Optimal Forecasting Technique in Reducing Supply Chain Amplification

Abstract

This paper investigates the optimal forecasting method in reducing supply chain amplification, a.k.a., the Bullwhip Effect. By using a simple replenishing policy and solving Stein Matrix Equations, the relationship between input and output covariance matrices is derived. Both analytical and simulational results support the opinion that proportional forecasting is superior in bullwhip mitigation. There exists trade-off in reducing variance amplifications in order and inventory. For different control objectives, we propose optimal proportional controllers.