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

• URL: http://arxiv.org/abs/2109.01784v1
• Date: Sat, 4 Sep 2021 04:16:04 GMT
• Title: Model retraining and information sharing in a supply chain with long-term fluctuating demands
• Authors: Takahiro Ezaki, Naoto Imura, Katsuhiro Nishinari
• Abstract summary: This study examines 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. Our results also indicate that sharing the forecasting model among the parties involved significantly reduces the bullwhip effect.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 However, in this approach, a model constructed from past data occasionally becomes outdated due to long-term changes in the environment, in which case the model should be updated (i.e., retrained) using the latest data.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.

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