian cuisines.The menus change daily and guests can make special dietary requests before commencement of the journey.The train offers on-board and off-board service providers at substantially lower rates for same quality/level of services.Three packages of 6 Nights/7 Days and one of 3 Nights/4 Days package have been designed to visit places like Udaipur,Jodhpur,Bikaner,Jaipur,Ranthambore,Agra,Khajuraho and Varanasi.The itineraries have been uploaded with departure dates on the website of the train i.e.www.the-.In 2020-21,IRCTC had cancelled all trips of Maharajas Express due to restriction of Government of India on Group movement&flight restrictions to contain the spread of COVID-19 pandemic in India.b.Golden ChariotThe Karnataka State Tourism Development Corporation(KSTDC)signed a memorandum of understanding with the Indian Railway Catering and Tourism Corporation(IRCTC)to market and operate the Golden Chariot train.The train was taken over during January 2020 and first phase of up-gradation of coach interiors and amenity coaches have been completed.It is proposed to operate around 15 departures with earnings of around 7 crores during the year 2021-22 seasons but due to ongoing pandemic affecting inbound travel as well as restrictions on group travel,the trips of Golden Chariot has been cancelled up till January 2021.Further domestic tourists are being targeted by aggressive marketing as well as competitive pricing,due to which IRCTC was able to operate two(02)trips of Golden Chariot with 55 passengers.The details of package tour&destination covered as well as online booking facility are available at website“https:/www.goldenchariot.org”.c.Palace of WheelsThe Palace on Wheels,a luxury train being operated by Indian Railways through Rajasthan Tourism Development Corporation,takes you on a scintillating journey into the royal land of sand dunes and regal palaces.Voted as the 4th best luxury train in the world,the Palace on Wheels carries with it an intrinsic ambience that goes perfectly well with the majestic charm and beauty spread so lavishly across the Indian terrain.The Palace on Wheels trains provides the most luxurious travel experience of its kind in Indian Railway.Guest cabins provide a calm,personal space where guests may read,relax,or simply watch the landscape Covid Care Coach SECRUTS centre at Patna SER23INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21spool by through the panoramic windows.A passionate team of chefs takes care of the gourmet meals that are served in the 2 dining cars on board.For an authentic dining experience of Royal Rajputana,the accent is on fresh local ingredients and traditional dishes.The Spa Car,Lounge Car and a Bar Car in Palace on Wheels ensure that you rediscover the finer aspect of travelling for the entire duration of your itinerary.So be prepared to step back into a more refined era n unhurried pace of life.In 2020-21,RTDC had also cancelled all trips of Palace of Wheels trains due to restriction of Government of India on Group movement&flight restrictions to contain the spread of COVID-19 pandemic in India.d.Deccan OdysseyThe huge success of the first Indian luxury train Deccan Odyssey paved the way to the birth of another deluxe rail journey in the south western part of the country.A brainchild of Maharashtra Tourism Development Corporation(MTDC),Deccan Odyssey is the fruitful result of the relentless efforts offer the most luxurious travel experience of its kind to passengers.Aligned with the best royal trains of the world such as Blue Train of South Africa or Orient Express of Europe,Deccan Odyssey was suffused with every modern facility that contributes towards making each luxury train tour to Maharashtra comfy and deluxe.The Deccan Odyssey train covers Forts&Palaces of India,Taj Mahal,Tiger&Lion forest reserves and UNESCO World Heritage Sites of Ajanta Ellora Caves&Hampi etc.Deccan Odyssey has the most variety of itineraries on offer as compared to any other luxury train in India.The Deccan Odyssey Train has been voted as the best luxury train in Asia at the World Luxury Travel Awards 3 times in a row.On the Deccan Odyssey luxury train journey,you travel overnight in comfort of richly furnished A/C cabins with ensuite bathrooms.In the daytime you enjoy pre-arranged sightseeing tours in diverse tourist destinations.The decoration of the train reflects a particular era of the Deccan milieu and each cabin is creatively outfitted with facilities like small wardrobe,personal safe,telephone,attached bathroom with toiletries,air-conditioning and a personal attendant on the round the clock service of the guests.Cost of daily three meals,sightseeing tours,travel&onboard stay is included in package cost.Onboard Deccan Odyssey train there are 21 luxurious coaches,which are comprised of 12 passengers saloons,two dining cars,one bar lounge,conference saloon with business centre,mini gym&ayurvedic spa facility.The entire train is interconnected with common passageway&dining cars are in the centre of the train with six passenger saloons on either side.Each of the 21 coaches of Deccan Odyssey is celestially decorated that give an insight view of the elegant travelling style of the Maharajas of the bygone era.All the destinations covered in a weeklong Maharashtra tour is sumptuously selected so that the tourists can witness the vividness of the state along with traversing through the diverse landscapes.It is certainly a surprise tour on the rolling wheels of Deccan Odyssey that transport the Tractor Automobiles Loaded from Mandideep for the 1st time on,WCRATVM at Ranchi station,SER24INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21travellers to a world where everything is regal,elegant and brainstorming.Due to restriction of Government of India on Group movement&flight restrictions to contain the spread of COVID-19 pandemic in India,MTDC had cancelled all trips of Deccan Odyssey trains in 2020-21,2.Deluxe Train:a.Buddhist Special Trains:Buddhist Circuit is a niche product of 7 Night/8 Days itinerary which covers all major Buddhist Pilgrim locations in India and Lumbini in Nepal.The major destinations covered follow the life span of Lord Gautam Buddha like Bodhgaya,Rajgir,Nalanda,Varanasi,Sarnath,Kushinagar,Lumbini(Nepal),Shravasti and Agra.The tour is mostly patronised by International clients and has been able to attract domestic clients too.The details of package tour&destination covered as well as online booking facility are available at Buddhist train micro-site“”.In 2020-21,IRCTC had to cancel the trips of Buddhist Circuit Special Train due to Lockdown imposed by Government of India as a result of COVID-19 pandemic situation.b.AC Deluxe Tourist Trains:IRCTC launched AC Deluxe Tourist train from North India using new modified LHB Buddhist rake in 2020-21.These tours are meant for mid-income domestic tourists to meet their demand who cannot afford the luxury train and also do not want to travel on the budget tourists trains like Bharat Darshan/Pilgrim Special Tourist Trains.In 2020-21,IRCTC has operated 01 trip of Deluxe Tourist Train and provided service to 84 passengers.3.Budget Segment Tourist Trains:a.Bharat Darshan/Aastha Circuit Tourist Trains:Bharat Darshan/Aastha Circuit is one of the most popular tourism products,are tourist trains for the budget segment tourists.These Trains cover major pilgrim and tourist destinations of India on different itineraries.This product is attractively priced at 900/-per day per person GST for non-AC sleeper class passengers and 1100/-per day per person GST for 3AC class passengers.The price is inclusive of rail&road travel,all meals,sightseeing,accommodation and accidental insurance upto a sum assured of 10 lakhs.Further details regarding itinerary and booking of Bharat Darshan/Aastha Circuit Tourist is available at tourism portal“”or by visiting nearest Tourism Information&Facilitation Centres of IRCTC.In 2020-21,IRCTC has operated 21 trips and provided service to 13,312 passengers.Due to impacts of COVID-19 pandemic,trips of Bharat Darshan trains were operated from Nov 2020 onwards.b.Pilgrim Special Tourist Trains:Pilgrim Special Tourist Train has been launched in 2020-21(i.e.Jan,21),which is having composition of sleeper class 05 and 3AC coaches 05.These trains cover major pilgrim and tourist destinations of India on different itineraries.This product is attractively priced at 1,100/-per day 25INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21per person GST for non-AC sleeper class passenger and 1,500/-per day per person GST for 3AC class passenger.The price is inclusive of rail&road travel,all meals,sightseeing,accommodation and accidental insurance of upto 10 lakhs.In 2020-21,IRCTC operated 04 trips and provided service to 2,426 passengers.4.Rail Tour Packages:These packages are offered with USP of confirmed berths of train journey along with all inclusive package services like road transfers,accommodation,meals and sightseeing at reasonable rates on the basis of itinerary.However,Due to COVID-19 pandemic and restriction in group travel during the 2020-21,Rail Tour Packages were just operated on limited destinations like Vaishno Devi,Kevadia(Statue of Unity),etc.The details and booking of these packages can be done at tourism portal“”or by visiting nearest Tourism Facilitation Centres(TFC)of IRCTC.5.Online Chartering of Trains and coaches:In 2017-18,IRCTC has been nominated as Single window agency for booking of Full Tariff Rate(FTR)Train/Coach&Saloon Cars.Online website and service of FTR booking was launched on 18.05.2018 for public,wherein public can register their demand for Charter Trains/Coaches/Saloon Cars.The Saloon Car has a living room,two air-conditioned bedrooms one twin bedroom and the other facilities similar to AC First Class coupe with attached baths,dining area and a kitchen.The additional service of one AC attendant and one saloon attendant was also provided for ensuring hassle free travel.Saloon Cars can be booked through IRCTC.During the 2020-21,IRCTC has operated 69 charter coaches/saloon cars and 25 charter trains.Passenger AmenitiesThe allocation under the Plan Head-53“Passenger Amenities”in 2020-21 was 2,725.63 crore(Budget Estimate)and the same was revised to 2,615.3 crore(Revised Estimate).1,253 stations were identified for development under the Adarsh Station Scheme,out of which 1,208 stations have since been developed under the said scheme.During the Year 2020-21,219 Foot over Bridges were constructed over Indian Railways.95 stations were provided with water coolers,54 stations were electrified and 156 passenger lifts and 120 escalators were provided at Railway Stations.Customer CareIndian Railways is imparting training to frontline staff with a view to improve their inter-personal skills and to equip them to deal with the rail customers in a better manner.This training is being imparted at New Delhi,Howrah,Mumbai and Secunderabad in a decentralized manner.Luxury Traveler Lounge at Prayagraj Jn.,NCR26INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21Accordingly,concerned Zonal Railways conduct the Customer Care Training at these locations for their nominated railways.The training aims at increasing the performance level of employees by sensitizing them to the needs of the customers.This also helps in improving the customer interface of the Indian Railways.a)Passenger Reservation System(PRS)Passenger Reservation System(PRS)is running at about 3,274 locations,and is handling more than 3,000 trains.Computerised reservation terminals have been expanded to remote corners through India Post PRS centers as well as non-railhead PRS facilities have been extended through State Government and local bodies.In addition,the facility of e-ticket has been made available for all Mail and Express trains through www.irctc.co.in website.The progress of proliferation of PRS locations over the years is as indicated below:No.of location with PRS facility10-1111-1212-1313-1414-1515-1616-1717-1818-1919-2020-212,3552,8293,0193,1463,2013,3503,4223,3843,4433,4453,274b.Unreserved Ticketing System(UTS)A pilot project was sanctioned for Unreserved Ticketing System(UTS)in 2002-03 and a nationwide project in 2003-04,UTS is now functioning at about 4,518 locations(working)on Indian Railways.This covers most of the important stations of IR.The details of proliferation of locations over the previous years is as indicated below:No.of locations with UTS facility10-1111-1212-1313-1414-1515-1616-1717-1818-1919-2020-214,7395,2565,6195,7785,8355,8605,9796,0006,3826,2424,512*This shows working Locations of UTS.Some locations were closed temporarily because of Covid.c.Automatic Ticket Vending Machines(ATVMs)/Coin-cum-Card Operated Automatic Ticket Vending Machines(CoTVMs).So far more than 1,532 ATVMs and CoTVMs have been installed over Indian Railways.The ATVMs facilitate purchase of unreserved tickets,platform and recharging of season tickets by the passengers by way of touch screen facility.Thus queuing at the counters is significantly reduced during the rush hours.d)Online and Mobile Ticketing on Indian RailwaysIndian Railway catering and tourism corporation(IRCTC)manages the website www.irctc.co.in for online booking of reserved tickets.The website was launched in 2002.Now it has emerged as one of the largest e-commerce website in the country and Asia Pacific.The IRCTC e-Ticketing System was replaced with Next Generation 27INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21E-Ticketing(NGeT)system in 2014.The system is being upgraded regularly and is supported by high capacity servers which have a capacity to book more than 26,000 tickets in a minute.E-tickets can also be booked on IRCTC Rail Connect Mobile Apps(Android&iOS platforms).IRCTC had launched a new Android App on its NGeT system in 2017.In 2020-21,on an average about 2.21 lakh tickets were booked daily.More than 46%of online tickets were booked through IRCTC Rail Connect Mobile App.IRCTC has a robust system of payment gateways with various payment options viz.,net Banking/Credit&Debit Card/Wallets/Bhim/UPI.Even foreign users can book tickets using international credit Card(issued outside India).Some Special Features of Ticket Booking on Indian Railwaysa)E-Ticketing system E-ticketing has been one of the most passenger-friendly initiatives of Indian Railways,as it obviates the need to come to Railway reservation counters.Passengers who have booked e-tickets can either take a print-out of the Electronic Reservation Slip(ERS)or can display the Short Message Service(SMS)sent by IRCTC containing all vital details to the on-board ticket checking staff for undertaking travel in reserved classes subject to appearance of passengers name in the reservation chart and carrying of any one of prescribed proofs of identity in original.Owing to convenience offered by e-ticketing,the share of e-ticketing has consistently increased over the years and accounted for approximately 79.81%of the total reserved tickets booked during 2020-21.b)Unreserved ticket booking through mobile phoneThe UTSONMOBILE APP has been launched to promote cashless transaction,Contactless ticketing and enhanced customer convenience.It aims to obviate the need for passengers to wait in queues at the ticket booking counters for purchasing the unreserved tickets and thereby facilitate seamless booking of unreserved tickets-journey,season tickets&platform tickets.Payment can be made through either Railway wallet(R-Wallet)(created with zero balance upon successful registration&linked with the mobile number)or through other digital modes like debit card,credit card,net banking,UPI through payment aggregator vis Paytm,Mobikwik and Freecharge.c)Rationalisation of fare of Humsafar train:Fare of Humsafar trains have been rationalised and decided to withdraw the variable fare scheme from Humsafar train.It has also been decided to attach Sleeper class coaches in addition to only 3rd AC coaches in Humsafar trains.d)Introduction of discounted scheme in CC and EC class:Power has been delegated to Zonal Railways to grant discount in the fare of AC Chair Car and executive class over part of the section,last/first leg,end to end,weekends etc.,in trains whose occupancy is below 50%.The element of discount can be decided between 10%and 25sed on the fare of alternative mode of transport.28INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21e)Information on Status of BookingFirst reservation chart is finalized automatically at least 4 hours before the scheduled departure of the train so that the waitlisted passengers can come to know about the final status of their bookings.Thereafter,the available accommodation,if any,can be booked across any computerized PRS counter or through internet.Second reservation chart is prepared between 30 minutes to 5 minutes before the scheduled/rescheduled train departure.Remaining berths,if any,are transferred to the next remote location.The passenger gets SMS on his registered mobile number indicating the coach and berth number allotted.f)Alternate Train Accommodation Scheme-VikalpWith a view to provide confirmed accommodation to waitlisted passengers and also to ensure optimal utilization of available accommodation,a scheme called Alternate Train Accommodation Scheme-VIKALP has been implemented.Under this scheme,Waiting list passengers can give choice,at the time of booking ticket,to opt to travel by alternate train in case the berth is not confirmed after preparation of the chart.g)Station Ticket Booking Agent(STBA)The STBA scheme was initially launched on the erstwhile E-category stations with a view to offloading Station Master/Assistant Station Master of the ticketing activity in order to concentrate better on train operations.STBA were,thus,engaged to issue unreserved tickets at E-category stations.However,the STBA scheme has now been revised and instructions have been issued to engage STBA to issue unreserved tickets at NSG-5 and NSG-6 category stations.During 2019-20,approximately 2,55,270 tickets per day were sold by STBA.During 2020-21,approximately 10,780 tickets per day were sold by STBA.Since there was a lull in unreserved ticketing during 2020-21 owing to COVID-19 pandemic,the number of tickets issued during 2020-21 was much lower than that in 2019-20.h)Yatri Ticket Suvidha Kendra(YTSK)With a view to expanding the facilities for issuing of tickets(both reserved and unreserved),public private partnership was allowed in establishment and operation of computerized PRS-cum-UTS terminals at centers called YTSK.i)Online concessional ticket booking facility to Divyangjan The scope of internet ticketing has been expanded to provide online concessional ticket booking facility to Divyangjan.j)Booking of Foreign Tourists through Internet upto 365 Days in AdvanceWith a view to provide confirmed reservation to foreign tourists through internet(e-ticketing),they are allowed to book accommodation in Executive Class/1st AC,2nd AC Class in all trains upto 365 days in advance The facility is available upto the time of opening of reservation in the train as per Advance Reservation Period.Thereafter,the Foreign Tourists can 29INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21book ticket against Foreign Tourist Quota which has been earmarked in certain mail/express trains based on demand pattern.k)Facility of Online Change of Boarding PointFor the convenience of passengers,the online facility of change of boarding point upto the time of preparation of first reservation charts has been extended both for the tickets booked through internet as well those booked through computerized Passenger Reservation System(PRS)counters.This facility is also available through 139 and across PRS counters(during working hours of PRS centers).In case of change of boarding point short of 24 hours,no refund is permissible in normal circumstances.Coaching VehicleInduction of Smart CoachesIn view of the latest development in rolling stock technology and increased level of passengers comfort.Indian Railways has introduced 88 smart coaches with ultra modern features like Smart Public address and passenger information system,Smart HVAC(Heating,Ventilation and Air Conditioning system),Smart security and surveillance system etc.in train service.In 2020-21,64 Smart coaches were introduced.2.Induction of semi-high speed Train-setsSemi High Speed Self Propelled Train-set was manufactured by Integral Coach Factory/Chennai with indigenous efforts,termed Train-18/Vande Bharat Express.Vande Bharat Express State-of-the-art Train-set Vande Bharat services have been introduced between New Delhi-Varanasi and New Delhi-Shri Mata Vaishno Devi Katra in 2019-20.These trains have ultra modem features like quick acceleration,Substantial reduction in travel time,having maximum speed of 160kmph,on board infotainment and GPS based passenger information system,automatic sliding doors,retractable footsteps and Zero discharge vacuum bio toilets etc.The Train-18 has contemporary features as per global standards.1st train started from 17th February 2019 between Delhi-Varanasi.2nd train started on 5th Oct,2019 between New Delhi-Shri Mata Vaishno Devi Katra.3.Complete switchover to LHBMinistry of Railways has decided for large scale proliferation of LHB coaches which are technologically superior with features like Anti climbing arrangement,Air suspension(Secondary)with failure indication system and less corrosive shell.These coaches have better riding and aesthetics as compared to the conventional ICF coaches.The Production units of Indian Railways are now producing only LHB coaches from April 2018 onwards.The production of LHB coaches are continually increased during the years:1,469 LHB coaches in 2016-17,2,480 LHB coaches in 2017-18,4,429 LHB coaches in 2018-19,6,277 LHB coaches in 2019-20 and 4,323 LHB coaches in 2020-21.4.Focus on amenities for unreserved passengersa.Antyodaya Train ServiceThese are long distance fully unreserved train comprising of LHB 30INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21general second class coaches with vestibules.These have additional facilities like cushioned luggage racks,additional hand hold in doorway area for the comfort of standing passengers,provision of J hooks near longitudinal luggage racks for hanging carry bags,enhanced number of mobile charging points,Fire extinguishers with anti-theft arrangement,more pleasing colour scheme for interior and exteriors,provision of MU cable in each coach for running train service with loco at both ends.At present 16 Antyodaya trains are running in service.b.Deen Dayalu coaches General second class coaches for unreserved passengers with additional facilities like Cushioned luggage racks,Additional hand hold in doorway area,provision of J hooks for hanging carry bags,Bio-toilets,Enhanced mobile charging facility,Water level indicator,Pleasing Interiors,Improved exterior colour scheme and polymerized floor coating in toilets.So far,around 2,800 Deen Dayalu coaches turned out by Production Units during 2016-17,2017-18,2018-19,2019-20 and are in service.Of these 585 Deen Dayalu coaches turned out in 2020-21.5.Focus on improving amenities for reserved passengersa.Humsafar TrainsHumsafar trains having additional amenities in the coaches have been introduced for providing comfortable Air-Conditioned III Tier travel.Following major features have been introduced:-GPS based Passenger information system,Passenger announcement system,Dust bins in each bay,4 lane coffee vending machine,improved aesthetics and pleasing colour scheme,Closed-Circuit Television(CCTV)based surveillance system,Integrated Braille displays etc.38 Humsafar trains have been introduced in service till date.b.TEJAS trainsIndian Railways has introduced Ultra modem TEJAS trains with speed potential of 200 KMPH have been introduced which runs on LHB platform with non-executive and executive chairs Car.At present,4 Tejas trains have been introduced in service over Indian Railways out of which 02 were introduced in 2019-20.These ultra modern trains have following major distinguished features:Automatic entrance doors,Infotainment system(LCD Screens),Passengers Information display system(Electronic Reservation chart System),GPS based Passenger information system,Fire and Smoke detection system,Superior toilet fittings,Sealed vestibules,LED lights,CCTV,Aesthetically pleasing colour scheme etc.c.TEJAS Rajdhani TrainsIndian Railways has introduced Ultra modern TEJAS trains with speed potential of 200 KMPH have been introduced which runs on LHB platform with sleeper coaches.RAJDHANI trains coaches are planned to be replaced with TEJAS Sleeper coaches.At present,One Tejas sleeper rake of Agartala-Anand Vihar Rajdhani Express has been introduced in 2020-21(since February,2021).These ultra modern trains have following major distinguished 31INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21features:Automatic entrance doors,Electro-pneumatic assisted brakes,Improved Inter-car Gangway,Automatic Coupler,Improved lavatory-vacuum assisted flushing with bio-toilets,Infotainment system&PIS and digital destination board,Smart windows with automatic venetian blinds,Sensorised taps,touch free Soap dispenser,flushing system,hand driers,tissue paper dispenser touch free COVID,Provision of Wi-Fi facility,Infotainment system(LCD Screens),Passengers Information display system(Electronic Reservation chart System),GPS based Passenger information system,Fire and Smoke detection system,Superior toilet fittings,Sealed vestibules,LED lights,CCTV,Aesthetically pleasing colour scheme etc.d.Uday trains Utkrisht Double Decker Air-conditioned Yatri(UDAY)trains have been conceptualized as double-decker rakes with improved amenities such as,a dedicated vending machine with dining facilities in each of the four coaches in the rake,Decorative vinyl wrapping on both exterior and interior of the coach,Water borne solar reflective coating on the roof and PU painting on the end walls,High quality and high aesthetic passenger friendly fittings in the toilets,Defused LED lighting,Powder coated seat frames and snack tables,Aesthetically designed seat covers,All luggage racks are spray painted for aesthetic look,All foot steps are buffed and powder coated,Vynatile floor provided with clear coat,All stainless steel items like passage door,vestibule door,moldings etc.,are buffed,PIS and infotainment system with Wi-Fi,7 dedicated LCD screens provided in coach and Dining table and chairs in middle deck etc.Two Uday rakes are running in service between Bangalore City Coimbatore(Train No.22665/56)and between VSKP-BZA.e.Vistadome coachesVistadome coaches provide panoramic view,through wider body side windows as well as through transparent sections in the roof,thus enabling the passengers to enjoy the scenic beauty of the places through which they travel.Presently,36 Vistadome coaches are available over various sections of Indian Railways.Seven LHB type BG Vistadome coaches were have been manufactured in 2020-21 by ICF/Chennai.One of these coaches was introduced in January 2021 in Train No.09247/48/49/50 Ahmedabad-Kewadiya Jan Shatabdi.6.Focus on improving safety in new manufacture coaches Instructions have been issued for provision of following items in coaches during manufacturing at Production Units to improve the safety features of these coaches:1.Fire detection and suppression system in all newly manufactured Power Cars and Pantry Cars.2.Fire and Smoke detection system in all newly manufactured AC coaches.3.Double Acting AC compartment doors in all newly manufactured AC coaches.4.Fire extinguishers in all newly manufactured coaches.32INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-215.Automatic plug type doors in all newly manufactured Humsafar and Uday train coaches.7.Improving interiors of Coaches-Up-gradation/modernization of rakes(i)Project Swarn:Project Swarn was started to upgrade the condition of Rajdhani and Shatabdi Express Trains,with the objective of significantly improving the passenger experience across the nine dimensions which include coach interiors,toilets,onboard cleanliness,staff behavior,catering,linen,punctuality,security,on-board entertainment.Real time feedback is also a part of Project Swarn.Under this scheme total 29 trains were targeted and have been covered.Later,under Project Swarn,all Rajdhani and all Shatabdi have been covered.Presently,all rakes of Rajdhani and Shatabdi Express Trains in services have been upgraded.