Modeling supply chain performance and stability

Purpose The purpose of this paper is to propose an integrated approach to modeling and measuring supply chain performance and stability using system dynamics (SD) and the autoregressive integrated moving average (ARIMA). Design/methodology/approach SD and ARIMA models were developed, respectively, for modeling and measuring supply chain performance and for further analyzing and projecting supply chain stability for long‐term management. A case study from a typical semiconductor equipment manufacturing company is used to illustrate and validate the proposed method. Findings Effectiveness and efficiency, with six corresponding indicators (product reliability, employee fulfillment, customer fulfillment, on‐time delivery, profit growth, and working efficiency), were found to be the most significant factors in the performance of the supply chain. The results of the combined model provide evidence that supply chain performance of the case company is up to standard (average OPIN= 0.64) and is considered stable, but still far from outstanding. Continuous improvement, especially in supply chain efficiency, is suggested in order to maximize performance. Originality/value This integrated approach is innovative and creates a new way for other disciplines. This study provides a practical and easy‐to‐use model that enables senior and top management decision makers and operation managers involved in the supply chain to assess, forecast, and take anticipatory action so that the supply chain can experience improvement in a timesaving and effective manner and achieve excellence in performance.