The impact of forecasting method, information sharing, demand pattern and capacity tightness on dynamic supply chain system: A chaos perspective

• Main Authors: Chiun-Hua Chen, 陳群樺
• Other Authors: Wei-Tsong Wang
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/16813043365997567052

碩士 === 國立成功大學 === 工業與資訊管理學系碩博士班 === 96 === Chaos theory, which is from mathematics and physics domains, has been widely applied to study issues in social systems. There has been abundant evidence indicating the advantage of applying the chaos theory to the area of supply chain management. However, such applications are still limited. Previous studies generally focus on verifying the existence of chaos phenomena in supply chain systems, but very few of them specifically aim to investigate how the dynamics of a supply chain system may lead to chaotic phenomena. Recently, a number of studies have investigated how supply chain factors (e.g., demand pattern, ordering policy, demand-information sharing, lead time and supply chain level) or complex interaction between supplier and customer may result in complex dynamics and chaos of supply chain systems. Generally, these studies all emphasize the negative effects of chaotic behaviors on effective management of a supply chain. Nevertheless, to the best of our knowledge, none of the existing studies have investigated the impact of forecast on the chaotic supply chain systems. A review of literature in the traditional supply chain management field indicates that the selection of forecasting methods can significantly influence the performance of a supply chain. In addition, demand patterns faced by retailers and capacity tightness faced by suppliers can significantly influence the performance of information sharing. In this study, we aim to investigate how various supply chain factors caused the systems to demonstrate complex dynamics and chaotic behaviors. Consequently, the primary purposes of this study are: (1) to understand, from the chaos perspective, what are the possible behaviors a dynamic supply chain system may generate in various scenarios composed of supply chain factors including forecasting methods, demand patterns, information sharing, and capacity tightness levels; (2) to investigate how and to what extent these supply chain factors contribute to the dynamics and chaotic behaviors of a supply chain system. The well-known beer distribution model was used in this study to examine the supply chain structure and behaviors, and observe the supply chain dynamics and chaos. Lyapunov exponent was used to measure and quantify the degree of system chaos. The result of study can help supply chain managers select appropriate forecasting methods and adopt adequate information sharing and capacity tightness policies given a specific demand pattern. This may, in turn, reduce the possibility of producing chaotic behaviors in the dynamic supply chain system and eventually reach better supply chain management performance and information sharing value.