(ii)Project Utkrisht:IR has also launched Project Utkrisht in order to improve the condition of ICF type coaches running in Mail/Express trains.Up gradation of 640 rakes of Mail/Express trains has been taken up under Project Utkrisht for improvement in patronized train services.Work in 463 rakes has already been completed under Project Utkrisht.Work has been completed in 81 rakes in 2020-21.8.Other facilities to improve train facilitiesa.Quick Watering FacilitiesQuick watering facilities are being provided for quick watering in trains within stipulated halt of the train.These facilities are essential to ensure availability of adequate water in coaches throughout the journey.At present,66 stations have been provided with Quick Watering Facilities.Out of these,30 stations have been provided with Quick Watering Facilities in 2020-21.b.Automatic Coach Washing Plants Automatic Coach Washing Plants have been installed over Zonal Railways to clean exterior of coaches more effectively and efficiently.In addition to excellent cleaning the direct water consumption also gets reduced avoiding wastage and recycling the water through water recycling plant integrated with this plant.127 locations have been identified for provision of ACWP.Instructions have already been issued to General Managers of Zonal Railways to provide automatic coach washing plants in all coaching depots.Now,Automatic Coach Washing Plants are available at 27 locations.Work has been completed in 11 locations in 2020-21.c.Proliferation of Bio-ToiletsAs a part of Swachh Bharat Mission,Indian Railway is proliferating bio-toilets on all its coaching stock so that no human waste is discharged from coaches on to the track.Indian Railways has completed fitment of Bio Toilets in all its coaches running on line.73,110 coaches(2,58,990 Bio Toilet)have been fitted with Bio-toilet.Of these,4,420 has been fitted in 33INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-212020-21.Details of provision of Bio-toilets are as follows:YearsBio-ToiletsCoaches2004-20149,5873,6472014-201759,73516,1232017-201857,42915,0172018-201969,16619,1372019-202046,98814,7662020-202116,0854,420Total2,58,99073,110Further,IR has planned to supplement the existing Bio-Toilet system with Vacuum flushing system toilet(Bio-Vacuum Toilets),which substantially reduces the requirement of water for flushing,while ensuring effective/proper flushing of fecal matter from the pans.Indian Railways have provided Bio Vacuum Toilet in total 1,250 LHB coaches and it has been decided to provide Bio Vacuum Toilets in newly manufactured AC LHB Coaches of premium rakes.Sanctions for 8,500 coaches are also given.9.AC-III Tier Economy CoachIR has planned to introduce AC III Economy class to cater the needs of general masses and fulfilling their expectation of travel in AC class.These coaches are planned to replace normal general sleeper class coaches in trains.One prototype coach was turned out in 2020-21.Manufacturing of 806 such coaches is included in Production Plan 2021-22.10.COVID-19 preparednessAll Zonal Railways was advised for COVID-19 preparedness.It was advised in the video conference held on 25.03.2020 that a few rakes may be converted into quarantine/isolation coaches in consultation with the Medical department,so as to augment the quarantine facilities being created.In this connection consultations were held with Armed Forces Medical Services,Medical Department of various zonal Railways,and Ayushman Bharat.Indian Railway may be required to convert up to 20,000 such coaches,with 5,000 coaches to be converted initially into quarantine/isolation coaches.As per requirement,5,601 Non-AC ICF sleeper coaches,hybrid sleeper and General coaches had been converted into isolation coaches.Cleanliness and HygieneCleanliness on Trains Intensive mechanized cleaning of coaches Mechanised cleaning of coaches is being carried out in the coaching depots through professional agencies.Heavy duty machines such as high pressure jet cleaners,floor scrubbers,vacuum suction cleaners etc.are deployed for the purpose.Clean Train Stations(CTS)scheme Clean Train Station Scheme is provided for limited mechanized cleaning attention to passing through trains during their halts at selected stations enroute.34INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21 On Board House Keeping Service(OBHS)On Board House Keeping Service has been prescribed in all Rajdhani,Shatabdi,Duronto and other important long distance Mail/Express trains for cleaning of coach toilets,doorways,aisles&passenger compartments during the run of the trains.This scheme had been implemented in more than 1000 pairs of trains.The Scheme is further planned to be extended to cover all long distance Mail/Express vestibuled trains excluding purely overnight trains.Clean My Coach/Coach Mitra service “Clean My Coach service has been upgraded to provide Coach Mitra service in about 1000 pairs of train for providing single window assistance to train passengers regarding cleanliness,linen,disinfestation,watering and petty repair.Provision of dustbins in AC and non AC coaches.Mechanized cleaning of coaches at both ends is being carried out through professional agencies in around 155 coaching depots.Machines like high pressure jet cleaners,floor scrubbers,wet&dry vacuum cleaners,hand held buffing machines etc.are deployed for the purpose.Instructions have been issued for proper sanitization of coaches with regular and frequent sanitation of common use areas like door handles,railings,taps,washrooms etc and availability of water.Cleanliness at Stations:Provision of Integrated Housekeeping Contracts at major stations,award of rag picking/garbage disposal contracts at stations.Mechanized cleaning being done at 950 stations.Rag picking contracts at 1,310 stations.Concrete washable aprons on platform tracks are provided to facilitate clearing of night soil on platform lines by washing with water jets.Provision of clean and hygienic toilets including pay and use toilets at around 900 stations and deluxe Pay&use toilets at 77 stations.Enforcement of Indian Railways(Penalties for activities affecting cleanliness at railway premises)Rules,2012 has been intensified.4.11 lakh persons penalized and a fine of 6.66 crore realized during 2019-20.Data for 2020-21 not available.Use of CCTVs is being extended for monitoring cleanliness work at 700 Stations.Social/Charitable Organisations/NGOs have also been associated in periodic cleanliness/awareness drives at 70 railway stations.Railways have taken up a pilot project for disposal of Municipal Solid Waste(MSW)being generated at major railway terminals in an environment friendly manner including segregation of waste and conversion of bio-degradable waste to energy(bio-methanation).35INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21PlanningIn the year 2020-21 the following assets were acquired:-S.No.HeadsIn Numbers1.Wagons(BLC Private Wagons)10,0622.Locomotives including Trade7543.Coaches including Trade6,277EMUs312MEMUs638DMUs54In addition,the following works were accomplished:-S.No.HeadsIn Kms.1.New lines 286.312.Gauge Conversion to BG from MG/NG469.933.Double/Multiple lines 1,614.184.Route Electrification6,0155.Track renewals(both primary&secondary renewal)4,363The Plan allocation(Revised Estimates)and Actual Net Expenditure for 2020-21 compared with 2019-20,were as follows:S.No.Plan Head2019-20 2020-21(in crore)Allocation(R.E.)Actual Net ExpenditureAllocation(R.E.)Actual Net ExpenditureCIVIL ENGINEERING1New Lines(Construction)22,974.2712,683.1726,772.2214,901.342Gauge Conversion#3,129.274,140.15#3,430.123,980.303Doubling$23,777.5822,385.67$22,213.5724,226.154Traffic Facilities-Yard Remodeling and Others%1,941.711,626.22%2,531.701,241.135Road Safety Works-Level Crossings546.44570.54799.83543.536Road Safety Works-Road Over/Under Bridges&4,718.883,520.92&6,329.564,137.447Track Renewals7,068.877,802.639,201.1611,657.528Bridge Works,Tunnel works&approaches751.83777.50877.88769.679Staff Welfare 516.84480.92504.83470.1010New Lines(const.)Dividend free Projects 3,300.00-TOTAL68,725.6953,987.7272,660.8761,927.18MECHANICAL1Rolling Stock42,670.5837,101.7843,362.0832,213.162Leased AssetsPayment of Capital Component10,557.5310,462.2111,966.7211,948.243Machinery and Plant430.92448.11 757.16672.764Workshops including Production Units2,121.022,119.122,176.302,330.42TOTAL55,780.0550,131.2258,262.2647,164.58ELECTRICAL ENGINEERING1Electrification Projects?7,593.557,124.63?6,590.756,141.022Other Electrical Works including Traction Distribution Works.*603.61481.30*739.69652.23TOTAL8,197.167,605.937,330.446,793.2536INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21SIGNAL AND TELECOMMUNICATION1S and T Works1,374.701,620.691,858.351,900.84TOTAL1,374.701,620.691,858.351,900.84OTHERS1Computerization423.45282.81563.00390.012Railway Research43.5826.8050.1057.263Users Amenities 2,583.391,902.90 2675.902,583.454Investment in Non-Govt.undertaking including JVs/SPVs16,634.9816,924.8815,620.0015,629.655Other Specified Works708.94455.73830.62482.856Training/HRD102.5585.73150.0086.937Inventories200.00915.500.00686.098M.T.Ps.1,577.501,515.181,690.461,543.90TOTAL22,274.3922,109.5421,580.0821,480.14GRAND TOTAL1,56,351.99!1,35,455.101,61,692.001,39,245.99Revised Estimates Includes 542 crore under EBR(IF),11,769 crore under EBR(Partnership)and 13,539 crore under EBR(Special).It also include 7,535 crore for National Project&Projects of National importance.Includes 2,900.50 crore for National Project and 544 crore for project of National Importance.It also include 599.26 crore under EBR(IF)and 14,506 crore under EBR(IF).#Includes 644 crore under EBR(IF),2,846 crore under EBR(S)and 122 crore for national Project.#Includes 6 crore for National Projects.It also includes 849.10 crore under EBR(IF)$Includes 1,429.69 crore under EBR(IRFC),1,000.00 crore under EBR(S)and 19,705 crore under EBR(IF)$Includes 1,407 crore under EBR(IRFC)and 21,746.14 crore under EBR(IF).%Includes 500 crore under EBR(IF)and 792 crore EBR(Partnership).%Includes 618.57 crore under EBR(IF)and 254.82 crore under EBR(PPP).Includes 800 crore under EBR(Special).&Includes 880 crore under EBR(Partnership),and 5,448.00 crore under EBR(S).&Includes 1,022.51 crore under EBR(PPP).Includes 10,500 crore under EBR(Special).Includes 862 crore under EBR(Special).Includes 200 crore under EBR(Special).Provision for Udhampur Srinagar-Baramulla National Project.Includes 33,137.31 crore under EBR(IRFC),1,559 crore under EBR(Partnership)and 6,739.98 crore under EBR(S).Includes 141 crore under EBR(Special)and 1,074 crore under EBR Includes 408.85 crore under EBR(Special)Includes 10 crore under EBR(IF)and 1,942.45 crore EBR(Special).Includes 24.50 crore under EBR(IF)and 1942 crore under EBR(Special).?Includes 6,599 crore under EBR(IF).?Includes 7,602.55 crore under EBR(IF).*Includes 647.94 crore under EBR(Special).*Includes 120 crore under EBR(PPP).Includes 1,857.08 crore under EBR(Special).Includes 250 crore under EBR(Special).Includes 0.10 crore under EBR(Special).Includes 900.60 crore under EBR(Special).Includes 702 crore under EBR(PPP).Includes 410 crore under EBR(Special).Includes 141 crore under EBR(Special).Includes 1,415 crore under EBR(Special).Actual Net Expenditure Excluding actual expenditure of 15,935.02 crores under EBR(PPP)during 2020-21.!Excluding actual expenditure of 12,609.38 crores under EBR(PPP)during 2019-20.Staff Quarters and Amenities for Staff merged&reclassified as staff welfare.Reclassified as other Electrical works including TRD Includes 3,098.42 crore reported by Railways under new lines(const.)dividend free projects now merged with new lines(const.)37INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21EngineeringDuring 2020-21,286.31 Kms of new lines were constructed,track of 469.93 Km was converted from MG/NG to BG and 1,614.18 Kms of double/multi-tracking line commissioned.The details of the same are given below:Gauge ConversionDuring 2020-21,469.93 Kms of track was converted from MG/NG to BG as detailed below:SNRailwaySectionKm.1ECRJhanjharpur-Tamuria9.002ECR-NSaraigarh-Raghopur 11.003NERPilibhit Shahbaz Nagar42.004SCRAkola-Akot44.405SECRBhandarkund Bhimal gondi23.006SECRChiraidongri Mandla fort24.007SECRLamta Samnapur GC25.008SECRNainpur-Bhoma55.629SECRChhindwara-Chourai32.7510SRUsilampatti-Andipatti21.0011SRSingaperumal Koil-Chengalpattu8.5012SRGuduvancheri-Singaperumal Koil11.0013WRVadnagar-Varetha21.0414WRDungarpur-Raigadh Road70.7315WRDabhoi-Chandod18.6616WRFatehabad Chandravati Ganj-Ujjain21.5417WRKalol-Dangarwa18.1918WRNimar Kheri-Sanawad12.50 Total469.93DoublingDuring 2020-21,1,614.18 Kms of double/multiple lines track was completed.New Lines During 2020-21,286.31 Km of new lines have been completed on the following sections:SNRailwaySectionKm.1ERPoreyahat-Godda16.502ECR/NSaraiagarh-Asanpurkupha13.003ECoRKendrapara(KDRP)-Paradeep39.544NRRohtak Elevated track 4.855NWRGangapur City-Piplai24.726SCRManoharabad-Gajwel31.007SCRJaklair-Makthal11.50Repair Work Near Godavari Bridge,SCRLHB Coaches IR597.20 469.93 800 600 400 200 0 GAUGE CONVERSIONS 408.49(Kms.)TRACK RENEWALS 5,000 3,750 2,500 1,250 0(Kms.)4,500 4,363 4,181 6,400 4,800 3,200 1,600 0 ANNUAL RAILWAY ELECTRIFICATION(ROUTE KILOMETRES)4,378(Kms.)6,015 2018-19 2019-20 2020-21 2019-20 2020-21 5,276 2018-19 2018-19 2019-20 2020-21 38INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21Digital laboratory,IRICENKosi Rail Mega Bridge,ECR8SECRKeoti-Antagarh16.959SECRKorichhapar-Dharamjaigarh Road30.0010SECRKatangi-Tirodi14.9311SWRGangavathi-Karatagi28.0012WCRJhalrapatan-Junakheda13.7013WRAlirajpur-Khandala9.7214WRChandod-Kevadiya31.90Total286.32Track Renewal During 2020-21,4,363 kms of Complete Track Renewal(CTR)units of track renewal was carried out.Year-wise details of Track Renewals carried out and expenditure incurred thereon are as under:Year Gross expenditure (in crore)Track Renewal carried out(in kms)2019-209,390.55 4,5002020-21 13,522.64 4,363One Complete Track Renewal(CTR)units comprises of one km of Through Rail Renewal(0.5 CTR units)and one km of Through Sleeper Renewal(0.5 CTR units).Track UpgradationThe track constitutes the basic infrastructure of a railway system and bears the burden of coping with ever increasing traffic.Higher speed and heavy axle load operation of IR has necessitated up-gradation of the track structure.Several policy initiatives have been taken in order to modernize the track.Track structure is upgraded at the time of renewals.Sleepers are being upgraded from wooden,steel and CST-9 to PSC(Normal/Wider Base)sleepers.Heavier section and high tensile strength 60kg 90UTS/R260 rails are used in place of 90R/52kg 72/90 UTS rails.Similarly,long rail panels or welded rails are predominantly used in place of earlier fish plated joints.The sturdier turnouts using thick web switches are being provided on trunk routes and high density routes.As on 31-03-2021,on BG main lines of IR,about 89.61%of the length is covered by long welded rails,99.36%with PSC sleepers and 98.28%with 52kg/60kg 90 or higher UTS rails.Welded RailsOn most of BG track,rails have been converted into long Welded Rails.Short-welded Rails of 39m length and Single Rails are limited to locations,where welded rails are not permitted on technical grounds.As on 31.3.2021,total length of welded tracks on main lines of Indian Railways was 93,248.7 km out of which 84,019.7 km was long welded rails and 9,229 km was short-welded rails.597.20 469.93 800 600 400 200 0 GAUGE CONVERSIONS 408.49(Kms.)TRACK RENEWALS 5,000 3,750 2,500 1,250 0(Kms.)4,500 4,363 4,181 6,400 4,800 3,200 1,600 0 ANNUAL RAILWAY ELECTRIFICATION(ROUTE KILOMETRES)4,378(Kms.)6,015 2018-19 2019-20 2020-21 2019-20 2020-21 5,276 2018-19 2018-19 2019-20 2020-21 39INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21(A)Track Modernization Indian Railways is working towards progressive mechanization of maintenance,laying,inspection and monitoring of track.Some of the major steps taken during 2020-21 are as below:-(i)Rail Grinding Machines(RGM):Rail grinding is a regular rail maintenance practice over world Railways for enhancing the life of the rails as well as to reduce the rail fractures,to improve the worn profile of rail head,rail wheel contact band,its location,to remove fatigued material having micro cracks and other surface defects on the rail head and to remove corrugations.IR has started rail grinding with deployment of 2 nos.72-stone RGMs in the year 2011-12,one each in NCR and SCR.96-stone high productivity rail grinding machines(30-40%more productivity than the available RGMs)are being introduced to cover IR network.(ii)Switch Rail Grinding Machine(SRGM):Besides the grinding of plain track,grinding of Turnouts is also very important activity for enhancing the life of assets and reducing the failures.Presently,grinding of mainline rails is done,but turnout switches,LC gates,sharp curves with check rails etc.are left without grinding.SRGMs are being introduced to grind the turnouts of all Zonal Railways.(B)Track Recording Cars(TR)are deployed for recording of track parameters at periodic intervals to enable planning of track maintenance.During 2020-21,a total of 1,76,205 km track recording was done.During 2021-22(Upto Sep.,2021)92 nos.of track machines were added to the fleet of Track Maintenance Machines of IR taking the total to 1,178 nos.This was done despite the restrictions in manufacturing due to COVID-19 pandemic.BridgesAs on 01.04.2021,Indian Railways has a total number of 1,55,278 Bridges out of which 729 bridges are important,12,493 bridges are major and 1,42,056 bridges are minor.During the year 2020-21,1,114 bridges were strengthened/rehabilitated/rebuilt to enhance safety of train operation.Level CrossingLevel crossings are meant to facilitate the smooth running of traffic in a regulated manner governed by specific rules and conditions.Status of level crossings on IR as on 01.04.2021 is as under:Total number of level crossings :20,395Number of manned level crossings :19,532(95.8%)Number of unmanned level crossings:863(4.2%)Indian Railways has decided to progressively eliminate the level crossings for the safety of Road users and train passengers.During the year 2020-21,961 nos.of manned level crossings have been eliminated.All unmanned level crossings on Broad Gauge have already been eliminated on 31.01.2019.40INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21Road Over/Under BridgesTo improve safety of train operation and reduce inconvenience to road users,level crossings are being replaced by Road Over/Under Bridges/Subways(ROBs/RUBs)in a phased manner based on the quantum of traffic.During the year 2020-21,174 ROBs and 959 RUBs/subways have been constructed under cost sharing,railway cost/accommodation works,Deposit/BOT term and by NHAI over IR.Bridge inspection and management SystemModern Bridge Inspection techniques have been adopted,which includes inspection by drones,under water inspections,monitoring the water level with the help of water level system,3D scanning of river bed etc.Land ManagementAs on 31.03.2021,Indian Railways(IR)owns about 4.84 lac hectares of land.About 90%of this land is under Railways operational and allied usages such as laying of new lines,doubling,gauge conversion,track,stations,workshops,staff colonies,etc.The break-up of the land is as under:S.No.DescriptionArea (in lac hectares)1.Track and structures including Stations,colonies,etc.3.602.Afforestation0.433.Grow More Food scheme0.024.Commercial licensing0.045.Other uses like pisciculture0.126.Encroachment0.017.Vacant land0.62Total4.84Creation of various infrastructure facilities for development of future rail network largely depends on the availability of land.Therefore,preservation and meaningful interim use of railway land is the main objective of IRs land-use policy.During 2020-21,Railway did mass plantation of 93 lac trees.Railways have already finalized a model agreement with Ministry of Environment&Forests to be entered by Zonal Railways with State Forest Departments.Moreover,now instructions have been issued to all Zonal Railways to make provision of 1%in all estimates to environment related matter.This will help in meeting the cost of plantation.As such,Railways are making all efforts to plant more and more trees.Besides,railway land is also licensed to railway employees belonging to Group C and D category under Grow More Food scheme,for growing vegetables,crops etc.Licensing of railway land is permitted for purposes directly connected with railway working.Plots of railway land at stations,goods sheds and sidings are licensed to other parties for stacking/storing of goods either 41INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21received or to be dispatched by rail.Railway land is also leased to Kendriya Vidyalaya Sangathan to open Kendriya Vidyalayas.A part of this land is also leased to Central/State Governments/Public Sector Undertakings on long term basis for public utility purposes like ROB/RUB,construction/widening of roads,etc.Railways have also taken up commercial use of such land which may not be required by the Railways for its immediate future use.Through an amendment to Railways Act,1989,Rail Land Development Authority(RLDA),under the Ministry of Railways was constituted on 1st November,2006 to undertake all tasks related to commercial development on railway land/air-space under the control of Ministry of Railways.87 sites measuring 266.68 hectares(approx.)were entrusted to RLDA for commercial development upto 31.03.2021.Necessary action for development of these sites is under process by RLDA.Besides commercial development of vacant Railway land,RLDA has also been assigned the task of development of Multi Functional Complexes(MFCs).Byappanahalli Railway station42INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21Railway ElectrificationWith a view to reduce the Nations dependence on imported petroleum based energy and to enhance energy security to the Country as well as to make the Railway System more eco-friendly and to modernize the system,Indian Railways have been progressively electrifying its rail routes.In pre-independence period,electrification remained confined to 388 Route kilometers(RKMs)and it is only in the post-independence period that further electrification was taken up.Since then,there has been no looking back and the Indian Railways have slowly but steadily electrified its routes.By March,2021,electrification on Indian Railways has been extended to 44,802 RKMs out of the total Broad Gauge(BG)rail network of 68,103 RKMs including Konkan Railway.This constitutes 65.79%of the total BG Railway Network.On this electrified route,67.3%of freight traffic&59.6%of Passenger traffic is hauled with fuel cost on electric traction being merely 38%of the total traction fuel cost on Indian Railways.Further,Indian Railways has planned to electrify remaining BG rail routes expeditiously in mission mode.With the progressive electrification,metro cities of Delhi,Mumbai,Kolkata and Chennai have already been interconnected with electric traction.Mumbai-Chennai route is also electrified except Mohol-Hotgi-Dudhani,on which electrification work is in progress and targeted for completion during 2021-22.II Progress of Railway Electrification(a)The progress of Electrification since independence is tabulated below:YearCumulative Electrified(RKM)1951388196174819713,70619815,34519919,968200114,856201119,607201829,228201934,319202039,329202144,802(b)During year 2020-21,6,015 RKM has been electrified.Electrification work in Tatanagar Badampahar section SER597.20 469.93 800 600 400 200 0 GAUGE CONVERSIONS 408.49(Kms.)TRACK RENEWALS 5,000 3,750 2,500 1,250 0(Kms.)4,500 4,363 4,181 6,400 4,800 3,200 1,600 0 ANNUAL RAILWAY ELECTRIFICATION(ROUTE KILOMETRES)4,378(Kms.)6,015 2019-20 2020-21 5,276 2018-19 43INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-21III Sections Electrified in(2020-21)S.No.SectionRailwayStateRKM1.Taj-sultanpur-Gulbarga-DudhaniCRKarnataka642.Dhalgaon-Kurduvadi-Mohol&Kurduvadi-BhigvanCRMaharashtra2543.Shindaane-JejuriCRMaharashtra274.Miraj-ShenoliCRMaharashtra635.Banka-Bhagalpur-ShivanarayanpurERBihar936.Madhupur-GiridihERJharkhand427.Rampurhat-Harinsing-Dumka ERJharkhand648.Manigram-New FarakkaERWest Bengal569.Muzaffarpur-Sitamarhi-DarbhangaECRBihar13010.Karaila Road-ShaktinagarECRUttar Pradesh3311.Dildar Nagar Jn.-TarighatECRBihar2012.Natesar-IslampurECRBihar2113.Kendrapara Road-ParadeepECOROdisha3614.Khurda Road-NayagarhECOROdisha6615.Jakhal-Duhri-Lehra MahabbatNRPunjab13216.Prayag-Prayag ghatNRUttar Pradesh217.Aonla-BareillyNRUttar Pradesh2818.Phaphamau-PratapgarhNRUttar Pradesh4619.Sultanpur-ChilbilaNRUttar Pradesh3620.Janghai-ZafrabadNRUttar Pradesh4821.Delhi cantt-Patel Nagar Cantt.NRDelhi422.Virbhadra-Yog Nagari RishikeshNRUttarakhand623.Rohtak Elevated Track NRHaryana524.Unnao-Balamau-SitapurNRUttar Pradesh16025.Noli-ShamliNRUttar Pradesh7926.Garhi Harsaru-FarrukhnagarNRHaryana1127.Raebareli-Unchhahar&Daryapur-DalmauNRUttar Pradesh5928.Batala Qudian&Amritsar-Chheharta NRPunjab2729.Amb-Andaura-DaulatpurNRHimachal Pradesh1630.Gohana-Pandu PindaraNRHaryana3731.Etawah-BhandaiNCRUttar Pradesh12632.Bhatni-AunriharNERUttar Pradesh12633.Kasganj-Bareilly-Pilibhit-Majhola PakariyaNERUttar Pradesh18534.Majhola Pakariya-TanakpurNERUttarakhand37Electrification work on North Western Railway44INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-2135.Sitapur-Lakhimpur NERUttar Pradesh4536.Duraundha-MashrakhNERBihar4237.Salempur-Barhaj BazarNERUttar Pradesh2038.Indara-PhephnaNERUttar Pradesh5039.Mandhana-BrahmavartNERUttar Pradesh940.Gorakhpur-AnandnagarNERUttar Pradesh4141.Gonda-SubhagpurNERUttar Pradesh642.New Jalpaiguri-New cooch behar-JoraiNFRWest Bengal17143.Jorai-Srirampur AssamNFRAssam1144.Bongaigaon-Rangiya-KamakhyaNFRAssam15145.Bassi-Jaipur-KanakpuraNWRRajasthan4246.Swarupganj-MavalNWRRajasthan3847.Bhiwani By pass line New lineNWRHaryana148.Ringas-Jaipur-Sheodaspura-PadampuraNWRRajasthan8249.Churu-Sadulpur-Gogameri-NoharNWRRajasthan16450.Madar-Bangurgram-BeawarNWRRajasthan5851.Thiruvarur-NagoreSRTamil Nadu3152.Nagore-KaraikalSRPuducherry1153.Mayiladuturai-ThanjavurSRTamil Nadu6854.Cuddalore port-VriddhachalamSRTamil Nadu5755.Nidamangalam-MannargudiSRKarnataka1356.Jokatte-PanamburSRTamil Nadu957.Medchal-ManoharabadSCRTelangana1358.Vijayawada-BhimavaramSCRAndhra Pradesh10759.Gudivada-MachilipatnamSCRAndhra Pradesh3660.Lingampet Jagityal-MortadSCRTelangana5161.Dharmavaram-KadiriSCRAndhra Pradesh6762.Vikarabad-Kohir deccanSCRTelangana4563.Akola-LohagadSCRMaharashtra3564.Falaknuma-UmdanagarSCRTelangana1465.Tenali-RepalleSCRAndhra Pradesh3366.Bankura-MasagramSERWest Bengal11767.Ramkanali-ChourashiSERWest Bengal668.Rupsa-BhanjpurSEROdisha5669.Tatanagar-Bahalda RoadSERJharkhand4270.Bahalda Road-GorumahisaniSEROdisha2345INDIAN RAILWAYS ANNUAL REPORT AND ACCOUNTS 2020-2171.Bhimalgondi-BhandarkundSECRMadhya Pradesh1872.Nainpur-Lamta-SamnapurSECRMadhya Pradesh5873.Chiraidongri-Mandla FortSECRMadhya Pradesh2374.Tumsar Road-Dongri BuzurgSECRMaharashtra2675.Dongri Buzurg-TirodiSECRMadhya Pradesh2076.Marauda-BalodSECRChhattisgarh5277.Padnur-KalgurkiSWRKarnataka9578.Rayadurg-MolakalmuruSWRAndhra Pradesh1679.Molakalmuru-Chi
2022-12-28
179页
5星级
AIR TRANSPORT COMPETITIVENESS INDEX IN LATIN AMERICA AND THE CARIBBEANAir transportation significant.
