Model retraining and information sharing in a supply chain with long-term fluctuating demands
This addresses supply chain optimization for businesses by showing how model retraining coordination can mitigate demand fluctuations, though it is incremental as it builds on existing forecasting and bullwhip effect research.
The study tackled the problem of outdated demand forecasting models in supply chains due to long-term environmental changes, demonstrating that uncoordinated model retraining causes the bullwhip effect, while sharing forecasting models among parties significantly reduces it.
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 However, in this approach, a model constructed from past data occasionally becomes outdated due to long-term changes in the environment, in which case the model should be updated (i.e., retrained) using the latest data.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.