Using Exponentially Weighted Moving Average to Improve Buffer Adjustment of Demand-Driven Replenishment Strategies

Abstract

Stock management is a critical issue in supply chain management. Previous studies have shown that the demand-pull inventory replenishment strategy combined with a buffer management mechanism (DPBM) suggested by the theory of constraints (TOC) performs better than traditional replenishment strategies in supply chain stock management. In DPBM, buffer management is applied for controlling the target amount of stock to keep, which is adjusted by some predetermined rules that instruct when and how much to adjust the target level. Previous studies found that when applying DPBM to making replenishment decisions for products with a high-variation demand, long production lead-time, and short product life cycle, the conventional DPBM is not very effective. This article proposes a novel method for improving the conventional buffer management mechanism. Instead of the predetermined rules suggested by TOC, this study borrowed the concept of the exponentially weighted moving average approach that has been successfully applied in statistical quality control to design a new set of rules to assist in making buffer adjustment decisions based on the trends shown in demand. To evaluate the proposed method's feasibility and validity, this article uses both real demand data provided by a wafer foundry factory in Taiwan and simulated data that reveal various demand patterns. As a result, the proposed method performs better than the conventional demand-driven replenishment strategies suggested by TOC, especially when demand has a large variance.