2022-12-28
82页
5星级
12022 EDITIONLogisticsGlobal HR Trends32Chapter 1 Overview of the logistics sector Chapter 2 Innovat.
2022-12-26
29页
5星级
THE FUTURE OF AUTOMOTIVEHow One Year in the Covid-19 Pandemic is Impacting MobilityConsumer Trends01.
2022-12-23
13页
5星级
Will they choose the right road? leaders prepare to seize big opportunities23rd Annual Global Automo.
2022-12-21
24页
5星级
Next-Generation Transportation MobilityThe pace of digitalization accelerates to meet growing custom.
2022-12-21
16页
5星级
Report on Sustainable Transport in China China Academy of Transportation Sciences PrefaceThe second .
2022-12-21
140页
5星级
October 2022techUK perspective Putting data and innovation at the heart of Great British Railways transition ContentsContents2Executive Summary 03 Introduction 05 1.The Customer Journey 06 2.Station and Retail 09 3.Data and Digital 11 4.Infrastructure and Utilities 13 5.Freight and Logistics 14 6.Funding and Financing 16 Conclusion 17 techUKs Intelligent Mobility 18 and Transport Programme References 19Executive SummaryExecutive Summary3This report from techUK outlines several strategies that a newly formed Great British Railways(GBR)could adopt as it looks to embed data and digital technology into how our railways are planned and operated.We have a generational opportunity to create a rail network that is low-carbon,cost efficient,reliable and customer focussed.Technology and data form a critical part of this ambition.However,barriers to digital adoption remain.These include a fragmented data landscape,legacy systems which are not easily interoperable and a disjointed approach to technology adoption.Addressing these issues will require partnership between the technology industry and Great British Railways.As the UKs technology trade association,we have begun signalling new approaches to solving some of these challenges.We hope this early thinking will start an ongoing conversation as we work together to revolutionise rail travel in Great Britain.Executive Summary4Summary of Recommendations:1.GBR should lead the creation of an industry eco-system to nurture innovation.2.GBR should ensure there remains a level playing field for Third Party Retailers to foster innovation in ticketing.3.GBR should build on the work of Network Rail in strengthening ties to the telecoms sector to improve connectivity.4.GBR should create a blueprint for stations to become digitally enabled smart-spaces.5.GBR should lead in the creation of a single point of access for data sharing.6.GBR should work closely with the technology sector to develop the right solutions to optimise infrastructure and utilities.7.GBR should work with rail freight operators to build awareness of how technology can support the sector.IntroductionIntroduction5The Williams-Shapps Plan for Rail stated that“rail needs to innovate and accelerate change if it is to remain relevant.1 The plan recommended the establishment of Great British Railways(GBR)as the“guiding mind”for the industry which will own the infrastructure,receive the fare revenue,run and plan the network and set most fares and timetables.The Great British Railways Transition Team(GBRTT)has been established to lead the formation of the new organisation.It has consulted closely on its plans to develop a long-term strategy for rail2,the legislative changes needed to deliver reform3 and on a new target for rail freight.4Deeply woven into GBRs thinking is a focus on data and technology to deliver a low-carbon,cost efficient,reliable and passenger-centric railway5 and we believe that the rate of technological change over the next 30 years for transport cannot be underestimated in delivering those outcomes.However,as the Rail Industry Association(RIA)highlighted recently,railway innovation is under-funded when compared to formal targets published by the Government in its recent Innovation Strategy,and under-supported,when compared to best practice.6On a technical level,barriers to adoption and integration remain.We have attempted within this short report to tackle some of these issues head on.Through drawing upon wealth of industry and technology experience available within our membership,we have begun signalling better approaches and innovative solutions to support the next generation of British railways,its ecosystem and supply chain.We do not intend to provide a fully comprehensive breakdown of every step GBR should undertake.Rather,we have taken GBRs six strategic pillars for transformation and outlined some first principles for ensuring data and technology can become embedded following a simple situation,challenge,recommendation and evidence format.We are positive about Great British Railways and what it can deliver for the technology sector and the UK more broadly.Through establishing stronger partnerships between GBR and the technology sector we believe the barriers to private sector investment in rail and innovation can be overcome.The Customer Journey61.The Customer JourneyTicketing Situation:The Williams-Shapps Plan for Rail recommended that Train Operating Companies(TOCs)will lose their ability to retail tickets.Instead,a new simplified system will be established where GBR will be responsible for setting fares and timetables and will retail tickets through a new retail arm.Third Party Retailers(TPRs)will also continue to retail tickets.Achieving this level of centralisation creates technical and commercial challenges for GBR.It will need to unite a fractious,difficult to navigate landscape and ensure a competitive market to bring customers the best price and experience.This is an ambitious and welcome objective.However,there are technical and commercial challenges created by the integrations needing to take place across the value-chain to achieve the seamless experience which passengers expect.In this section we have taken two key areas for passengers;ticketing and broadband connectivity;and set out recommendations for improving these processes using technology.GBR will be accountable for the end-to-end customer journey,from the decision to travel,the core train service,through to post journey.1.The Customer Journey7Challenge:GBRs retail arm will act as a gatekeeper to the entire rail retail environment.However,as true of other regulated retail markets,GBR will need to ensure that level playing field safeguards are in place to ensure a competitive and dynamic market.This includes ensuring product and feature parity between GBR and TPRs.From a technical perspective,TOCs all currently utilise their own mobile and web applications which may be based on multiple development platforms and not easily interoperable.While steps have been made to reduce complexity through cloud technology platforms such as AWS and Azure,the prolific nature of application development means integration may be difficult and expensive if GBR is to achieve true centralisation.Safeguards,clearly defined within GBRs licence and legislation,will be essential for ensuring that all retailers are treated on an equal and non-discriminatory basis under a commitment to competition in the rail retail market.From a technology viewpoint,interoperability will play a huge part in how the railway can attract new customers.We believe that a common integration platform for application development will lead to simplification and a more competitive environment.An integrated system will also enable the greater uptake of digital ticketing,unlocking the benefits of mobile technology,disruption notification alerts and other innovative features being developed by TPRs to improve customer experience and satisfaction.Evidence:The Netherlands has a strong tradition of integrated ticketing for public transport.In 1980,it was the first country to have a national ticketing and fare system for local and regional public transport.It was the first country to adopt nationwide ticketing,with more than 17,500 station validators,6,000 gates and 450 ticket vending machines.7The implementation of contactless ticketing across the TfL Network,originally achieved with the Oyster payment card,and later improved to accept retail credit and debit cards is another good example.Contactless technology was not new,nor was it a rail-specific development,but the change to the passenger experience was radically different.The underlying RFID technology has been in use for many years in applications outside rail.8Broadband connectivitySituation:It is essential to ensure the appropriate connectivity on rail journeys for passengers and staff.Currently,users can access the internet directly via their mobile phones or can connect to Wi-Fi networks provided by train operators.These Wi-Fi links are normally served by connections to mobile masts.This means that train-based access to the internet is entirely dependent on the availability of terrestrial mobile coverage,often leading to intermittent connectivity which falls short of expectations.Challenge:Broadband on trains is a major selling point in support of rail travel and considerable effort has been made to improve it.Nevertheless,basic problems remain,namely that trains routinely travel through sparsely populated areas with limited terrestrial coverage.Installing new infrastructure to improve this can also be costly.Recommendation:GBR will need to ensure that there is a level playing field for TPRs to foster innovation in ticketing.8Network Operators,full connectivity will be delivered on the 51-mile stretch,improving the experience of 300,000 passengers a day at its busiest.9techUK member CGI is leading a consortium of businesses including iComera,All.Space,5G3i and rail operators Network Rail,ScotRail and Northern Trains to the use of satellite communications(satcoms)to achieve greater connectivity and solve the issue of terrestrial gaps in service.10 In London,techUK member BAI Communications has agreed a 20-year partnership with Transport for London to deliver connectivity across the tube network.To date,over 400km of dark fibre has been laid,already delivering 4G coverage to the Jubilee Line and is due to expand to the wider network in the coming year.11 This has been supported by fellow techUK member Capitas data network infrastructure.teckUK member Freshwave has also supported HS1 in deploying a 5G Mobile Private Network in St Pancras station to investigate how the use of 5G can improve efficiency in operations and maintenance,and improve the customer experience.12 The solutions adopted need to provide service into the hard to reach and high usage locations to ensure a highly reliable end-to-end mobile connection throughout the passenger journey.This is going to be essential to enable passengers and staff to access the wide range of digital apps and centralised servers that the new vision for the customer journey requires.This means solving mobile connectivity in mainly the tunnels and underground locations where signal(including Wi-Fi)is currently lost together and ensuring high-capacity systems in the busiest stations.Solving this requires joined-up approach across shared infrastructure to drive the best and most competitive solutions.Evidence:techUK member Cellnex UK is working with Network Rail to improve cellular and mobile data coverage on the London to Brighton line.Through the installation of new infrastructure such as towers and fibre cabling for Mobile Recommendation:GBR should build on the work of Network Rail in strengthening ties to the telecoms sector through embracing a partnership and outcomes-led approach.2.Station and Retail92.Station and Retail The creation of a common blueprint for stations can be used to facilitate shared information flow and collaboration between all actors in the station environment and the passenger.This solution could provide a more pleasant,sustainable,and profitable station environment.For example,utilising digital twins technology for future stations could make these part of the metaverse,enabling new dimensions for engaging passengers within stations and driving an attractive virtual economy.High capacity,high reliability mobile and Wi-Fi infrastructure needs to be provided.However,the creation of new data streams,especially those pertaining to passengers,introduces significant new challenges regarding the information and cyber security of that data and the privacy of the public who generate it.A carefully considered cyber security and privacy approach is therefore critical.Situation:As we recover from the pandemic and life begins to return to our cities and their businesses,there is a need to innovate in attracting people back to city spaces and to promote rail travel as an enjoyable,relaxing passenger experience.Currently,urban stations are often seen as an unwelcoming,sterile,transactional environment,which passengers view as somewhere to get through quickly simply to get on a train.This fails to maximise the value of the station as a retail asset and attract business investment in the station environment.Challenge:Transforming the station experience requires consideration of many interrelated aspects including avoidance of travel disruption,integrated digital services(e.g.,location-based retail/marketing alerts,personalised travel guidance,etc),passenger safety,high-capacity data connectivity,security and privacy,ease-of-use and accessibility,and automation of operational maintenance.GBR will take over all asset management including stations and depots.It will be responsible for improving the experience within stations to encourage passengers to stay for longer and improve the viability of its retail offer.Recommendation:GBR should consider stations as digitally enabled smart-spaces.2.Station and Retail10Ipsotek,an Atos company and techUK member,is also pioneering the use of artificial intelligence-based video analytics to enhance station safety and security,creating smart spaces that drive operational efficiency and decision-making improving passenger experience.Evidence:In Reading,techUK member Atkins is working with Network Rail to create a digital twin of Reading station that will improve its energy performance by up to 20%.The roll out of sensors will provide real-time data on the stations energy usage,revealing where energy is being overused and could be reduced.Data collected by the sensors will also provide insights on passenger numbers and help to understand station-user behaviour.133.Data and Digital113.Data and DigitalWith rapid digitalisation across sectors,legacy systems will likely be left behind in terms of skills and investment.This will stifle innovation and lock the rail community into incumbent suppliers.The current approach of developing a Rail Data Marketplace,an API layer over existing datasets,does not remove barriers to unlocking new value as the data inputs are of varying quality.We need a new approach to cataloguing,ingesting and synchronising data from legacy systems into a common data architecture via suitable data pipelines.This will enable the efficient delivery of projects with a data sharing element or with data as a primary focus,through a consolidated platform intended to save time and money.Situation:The rail industry needs to become part of the evolving connected data-driven transport and smart city ecosystem.For example,rail is expected to become a key part in the growth in Mobility-as-a-Service offerings(MaaS).However,the industry currently lacks a single view of data across its various functional,services and engineering units,let alone the wider ecosystem and supplier base.This is exacerbated by a lack of data sharing frameworks both commercially and technically,resulting in“orphan”industries working in isolation from one other in the same ecosystem.Challenge:As many rail information systems have grown organically over time,the data required is likely to be in multiple existing legacy systems,in diverse,non-standard formats.Attempting to re-engineer the data landscape to build a centralised data model from the ground up may be problematic and extend time to commercialisation.Data sharing is also often marred by lack of clear guidelines including unclear ownership,IP barriers and lack of a single coherent vision.This leads to difficulties in maintaining data quality standards,the need to maintain legal and data sharing terms,an inconsistent approach to managing IP and a lack of a single custodian for the rail data ecosystem.Data and digital services are intended to run at the core of GBR,permeating multiple elements of service provision and delivery.Recommendation:GBR should lead in the creation of a single point of access for data sharing supported by a robust governance framework enabling cross-functional collaboration.3.Data and Digital12Evidence:Transport for Londons(TfL)creation of the TfL API has nourished significant innovation,delivering improved experiences for passengers and stimulating a dynamic industry.Businesses such as Waze,Twitter,Google,Apple,Citymapper,as well as many academics and professional developers partner with TfL and use this data to create new commercial and non-commercial customer-facing products and services.A report published by Deloitte in 2017 found that the release of open data by TfL is generating annual economic benefits and savings of up to 130m for travellers,London and TfL itself.14In addition,techUK member Capitas data network infrastructure enables London Underground to continue running,from stations to back-office functions.The system allows TfL to manage,monitor,and provide preventative maintenance,allowing the Tube to run smoothly.The service was recently enhanced to include Wi-Fi connectivity in stations and on the Jubilee Line,the first cellular and mobile network of its kind in the UK.15Benefits include a reduction in technical and back office(e.g.,legal)overheads and improving the financial sustainability of the railways.There is also potential for revenue to be generated from data initiatives to further strengthen financial standing.Finally,putting data from passenger and freight end-users at the centre of decision making will enable GBR to deliver on its ambition of becoming a truly customer-centric railway.More broadly,the proliferation of MaaS is also something we can expect to continue to develop overtime,fuelled by the need for carbon reduction and as passenger demands for a seamless journey experience across multiple modes of transport increase.The development of a common technology strategy for a cloud-based system of data management,alongside an open data architecture and standard APIs will be key to enabling MaaS and bringing the railway closer to other transport modes.4.Infrastructure and Utilities134.Infrastructure and UtilitiesDeriving the insights necessary to reduce cost,improving passenger experience and supporting the decarbonisation of the railway will also require close co-operation between industry providers to create a standardised approach to the underlying information architecture.Evidence:Atos is engaged with Network Rail in the development of digital twins and other immersive environments to enable powerful innovative learning pathways aligned to the adoption of new technologies(e.g.ETCS L3),as well as driving a reduction in cost of physical training assets.Atos is also delivering the Phase 1 options selection for Network Rails proposed future synthetic environment,that seeks to address the major challenge of replacing aging UK signalling infrastructure by improving design efficiency and reducing the cost of future signalling systems through the application of an immersive,integrated automated design,simulation,visualisation,and test environment.16 Situation:Technologies to improve infrastructure maintenance and performance are rapidly evolving in many industries,applying modelling,artificial intelligence and machine learning to drive operational and strategic decision making.There is a significant opportunity for the application of digital twins and immersive technologies for rail,however,their application is currently fragmented.This also prevents the integration of data from other external sources(e.g.,highways and air travel)and supply sectors(e.g.,utilities)that will be critical if GBR is to take responsibility for the end-to-end passenger experience.Challenge:While there are several vendor solutions addressing concerns in rail infrastructure and utilities,their development is somewhat uncoordinated and there is no agreed common reference architecture supporting their integration.This understanding combined with selecting the right operational processes and defining the expected decision-making outcomes is essential when rolling-out innovation.GBR will be responsible for maintaining and upgrading physical infrastructure and ensuring efficient utility management.Recommendation:GBR should work with industry closely to analyse the challenges it wishes to tackle before defining technology solutions.The Customer Journey145.Freight and LogisticsAs a key freight corridor,this blockage created a ripple effect across the entire freight system,resulting in goods on the other side of the world finding themselves in a standstill(economists and insurers calculated that the blockage reduced annual trade growth by 0.2 to 0.4%).The lasting impact of COVID-19 has caused multiple disruptions and expense in the transportation of goods with costs ultimately passed on to the end-consumer.Situation:Currently,there are no Darwin data feeds17 for rail freight trains as there are for passenger services.The availability of data is,however,critical when increasing rail freight volumes to ensure better coordination between freight and passenger services and maximise efficiency.Challenge:We must improve data and technology coordination for rail freight.Global freight systems and supply chains are linked to frequent service disruptions.For example,in March 2021 a container ship blocked the Suez Canal in Egypt.GBR will have a duty to promote rail freight,setting a growth target for the sector,and showcase its economic,environmental,and social benefits.5.Freight and Logistics15Evidence:The global freight sector is rapidly embracing an“ecosystem”approach;collaborative arrangements that enable companies to coordinate efforts to satisfy customer needs underpinned by data sharing.A study conducted by techUK member IBM published in 2021 found that 88%of businesses in the global shipping and supply industry expected ecosystems to grow and 52%of experts view ecosystems as a way of improving interoperability.18 A separate report published by the thinktank The Coalition of Reimagined Mobility in 2022 found that if freight data was shared in near real time,it could eliminate 2.6 billion barrels of oil per year from global supply chains and lead to 6%cost savings per ton-kilometre.19 This would modernise the sector while facilitating integration with the railways and the logistics sector more broadly,including last mile couriers.This improved communication would allow stakeholders to conduct analyses to have better predictions of goods availability across rail freight terminals,making it easier for road haulage companies dealing with their own labour and equipment shortages to be responsive to real world conditions.Recommendation:GBR should work with rail freight operators to build awareness of how technology can support the sector and lead in the creation of an open data standard.6.Funding and Financing166.Funding and Financing GBR will also need to ensure it has the right people,well versed in research,development,and innovation activities.Evidence:Transport infrastructure research at the University of Southampton has achieved significant performance and reliability improvements for railway and other critical infrastructure systems.It has led to substantial cost and carbon savings,supported government decision-making and enabled industry innovation for economic gain.The University of Southampton leads the UKRRIN Infrastructure centre of excellence.Amongst other outputs,the research has led to specific and measurable innovations such as the restart of the UKs rail electrification programme,delivering savings worth an estimated 650m to the UK economy.It has also estimated cost reduction of HS2 noise barriers by 65m and HS2 geotechnical works by 100m,thereby reducing the risk of further costly delays to the project.20Situation:Innovative technology for rail remains difficult to implement and the rail industry lags other sectors of the economy and even other transport systems in deploying technology that drives cost reductions and efficiencies.Rail and strategic connectivity are also too often considered in isolation in businesses cases and investment decisions,not linked to holistic solutions which can drive multiple benefits across the wider transport network.Challenge:GBR needs to support a clear path to market for innovation.Currently,rollout is often prevented by policy,procurement,cultural or industry issues which are beyond the control of private sector innovators and investors.As the“guiding mind”it can take responsibility for allocating funds and teams for the first deployment of promising innovation in the regions.Enabling this collaborative environment,underpinned by a common goal will give confidence to the private sector to invest in skills,facilities,supply chain,and product development.GBR is tasked with delivering financial sustainability for the sector and considering ways it could support greater efficiency.There is a strong case for an increased focus in the delivery of new technologies as a basis for driving efficiencies and improving cost performance throughout the sector.Recommendation:GBR should lead the creation of an industry eco-system to nurture innovation.Conclusion We hope that by adopting the recommendations outlined in this report,GBR can achieve its ambition of becoming a data and technology driven network,capable of meeting todays demands.techUK and the wider technology industry stands ready to support this transformation.We are positive about this new era of rail travel and look forward to working hand-in-hand with the entire rail ecosystem.17About techUKs Intelligent Mobility and Transport ProgrammeAcknowledgements18techUKs Intelligent Mobility and Transport Group aims to deliver a digitally-enabled,interoperable,integrated and inclusive transport network that connects people and services with multiple modes of mobility.This programmes focus is to improve regulatory environment of the wider mobility services ecosystem by working together with Government and engaging with industry around priorities and key challenges.ReferencesReferences191.https:/www.gov.uk/government/publications/great-british-railways-williams-shapps-plan-for-rail2.https:/gbrtt.co.uk/wisp/3.https:/www.gov.uk/government/speeches/consultation-on-primary-legislative-changes-to-reform-our-railways4.https:/gbrtt.co.uk/rail-freight-growth-target/5.https:/ is a membership organisation that brings together people,companies and organisations to realise the positive outcomes of what digital technology can achieve.We collaborate across business,Government and stakeholders to fulfil the potential of technology to deliver a stronger society and more sustainable future.By providing expertise and insight,we support our members,partners and stakeholders as they prepare the UK for what comes next in a constantly changing techUK
2022-12-16
20页
5星级
Delivering insight today,creating value tomorrow.6.0Page 1/152Powered byDHL Trend ResearchContentsWH.
