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Modeling supply chain performance and stability

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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Industrial Management & Data Systems Emerald Publishing

Modeling supply chain performance and stability

W.H. Ip; S.L. Chan; C.Y. Lam
Industrial Management & Data Systems , Volume 111 (8): 23 – Aug 30, 2011
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References (75)

Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
0263-5577
DOI
10.1108/02635571111171649
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Industrial Management & Data Systems – Emerald Publishing

Published: Aug 30, 2011

Keywords: Supply chain management; Supply chain performance and stability; System dynamics; Autoregressive integrated moving average; Overall performance index number; Manufacturing industries

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