Model retraining and information sharing in a supply chain with long-term fluctuating demands

Takahiro Ezaki,Katsuhiro Nishinari,Naoto Imura

doi:10.1038/s41598-021-99542-z

Abstract

Demand forecasting based on empirical data is a viable approach for optimizing a supply chain. However, in this approach, a model constructed from past data occasionally becomes outdated due to long-term changes in the environment, in which case the model should be updated (i.e., retrained) using the latest data. In this study, we examine the effects of updating models in a supply chain using a minimal setting. We demonstrate that when each party in the supply chain has its own forecasting model, uncoordinated model retraining causes the bullwhip effect even if a very simple replenishment policy is applied. Our results also indicate that sharing the forecasting model among the parties involved significantly reduces the bullwhip effect.

Highlights

Demand forecasting based on empirical data is a viable approach for optimizing a supply chain
We demonstrate that uncoordinated model retraining causes the bullwhip effect, and that sharing the forecasting model improves the performance of the supply chain (SC)
We demonstrated that retraining a forecasting model can cause the bullwhip effect in an SC

Summary

Introduction

Demand forecasting based on empirical data is a viable approach for optimizing a supply chain. The target inventory level in the policy is determined by the forecasting model of each echelon, which is described in Sec 2.2. Note that we do not consider the backlog of orders; the amount of demand that exceeds the inventory level is lost, which is recorded as the lost sales opportunity.

Results

Conclusion