2022-12-14
151页
5星级
Decarbonising Hong Kongs Roads:Pathways towards a Net-Zero Road Transport System1WRI.ORG.CNLULU XUE .
2022-12-09
106页
5星级
2022 KPMG Assurance and Consulting Services LLP,an Indian Limited Liability Partnership and a membe.
2022-12-08
18页
5星级
Moving into the software-defined vehicle fast laneWhy transformed products and services hold the key.
2022-12-07
25页
5星级
2022ANNUAL FINANCIAL PROFILE OF AMERICAS FRANCHISED NEW-CAR DEALERSHIPSNATIONALAUTOMOBILEDEALERSASSO.
2022-12-07
19页
5星级
Climate Goals and Recommendations for Action in Chinas Transport SectorImplemented byImprintAs a fed.
2022-12-07
46页
5星级
On behalf ofAll in One?“Developments and Perspectives for Mobility Stations in the Urban Space Impri.
2022-12-07
30页
5星级
Transportation trends 202223Making the most of a huge infusion of federal fundsA report from the Del.
2022-12-06
44页
5星级
AUGUST|2022Brett C.SmithEdgar FalerMichael GerNarges LahijiFrom Internal Combustion to Battery Electric Vehicles:Enabling Digital Manufacturing CENTER FOR AUTOMOTIVE RESEARCH2About the AuthorsAcknowledgmentsExecutive SummaryIntroduction -MethodologyICE to BEV Production Most Critical ChallengesImpact on Production Processes -New BEV Operations -Focus:Optimizing Production Scheduling -Focus:Optimizing Production Throughput -Focus:Optimizing Quality AssuranceImpact to Technology -Production Technology -Digital Technology(Data,Analytics,Automation,and Machine Learning)Impact to the Organization -Organization Alignment -SkillsWhere Can Partners Help?Conclusion34566789911141717192020202121TABLE OF CONTENTS CENTER FOR AUTOMOTIVE RESEARCH1The Center for Automotive Research is an independent non-profit that produces industry-driven research and fosters dialogue on critical issues facing the automotive industry and its impact on the U.S.economy and society.CAR researchers closely track current and future global automotive industry and technology trends and assess their impacts.CAR researchers also study international collaborations and stay abreast of changes in international trade and regulatory environments,the development of technology standards,and the deployment of new vehicle technologies.Brett C.Smith,Director,Edgar Faler,Senior Industry AnalystMichael Ger,Digital Transformation ConsultantNarges Lahiji,Industry AnalystHitachi America Research and Development Division(R&D)brings together some of the worlds greatest minds to research and innovate for a better future.For three decades,Hitachi America R&D has collaborated with business and research leaders worldwide to address industry and social issues,creating next-generation solutions that make the planet a safer,smarter,healthier and more secure place.Quan(Jason)Zhou,Smart Manufacturing Researcher,R&D DivisionSadanori Horiguchi,Lab Manager and Senior Researcher,R&D DivisionWei Yuan,Senior Manager,R&D DivisionABOUT THE AUTHORS CENTER FOR AUTOMOTIVE RESEARCH2The successful completion of this research resulted from many individuals contributing to and supporting the project.The Center for Automotive Research would like to thank Hitachi America(HAL),Limited for having the foresight to fund this project.The Funders research team provided critical insight and momentum to keep this effort moving forward.Notably,the Funders were very supportive of CAR presenting independent research.CAR did not share any respondent information with the funders,nor did the funders influence any research outcomes.CAR and HAL would also like to thank the industry respondents who participated in this study.The industry partners participated thoughtfully in interviews,survey responses,research reviews,and commentary for this effort.Their commitment to open dialogue on this critical effort created the foundation for the information in this report.Their participation represents significant effort,analyses,and contemplation.Research team appreciates the importance,honesty,and sincerity these individuals placed on this task and their commitment to a collaborative process.Their open discussion and critiques contributed to the integrity of our research.We cannot overstate our appreciation of their efforts.Finally,the research team would like to thank Steve Wyatt.During this project,Mr.Wyatt worked diligently to help the researchers capture the perspective of production workers and technicians.Over decades of work,Mr.Wyatt has gained tremendous respect and connections within the U.S organized labor field.His efforts contributed to a robust consideration of the role production workers play in the ICE to BEV and the digital transformation of the industry.For citations and references to this publication,please use the following:Smith,B.,Faler,E.,Ger,M.,and Lahiji,N.(2022).From ICE to BEV.Center for Automotive Research,Ann Arbor,MIACKNOWLEDGEMENTS CENTER FOR AUTOMOTIVE RESEARCH3Todays automotive industry faces a historical shift from internal combustion engine(ICE)vehicles to battery electric vehicles(BEV).This shift is profound,dramatically altering the structure of the automotive value chain and the vehicle manufacturing process.This conversion occurs as the industry undergoes a digital transformation.The ICE to BEV project builds a 2020 CAR project on digital transformation by considering the implications of a transition in propulsion technology and a digital transformation in manufacturing for the North American automotive sector.The project begins to identify the“white spaces”in manufacturing enabled by a transition to BEV and digital manufacturing.For this project,the research team conducted long-form interviews to support a targeted technology survey of manufacturing(operations)and information technology decision-makers at five vehicle manufacturers.CAR researchers also conducted interviews with leading labor representatives.The gathered information provides a snapshot of how the North American automotive industry is digitally converting vehicle manufacturing and the critical role of BEVs in that conversion.CAR considered three building blocks to help place structure around the digital transformation enabled by BEV manufacturing:technology(production and digital)process(scheduling,throughput,and quality assurance)and organizational(alignment and skills).CAR researchers also investigate the role of partners in the transition.Perhaps the most challenging aspect of the ICE to BEV transformation is the sheer breadth of changes to the production process-from new BEV-specific parts,processes,and suppliers to new technology opportunities made possible via digital technologies.Survey results identified the production of the battery cell/module/pack and the selection of suppliers as the primary challenge of the new BEV manufacturing paradigm.A common theme from the interviews was the uncertainty of demand for BEVs.Several manufacturers are pursuing dual-mode production lines,which will require more flexibility,specifically for ICE and BEV configurations.Digital tools are being implemented to support planning.Based on the survey results,improved supply chain integration and more timely data are the most common ingredients being leveraged for process improvements in production scheduling.Respondents indicated enhanced supplier integration would be needed as EV production increases to avoid disruption and fully leverage analytics to optimize production throughput rapidly.OEM and Organized Labor respondents agreed that the rate of change in the automotive industry is driving the need for enhanced skills.However,a recurring theme from interviewees punctuated this point by pointing out that these skills were digital transformation-related,much more than Executive Summary CENTER FOR AUTOMOTIVE RESEARCH4Introductionbeing only BEV-related.In other words,skills associated with new digital technologies(i.e.,data management requirements,advanced analytics,machine learning,and automation)remain a development priority.Still,they pertain equally to both BEV and ICE vehicles.While automakers have specified the need to develop new digital transformation and production skills internally within their organizations,they also recognize that there are focused capabilities in which partners can most definitely enhance these efforts.The research concluded that product and service partners in the areas of hardware/software supply,business process consulting,technology consulting,and IT consulting were dispersed.However,“very important”(the very best significance level)rankings did appear at least somewhat in all three business process areas surveyed and across the different partner products and services.This is not entirely surprising,given the current marketplaces high demand for digital transformation skills.Todays automotive industry faces a historical shift from internal combustion engine(ICE)vehicles to battery electric vehicles(BEV).This shift is profound,dramatically altering the structure of the automotive propulsion system value chain and manufacturing process.The propulsion value chain is changing from one dominated by ICE-based OEMs and suppliers to a new BEV-based value chain comprising BEV offerings from existing OEMs and suppliers.Additionally,newly emergent“BEV-only”OEMs,e.g.,Tesla,Rivian,Lucid,and suppliers,e.g.,CATL LG Energy Solutions and American Battery Solutions(ABS),have added to a broad competitive landscape.While much recent discussion has been about the new entrants,the shift in propulsion technologies has profoundly affected traditional manufacturers and propulsion suppliers.Delphis(renamed Aptiv)2017 spin-out of its propulsion group Delphi Technologies(later acquired by BorgWarner)is an example of the change within the propulsion supply sector.Fords announcement to separate its ICE development and manufacturing into Ford Blue and its BEV development into Ford Model e is a recent example of remarkable change.The BEV manufacturing conversion occurs as the industry undergoes a digital transformation.The digital transformation of automotive manufacturing is complex,massive,and rapidly evolving.CAR considered three building blocks to help place structure around the digital transformation:technology,process,and organizational,i.e.,people and change.Many often approach digital transformation as a technology-driven challenge.However,technology is only one aspect determining success in digitally transforming manufacturing.Technology adoption often creates problems without a coherent process and organizational change plan.CAR published a report in 2020 that considered the importance of the strategic vision connecting the technology,process,CENTER FOR AUTOMOTIVE RESEARCH5The research team conducted long-form interviews to support a targeted technology survey of manufacturing(operations)and information technology decision-makers at five vehicle manufacturers.The combined inputs enabled the research team to gather a snapshot of how the North American automotive industry is digitally transforming vehicle manufacturing and the role BEVs play in that transformation.The industry participants included multiple representatives from FOUR traditional vehicle manufacturers and one new entrant.The respondents agreed to participate under the condition of no attribution.The report will compare and contrast the survey responses for each OEM but will not identify the company.The number of companies participating is low and not necessarily representative of industry trends.However,three points are essential.First,the respondents hold positions at their companies highest manufacturing and information technology groups.These individuals set the vision for their respective companies and demonstrated an impressive knowledge of the challenges Methodologyand organization elements of digitally transforming vehicle manufacturing in the automotive sector.1 The ICE to BEV project follows the 2020 effort by considering the combined implications of a transition in propulsion and a digital transformation in manufacturing in the North American automotive sector.It also explores the opportunities the transition creates for rapidly advancing data-driven manufacturing.The project attempts to identify the“white spaces”in manufacturing enabled by a transition to BEV and digital manufacturing.This research effort aims to view automotive industry-wide challenges and opportunities associated with the shift to BEV manufacturing.We do not seek to collect proprietary or confidential information driving competitive advantage among companies but rather to provide a mutually beneficial“2022 snapshot”of industry challenges and practices in managing the historical shift from ICE to BEV production.The shift from ICE to BEV-based vehicles has important implications for automotive manufacturing-impacting people,processes,and technology opportunities across the automotive value chain.At this nascent and rapidly evolving point in BEV history,automotive OEMs and suppliers are charting new ground,often working independently and in isolation from similar efforts across the industry value chain.While this can sometimes be a source of competitive advantage,this often leads to missed opportunities for understanding mutual challenges and leveraging best practices in adapting to the new BEV reality.The CAR ICE to BEV project is a solid first step to understanding those challenges and creating a proactive stakeholder-led effort to address them.1.Smith,B.,Dennis,E.P.,Modi,S.,and Nriagu,E(2020).The State of Industry X in Automotive,Center for Automotive Research,Ann Arbor,MI.CENTER FOR AUTOMOTIVE RESEARCH6Perhaps the most challenging aspect of the ICE to BEV transformation is the sheer breadth of changes to the production process-from new BEV-specific parts,processes,and suppliers to new technology opportunities made possible via digital technologies.Figure 1:Greatest BEV Challenges Against this backdrop,we asked survey respondents to outline their greatest BEV-related challenges.Figure 1 illustrates that survey responses reveal key similarities and differences between OEMs.Key HighlightsFirst,respondents were nearly unanimous that two challenge areas rise among all others:Uncertain BEV Demand:Four of the five survey respondents rated demand uncertainty as a“great challenge,”the highest level possible,with the remaining respondent indicating this as“somewhat a challenge.”The interview phase of the project added additional color to this challenge,with interviewees consistently describing new BEV offerings and uncertainty regarding the ultimate rate of consumer acceptance.This concern was particularly acute for traditional OEMs,who expressed the necessity to“hedge bets”by flexibly altering the mix of ICE and BEV production wherever possible.and opportunities.Second,the traditional companies selected have a long history of electrification and digital transformation.While identifying“leaders”is challenging,these companies have a proven track record in both areas.Finally,the interview discussions and survey responses strongly indicate that the process can serve as a foundation for future work to support the industry in the conversion.The research team believes the project is an outstanding first step in a much more comprehensive effort.ICE to BEV Production-Most Critical Challenges CENTER FOR AUTOMOTIVE RESEARCH7 Limited BEV production capacity.All respondents identified limited capacity as a“great challenge.”This is not surprising given the current high levels of BEV demand with a limited supply of vehicles available.During the interviews,traditional OEMs quickly pointed out the need for flexible capacity in the short term to mitigate the risks of ICE/BEV demand fluctuations.While new BEV labor skills were rated as a significant challenge by three OEMs,it was only rated as a moderate challenge for the remaining two.Interviews provided additional insights regarding skills,with respondents clarifying the skills surrounding digital transformation being the broader concern,incorporating many of the new skills required for modern BEV production.Also evident from figure 1,respondents provided less correlated responses to the remaining challenge areas.While new BEV processes and new BEV technologies were both deemed as challenges,they did not rise to the highest level of importance.Responding to the BEV challenges outlined above will involve significant changes to production processes,technologies,and people across the manufacturing enterprise.The remainder of this whitepaper considers changes to each of these areas.The shift from ICE to BEV manufacturing impacts many elements of the production process,requiring new vehicle components and systems,production processes,and resource requirements.This section of the report focuses on BEV impacts on the production process in greater detail.Figure 2:Greatest Production Challenges from ICE to BEV Figure 2 presents responses to a survey question probing for the“greatest production-related challenges associated with the shift from ICE to BEV production.”Several insights can be gleaned:Key HighlightsOnce again,while all challenge areas for this question were deemed important,two challenges stood out:Impact on Production Process CENTER FOR AUTOMOTIVE RESEARCH8 New Supply Base:survey respondents were unanimous when describing the“great”level of challenges associated with developing a new supply base catering to BEV technologies.During the interviews,participants elaborated on the challenges ranging from difficulty identifying and selecting new suppliers,training new suppliers,to certifying new suppliers.One interviewee also described more stringent requirements for suppliers providing advanced in-vehicle software solutions,particularly in software configuration management(vehicle-level software version control).This participant pointed out that this is not necessarily a BEV-only requirement but a requirement for the“software-controlled vehicle”more generally,including ICE vehicles.Sufficient Plant Capacity:respondents were near-unanimous in rating this as a“great”challenge.One survey respondent dissented from this assessment,rating this as“somewhat”of a challenge.However,upon follow-up with this participant during an in-person interview,the respondent clarified that BEV capacity was indeed important.Still,flexible capacity was more important given the uncertainty of demand concerns outlined previously.The remaining challenge areas in Figure 2(ramping to production,new production processes,new testing requirements,and new skills required)were considered important to respondents.Still,the level of importance of each OEM relative to others was far more dispersed.To gain greater insights into these challenges,we will address each in greater detail later in this document.New BEV OperationsWhile many production processes from ICE to BEV vehicles remain unchanged,several others(i.e.,battery and electric powertrain operations)are entirely new.This section identifies new BEV production processes and assesses the relative challenges imposed by each.Figure 3.Most Challenging New BEV Operations Figure 3 presents responses to a survey question asking participants to identify“which EV processes are particularly challenging.”Several insights can be gleaned:Key HighlightsOnce again,while all challenge areas for this question were deemed important,two challenges stood out:While each process area is new and potentially challenging,battery cell production emerged as the greatest challenge.This is perhaps not surprising given the dramatic advancements in battery cell chemistry,form factor optimization,and supplier selection challenges.While survey respondents were near-unanimous on this assessment,there was one exception from OEM#3,who cited many years of working with an existing supplier to alleviate significant concerns.Closely trailing battery cell production as the primary challenge was battery pack assembly.While three respondents rated this as a“great”challenge,the remaining two rated this as“somewhat”of a challenge.Through insights gained in subsequent interviews,this diminished challenge level can be understood via two explanations.First,respondents cited prior battery pack assembly experience gleaned from manufacturing hybrid and BEV vehicles.Second,other respondents alleviate such challenges by outsourcing this process and purchasing fully assembled packs.An interesting observation in this area arises from OEM perceptions of potential competitive advantage driven by the battery“make versus buy”strategy.Based on interview discussions,several OEMs are pondering this question.Will the battery emerge as a source of competitive advantage(as defined by the customer)?Or will batteries devolve into commodity status items over time?Focus:Optimizing Production SchedulingBEV production also has implications for the production scheduling process.First,as previously discussed,demand uncertainty leads manufacturers to design increased flexibility into plant operations,particularly for dual-mode(ICE/BEV)production lines.At the same time,BEV demand is outstripping the supply of BEV vehicles on the market.This leads to,essentially,a demand-driven build-to-order(BTO)production process.This section focuses on production scheduling challenges and opportunities within this demand-driven production environment.Figure 4:Production Scheduling Challenges Figure 4 presents responses to a survey question asking respondents to identify“current challenges in optimizing production scheduling.”Responses are analyzed below.Key HighlightsTwo of the five challenges presented to respondents were consistently mentioned as“very important”challenges.How are OEMs working to alleviate production scheduling concerns?Figure 5(below)summarizes responses to the question,“in what areas are you investing in production scheduling in the future?”Immediately evident is that supply chain integration appears to be a“very important”production scheduling challenge to four of the five surveyed OEMs,with the remaining OEM ranking this as“somewhat important.”From in-person interviews of respondents,the root cause of these challenges was almost universally associated with supply disruption risks stemming from global supply shortages(i.e.,semiconductors,electronic modules)and limited supplier capacity for emerging technologies(batteries).Closely trailing supply chain integration as the primary challenge was poor visibility to demand,with all but two respondents rating this challenge as“very important.”During the interview processes,however,we realized that this was,in fact,a unanimous consensus due to the vagueness surrounding the term“poor visibility of demand.”When,instead,we refined the survey wording choice to being“uncertainty of demand,”all respondents rated this as“very important,”the highest level of challenge presented.The remaining challenge areas in Figure 4(lack of real-time data,insufficiently advanced planning tools,and worker skills)were considered to be at least“somewhat important”to respondents.Still,the level of importance among respondents was far more dispersed.To gain greater insights into these challenges,we will address each in greater detail later in this document.Figure 5:Investments in Production Scheduling Figure 4 presents responses to a survey question asking respondents to identify“current challenges in optimizing production scheduling.”Responses are analyzed below.Key HighlightsOverall,it appears that OEMs are embarking on various investment priorities when tackling the challenge of production scheduling.That said,it appears as though improved supply chain integration and more timely data are the most common ingredients being leveraged for process improvements in this area.This was reinforced during the interview process,with participants stressing the need for“rapid communication”and“timely responses”regarding changing production conditions.Focus:Optimizing Production ThroughputIn 2022,the biggest EV production challenge may be scaling up to higher production levels and as such,optimizing production throughput in plants is a goal shared by many automakers.This section considers challenges and opportunities in this area,investigating key production constraints and strategies to alleviate these constraints.Figure 6 presents responses to a survey question asking respondents to“identify current challenges in optimizing production throughput.”Analysis of these responses,in addition to follow-up interviews with participants,provide interesting observations.Key HighlightsFigure 6:Production Throughput Challenges Not surprisingly,process bottlenecks represented a significant challenge to optimizing production throughput.Interestingly,these challenges were evaluated as being“very important”to three OEMs with distinctly different characteristics two being large traditional OEMs.At the same time,the other respondent was a much smaller new entrant OEM.As expected,interviews with participants confirmed that battery-related constraints constituted the great majority of BEV bottlenecks.However,the complexities of mixed-mode(ICE and BEV)production on single production lines has the potential to add constraints as well.BEV-specific constraints will be examined in greater detail in the next section of the report.Supply chain integration again rose to a high level of importance,with two of five OEMs considering this a“very important”challenge.Interview insights pointed to challenges associated with developing new supplier relationships based on new EV technologies and ensuring sufficient and reliable supplier capacity.The remaining challenges in Figure 6(uncertainty of demand,lack of real-time data,and labor skills)all represented significant,though somewhat lower,levels of challenges.Future sections of this report will examine many of these factors in greater detail.The survey identified battery cell production as the primary BEV production constraint,with four respondents rating these constraints as“very constrained.”The lone dissenter(OEM#3)rated this constraint less severely,describing the battery cell production as“somewhat constrained.”In a subsequent follow-up interview,probing for an explanation for this,we learned that the“somewhat constrained”characterization was based primarily on historical battery supply experiences with existing hybrid vehicles,adding that as the company pivots to full BEV production in the future,battery cell production constraints are a“real concern.”Battery module production provided more varied survey responses,with three OEMs rating this as“very constrained.”As gleaned in subsequent interviews,responses to this question correlate closely to whether the OEM manages the module assembly processes“in-house”or whether this process is outsourced to suppliers.As discussed previously,according to our research,battery-related bottlenecks constitute a large portion of BEV production constraints.We will now consider these constraints in greater detail.Figure 7:BEV Areas with Greatest Constraints Figure 7 presents responses to a survey question asking respondents to“identify areas in which you are experiencing the greatest production constraints.”Key HighlightsThe research shows that BEV-related production constraints can represent a significant source of production throughput concerns.This raises interesting questions regarding OEM strategies to alleviate these constraints.Figure 8:Strategies to Alleviate Production Constraints Figure 8 presents responses to a survey question asking respondents to“identify your strategy to alleviate BEV production constraints.”Key HighlightsThe survey responses indicate several interesting findings:As evidenced above,automakers are expending significant efforts to optimize BEV production throughput.The following section examines the types of investments OEMs are undertaking to achieve this goal.In general,all OEM respondents appear to be leveraging the full menu of options to alleviate production constraints:new plants,greater throughput in existing plants,and outsourcing.Larger OEM respondents are more likely to leverage new plants and greater throughput in existing plants as strategies to alleviate production constraints.In interviews,larger OEMs indicated a greater propensity to bring battery manufacturing(both cells and modules)“in-house”to alleviate potential constraints.In both the survey and subsequent interviews,one OEM highlighted greater throughput in existing plants to alleviate production constraints and indicated the importance of improved analytics as a mechanism to achieve this goal(figure 9).Figure 9:Investments to Improve Production ThroughputFigure 9 presents responses to a survey question asking respondents to“identify areas in which you are making investments to optimize production throughput in the future.”Key HighlightsFocus:Optimizing Quality AssuranceOver the last several decades,automakers have continuously been striving to improve product quality.Industry 4.0(connected manufacturing,machine learning,and analytics)has provided new capabilities to continue this effort.Against this backdrop,this section investigates current BEV production quality assurance challenges and opportunities and strategies to continuously improve BEV quality in the future.Judging from the survey results,improved supply chain integration is a critical tool to improve production throughput,with four respondents indicating a“high investment“in this area.Such investments include more rigorous supplier selection processes to reduce supplier risk and improved supplier communications initiatives to avoid supply disruptions.Investments to improve analytics and applications were also highlighted as opportunities to optimize production throughput,with four respondents indicating“high investment”levels for these initiatives.Interview results from one large OEM indicated big data analytics initiatives to dynamically identify production constraints and prioritize corrective actions to the constraints most negatively impacting production.Figure 10:Production Quality Control Challenges Figure 10 presents responses to a survey question asking respondents to“identify your current challenges surrounding production quality control.”Key HighlightsOEM respondents pointed out that many quality control challenges above apply equally to BEV and ICE vehicle production.The next question considers quality control challenges in the