序列数据的因果推断在仓储管理的应用.pdf

1、2023 DataFunSummitCausal Analysis with Application to Inventory Control Erli Wang（王尔立）NEC Labs,ChinaApr 22,2023目录Inventory control description Background:time series,causalityCausality helps demand forecastCausality helps replenishment strategyC Contentsontents01 01 Inventory control description Inv

2、entory control description Causality helps inventory control Goal:a good balance between maximizing the amount of high-valued customer demands that can be fulfilled and minimizing storage,delivery,and waste costs.Historical tradingCalendarActivityCustomerInventory control processInventory:good 2T1In

3、ventory good 1ObservationDemand forecast Optimal orderT2Demand forecast Causality helps inventory control As-Is:According to our investigation,many giant companies still,The demand forecasting are based on past experience,rather than data-driven,making difficult to improve further.The inventory poli

4、cy are simply(s,S)strategy without considering realistic uncertainties,such as erroneously stocks.Causal analysis helps to understand why a business process happens in an explanatory manner.TimeBrowseExposurePriceOrder4/19/20234513241784/18/20231103191564/17/20237802882054/16/20235613052384/15/20233

5、70296199Historical observationsWhich one should be trusted?tt-1t-2OrderPriceExposureBrowset-3Auto-determine the relation=.=.+.=+.=.+.=.+.=+.().()Forecast relationVisualize key factorsApproach to the best decision Key to inventory control is to map each state to action(s),satisfying Roadmap:Approach-

6、1:improve demand forecast D;One of the biggest challenges is forecasting demand accurately.We deliver explainable forecast,and multi-target intervention as a web-based service.Approach-2:efficient manage inventory across different environments B;Relearn the policy for each environment are costly.We