愈來愈多的研究將來自數學與物理領域的混沌理論應用到社會系統。儘管多方面表示混沌理論具有明顯的可應用性於現代企業問題與供應鏈管理領域，然而，在這方面的應用仍是有限。過去這一方面的研究主要是在證明供應鏈系統存在混沌現象之事實，但對於供應鏈系統之動態性如何產生混沌行為卻是研究上之缺口。在最近，許多研究調查供應鏈因子(例如，需求模式、訂購政策、資訊分享、前置時間與供應鏈階層等)或供應商與顧客之間複雜的互動過程如何產生系統之動態與混沌，這是由於這些因子與決策流程很有可能會直接影響供應鏈之動態性，並使系統產生混沌之行為。基本上，上述的研究皆強調混沌行為對於有效管理供應鏈之負面影響，但就我們所知，在供應鏈管理領域，尚未找到有文獻探討預測對系統混沌狀態之影響與關係。在傳統的供應鏈管理研究已發現預測方法之選擇會影響供應鏈變異被誇大之程度與供應鏈之績效。此外，零售商面對的需求模式與供應商產能緊度政策也都會影響資訊分享之價值。
在本研究中，試圖調查各項供應鏈因子如何造成系統複雜之動態與混沌行為。本研究之目的有二：(1)以混沌之觀點了解在不同預測方法、需求模式、資訊分享與產能緊度水準所組成之情境下，動態供應鏈系統可能產生之行為；(2)調查各供應鏈因子如何影響系統之動態性甚至是混沌行為，並了解其對系統混沌之貢獻程度。本研究使用在系統動力學方法中探討供應鏈結構與行為之啤酒配銷模型，觀察供應鏈動態與混沌如何受到各項因素之影響。Lyapunov exponent將被使用以測量系統之混沌程度。研究結果將提供供應鏈管理人員在特定之需求模式下，應選擇何種預測方法與採取之資訊分享及產能緊度政策，降低動態供應鏈系統產生混沌行為之可能性，將可達到較佳之供應鏈績效表現以及資訊分享價值。

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.

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