context of BEV-specific processes.Based on the survey results,respondents face“across the board”challenges(incorporating all surveyed challenge areas)in their efforts to optimize production quality.Within the interviews,three companies stressed that the current trend towards“software-defined vehicles”is creating heightened requirements to accurately“trace”the versions of software components installed within the vehicle(also known as software configuration management).Furthermore,this information should be captured within the OEMs manufacturing execution system(MES)for future traceability.Within the interviews,the increasing use of computer vision for quality inspections was a recurrent theme.Figure 11:BEV Processes Presenting Challenges from Quality Perspective Figure 11 presents responses to a survey question asking respondents to“identify which BEV production processes present challenges from a quality assurance perspective.”Key Highlights Battery cell production was unanimously deemed a“highly challenging”BEV-specific process area from a quality control perspective.Subsequent interviews explained both the importance of maintaining battery cell quality and the“newness”of this process from an OEM perspective.Battery module production was raised as a“highly challenging”quality control challenge by three OEM respondents,while two others rated it as“somewhat challenging.”Interviews of those rating this as“highly challenging”revealed that battery pack“testing at scale”represented a significant challenge.In contrast,those rating module quality control as only“somewhat challenging”either had previous experience producing battery modules or outsourced this function altogether.Three survey respondents saw battery pack installations as only“somewhat challenging,”while two considered this“highly challenging.”Asked for clarification during subsequent interviews,these respondents pointed out significant challenges associated with battery pack functional and leakage tests.Electric drive component installation was only“somewhat challenging”based on survey responses.Interviews clarified that the quality control processes for this function were very similar to powertrain installation processes in ICE vehicles.Human skills or factors were seen as“highly challenging”by one OEM respondent but only“somewhat challenging”by the remaining four respondents.In a subsequent interview,OEM#4,who rated human skills as“highly challenging,”identified high voltage component handling and testing as a critical new BEV skill.Other interview participants stated that overall,quality control skills for BEV production were broadly similar to those required for ICE production.Human skills will be considered in greater detail in the later“Impact to the Organization”section of this report.Given the broad adoption of Industry 4.0 technologies(connected manufacturing,analytics,and machine learning)by manufacturers in general,we now consider how these technologies are being utilized to support quality assurance functions within BEV production organizations.Figure 12:Use of Big Data,Analytics,Artificial Intelligence,and Applications Figure 12 presents responses to a survey question asking respondents,“in which areas have you adopted Big Data analytics,artificial intelligence(AI),or other applications?Below,we summarize both survey and interview responses.Key Highlights Survey results appear to confirm OEM respondent interest in implementing connected manufacturing and advanced analytics solutions to automate quality inspections via computer vision,with three large traditional OEMs indicating“high adoption”of this technology,one somewhat smaller OEM indicating“some adoption,”and on new-entrant OEM indicating“little adoption”of this technology.Asked to explain the limited adoption of computer vision during the interview process,the traditional OEM explained that quality inspections were performed by computer vision AND people.At the same time,the new entrant planned greater levels of computer vision adoption in the future.All respondents indicated that this technology was equally likely to be used in ICE and BEV production.Survey respondents also indicated the need to integrate data to improve forensic analysis,with three large traditional OEMs indicating“high adoption”of this technology.This requirement can be explained as the need to capture detailed manufacturing process data,at a vehicle level,from production sensors and transaction systems to provide the ability to“trace”the root cause of vehicle quality problems found later in the field.Interviews revealed the need to capture“end-to-end quality traceability from design and manufacturing perspectives,”with another OEM adding,“this is particularly important given the greater level of software being contained within vehicles.”Likewise,three large traditional OEM respondents indicate“high adoption”of advanced solutions for predicting equipment failures and reducing equipment maintenance costs by performing maintenance operations.In general,the use of advanced solutions for quality assurance was much more likely to be adopted by the larger and traditional OEMs than by the new-entrant OEMs.Asked to explain during the interview,the new entrant OEM explained that“these solutions will come in time,as production ramps up.”Figure 12 presents responses to a survey question asking respondents,“in which areas have you adopted Big Data analytics,artificial intelligence(AI),or other applications?Below,we summarize both survey and interview responses.Key Highlights Survey results appear to confirm OEM respondent interest in implementing connected manufacturing and advanced analytics solutions to automate quality inspections via computer vision,with three large traditional OEMs indicating“high adoption”of this technology,one somewhat smaller OEM indicating“some adoption,”and on new-entrant OEM indicating“little adoption”of this technology.Asked to explain the limited adoption of computer vision during the interview process,the traditional OEM explained that quality inspections were performed by computer vision AND people.At the same time,the new entrant planned greater levels of computer vision adoption in the future.All respondents indicated that this technology was equally likely to be used in ICE and BEV production.Survey respondents also indicated the need to integrate data to improve forensic analysis,with three large traditional OEMs indicating“high adoption”of this technology.This requirement can be explained as the need to capture detailed manufacturing process data,at a vehicle level,from production sensors and transaction systems to provide the ability to“trace”the root cause of vehicle quality problems found later in the field.Interviews revealed the need to capture“end-to-end quality traceability from design and manufacturing perspectives,”with another OEM adding,“this is particularly important given the greater level of software being contained within vehicles.”Likewise,three large traditional OEM respondents indicate“high adoption”of advanced solutions for predicting equipment failures and reducing equipment maintenance costs by performing maintenance operations.In general,the use of advanced solutions for quality assurance was much more likely to be adopted by the larger and traditional OEMs than by the new-entrant OEMs.Asked to explain during the interview,the new entrant OEM explained that“these solutions will come in time,as production ramps up.”Impact to TechnologyAs if the shift from ICE to BEV itself were not consequential enough,this shift occurs against the backdrop of the digital transformation of industry in general.Given this fact,does this provide BEV production opportunities to accelerate the adoption of new digital transformation technologies?The team surveyed respondents on two broad questions,first to assess production challenges encountered and then to assess the technologies pursued to address these challenges.Production TechnologyFigure 13:Greatest BEV Production Technology Challenges Figure 13 presents responses to a survey question asking respondents,“when considering the shift from ICE to BEV manufacturing,what are your greatest production technology challenges?”.From this data and subsequent interviews,we draw the following conclusions.Key Highlights While results appear to confirm challenges in all surveyed areas,the extent of these challenges appears somewhat dispersed.One surprise in the survey results for this question was the relatively low ranking associated with the challenge of flexible production,given its importance in addressing the BEV demand uncertainty described earlier in this report.Overall,two of the four traditional OEMs addressed this as a“great challenge,”while the remaining two traditional OEMs rated this as“somewhat a challenge.”During the interview,the two dissenters explained that this was becoming less of a challenge as the company moved from multi-mode(ICE and BEV)production lines to dedicated production lines.Likewise,the new-entrant OEM had less of a need for flexibility given the requirement for BEV-only production.Overall,integrating the supply chain was considered to be a“great challenge”by three of five respondents.Interviews revealed that obtaining a sufficient and steady supply of battery-related materials was of greatest concern,followed by technology solutions to provide greater supply chain visibility.Standardizing technologies globally was considered a“great challenge”by two of the five respondents.Interviews revealed that this often results from tensions between centralized technology decisions made at the OEM headquarters and more localized and regional needs.Predictably,this was seen as less of a challenge for the new entrant OEM.Automating operations was seen as a“great challenge”for two large traditional OEMs and only“somewhat of a challenge”by the other respondents.Asked to elaborate during interviews,a consensus emerged that while automation was a challenge,it was equally challenging within ICE and BEV production environments.Figure 13 presents responses to a survey question asking respondents,“when considering the shift from ICE to BEV manufacturing,what are your greatest production technology challenges?”.From this data and subsequent interviews,we draw the following conclusions.Figure 14:EV Production Technologies Pursuing Figure 14 presents responses to a survey question asking respondents,“which EV production technologies are you pursuing?”.Analysis of the responses follows below.Key Highlights Judging from the survey results,while respondents are pursuing technologies within all surveyed areas,the nature of these pursuits appears somewhat dispersed.Computer vision appeared to be the most adopted of these technologies,with four of five claiming that they were“strongly pursuing”this approach.During interviews,participants confirmed that quality inspections represented the greatest use of this technology.Once again,the new-entrant OEM did not discount the value of such solutions but rather pointed out the“early days”of production for the company and an intent to implement more advanced solutions as time progresses.OEM respondent organizations reported all other technologies areas as being at least“somewhat pursued.”Interviews provided some context to this.While important,these technologies apply to both ICE and BEV production and have been considered for some time.Digital Technology(Data,Analytics,Automation,and Machine Learning)Perhaps one of the more significant manufacturing advances of the last decade involves the advent of connected manufacturing,with entire plant operations being newly equipped with low-cost sensors attached to equipment,easier sensor connectivity via internet-based protocols,and new abilities to leverage this sensor-based data to optimize manufacturing performance via a new generation of analytical and machine learning applications.However,as is often the case,new technologies are often associated with new challenges.The following question seeks to investigate such issues.Figure 15:Greatest Digital Technology Challenges Figure 15 presents responses to a survey question asking respondents,“for BEV production,what are your data management and analytics-related challenges?”.Key HighlightsTo begin with,interviews made clear that the data and analytics technology challenges discussed in this question were not unique to BEV production,pertaining equally to ICE production environments.That being said,the following trends become evident.Effort building analytics and AI applications was identified as a“great challenge”by four of the five survey respondents and only“somewhat of a challenge”by one traditional OEM,who indicated previous heavy investments in building internal capabilities in this area.In interviews,common themes were the shortage of advanced analytics and machine learning skills available in the marketplace and the need for ecosystem partners to provide“complete end-to-end business solutions and not point solutions.”Scaling IT systems to huge IoT data volume was identified as a“great challenge”by three of the four traditional OEM survey respondents.Interviews reinforced the large amounts of data being collected and ongoing discussions about where(on-site,edge,or Cloud)this data would be stored and analyzed.,Unsurprisingly,this was less of an issue for the new entrant OEM.Still,it was emphasized that this could change as operations reach greater scale.Combining OT and IT data for analytics was identified as a“great challenge”to two of the four traditional OEMs and only“somewhat of a challenge”to the remaining OEMs and the new entrant OEM.Interviews surfaced explanations for both“somewhat of a challenge”respondents.One traditional OEM attributed their success in combining OT and IT data for analytics to the fact that they hired OT personnel within the IT organization and then standardized accrued learnings across their global operations.Conversely,the new entrant OEM stated that OT/IT convergence was not yet an issue,given the newness of their operations.Impact to the OrganizationOrganization AlignmentIt is often said that effectively implementing significant organizational changes“takes a village,”incorporating all levels and domains within the enterprise.Of particular interest when implementing technology-intensive initiatives such as BEV production successes and failures in aligning Management,Labor,and Information Technology(IT)functions to ensure success.To assess the effectiveness of how automakers have aligned to meet new BEV production and digital transformation-related changes,we interviewed representatives from OEMs and Organized Labor and asked them to comment on“successes and failures experienced in aligning Management,Labor and Information Technology(IT)functions in meeting core strategic BEV production and digital transformation-related initiatives.”Overall,interview respondents commented that organizational alignment“takes a lot of effort”and that“challenges exist.”From an OEM perspective,challenges in aligning Engineering and Manufacturing functions were highlighted,in addition to difficulty aligning Manufacturing and Information Technology functions.From an Organized Labor perspective,opportunities were seen to enhance Labor involvement and decision-making earlier in the OEM product development process,allowing more strategic input into issues such as labor allocation(specifying the product development and manufacturing tasks performed by labor)and skill development priorities and programs.SkillsTechnology and process transformation are typically accompanied by the need for new and improved organizational skills(management,IT,labor).To assess challenges and opportunities in this area,we asked OEM and organized labor interview participants to describe“in which areas(i.e.,Management,Information Technology,Labor)are you seeing skill development opportunities in shifting from ICE to BEV production processes?”.Overall,both OEM and Organized Labor agreed that the rate of change in the automotive industry is driving the need for enhanced skills.However,a recurring theme from interviewees punctuated this point by pointing out that these skills were digital transformation-related,much more than being only BEV-related.In other words,skills associated with new digital technologies(i.e.,data management requirements,advanced analytics,machine learning,and automation)remain a development priority.Still,they pertain equally to both BEV and ICE vehicles.In the words of one Organized Labor interviewee,“skills are shifting from more manual labor to digital,with skilled trades taking on more digital tasks including troubleshooting,fixing and reprogramming.”Interestingly,while a consensus exists regarding the need for more advanced skills,perspectives seemed to diverge somewhat when discussing where these skills should reside organizationally.OEM interviewees unanimously described efforts to build digital and new BEV-related technical skills within their organizations.Likewise,although Organized Labor described a similar desire,interviewees described OEMs as potentially reluctant to allocate more advanced and higher value activities outside their organizations.Where Can Partners Help?While automakers have indicated the need to develop new digital transformation and production skills internally within their organizations,they also acknowledge that there are focused capabilities in which partners can most definitely augment these efforts.This section investigates areas of potential need.Figure 16:Areas Where Partners Can Help,Quality Assurance Figure 16 presents responses to a survey question asking respondents“where partners can help”in select business process areas(production scheduling,optimizing production throughput,and optimizing quality assurance).Simultaneously,we asked which type of partner products and services(hardware/software supply,business process consulting,technology consulting,and IT consulting)are most required for each business process area.In general,survey responses were somewhat dispersed across the board.However,“very important”(the highest importance level)ratings did appear at least somewhat in all three business process areas surveyed and across the different partner products and services.This is not entirely surprising,given the current marketplaces high demand for digital transformation skills.The interview process provided additional insight when discussing areas where partners can assist OEMs.Three of the five OEM participants pointed to the need for more complete and automotive industry-focused solutions.Interviewees quickly pointed out the value of“turn-key”application solutions that would minimize OEM resource requirements for implementation.ConclusionOverall,this study confirmed the epic changes in todays automotive industry.While the shift from internal combustion vehicles to battery electric vehicle production is hugely challenging individually,the ongoing digital transformation of the automotive industry presents challenges that are at least equally paradigm-shifting.It is crucial to identify and analyze the drivers that stimulate the digital conversion in manufacturing processes,specifically in automotive industries.Based on the interviews conducted throughout the research,digital transformation involves profound changes in the business model of the organizations,which foster modifications in processes,resources,operational methodologies,and quality assurance standards.So,what are our key takeaways?First,from a production perspective,though many ICE and BEV assembly processes will remain similar,automakers are being challenged to scale-up new BEV production processes,particularly in the area of battery cell production.Second,the industry appears to be in the midst of a fundamental restructuring of the automotive supply chain,with new BEV technologies driving the need for new supply chain partners able to meet growing market demand.Further,in the face of uncertain BEV demand,established OEMs are pursuing greater levels of production flexibility to accommodate changing ICE/BEV production mixes over time.Finally,the shift to BEV production coincides with the digital transformation of the automotive industry.Automakers are developing new skills within their extended enterprises(consisting of internal,organized labor,and partner resources),paving the way for a rapidly evolving automotive manufacturing future.
2022-12-04
26页
5星级
Cost benchmarking of Network Rails maintenance and renewals expenditure Annual report:year 3 of Control Period 6(2021-22)14 November 2022 Office of Rail and Road|2 Contents Executive summary 3 1.Introduction 8 2.Maintenance 19 Route-level analysis.19 MDU-level analysis.32 3.Renewals 50 Annex A:Route models results comparison 67 Annex B:Network Rails geographic routes and regions 68 Annex C:Mapping of Network Rails regions,routes and MDUs 69 Office of Rail and Road|3 Executive summary Context 1.The Office of Rail and Road holds Network Rail to account for its management of the rail network in Great Britain.Understanding the main drivers of Network Rails expenditure(including the reasons expenditure changes from year to year)and assessing the scope for it to improve its efficiency are central to this work.To achieve this,we use different analytical approaches,ranging from a bottom-up assessment of Network Rail business plans,projects and efficiency improvement measures to top-down cost benchmarking using statistical methods.2.This report presents our latest cost benchmarking statistical analysis,which compares maintenance expenditure and conventional track renewals unit costs(in simple terms,renewals expenditure divided by work volume)over time and across Network Rails regions,routes and maintenance delivery units(MDUs),after normalising1 for the effect of the observable underlying differences between them2.3.The methodology in this years report is broadly similar to the methodology in our year 2 of CP6 cost benchmarking report that we published in July 2021.4.Our cost benchmarking was one part of the evidence that informed our initial advice to the UK and Scottish governments over the summer,as they prepare their funding and high-level output specifications for the next control period(control period 7 or CP7).We will undertake a similar analysis next year to assess Network Rails strategic business plans for CP7.This will inform ORRs PR23 work on efficient costs.Key messages Maintenance expenditure Key message 1:There has been an average annual increase in maintenance expenditure of 6%per year(in real terms3)since 2013-14,after normalising for 1 By normalising,we mean we take account of some of the underlying differences between regions that affect expenditure,e.g.length of the network.2 For renewals,we have also analysed average unit costs(expenditure divided by work volume)separately by the main asset classes and for different types of renewals activity.3 In real terms means after adjusting for the effect of inflation(measured by the Consumer Price Index(CPI).Office of Rail and Road|4 factors such as traffic and network complexity.This may be due to inefficiency,or other factors.5.Our analysis suggests that there has been an average annual increase in maintenance expenditure of 6%per year(in real terms)since 2013-14,after normalising for factors such as traffic and network complexity.This may be due to inefficiency,or other factors.This long-run trend of maintenance expenditure rising has reduced from the last two years(from 9%in 2019-20 and 8%in 2020-21)but it is not clear if this reflects actual cost changes(e.g.inefficiency)or other factors,such as a change in the accounting of maintenance expenditure in the years from 2019-20.6.However,inconsistencies in the data,especially regarding how centrally managed expenditure is treated,make comparisons difficult.In particular,before 2019-20,Network Rail used to provide us the data with its centrally managed expenditure allocated to the routes within each region.From 2019-20 onwards,Network Rail told us that it was not able to do that anymore,so our model(and comparisons)do not include this expenditure.The excluded costs were 79m(4%of maintenance expenditure)in 2019-20,399m(20%)in 2020-21 and 391m(20%)in 2021-22.This means it is difficult to robustly compare Network Rails maintenance expenditure in 2021-22,with the historic/background trend.7.Network Rail has not provided clear guidance on what should be included in the maintenance and renewals expenditure that we use in our analysis,especially at a route and MDU level.This may mean an inconsistent approach has been used across Network Rail.We will work with Network Rail to agree on a process that will allow regional teams to be clearer on what should be in these expenditure categories,and to validate the data before we can analyse it.Key message 2:Maintenance expenditure at regional level this year is between -17%and 17%of what our model would expect.This range is slightly larger than that implied in last years analysis(-18%to 12%).Similar to last year,Scotlands unexplained difference is the lowest(least costly)and Easterns is the highest(most costly).8.Figure 1 below presents our results,comparing the outturn and modelled maintenance expenditure by Network Rails regions,in 2021-22.Office of Rail and Road|5 Figure 1:Deviation between outturn and expected(modelled)maintenance expenditure by Network Rail region,2021-224 9.The figure shows that maintenance expenditure at the regional level,was between-17%and 17%of that predicted by our model for 2021-22.Similar to last year,Scotland and Eastern are the largest outliers.10.The Eastern regions actual maintenance expenditure was 17ove the models prediction.Last year it was 12ove the models prediction.The region suggested that a factor that could explain this difference is the complexity of maintenance work carried out by different regions.We will work with Network Rail to better understand this issue.11.It is also not clear why Scotlands maintenance expenditure continues to be below the model prediction compared to other regions(-17%in 2021-22 from -18%in 2020-21).Network Rail Scotland said the variance may be explained by improved co-ordination in the planning and delivery of maintenance and renewals,4 Given the uncertainty associated with any statistical model,we consider any region that is within /10%of our modelled prediction(as shown by the x-axis at zero)is not an outlier.These regions are marked grey.Regions that are marked blue are considered outliers.The lines surrounding the central estimate of a given regions deviation between outturn and modelled cost indicate a 95%confidence interval.In other words,given the data available and the robustness of our model,there is a 95%probability that this estimated confidence interval contains the actual number representing the deviation between outturn and modelled cost.Office of Rail and Road|6 though it is unclear to what extent Network Rail Scotland did this better than other regions,to the point where it can justify this unexplained difference.We will continue to work with Network Rail to better understand this issue.Conventional track renewals unit costs Key message 3:There has been an average annual increase in the average unit costs of conventional track renewals of 2.0%per year(in real terms)since 2014-15,after normalising for factors such as traffic and network complexity.This may be due to inefficiency,or other factors.12.After normalising for factors such as traffic and network complexity,the analysis shows that there has been an average annual increase in the average unit costs for conventional track renewals of 2.0%per year(in real terms)during the period from 2014-15 to 2021-22(same as for the period 2014-15 to 2020-21,in the year 2 of CP6 report).13.In 2021-22,the rate of growth in the average unit costs for conventional track renewals increased to 2.7%compared to the long-term trend of 2.0%.According to last years report,this rate of increase(from the long-term trend)was 2.6%in 2020-21,which means the increase in conventional track renewals unit costs was well above the trend in each of the last two years.We consider that this may be due to inefficiency,headwinds or some other factors including some project-specific factors(e.g.project location),which cannot be taken account of in a top-down analysis of this sort.For example,some of this increase may be due to rising input price inflation(i.e.changes in prices above the Consumer Price Index(CPI)as discussed in our 2021-22 Annual Efficiency and Finance Assessment report.Key message 4:Conventional track renewals average unit costs at the regional level are between-3%and 7%of what our model would expect.This range is smaller than in last years analysis(-10%to 10%).Compared to last year,Eastern is still at the lower(least costly)end of the range,whilst Southern has replaced Wales&Western at the top(most costly)end of the range.14.Figure 2 below presents our results,comparing the outturn and modelled unit costs for conventional track renewals by Network Rails regions,in 2021-22.Office of Rail and Road|7 Figure 2:Deviation between outturn and expected(modelled)unit costs for conventional track renewals by Network Rail region,2021-22 15.The figure shows that conventional track renewals average unit costs at the regional level are between-3%and 7%of what our model would expect.This range is smaller than in last years analysis(-10%to 10%).16.Compared to last year,Eastern is still at the lower end of the range(-3%),whilst Southern has replaced Wales&Western at the top end of the range( 7%).While in our analysis last year,Wales&Westerns average conventional track renewals unit costs appeared to be 10%more than our model prediction,this has reduced to 3%less than our models prediction.17.It is important to note that the unit costs of renewals are influenced by a wide variety of project-specific factors,which cannot be taken account of in a top-down analysis of this sort.So,the results above should be read as indicative of the relative position of different regions.18.We will continue to work with Network Rail over the next few months to look into the potential underlying causes for these results,encouraging regions to share