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 However, in this approach, a model constructed frompast data occasionally becomes outdated due to long-term changes in theenvironment, in which case the model should be updated (i.e., retrained) usingthe latest data.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

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