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

TL;DR

This paper investigates how model retraining and information sharing among supply chain parties influence demand forecasting accuracy and the bullwhip effect, highlighting that uncoordinated updates can worsen fluctuations, while sharing models mitigates this issue.

Contribution

It demonstrates that uncoordinated model retraining causes the bullwhip effect and shows that sharing forecasting models among supply chain parties reduces demand variability.

Findings

Uncoordinated model updates increase demand fluctuations.

Sharing forecasting models reduces the bullwhip effect.

Model sharing improves supply chain stability.

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 However, in this approach, a model constructed from past data occasionally becomes outdated due to long-term changes in the environment, in which case the model should be updated (i.e., retrained) using the latest data.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.