good practice,and to improve our model where possible.Office of Rail and Road|8 1.Introduction 1.0 The Office of Rail and Road holds Network Rail to account for its management of the rail network in Great Britain.Understanding the main drivers of Network Rails expenditure(including the reasons expenditure changes from year to year)and assessing the scope for it to improve its efficiency are central to this work.To achieve this,we use different analytical approaches,ranging from a bottom-up assessment of Network Rail business plans,projects and efficiency improvement measures to top-down cost benchmarking using statistical methods.1.1 This report presents our latest cost benchmarking statistical analysis,which compares maintenance expenditure and conventional track renewals unit costs(in simple terms,renewals expenditure divided by work volume)over time and across Network Rails regions,routes and maintenance delivery units(MDUs),after normalising for the effect of the observable underlying differences between them5.1.2 Our previous reports demonstrated that it is possible to build a statistical model that can explain the majority of the variation in some types of expenditure between Network Rail business units as a function of a few key cost drivers.We noted that these results should be seen strictly as a comparison of maintenance or renewals unit cost expenditure across business units rather than as an indication of Network Rails overall efficiency.The same caveat applies to this years analysis.1.3 The methodology in this years report is broadly similar to the methodology in our year 2 of CP6 cost benchmarking report that we published in July 2021.We use historical data to establish a statistical relationship between expenditure and underlying cost drivers.We use the model to predict expenditure for the latest year as a function of observable cost drivers at the region,route and/or MDU level;and then compare that figure against actual expenditure.We refer to the difference between these two figures as the unexplained difference.The larger the unexplained difference,the more important it is to understand what is different about the business unit in question relative to others and relative to previous years,be it efficiency,inefficiency,headwinds(cost increases outside of Network 5 For renewals,we have also analysed average unit costs(expenditure divided by work volume)separately by main asset classes and for different types of renewals activity.Whilst part of this analysis is discussed in the“Context”section of chapter 2,we are only publishing our detailed analysis on conventional track renewals as this compares better with last years analysis.Office of Rail and Road|9 Rails control),tailwinds(cost reductions outside of Network Rails control),data reporting or some other factor.1.4 Our analysis aims to provide a comparison of expenditure across Network Rails business units and to improve our understanding of underlying cost drivers.Together with other strands of ORRs work,such as our Annual Efficiency and Finance Assessment,it provides a deeper context for our overall assessment of Network Rail.We intend that this analysis will be an increasingly influential part of our reporting toolkit.1.5 The methodology and most of the data that is the basis of this report formed the basis for the cost benchmarking analysis that we undertook on the CP7 plans that Network Rail submitted to us in March 2022,as part of PR23.Firstly,using Network Rail historical data and CP7 forecasts,we estimated both the maintenance expenditure and conventional track renewals average unit costs for CP7 for each region.Secondly,we used the findings in our year 2 of CP6 report together with other studies(both in the literature and those commissioned by Network Rail)to form a view about potential savings that Network Rail could make in CP7,following the reduction in traffic brought about by the coronavirus(COVID-19)pandemic.The findings of that analysis were used as one element of the evidence that informed ORRs initial advice to the UK and Scottish governments over the summer,as they prepare their funding and high-level output specifications for the next control period(CP7).We will undertake a similar analysis next year to assess Network Rails strategic business plans for CP7.This will inform ORRs PR23 work on efficient costs.What is cost benchmarking?1.6 Cost benchmarking involves comparing expenditure across organisations or business units,after controlling for the effect of observable underlying differences.By controlling for we mean that we separate out the effect that differences in observable cost drivers are expected to have on overall expenditure.We do this by identifying statistical patterns in past data using statistical models.1.7 Cost benchmarking results can be used for a number of purposes.These include:to set efficiency targets(for example as part of a periodic review),to identify unexplained cost differences and underlying sources of good or bad practice;to set prices(or access charges in the case of rail infrastructure);or to forecast future costs as the result of changes in outputs.Office of Rail and Road|10 1.8 Our analysis can be used in part as a reputational tool to help drive improved performance within Network Rail,and in part as an indication of where ORR should focus its detailed analysis,monitoring and engagement.Applicability and limitations 1.9 Any statistical model is only as good as the data it is based on.Measurement error(for example,by wrongly attributing cost incurred in one area to another),omitted variables(the absence of important cost drivers from the model),or too small a sample size,can all weaken the robustness of results.1.10 Despite some outstanding issues as discussed in para 1.23 below,we consider that the quality and size of our dataset,and the model specification we have used,are robust enough to enable a meaningful comparison of maintenance expenditure and of conventional track renewals unit costs between regions.This evidence base is also able to provide a reasonable range of estimates of future expenditure and renewals unit costs to benchmark business plans.1.11 On the other hand,we have only partly been able to resolve the issues around Network Rails recording of maintenance expenditure at the MDU level that we suggested could be behind the unexpected MDU-level results in our previous years report.We have identified a workaround in collaboration with Network Rail but this will likely have introduced some measurement error.We are therefore placing little weight on the comparison of maintenance expenditure across MDUs and continue to work with Network Rail to resolve these issues.1.12 More generally,it is important to underline that benchmarking is a high-level tool.It is useful in identifying significant discrepancies across organisations/business units,and in producing reasonable,though not highly precise,expenditure forecasts.We should also not expect cost benchmarking to provide in-depth insights into the reasons between such discrepancies.Background 1.13 Cost benchmarking has been used by ORR to help set efficiency targets for Network Rail in the 2008 and 2013 periodic reviews(respectively,PR08 and PR13).In both cases,we compared Network Rail,as a whole,against a number of European peers.Whilst we used this international comparison to inform our determinations,we also recognised that there are limitations in this type of analysis,especially in the absence of high quality and consistent data across countries.Office of Rail and Road|11 1.14 From PR18,ORR decided to focus on Network Rails regions.As part of that our cost benchmarking approach also shifted towards comparing Network Rails business units(i.e.its regions,routes and MDUs),building on internal analysis undertaken by Network Rail during PR13.1.15 In our PR18 final determination,we committed to updating this evidence base annually and stated our intention to make greater use of comparative regulation in control period 6(CP6),with cost benchmarking playing an important role.1.16 Although we recognised that there remained inherent differences between these business units that could not be controlled for,this analysis provided a useful top-down check on efficiency targets calculated through a more granular,bottom-up,assessment of Network Rails business plans.This analysis has also produced more meaningful discussions with Network Rail,including with its regions,where possible reasons for higher or lower than modelled expenditure and potential actions for improvement are discussed.1.17 We published our year 2 of CP6 cost benchmarking report in July 2021 and the present document is the third report in this series.Reporting our results 1.18 The key focus of this analysis is the comparison of outturn maintenance expenditure and conventional track renewals average unit costs in 2021-22,against expected expenditure derived from our statistical models,which are calibrated on past data.Results are presented as percentage deviations from expected expenditure/average unit costs a positive number means that outturn expenditure has been higher than that predicted by the model and vice versa.These results represent cost variances that cannot be statistically explained by observable business unit characteristics and therefore merit further investigation.1.19 We present results at the level of Network Rails regions,routes and MDUs,and highlight the largest outliers.1.20 We have discussed our key findings with Network Rail,and this has been helpful in sense checking our interpretation of the results and in identifying other potential factors at play.1.21 Whilst we have sought to reflect Network Rails input in this report,we would note that it only had a small amount of time to digest the results and provide a response.We will continue to engage with Network Rail to discuss its views on the methodology and data that support this analysis;on the factors that could explain Office of Rail and Road|12 our results;and on possible actions that it could undertake to continue to improve both its cost information and efficiency.Data improvements in our future reports 1.22 Since we published our PR18 cost benchmarking report,we have continued to improve both the modelling and the quality of the underlying data.This was recognised by Deloitte when reviewing our year 2 of CP6 report on behalf of Network Rail which stated that within the econometric literature,the ORRs year 2 of CP6 study offers the most relevant evidence on the relationship between Network Rails maintenance expenditure and traffic.Although Deloitte also considered there were some weaknesses in our analysis and stated that its findings should not be thought of as being directly suitable for making decisions on funding arrangements.1.23 After publication of this report,we will work with Network Rail and its regional teams to resolve the remaining data issues,and agree on a process that will allow regional teams to validate the data before we can analyse it.In particular,we will work together to resolve the following data issues in order to improve the relevance of this analysis further,especially for the purpose of informing ORRs PR23 work on efficient costs.These are:(a)accounting for centrally managed maintenance expenditure:before 2019-20,Network Rail used to provide us with the data with its centrally managed expenditure allocated to the routes within each region.From 2019-20 onwards,Network Rail told us that it was not able to do that anymore,so our model(and comparisons)do not include this expenditure.The excluded costs were 79m(4%of maintenance expenditure)in 2019-20,399m(20%)in 2020-21 and 391m(20%)in 2021-22.Moreover,centrally managed expenditure represents a different proportion of total maintenance expenditure for different regions.For example,in 2021-22,centrally managed expenditure was 7%of Easterns maintenance expenditure,whilst it was around 20%of North West&Centrals maintenance expenditure.This means that we have been able to model 93%of Easterns maintenance expenditure,whilst only modelling 80%of North West&Centrals maintenance expenditure.We did not find a credible way to allocate these costs to routes.So,we decided to exclude them from the analysis and we controlled for this change by adding a dummy variable for 2019-20 in the model.The issues with centrally managed maintenance expenditure mean that it is difficult to robustly compare Network Rails expenditure in 2021-22 with the historic/background trend.Office of Rail and Road|13(b)data recording:in our discussions with regions,they identified hosting arrangements(i.e.whereby one MDU undertakes maintenance activities on some infrastructure(e.g.overhead line)on behalf of other MDUs but the costs continue to be paid by the hosting MDU and are not charged to the MDU where the asset is located)as one main reason for the large unexplained differences that we observed in our MDU analysis.In our future analysis,we will work with Network Rail to identify where these hosting arrangements exist and to agree on ways to allocate the costs to the MDUs where the infrastructure maintained is located.(c)expenditure classification:in our discussions with regions,we identified some potential issues with expenditure classification.We will work with Network Rail to better understand them.(d)lack of clear guidance:Network Rail has not provided clear guidance on what should be included in the maintenance and renewals expenditure that we use in our analysis,especially at a route and MDU level.This may mean an inconsistent approach has been used across Network Rail.We will work with Network Rail to agree on a process that will allow regional teams to be clearer on what should be in these expenditure categories,and to validate the data before we can analyse it.Quantitative context 1.24 Below we provide some high-level quantitative information by way of context for the analysis that follows.1.25 In this report,we cover maintenance and a proportion of renewals.As shown in Figure 3,maintenance represents 19%of Network Rails total expenditure(excluding financing costs)for 2021-22;renewals represent(in total)38%.The proportion of renewals that we concentrate on in this report(conventional track renewals)represents 12%of both the renewals expenditure for 2021-22 and average renewals expenditure over 2014-15 to 2021-22.1.26 Figure 4 shows the trends in total maintenance and renewals expenditure,in 2021-22 prices.Maintenance expenditure has fallen slightly in 2021-22,after having been on a steady upward trend since 2013-14.Renewals expenditure has fluctuated considerably since 2013-14.Office of Rail and Road|14 Figure 3:Breakdown of expenditure categories(excl.financing costs),2021-226 Figure 4:Total maintenance and renewals expenditure,2013-14 to 2021-22(2021-22 prices)6 Maintenance and renewals figures are based on the bespoke data that we received directly from Network Rail for the purpose of this analysis in June 2022.Maintenance figures do not match the figures in the 2021-22 Annual Efficiency and Financial Assessment(AEFA)as that report uses the latest information.Enhancements and operating expenditure figures were taken from the AEFA.The enhancements expenditure figure excludes third-party funded expenditure.The operating expenditure figure includes Schedule 4&8 payments,network operations costs,support costs,traction electricity and industry costs and rates.Office of Rail and Road|15 1.27 Figure 5 shows the breakdown of average annual maintenance and renewals expenditure by region,normalised by network size(expressed in track-kms).There is considerable variation across regions.A key purpose of cost benchmarking is to control for the proportion of this variation that is due to observable factors,so that comparisons across regions are made on a more like-for-like basis.Figure 5:Breakdown of average total maintenance and renewals expenditure per track-km,2013-14 to 2021-22(2021-22 prices)1.28 One of the key drivers of maintenance and renewals expenditure is traffic.Figure 6 shows average annual traffic density across regions(split into passenger and freight traffic).It can be seen that there is a strong correlation between this variable and the expenditure per track-km(as shown in Figure 5 above).Figure 6:Average traffic density(train-km per track-km),2013-14 to 2021-22 Office of Rail and Road|16 1.29 Figure 7 shows the average proportion of electrified track for the period from 2013-14 to 2021-22.We observe that there is a high degree of variation in the proportion of electrified track between regions and that there is some correlation between this variable and the expenditure per track-km(as shown in Figure 5 above).Figure 7:Average proportion of electrified track,2013-14 to 2021-22 1.30 The network is classified into five criticality bands7.Figure 8 shows the proportion of track-km that is classified into either criticality band 1 or 2.We observe that according to our data,there is no clear correlation between this variable and the expenditure per track-km(as shown in Figure 5 above).7 Network Rail defines route criticality as a“measure of the consequence of the infrastructure failing to perform its intended function,based on the historic cost of train delay per incident caused by the track asset”.Using this measure,each strategic route section(SRS)of the network has been assigned a route criticality band from 1 to 5.The lower the number of the criticality band,the more a delay is likely to cost should infrastructure fail.The classification of each SRS into criticality bands is used in the development of Network Rails asset policy as a first step to matching the timing and type of asset interventions.Office of Rail and Road|17 Figure 8:Criticality 1 and 2 track-km as a proportion of total track-km,average 2013-14 to 2021-22 1.31 The network is also classified into seven track category bands8.Figure 9 shows the proportion of track-km that is classified into criticality bands 1A,1 or 2.We observe that according to our data,there is no clear correlation between this variable and the expenditure per track-km(as shown in Figure 5 above)Figure 9:Category 1A,1 and 2 track-km as a proportion of total track-km,average 2013-14 to 2021-22 8 Each track line is assigned a category from 1A to 6 based on a function related to its Equivalent Million Gross Tonnes per Annum(EMGTPA).The EMGTPA measures the annual tonnage carried over a section of track but takes into account variations in track damage caused by different types of rolling stock.Category 1A is the highest-125mph or higher and Category 6 is the lowest 20mph and below.Office of Rail and Road|18 1.32 Our analysis aims to control for the effect of cost drivers including those described above on maintenance expenditure and average renewals unit cost across Network Rails business units.Office of Rail and Road|19 2.Maintenance Introduction 2.0 Maintenance expenditure relates to activities that sustain the condition and capability of the existing infrastructure to the previously assessed standard of performance.2.1 Most maintenance activity on Network Rails infrastructure is carried out by Maintenance Delivery Units(MDUs).MDUs are operating units within Network Rails routes,responsible for the majority of the day-to-day upkeep of their designated part of the network.MDUs are not responsible for renewals.2.2 Most maintenance is carried out,or procured,at the route or regional level.Each MDU is part of a route,and each route is part of a region.On average,MDUs accounted for around 67%of total network maintenance expenditure during the period covered by our analysis.The remaining 33%was centrally managed,covering activities such as structures examination,major items of maintenance plant and other HQ managed activities.2.3 We carry out our analysis by,first,comparing total maintenance expenditure aggregated to the route level and the regional level,and then by comparing expenditure across MDUs.The control period 4(CP4)ten routes level is the level at which we conducted the analysis underpinning the regional comparisons.However,given the CP4 ten routes no longer match the current organisational structure of Network Rail,we have presented only the regional and MDUs comparisons in the main report,with the route comparisons made available in Annex A.The regional level analysis is more robust than the MDU-level analysis,but the MDU-analysis is more local and granular.Whilst the two types of analysis broadly agree in their conclusions,there are some differences which we discuss at the end of this chapter.Route-level analysis Introduction 2.4 In this part of the chapter,we describe our route-level analysis and results.This is for the ten routes that were introduced in CP4.At the start of control period 5(CP5),the number of routes fell to eight as the result of a re-organisation.At the beginning of CP6,Network Rail once again reviewed its organisational structure,Office of Rail and Road|20 resulting in the creation of five geographical regions sitting above 14 routes9.Apart from this year,Network Rail has continued to supply us with the data at the CP4 ten routes level,despite these changes.The reasons we have continued to base our statistical model on ten CP4 routes are:(1)using routes rather than regions increases the number of data points thereby increasing the sample size,which is likely to result in more robust estimates;(2)it maintains comparability over time,which is also important for the statistical robustness of this work;(3)Network Rail has only relatively recently changed to a regional structure;and(4)there is a clear statistical relationship between maintenance expenditure and key cost drivers at this level of analysis.2.5 These route-level results are then aggregated and reported at regional level in order to be consistent with the current Network Rails organisational structure.In future,we aim to base our model on regional level data as it becomes available and large enough to inform a robust statistical model.We successfully did this in our analysis that informed our PR23 advice to UK and Scottish governments.This was possible because we were able to increase the size of our dataset by combining historical data and forecast data.However,this was not possible for this report as the size of the dataset(covering only historical data)is still very small at regional level.2.6 This part of the chapter is organised as follows:we first describe our data and modelling approach(in the Route Analysis section).We then use this information to compare expenditure across regions(under the Regional Benchmarking results section).Route Analysis Data 2.7 The analysis is based on data for financial years 2013-14 to 2021-22,recorded at the level of the ten routes that were introduced by Network Rail in CP4.Dependent variable 2.8 The dependent variable is annual total maintenance expenditure at the route level.For years 2013-14 to 2018-19,maintenance expenditure comes from Statement 1 of Network Rails Regulatory Financial Statements.For years 2019-20 and 2020-21,the information was provided to us directly by Network Rail for the purpose of this analysis.In 2021-22,Network Rail provided us with the data at regional level only,as it no longer reports expenditure at route level.To be consistent with our historical data,we allocated this regional expenditure to the ten routes by giving to 9 Annex B compares the CP4 ten-route organisational structure and the current CP6 14-route structure Office of Rail and Road|21 each route the same proportion of expenditure as last year10.All expenditure data is inflation-adjusted to 2021-22 prices,using the Consumer Price Index(CPI).Independent variables 2.9 Table 1 summarises the explanatory variables we retained in the final model,alongside the expected direction of the relationship to maintenance expenditure and the reasoning behind this.2.10 During our discussions with regions after we published our year 2 of CP6 report,some of them suggested that we use track category as an explanatory variable in lieu of track criticality.In last years analysis of maintenance expenditure we did not include either.This year,we tested both variables and decided to retain track category as it gave us more meaningful results.Table 1:Independent variables used in the route-level maintenance model Variable Expected direction for relationship Reason for relationship Track-km(length of track)11 Positive A larger network requires more maintenance.Passenger traffic density12(train-km/track-km)Positive More traffic on the network would likely cause greater wear and tear.In addition,it is likely that maintenance work is more difficult to undertake in more heavily used areas of the network.Freight traffic density(train-km/track-km)Positive Switches and crossings(S&C)density(number of S&C/track-km)Positive A network with more switches and crossings per track-km is more complex and therefore requires more costly maintenance.Average rainfall13(mm)Positive Higher rainfall is likely to cause more frequent and more damaging infrastructure failure(e.g.landslides)therefore requiring more regular maintenance.Higher rainfall 10 This has probably introduced some errors in the analysis but it was the best way forward available.We minimised the impact of this allocation by aggregating the route level results back to regions and then basing our conclusions on these regional results.We attempted to analyse the existing regional level data but this analysis did not provide reliable estimates as the dataset was too small.11 Where one km of double-tracked route counts as two track-km.12 This model specification gives us similar results as when we use the absolute number of passenger and freight train-kms.We have chosen to retain this density variable for ease of comparison with last years analysis.13 Annual average of monthly total rainfall,published by the Met Office.Office of Rail and Road|22 Variable Expected direction for relationship Reason for relationship may also make it more difficult to undertake infrastructure work.Average days per Possession(Number of possession days/number of possessions)14 Positive More longer possessions of the network mean that Network Rail would be likely to spend more in terms of labour costs,materials,etc.Average number of tracks(track-km/route-km)Negative Time windows for maintenance activities may be wider on multiple track sections of the network,which means the teams can do the work more efficiently.In addition,there may be relatively less volume of work involved when maintaining one km of double-track route than two km of single-track route(for example,due to the volume of ballast and drainage assets).Wage levels(Network Rails average wage in per year)Positive It is expected that maintenance expenditure will be higher in areas where maintenance work is done by staff on higher wages.Proportion of electrified track(electrified track-km/track-km)Positive The presence of electricity and of power supply infrastructure is likely to increase the complexity of track maintenance work.Renewals expenditure(m)Positive Undertaking additional work(frequently a different type of work)on the network at the same time may create for example additional pressure on supply chains,which may lead to increased costs.Proportion of track category 1A,1 and 215(category 1A,category 1&category 2/track-km)Positive A network with higher proportion of track in category 1A,1 and 2 is likely to require more frequent maintenance(as set out in technical standards)and may need to be kept in a better general condition than other parts of the network.It may also be more difficult to undertake engineering work on such sections of the network(for 14 Network Rail needs to restrict access to its network to carry out many of its maintenance and renewals activities.These restrictions of access are referred to as possessions.15 See footnote 8 for the definition.Office of Rail and Road|23 Variable Expected direction for relationship Reason for relationship example,due to higher train speeds and usage)and their access time window may be narrower.This effect may also be covered,in part,by the traffic variable.Year N/A The purpose of this term is to separate out the common annual trend in maintenance expenditure across routes that cannot be attributed to observable cost drivers.The coefficient on Year can be interpreted as an annual growth rate.Year-specific dummy variable(applies to 2020-21)N/A The purpose of this term is to separate out the common change in expenditure across routes due to year-specific exogenous factors that cannot be attributed to observable cost drivers.The coefficient can be interpreted as a deviation from the average annual growth rate given by the coefficient on the Year variable.Given the pandemic was an event that significantly affected Network Rails operations,especially during the year 2020-21,we use a dummy for year 2020-21 to isolate its impact.Year-specific dummy variable(applies to 2021-22)N/A The purpose of this term is to separate out the common change in expenditure across routes due to the 2021-22 year-specific exogenous factors that cannot be attributed to observable cost drivers.The coefficient can be interpreted as a deviation from the average annual growth rate given by the coefficient on the Year variable.Descriptive statistics 2.11 Table 2 below presents some summary statistics that describe the variables in our model:Table 2:Summary of variables Variable Mean Std.Dev.Min Max Maintenance expenditure(m)149 88 55 427 Office of Rail and Road|24 Variable Mean Std.Dev.Min Max Track-km(km)3,109 1,707 1,124 6,917 Passenger traffic density(train-km/track-km)17,669 5,870 6,588 31,999 Freight traffic density(train-km/track-km)1,178 564 171 2,229 Switches and crossings density(number of S&C/track-km)0.6 0.1 0.4 0.9 Average rainfall(mm)85 29 41 150 Average days per possessions(number of possession days/number of possessions)0.2 0.1 0.0 1.1 Average number of tracks(track-km/route-km)2.0 0.2 1.6 2.6 Average Wage levels(/year)35,333 1,728 31,317 3,9258 Proportion of electrified track(%)481%0%Renewals expenditure(m)336 172 86 1,019 Proportion of track category 1A,1&2(%)36%8T%Model specification 2.12 We have adopted the same functional form as in last years report,namely the Cobb Douglas log-log formulation(i.e.where the dependent variable and most explanatory variables are entered in natural logarithms).With this functional formulation,most coefficients can be interpreted as constant elasticities that measure the percentage change in cost resulting from a percentage change in the relevant cost driver.2.13 For this updated analysis,we have estimated a number of variants of the following model but settled on the following specification16:16 A bold font means the variable is new relative to our year 2 of CP6 report.Office of Rail and Road|25 ln()=0 1ln() 2ln() 3ln() 4ln(&) 5ln() 6() 7ln() 8ln() 9() (,&) 11ln() 12() 13(202021) 14(202122) 2.14 Relative to last years report,we have made changes to our model to reflect feedback from Network Rail following publication of last years report.These include controlling for the proportion of track category 1A,1 and 2(i.e.(track category 1A category 1 category 2)/track-km)as an additional cost driver.Moreover,we have now modified the way we measure the possession duration.Instead of calculating it as number of possession days/track-km we now calculate it as number of possession days/number of possessions.Network Rail consider this to be a better measure of average possession duration as it also reflects efficiency in each possession.This is because the number of possession days/track-km effectively only measures the volume of work carried out whilst the number of possession days/number of possessions reflects both the volume of work and that shorter possessions are less efficient as the amount of actual working time between setting up and handing back is squeezed.2.15 Another improvement comes from the way we have measured wage levels.In our previous reports,we used weekly wages data from the ONS,which we collected by mapping local authorities to Network Rails MDUs and then aggregating it at route level.This data was used as a proxy for Network Rails wage levels but in reality,the data only reflected the level of wages(in general)in each MDUs geographical area of operation rather than the actual wages paid by Network Rail.In this years analysis at route level,we have used Network Rails specific maintenance average wage data.However,whilst this constitutes an improvement,we are also aware that there is a degree of harmonisation of terms Office of Rail and Road|26 and conditions across Network Rail,which may reduce the effect of regional differences in wages.Estimation approach 2.16 As in last years report,we have used the pooled ordinary least squares(OLS)method to estimate our model17.This approach has the advantage of being simple to implement and its results easy to understand.2.17 With OLS,we estimate a line that passes through the centre of the observed data points.This means that,given the information available,the OLS line defines the average cost that a business unit should incur given the cost drivers we control for in our model.The distance between the OLS line and observed/outturn points is the residual.We use these residuals to describe the business units performance relative to the average of the peer group,after controlling for differences in relevant cost drivers18.2.18 This is illustrated in Figure 10 below.Observations above the line imply that the business unit in question spent more than expected,while those observations below the line mean that the business unit spent less than expected.The larger the distance between the individual observation and the line(i.e.the residual)the more important it is to find out what is different about the business unit in question relative to others and relative to previous years,be it efficiency,headwinds,tailwinds,data reporting or some other factor.Figure 10:Theoretical OLS regression line and cost performance(for illustration only)17 We also tested panel methods and stochastic frontier methods.18 See our previous reports for more details on how this is done.Office of Rail and Road|27 Model estimates 2.19 Below,we present and analyse the results of our OLS model estimates.Table 3:OLS coefficient estimates results for maintenance expenditure model Variable Coefficient Track-km 0.83*Passenger traffic density 0.36*Freight traffic density 0.11*Switches and crossings density 0.53*Average rainfall-0.04 Average days per possession 0.03*Average number of tracks-0.25 Average wage levels 0.67 Proportion of electrified track 0.06 Proportion of track category 1A,1&2 0.09 Renewals expenditure 0.09 Year(average annual unexplained growth rate in maintenance expenditure)0.06*Dummy for 2020-21(deviation from the annual growth rate due to COVID-19)-0.10 Dummy for 2021-22(deviation from annual growth specific to the year 2021-22)-0.27*Constant19-13*Number of observations 90 R2 0.96 19 The constant has no meaningful physical interpretation.Its role is to improve the fit between the model and the data.The coefficient is provided here for completeness and so that our calculations can be repeated by other people.Office of Rail and Road|28 Variable Coefficient*Statistically significant at the 99%confidence level20*Statistically significant at the 95%confidence level*Statistically significant at the 90%confidence level 2.20 Table 3 above shows that there is a statistically significant relationship(at the 95%confidence level)between the amount that a route spends on maintenance and:the size of the network it maintains,i.e.track-km;traffic density(both of passenger and freight trains);the average days per possession;and the density of switches and crossings.2.21 The models R2 is 0.96.R2 is a measure of goodness-of-fit.It represents the proportion of the variance in maintenance expenditure that can be statistically explained by the independent variables in the model.This means that our model can explain 96%of the variance in maintenance expenditure across routes and over time,which suggests that the model is a very good predictor of outturn maintenance expenditure.2.22 Our results suggest no clear relationship between maintenance expenditure and:average rainfall,average number of tracks,average wage levels,proportion of electrified track,track category or renewals expenditure.These variables may well influence maintenance expenditure but there is no clear statistical relationship in the data that is not already accounted for through other variables.Issues such as measurement errors,correlation between other variables already in the model might be behind this lack of statistical significance.2.23 The results in Table 3 above show that,all other factors held constant:(a)increasing track length by 1%,is associated with 0.36! higher maintenance expenditure.This suggests that there are economies of scale in 20 Technically,statistical significance(as produced by the model and expressed by the number of stars in the table)tells us that the patterns in the data provide evidence for a strong relationship between the dependent and the independent variables and that this is unlikely due to chance,while the size of coefficients tells us what the scale/magnitude of the relationship is.The higher the number of stars the more confident in the results we are.More precisely,when we say that a coefficient is statistically significant at the 99%level,this means that there is a 99%probability that the actual underlying parameter is different from zero.In other words,we are almost entirely certain that the parameter is different from zero.This assessment is based on the assumption that the parameter follows a normal,or bell-shaped,probability distribution across the population,with its most likely value being the parameter estimated.21 We calculate this as the difference between the track-km coefficient and the sum of the traffic density coefficients 0.83-(0.36 0.11)=0.36.This is because the traffic density coefficient reflects both the effect of an increase in traffic and of an increase in track-kms.To obtain the overall effect of a change in track-kms we therefore need to take account of all three coefficients that contain that variable.Mathematically,the Office of Rail and Road|29 network size,i.e.maintenance expenditure increases less than proportionally with the length of track;(b)increasing passenger traffic by 1%,increases maintenance expenditure by 0.36%;also,an independent 1%increase in freight traffic increases maintenance expenditure by 0.11%.These results show economies of density costs increase less than proportionally with traffic;(c)increasing the density of switches and crossings by 1%increases maintenance expenditure by 0.53%.It is likely that this variable is picking up the effect of network complexity more generally;and(d)10%longer network possessions are associated with 0.3%higher maintenance expenditure.2.24 Our analysis suggests that there has been an average annual increase in maintenance expenditure of 6 per year(in real terms)since 2013-14,after normalising for factors such as traffic and network complexity.This may be due to inefficiency,or other factors.This long-run trend of maintenance expenditure rising has reduced from the last two years(from 9%in 2019-20 and 8%in 2020-21)but it is not clear if this reflects actual cost changes(e.g.inefficiency)or other factors,such as a change in the accounting of maintenance expenditure in the years from 2019-20,as mentioned in chapter 1.2.25 Note that the main purpose of the present work is to compare maintenance expenditure across business units in the most recent year,whilst controlling for differences in observable cost drivers,rather than to measure business units against an external efficiency benchmark or to examine performance changes over time.We therefore do not have a view here on the cause of the trend identified above.ORRs separate publication,the Annual Efficiency and Finance Assessment,provides a view on Network Rails efficiency;our PR18 final determination sets out our expectations for Network Rails efficiency improvement over CP6.Regional benchmarking results 2.26 The present analysis compares outturn maintenance expenditure against expected spend as predicted by our model,given each regions characteristics.As mentioned earlier,whilst the underlying analysis was conducted using route level elasticity of maintenance expenditure with respect to track-kms equals the coefficient on track-kms minus the sum of the coefficients on the traffic density variables.22 Calculated as(0.06 1).Office of Rail and Road|30 data,we have aggregated our route level results to the regional level and that is what we present in this section23.We order the regions according to the amount of unexplained variation(i.e.the difference between outturn and predicted expenditure).2.27 Figure 11 below shows,for each region,the proportion of unexplained cost variance in 2021-22.A negative number means that the region spent less than expected(according to our statistical model)while a positive number means that the region spent more than expected(according to our statistical model).Figure 11:Deviation between outturn and expected(modelled)maintenance expenditure,2021-22-Regional comparisons24 23 This allows the interpretation of these findings to be consistent with Network Rails current organisational structure as reflected in the five regions.However,the routes comparisons are available in Annex A.24 Given the uncertainty associated with any statistical model,we consider any region that is within /10%of our modelled prediction(as shown by the x-axis at zero)is not an outlier.These regions are marked grey.Regions that are marked blue are considered outliers.The lines surrounding the central estimate of a given regions deviation between outturn and modelled cost indicate a 95%confidence interval.In other words,given the data available and the robustness of our model,there is a 95%probability that this estimated confidence interval contains the actual number representing the deviation between outturn and modelled cost.Office of Rail and Road|31 2.28 The figure shows that maintenance expenditure at the regional level,was between-17%and 17%of that predicted by our model for 2021-22.Similar to last year,Scotland and Eastern are the largest outliers.2.29 The Eastern regions actual maintenance expenditure was 17ove the models prediction.Last year it was 12ove models prediction.The region suggested that a factor that could explain this difference is the complexity of maintenance work carried out by different regions.We will work with Network Rail to better understand this issue.2.30 Furthermore,it is not clear why Scotlands maintenance expenditure continues to be below the model prediction compared to other regions(-17%in 2021-22 from -18%in 2020-21).Network Rail Scotland said the variance may be explained by improved co-ordination in the planning and delivery of maintenance and renewals,though it is unclear to what extent Network Rail Scotland did this better than other regions,to the point where it can justify this unexplained difference.We will continue to work with Network Rail to better understand this issue.2.31 In last years analysis,Wales&Western appeared to spend c.4%on maintenance more than our models prediction.This year,the region appears to have spent 3%less than our models prediction.The region stated that this is the outcome of the work they have undertaken to reduce costs.For example,the region said that it successfully delivered 26.2m opex efficiencies and continues to exercise robust cost control.The Wales&Western region also re-aligned the accounting classification of some minor maintenance works expenditure to be consistent with practice in other regions.According to the region,this also resulted in reduced maintenance expenditure.2.32 Other possible factors that could account for differences between regions arising from wider discussions with Network Rail include:the proportion of staff based in,and the proportion of work carried out in and around the London area(though we note Southern is actually below the models prediction);and the need to carry out work at night and weekends(over and above that implied by higher traffic volumes alone).2.33 We will continue to work with Network Rail over the next few months to look into the potential underlying causes for these results,and to improve our model where possible.Office of Rail and Road|32 MDU-level analysis Introduction 2.34 Maintenance Delivery Units(MDUs)are operating units within Network Rails routes(each route is part of a region),responsible for the majority of the day-to-day upkeep of their designated part of the network.They ensure that the infrastructure(ranging from signals and power supplies to track and structures)is in good working order.MDUs are not responsible for renewals,so we only cover MDU maintenance expenditure.2.35 Network Rail previously reduced the number of MDUs from 37 to 35.Woking closed in 2017-18 with activities previously undertaken by Woking moved to Clapham and Eastleigh,which then became Wessex Inner and Wessex Outer from 2018-19.Similarly,in 2019-20,Bristol,Plymouth,Reading and Swindon MDUs were restructured into Western Central,Western East and Western West.2.36 To maintain comparability with historical data,we have previously analysed maintenance expenditure using the 37 MDU structure.However,we have always sought to analyse the MDUs in their actual structure,as far as the data can be accurately reported at that structure.This year we have undertaken the analysis for 36 MDUs as we have been able to re-allocate data from Woking,Eastleigh and Clapham to Wessex Inner and Wessex Outer,as explained below in paragraph 2.48.We have not been able to do the same for Western Central,Western East and Western West,so we continue to use the Bristol,Plymouth,Reading and Swindon MDU structure instead.Annex C maps the 36 MDUs to Network Rails CP4 ten route structure(used in our route benchmarking analysis)25 and to the five regions.2.37 On average,MDUs accounted for around 67%of total network maintenance expenditure during the 8 years covered by this analysis.The remaining 33%is centrally-managed and it covers activities such as structure examination,major items of maintenance plants and other HQ managed activities.2.38 This part of the chapter is organised as follows:we first compare the 36 MDUs in terms of their respective expenditure,asset characteristics and network usage to provide context to the analysis(in the MDU context section).We then describe our data and modelling approach(in the MDU Analysis section).Finally,we use this information to compare expenditure across MDUs and we compare these 25 Using the former geographical boundaries of the CP4 ten routes,we can localise each MDU within that structure.Office of Rail and Road|33 findings with those from our regional level analysis(in the MDU Benchmarking results section).MDU context 2.39 Maintenance expenditure:Figure 12 below shows that MDUs spent,on average,c.35k per track-km each year.Euston spent the most,at 94k per track-km,whilst Perth spent the lowest amount,at 16k per track-km.Figure 12:Average maintenance expenditure per track-km,2014-15 to 2021-22(2021-22 prices)2.40 Traffic Density:Figure 13 below shows that traffic density(passenger and freight traffic per track-km)varied widely across MDUs.Croydon carried 39,238 train-km per track-km,on average,per year.On the other hand,Perth carried 7,665 train-km per track-km per year.The average GB-wide track density was 17,687 train-km per track-km.Office of Rail and Road|34 Figure 13:Average traffic density(train-km/track-km),2014-15 to 2021-22 2.41 Network size(track-km):as shown in Figure 14 below,Lancashire&Cumbria(Lancs&Cumbria)is responsible for the longest section of network with 1,556 track-km,whilst Euston maintains the shortest with 358 track-km.The average length of track covered by an MDU over the period 2014-15 to 2021-22 is 864 track-km.Figure 14:Average track-km,2014-15 to 2021-22 Office of Rail and Road|35 2.42 Wage levels:As per last years analysis,we have used median wages across the local authority areas covered by each MDU26.Figure 15 below compares this wage for each MDU.The average median weekly wage across all MDUs between 2014-15 and 2021-22 was 623 per week.2.43 Average median weekly wages were highest in the local authority that covers London Bridge at 804.In contrast,Sandwell&Dudley had the lowest average median weekly wage at 541,followed closely by Shrewsbury at 545.Figure 15:Average median weekly wages,2014-15 to 2021-22(2021-22 prices)2.44 Average length of possessions(number of possession hours/number of possessions):As shown in Figure 16 below,Liverpool has the longest length of possessions at 14.1 hours per possession,whilst Reading has the shortest at 2.9 hours per possession.The average length of possessions for an MDU over the period 2014-15 to 2021-22 is 5.7 hours27.26 Data is sourced from the Office for National statistics(ONS)on weekly earnings by local authority.We matched these local authorities with each of the 36 MDUs geographical area of operation.Note that this weekly wages data is not Network Rail specific.It simply reflects the level of wages in each geographical area covered by MDUs.27 In the analysis we used number of days/number of possessions,which is the same variable we use in the route analysis.However,to facilitate a better visual comparison of MDUs in this figure,we chose to show the Office of Rail and Road|36 Figure 16:Average length of possessions hours,2014-15 to 2021-22 2.45 Average number of tracks(track-km/route-km)across all MDUs in 2021-22 was 1.97.Bedford had the highest average number of tracks at 3.30,followed by Euston and Peterborough at 3.22 and 3.18,respectively.Perth MDU had the lowest average number of tracks at 1.34,followed by Glasgow at 1.49.2.46 Average electrification across all MDUs was 43tween 2014-15 and 2021-22.Shrewsbury had no electrified sections,followed by Derby,Perth,Sheffield,Plymouth,Bristol,Cardiff,and Saltley,all with negligible proportions of electrified track(10%).On the other hand,Croydon was almost fully electrified,followed by Euston,Orpington,London Bridge,Peterborough,and Romford,all with above 95%of track electrified.2.47 The network is classified into five criticality bands.The MDU with the highest percentage of its track length within criticality bands 1&2(combined)in 2021-22 is Reading at 94%,followed by London Bridge at 93%.Shrewsbury,Perth,and Sheffield have none of their track length in criticality bands 1&2.variable as the number of hours/number of possessions,as it has larger numbers,whose representative bars are easier to compare in a figure like this one.Office of Rail and Road|37 MDU Analysis Data 2.48 We have previously analysed maintenance expenditure using the 37 MDUs structure.From 2017-18,Network Rail reported Wessex MDUs as Wessex Inner and Wessex Outer in lieu of Woking,Eastleigh and Clapham.This reduced the MDUs from 37 to 36.This year,the analysis is based on data for Network Rails 36 MDUs for financial years 2014-15 to 2021-22.To move from the 37 to the 36 MDUs structure,we re-allocated data from Woking,Eastleigh and Clapham to Wessex Inner and Wessex Outer.For 2017-18 to 2021-22,we calculated the expenditure for Wessex Inner and Wessex Outer separately as a proportion of the total expenditure for Wessex Inner and Wessex Outer,and then applied those proportions to the total for Woking,Eastleigh and Clapham for the years 2014-15 to 2016-201728.2.49 This analysis builds on the model employed in our year 2 of CP6 report(2020-21),using mostly the same variables but with the addition of another years worth of data,and two new variables:average possessions length and average rainfall.Note that average rainfall is calculated at route level and is assigned to MDUs within that route.2.50 Last year,Network Rail was unable to supply passenger and freight traffic data at the MDU-level due to a data recording hiatus,whilst it transferred between systems.We therefore estimated MDU level traffic for 2020-21 by splitting route-level traffic data,based on the proportion of the relevant routes 2019-20 traffic that each MDU accounted for.This issue has been resolved and Network Rail was able to provide us with actual passenger and freight traffic data at the MDU-level for the year 2021-2229.This improved our results as our model has consistently shown that traffic is one of the main drivers of maintenance expenditure.Dependent variable 2.51 The dependent variable is maintenance expenditure,allocated to the MDU level.This excludes centrally managed expenditure(covering activities such as 28 This probably introduced some errors in the analysis,but it was the only way forward as we try to report our analysis in a structure that matches Network Rails current structure.Since the allocation is done for the years that are the 3 oldest in our analysis(out of the total of 8 years),this may not have a significant impact on our comparisons for the latest year.29 Network Rail was not able to provide us with the passenger and freight traffic data for 2020-21.This means that for 2020-21,we continued to use the same data we calculated for last year.Office of Rail and Road|38 structures examination,major items of maintenance plant and other HQ managed activities).2.52 In 2020-21,there was a significant drop in traffic due to the impact of the pandemic.This was accompanied by a 5%increase in maintenance expenditure at the MDU level from 2019-20,as social distancing,reduced staff availability and supply chain pressures,among other factors,made it more difficult and costly to carry out work on the infrastructure.In 2021-22,maintenance expenditure at the MDU level has returned to pre pandemic levels.Independent variables 2.53 Table 4 below presents the full list of independent variables that we have included in our analysis.Table 4:Independent variables used in the MDU-level model Variable Expected direction of relationship Reason for relationship Track-km(length of track)Positive A larger network requires more maintenance.Passenger train-km Positive More traffic on the network would likely cause greater wear and tear.In addition,it is likely that maintenance work is more difficult to undertake in more heavily used areas of the network Freight train-km Positive Average number of tracks(track-km/route-km)Negative Time windows for maintenance activities may be wider on multiple track sections of the network,which means the teams can do the work more efficiently.In addition,there may be relatively less volume of work involved when maintaining one km of double track route than two km of single track(for example,due to the volume of ballast and drainage assets).Proportion of electrified track(electrified track-km/track-km)Positive The presence of electricity and of power supply infrastructure is likely to increase the complexity of track maintenance work.Office of Rail and Road|39 Variable Expected direction of relationship Reason for relationship Switches and crossings(S&C)density(number of S&C/track-km)Positive A network with more switches and crossings per track-km is more complex and therefore requires more costly maintenance.Criticality 1&2 density(criticality 1&2 km/track-km)Positive More critical sections of the network are likely to require more frequent maintenance(as set out in technical standards)and may need to be kept in a better general condition than other parts of the network.It may also be more difficult to undertake engineering work in more critical parts of the network(for example,due to higher train speeds and usage)and the access time window may be narrower on those sections of line.This effect may also be covered,in part,by the traffic variable.Wage levels(per week)30 Positive Maintenance work in each MDU is carried out largely by a local labour force.It is expected that maintenance work will cost more in areas where labour costs are higher.In practice,this effect may be significantly reduced by the use of national terms and conditions.Average days per Possession(Number of possession days/number of possessions)Positive More longer possessions of the network mean that Network Rail would be likely to spend more in terms of labour,materials costs,etc.Average rainfall(mm per year)Positive Higher rainfall is likely to cause more frequent and more damaging infrastructure failure(e.g.landslides)therefore requiring more regular maintenance.Higher rainfall may also 30 ONS seasonally adjusted median average weekly earnings(AWE)per local authority.We obtained the data by mapping local authorities to Network Rails MDUs.We adjusted these weekly wages data for inflation and they represent real median earnings.The data only reflects the level of wages(in general)in each MDUs geographical area of operation rather than the actual wages paid by Network Rail.We are also aware that there is a degree of harmonisation of terms and conditions across Network Rail,which may reduce the effect of regional differences in wages.Office of Rail and Road|40 Variable Expected direction of relationship Reason for relationship make it more difficult to undertake infrastructure work.Year N/A The purpose of this term is to separate out the common annual trend in maintenance expenditure across MDUs that cannot be attributed to observable cost drivers.The coefficient on Year can be interpreted as an annual growth rate.Year-specific dummy variable(applies to 2020-21)N/A The purpose of this term is to separate out the common change in expenditure across routes due to year-specific exogenous factors that cannot be attributed to observable cost drivers.The coefficient can be interpreted as a deviation from the average annual growth rate given by the coefficient on the Year variable.Given the pandemic was an event that significantly affected Network Rails operations,especially during the year 2020-21,we use a dummy for year 2020-21 to isolate its impact.Year-specific dummy variable(applies to 2021-22)N/A The purpose of this term is to separate out the common change in expenditure across MDUs due to the 2021-22 year-specific exogenous factors that cannot be attributed to observable cost drivers.The coefficient can be interpreted as a deviation from the average annual growth rate given by the coefficient on the Year variable.Descriptive statistics 2.54 Table 5 below presents summary statistics for the variables in our model.Office of Rail and Road|41 Table 5:Summary of variables(all monetary variables in 2021/22 prices)Model specification 2.55 We have adopted the same functional form as in the route analysis:the Cobb-Douglas log-log formulation(i.e.where the dependent variable and most explanatory variables are in natural logarithms).As mentioned above,this functional formulation allows most coefficients to be interpreted as constant elasticities,i.e.the percentage change in cost resulting from a percentage change in the relevant cost driver.2.56 We have estimated a number of variants of the following model but settled on the following specification31:31 A bold font means the variable is new relative to our year 2 of CP6 report.Variable Mean Std.Dev.Min Max Maintenance expenditure(m)30 9 16 60 Track-km(km)864 319 353 1623 Passenger train-km(million train-km)14.1 4.3 5.9 25.2 Freight train-km(million train-km)1.2 0.7 0.1 3.7 Average number of tracks 2.1 0.5 1.3 3.3 Proportion of electrified track(%)515%00%Switches and crossings density(S&C per track-km)0.7 0.3 0.3 1.5 Criticality 1&2 density(%)33(%0%Wage levels(/week)622 65 520 846 Average days per Possession(days)0.3 0.2 0.0 1.7 Average Rainfall(mm per year)87 28 41 150 Office of Rail and Road|42 ln()=0 1ln() 2ln() 3ln() 4ln(&) 5() 6(1&2) 7ln() () 9ln() () 11() 12(202021) 13(202122) 2.57 We have made changes to our model in previous reports to reflect feedback from Network Rail.These include controlling for rainfall and average days per possessions as additional cost drivers in the MDU model.This,alongside the increased size of the dataset,has improved the robustness of our results.2.58 In its feedback last year,some of Network Rails regions suggested that we use track category instead of track criticality,as they considered track category to be a better driver of maintenance expenditure than track criticality.We have tested this hypothesis in this model,however the coefficient for track category came up with a negative sign which is counterintuitive.We therefore decided to keep track criticality in our model as in previous reports.2.59 In this part of the chapter,we have continued to use the weekly wage data from the ONS instead of Network Rails average annual wage data that we used in the routes analysis section.Although we received this data at MDU level and aggregated it at route level,the analysis showed that this data produces more meaningful results at route level than at MDU level.We consider that one possible reason for this is because the disaggregated data at MDU level does not account for hosting:when an MDU performs work on behalf of other MDUs(usually within the same route/region),the wage paid to the staff doing the work is paid by the hosting MDU.This has the implication of inflating the cost of wages for the hosting MDU whilst reducing it for the MDUs where the work is located.Therefore,given that hosting is a widespread practice within Network Rail,this could explain why using Network Rails average annual wage data at MDU level leads to counterintuitive estimates.As we aggregate the data to route/region level,the Office of Rail and Road|43 impact of hosting is smoothed out.On the other hand,the ONS data is used here as a proxy for the level of wages in each MDUs geographical area rather than Network Rails actual wage levels.This is not affected by hosting or any other issues in data recording by Network Rail.Estimation approach 2.60 Similar to last years analysis,we have used the pooled ordinary least squares(OLS)method to estimate our model.This approach has the advantage of being simple to implement and its results easy to understand.2.61 With OLS,we estimate a line that passes through the centre of the observed data points.This means that,given the information available,the OLS line defines the average cost that a business unit should incur given the cost drivers we control for in our model.The distance between the OLS line and observed/outturn points is the residual.We use these residuals to describe MDUs performance relative to the average of the peer group,after controlling for differences in relevant cost drivers.Model estimates 2.62 Below,we present and analyse the results of our OLS model estimates.Table 6:OLS estimated results Variable Coefficient Track-km 0.28*Passenger train-km 0.36*Freight train-km 0.15*Average number of tracks 0.48*Proportion of electrified track 0.46*Switches and crossings density 0.25*Criticality 1&2 density 0.10 Wage levels(median weekly wage)0.51*Average days per possession 0.08*Average rainfall 0.11*Office of Rail and Road|44 Variable Coefficient Year 0.02*Dummy for 2020-21 0.18*Dummy for 2021-22 0.09*Constant 10.04*Number of observations 285 R2 0.67*statistically significant at the 99%confidence level*statistically significant at the 95%confidence level*statistically significant at the 90%confidence level 2.63 The results show a statistically significant relationship between the amount that an MDU spends on maintenance and:the level of traffic(both passenger and freight),network complexity(measured by the average number of tracks,electrification and S&C density),the level of wages in the local authority covered by that particular MDU,the size of the network(track-km),the average rainfall and the average length of possessions.The proportion of the network in criticality bands 1&2 does not seem to have a clear effect on expenditure,as its coefficient is not statistically significant at the 90%level.2.64 Similar to last year,the analysis also shows that there has been an average annual increase in maintenance expenditure of 2.02 per year(in real terms)over the period covered by our sample,which cannot be explained by changes in network size,traffic or other observable factors.Also,after accounting for observable differences between MDUs,maintenance expenditure in 2021-22 appears to be 9.03 above this historical/background trend.This may be due to inefficiency,headwinds(cost increases outside of Network Rails control),or some other factors.2.65 The models R2 is 0.67.R2 is a measure of goodness-of-fit.It represents the proportion of the variance in maintenance expenditure that can be statistically explained by the independent variables in the model.This means that our model can explain 67%of the variance in maintenance expenditure across MDUs and over time.This is an improvement as compared to our model last year which had an R2 of 0.61.This improvement comes from the use of a bigger dataset,the 32 Calculated as(0.02 1).33 Calculated as(0.09 1).Office of Rail and Road|45 inclusion of the new explanatory variables as well as the better measurement of traffic data as discussed above.2.66 The results in Table 6 above show that,all other factors held constant:(a)increasing track length by 1%,whilst keeping traffic(and all other variables)constant,would increase maintenance expenditure by 0.28%.This suggests that there are economies of scale,i.e.costs increase less than proportionally with the length of track;(b)an increase in passenger train-km of 1%,would increase maintenance expenditure by 0.36%.The same independent 1%increase in freight traffic would increase costs by 0.154.These results show economies of density costs increase less than proportionally with traffic;(c)increasing the proportion of electrified track by 1%would increase maintenance expenditure by 0.46%.That is,if an MDU went from 50%to full electrification,our model indicates that its maintenance expenditure would be 38%higher35;(d)increasing the density of switches and crossings by 1%increases maintenance expenditure by 0.25%;(e)a 1%difference in local wages is associated with a 0.51%difference in maintenance expenditure;(f)maintaining a given length of track as a single-track route is expected to cost 39%more than maintaining the same length of track as a double-track route36;(g)10%longer network possessions are associated with 0.8%higher maintenance expenditure;and(h)10%higher rainfall is associated with 1.1%higher maintenance expenditure.34 Freight traffic is heavier but slower than passenger traffic.This means weight and speed may work in different directions in the analysis,which may make it difficult to make a prediction on the relative sizes of their coefficients.However,if we consider that in our data,freight traffic is very small as compared to passenger traffic,these coefficients are as expected.This is because the small amount of freight traffic means that the average cost for freight is higher than the average cost for passenger traffic,implying that for a similar marginal cost increase,the elasticity(i.e.coefficient)of freight must be smaller than the one on passenger traffic.Note that marginal cost=elasticity average cost.35 The percentage increase is calculated as(10.460.50.46)1=0.38 36 The percentage difference is calculated as(1 0.72)1=0.39.Note that one km of double-tracked route counts as two track-km.The cost of maintaining a one km line as single-track is therefore 10.28 10.48=1,whereas the cost of maintaining a one km line as double-track is 10.28 20.48=0.72.This indicates that it is cheaper to run the same length of line as a double-tracked network.Office of Rail and Road|46 MDU benchmarking results 2.67 Here we compare outturn maintenance expenditure against expected spend as predicted by our model,given each MDUs characteristics.We then order the MDUs according to the size of the unexplained variation.2.68 Figure 17 below shows the proportion of unexplained cost variance for each MDU in 2021-22.A negative number means that the MDU spent less than expected(according to our statistical model),whilst a positive number means that the MDU spent more than expected.Figure 17:Deviation between outturn and expected(modelled)maintenance expenditure,2021-2237 37 Given the uncertainty associated with any statistical model,we consider any MDU that is within /20%of our modelled prediction(as shown by the x-axis at zero)is not an outlier.These MDUs are marked grey.MDUs that are marked blue are therefore considered outliers.The lines surrounding the central estimate of a given MDUs deviation between outturn and modelled cost indicate a 95%confidence interval.In other words,given the data available and the robustness of our model,there is a 95%probability that this estimated confidence interval contains the actual number representing the deviation between outturn and modelled cost.Office of Rail and Road|47 2.69 Given that there is uncertainty in any statistical model,we classify MDUs into three broad bands based on the deviation between outturn maintenance expenditure and expected,or modelled,maintenance expenditure:(a)MDUs for which outturn spend is lower than expected by 20%or more;(b)MDUs for which outturn spend is higher than expected by 20%or more;and(c)MDUs for which outturn spend is within /-20%of that expected by the model.2.70 The analysis shows that,in 2021-22,the Doncaster,Edinburgh,Wessex Inner and Orpington MDUs are in the first category( 20%).At the extremes,Doncaster spent 38%less than predicted by our model whereas Lancashire&Cumbria spent 47ove our models prediction.The ordering of MDUs is broadly similar to that generated from last years analysis.However,the range of unexplained differences in 2021-22(i.e.-38%to 47%)is narrower than that implied in last years analysis(-55%to 39%).This is an indication of an improvement in our model due to a larger dataset and the inclusion of the new explanatory variables.2.71 This analysis shows that,for a minority of MDUs,there is a large proportion of unexplained variance between outturn expenditure and that suggested by our statistical model.One general explanation that the regions provided was“hosting”.This involves one MDU undertaking maintenance activities on some infrastructure(e.g.overhead line)on behalf of other MDU(s)but the costs continue to be paid by the hosting MDU and are not charged to the MDU where the asset is located.The regions stated that this type of hosting arrangement is common and may therefore help to explain some of the outliers.2.72 Similar to last year,Shrewsbury and Cardiff(both in the Wales route and Wales&Western region)are both towards the more costly end of the distribution.The Wales&Western region explained that such a big positive difference between outturn and predicted expenditure could be due to the geographical spread of the MDUs and the age of their infrastructure relative to other MDUs.2.73 Out of the six most costly MDUs as compared to our models prediction,four(i.e.Lancashire&Cumbria,Euston,Liverpool and Bletchley)are in the North West&Central region.The region has pointed out that the Lancashire&Cumbria and Liverpool MDUs are some of the most geographically dispersed MDUs,with a Office of Rail and Road|48 number of satellite units delivering work in more remote areas.In particular,according to the region,Lancashire&Cumbria covers some difficult to access rural areas and includes older infrastructure alongside a section of the West Coast Main Line(WCML).The region also noted the high concentration of running line jointed track and mechanical signalling in the area,which requires more frequent maintenance than other types of track and signalling systems.2.74 In addition,the North West&Central region has explained that some of these MDUs cover a network with increased maintenance requirements due to additional traffic carrying HS2 materials.For Euston,the region mentioned that the unexplained difference may be a result of increased line blockage working,due to track worker safety requirements(leading to an increase in non-time on tools)this is particularly significant for WCML access.2.75 All the MDUs in Scotland seem to spend less than our models prediction,which is in line with the regional results.Consistency between regional and MDU results 2.76 In Figure 18 below,we compare the regional level results to those implied by the MDU analysis.To do this,we map MDUs to regions,and then sum outturn and expected(modelled)cost from the MDU data/model up to region level.2.77 Note that we do expect some differences in the regional and MDU level results as the two models are different in terms of the costs modelled and the cost drivers controlled for.As described earlier,all MDUs accounted for around 67%of regional maintenance expenditure during the period covered by our analysis,with the remaining 33ntrally managed and covering activities such as structures examination,major items of maintenance plant and other HQ managed activities.However,this comparison helps us to draw out some insights regarding the robustness of the two analyses,by looking at whether the results for individual business units point in the same direction and by comparing the scale of unexplained differences.Office of Rail and Road|49 Figure 18:Comparison of region and MDU deviations from expected(modelled)maintenance expenditure,2021-22 2.78 Figure 18 shows that the results at both MDU and regional level for Scotland,Southern and North West&Central point in the same direction,with a relatively small difference in the scale.2.79 Although results for Wales&Western also point to opposite directions with a relatively sizeable difference in scale,the MDU results are comparable with route level results whereby the Wales route(and all its MDUs)appears to have spent more than our models prediction whilst the Western route(and all its MDUs)spent less than our models prediction.The results for Eastern have the largest difference in scale.Office of Rail and Road|50 3.Renewals Introduction 3.0 Renewals relate to activities to replace,in whole or in part,network assets that have deteriorated such that they can no longer be maintained economically.Renewal of an asset restores the original performance of the asset and can add additional functionality as technology improves.3.1 In PR08,PR13 and PR18,we modelled maintenance and renewals expenditure together.The potential advantages of this approach include that it can capture potential interdependency between maintenance and renewals activities.For example,renewing an asset in one year may reduce maintenance requirements in subsequent years.3.2 In practice,these two activities are different in nature and may be driven by different factors.Maintenance activities at the route level are less variable over time than renewals,which tend to be undertaken less often and as larger one-off projects to renew specific assets or specific parts of the network.3.3 Therefore,in our year 1 of CP6 report,we estimated separate models for maintenance and renewals.Whilst this change greatly improved our modelling of maintenance expenditure,it also highlighted that our approach to the modelling of renewals needed further improvement.Notably,the renewals model could not account for natural annual fluctuations in expenditure arising from the lumpy nature of the renewals work(e.g.fluctuations due to differences in work mix,decisions to defer some works,etc.)which,if not accounted for,could be misinterpreted as poor/good performance.Also,different types of work are likely to be delivered at different costs.3.4 In last years analysis(year 2 of CP6),we addressed those shortcomings by comparing renewals unit costs(in simple terms,expenditure divided by work volume)and did this separately by main asset class and for different types of renewals activity.3.5 We have followed the same approach for this years analysis as it allows for more meaningful comparisons.It also deals with the problem of large fluctuations in total expenditure from year to year.Average unit costs for a given asset and work type should remain relatively stable even if volumes of work fluctuate significantly.3.6 We have analysed the average unit costs(expenditure divided by work volume)separately by main asset classes(Track,Signalling,Civils and Buildings)and for different types of renewals activity.Whilst part of this analysis is discussed in the Office of Rail and Road|51“Context”section of this chapter,we are only publishing our detailed analysis on conventional track renewals as this compares better with last years analysis.3.7 This chapter describes the statistical model we have estimated to explain conventional track renewals unit costs at a route level as a function of key cost drivers.These results are then aggregated at regional level.Unlike in our maintenance expenditure analysis,where Network Rail provided us with data only at regional level,Network Rail was able to supply us with renewals data at the level of the existing 14 routes.To adjust this data to the level of the ten CP4 routes,we aggregated the East Coast and North&Eastern routes into the LNE route and we aggregated the Central,North West and West Coast Mainline South into the LNW route.All other routes stayed the same.3.8 Although we conducted our analysis at the level of the CP4 ten routes,we present only the regional comparisons in the main report,with the routes comparisons made available in Annex A.This allows us to be consistent with Network Rails current organisational structure,as Network Rail is currently regulated at a regional level.3.9 This chapter is organised as follows:the next section(Context)provides a description of the make-up of Network Rail renewals activity and how regions compare in terms of their overall expenditure and volume of work,asset characteristics and network usage.The following section(Analysis)describes the data and modelling approach.In the final section(Benchmarking results)we use this information to compare conventional track average unit costs across regions.Office of Rail and Road|52 Context Renewals across asset classes 3.10 Breakdown of Network Rails renewals expenditure by asset class:Figure 19 shows the breakdown of average total renewals expenditure by asset class between 2014-15 and 2021-22.Figure 19:Breakdown of average total renewals expenditure by asset class,2014-15 to 2021-22(2021-22 prices)38 3.11 As indicated by the inner ring,expenditure on Track,Signalling,Civils and Buildings accounted for 77%of the total.Asset classes are further split into sub-asset class or work type in the outer ring of the figure.For instance,the Track and Switches&Crossings sub-asset classes accounted for 85%of average total Track renewals expenditure.38 EW stands for Earthworks;S&C stands for Switches and Crossings.The Other categories represent expenditure not captured in our analysis(as we were unable to accurately match expenditure and volumes at the work type level for this data).The Other category in the inner ring of the chart includes expenditure on Electrical Power and Fixed Plant,Telecoms,Wheeled Plant and Machinery and IT,Property and Other renewals.Office of Rail and Road|53 3.12 Variation in average renewals unit costs:Figure 20 shows the 8-year average renewals unit costs,by asset and sub-asset class,and by region,with regions ranked for each asset according to their average unit cost.A rank of 1 represents the region with the lowest unit cost for a given asset class and a rank of 5 represents the region with the highest.The size(width)of the bubbles shows how large each regions average unit cost is relative to the median region in each asset and sub-asset class.Southern and North West&Central have some of the highest average unit costs across the majority of asset classes.In comparison,Eastern and Scotland have some of the lowest average unit costs across the asset classes.Figure 20:8-year average unit cost rankings per asset class,2014-15 to 2021-22 Conventional track renewals 3.13 There are three main types of track renewals:(a)conventional track renewals(work intended to fully replace the existing track asset utilising conventional track renewal methodologies);(b)track refurbishment(work intended to extend the life of the existing track asset rather than fully renew it);and (c)high-output track renewals(work intended to replace the existing track asset through utilisation of the specialised high-output machines).The high-output technology is only appropriate for simple stretches of track without switches and crossings,platforms or viaducts.Office of Rail and Road|54 The following paragraphs discuss conventional track renewals,which is the main focus of this chapter.3.14 Proportion of track renewed:Figure 21 shows the volume of track renewed as a proportion of total region track-kms.On average,Network Rail renewed 3.5%of its track each year between 2014-15 and 2021-22.The Scotland region renewed its track at the highest rate(4.4%,2.0%of conventional track renewals and 2.4%of other types of track renewal),whilst North West&Central renewed at the lowest rate(2.4%,1.3%of conventional track renewals and 1.1%of other types of track renewal).Figure 21:Average proportion of track renewed each year,2014-15 to 2021-2239 3.15 Conventional track renewal average unit cost and volumes:Figure 22 shows the 8-year average unit cost and volumes for conventional track renewals by region.The average across all regions is 735k per track-km for unit costs and 109km for volume renewed.On average,Wales&Western has the highest average unit cost(850k per track-km)and lowest volume renewed(75km),whilst Eastern has the lowest average unit cost(653k per track-km)and the highest volume renewed(182km).The figure suggests that there are economies of scale,i.e.the greater the number of conventional track-km renewed,the lower the unit cost becomes.39 Proportion of conventional track renewed per route is calculated as conventional track renewals costs divided by track-km.Proportion of other track renewals per route is calculated as the sum of high-output renewals and track refurbished,divided by track-km.Office of Rail and Road|55 Figure 22:Conventional track renewal 8-year average unit cost and volumes,2014-15 to 2021-22(2021-22 prices)3.16 Trends in conventional track renewal unit costs and volumes(Network Rail):Figure 23 shows the trend in the 8-year average unit cost and volumes for conventional track renewal for Network Rail as a whole.Real terms unit costs have been on an upward trend since 2017-18.This could be due to inefficiency,changes in work mix or other factors.Volumes have also risen every year since 2017-18,apart from in 2021-22 where they fell by 19%as compared to 2020-21.Trends in unit costs and volumes are less clear prior to 2017-18.Office of Rail and Road|56 Figure 23:Trends in Conventional track renewals average unit cost and volumes,2014-15 to 2021-22(2021-22 prices)Analysis Data 3.17 The analysis is based on data for financial years 2014-15 to 2021-22,recorded at the level of the ten routes that were introduced by Network Rail in CP4.For the year 2021-22,Network Rail supplied us with data at the level of the 14 routes.To adjust this data to the level of the ten CP4 routes,we aggregated the East Coast and North&Eastern routes into the LNE route and we aggregated the Central,North West and West Coast Mainline South into the LNW route.All other routes stayed the same.Dependent variable 3.18 The dependent variable is annual average unit costs at the route-level for conventional track renewals.We obtain this variable by dividing total annual expenditure on conventional track renewals by the amount of track-km renewed using conventional track renewals methods.For years 2014-15 to 2018-19,expenditure data comes from Statement 9b in Network Rails Regulatory Financial Statements and volume data comes from Network Rails published Annual Returns.For years 2019-20 and onwards,both expenditure and volume data were provided to us directly by Network Rail for the purpose of this analysis.All expenditure data is inflation-adjusted to 2021-22 prices,using the Consumer Price Index(CPI).Office of Rail and Road|57 Independent variables 3.19 Table 7 below summarises the explanatory variables retained in the final model,alongside the expected direction of the relationship to conventional track renewals unit costs and the reasoning behind this.3.20 Network Rail reports against five types of work under the Track asset category:(a)conventional track renewals;(a)track refurbishment;(b)high-output track renewals;(c)switches and crossings;and(d)other.3.21 In the present report,we focus on conventional track renewals.However,it is possible that there may be an interaction between the unit cost of conventional track renewals and the volume of other types of work,e.g.refurbishments and high-output work.For example,carrying out refurbishment work on the network may change the balance between the volume and unit cost of conventional track renewals.Or it could be that an increase in the use of high-output renewals could leave the most challenging track sections to be renewed through conventional methods,therefore pushing up the unit cost of conventional track renewals.We therefore include the volume of track refurbished and high-output renewals as explanatory variables in our model.3.22 We also tested whether the intensity40 of maintenance and enhancements expenditure has a bearing on conventional track renewals unit costs,e.g.through increased pressure on the supply chain.Model estimates came up with counterintuitive relationships and we therefore excluded these variables from the final model.This was also the case for average wage levels.3.23 In addition,we tested the following variables:track-km,average number of tracks(total length of track divided by total route length),and route-km.All these variables are highly correlated with other variables in the model,which means that it is difficult to separately estimate their respective effects as these effects are also captured by those variables in the model.40 Measured as maintenance and enhancements expenditure divided by track-kms.Office of Rail and Road|58 Table 7:Independent variables used in the route-level conventional track renewals model Variable Expected direction of relationship Reason for relationship Number of track-km renewed using conventional methods(km)Negative A greater number of track-km renewed should lead to a lower average unit cost as we expect there to be economies of scale.Number of refurbished track-km(km)Ambiguous Carrying out refurbishment work on the network may change the balance between the volume and cost of renewals.Number of track-km renewed using high-output technology(km)Positive High-output technology is currently only appropriate for simple stretches of plain line.So,an increase in high-output volumes could mean that conventional track renewals are used on average for more complicated parts of the network.Train-km(passenger train-km freight train-km)41 Positive More traffic on the network would likely cause greater wear and tear.In addition,it is likely that renewals work is more difficult to undertake in more heavily used areas of the network.Average rainfall(mm)Positive Higher rainfall is likely to cause more frequent and more damaging infrastructure failure(e.g.landslides)and may therefore require more costly renewals work.Higher rainfall may also make it more difficult to undertake infrastructure work.Proportion of track category 1A,1&242(category 1A,1&2 km/track-km)Positive A network with a higher proportion of track in category 1A,1 and 2 is likely to require more frequent and more costly renewals and may need to be kept in a 41 We use this variable instead of passenger and freight train-kms separately(as we did in our route maintenance model)because they are highly correlated with the number of track-km renewed using conventional methods.Also,given the relatively small size of our dataset,reducing the number of variables in our model(by combining the two traffic variables)improves on degrees of freedom.This in turn improves the robustness of our model.42 See footnote 8 for the definition.Office of Rail and Road|59 Variable Expected direction of relationship Reason for relationship better general condition than other parts of the network.It may also be more difficult to undertake engineering work on such sections of the network(for example,due to higher train speeds and usage)and their access time window may be narrower.This effect may also be covered,in part,by the traffic variable.Proportion of electrified track(electrified track-km/track-km)Positive The presence of electricity and of power supply infrastructure is likely to increase the complexity of track renewals work.Switches and crossings(S&C)density(number of S&C/track-km)Positive A network with more switches and crossings per track-km is more complex and therefore requires more costly renewals.Average days per Possession(Number of possession days/number of possessions)Positive A high number of possession days may imply that the renewals works to be done are more complicated.More possessions of the network mean that Network Rail would be likely to spend more,in terms of labour cost,materials,etc.Year N/A The purpose of this term is to separate out the common annual trend in unit costs across routes that cannot be attributed to observable cost drivers.The coefficient on Year can be interpreted as an annual growth rate.Year-specific dummy variable(applies to 2020-21)N/A The purpose of this term
2022-12-01
70页
5星级
罗兰贝格：预见2026：中国行业趋势报告（90页）.pdf
智源研究院：2026十大AI技术趋势报告（34页）.pdf
中国互联网协会：智能体应用发展报告（2025）（124页）.pdf
三个皮匠报告：2025银发经济生态：中国与全球实践白皮书（150页）.pdf
三个皮匠报告：2025中国商业航天市场洞察报告-中国商业航天新格局全景洞察（25页）.pdf
国声智库：全球AI创造力发展报告2025（77页）.pdf
中国电子技术标准化研究院：2025知识图谱与大模型融合实践案例集（354页）.pdf
三个皮匠报告：2025中国情绪消费市场洞察报告（24页）.pdf
中国银行：2026中国高净值人群财富管理白皮书（66页）.pdf
亿欧智库：2025全球人工智能技术应用洞察报告（43页）.pdf
0731-84720580
商务合作：really158d
友链申请 (QQ)：1737380874
最新报告
中英对照
全文搜索
报告精选
PDF上传翻译
多格式文档互转
入驻&报告售卖
会员权益
机构报告
券商研报
财报库
专题合集
英文报告
数据图表
会议报告
其他资源
研报
新质生产力
DeepSeek
低空经济
大模型
AI Agent
AI Infra
具身智能
自动驾驶
宠物
银发经济
人形机器人
企业出海
算力
微短剧
薪酬
白皮书
创新药
行业分析
个股研究
年报财报
IPO招股书
会议纪要
宏观策略
政策法规
其他
人工智能
信息科技
互联网
消费经济
汽车交通
电商零售
传媒娱乐
医疗健康
投资金融
能源环境
地产建筑
传统产业
英文报告
其它
行业聚焦
芯片产业
热点概念
全球咨询智库
人工智能
500强
新质生产力
会议峰会
新能源汽车
企业年报
互联网
公司研究
行业综观
消费教育
科技通信
医药健康
人力资源
投资金融
汽车产业
物流地产
电子商务
传统产业
传媒营销
其它
十五五规划系列报告合集（共48套打包）
2026低空经济/低空产业报告合集（共47套打包）
AI、科技与通信
广告、传媒与营销
消费、零售与支付
HR、文化与旅游
金融、保险与投资
能源、环境与工业
医疗制药与大健康
物流、地产与建筑
其他行业
AI ▪ 科技 ▪ 通信
数字化
金融财经
智能制造
电商传媒
地产建筑
医疗医学
能源化工
其他行业
收藏
下载
2026-02-02
AI查数
行业数据
政策法规
商业模式
产业链
竞争格局
市场规模
产业概述
其它
2026年
AI读财报
年报
一季报
半年报
三季报
IPO招股书
社会责任报告
A股
IPO申报
港股
美股&全球
新三板
微信扫码登录
手机快捷登录
账号登录
