مدل سازی و شبیه سازی تکامل شبکه های تأمین مبتنی بر سیستم انطباقی پیچیده و چشم انداز مناسب
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|11925||2009||15 صفحه PDF||سفارش دهید|
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|شرح||تعرفه ترجمه||زمان تحویل||جمع هزینه|
|ترجمه تخصصی - سرعت عادی||هر کلمه 90 تومان||11 روز بعد از پرداخت||674,370 تومان|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Computers & Industrial Engineering, Volume 56, Issue 3, April 2009, Pages 839–853
A supply network (SN) is a complex adaptive system, and its structure and collaboration mechanism evolves over time. However, most literature views SN as a static system and the study on the evolution of SNs is very limited. Based on complex adaptive system and fitness landscape theory, this paper first proposes an evolution model of SNs in order to understand the general principle of SN evolution. Then the paper conducts a multi-agent simulation on the evolution model, and discloses that the SN emerges and evolves from firms’ dynamic interaction under the dynamic environment. Dominated by the environment and firms’ internal mechanism, the evolution is highly sensitive to the initial condition, and it is path-dependent and difficult to predict precisely. Although the dynamics of environments is different, a SN enjoys the stable structure in different environments. Higher level of structure stability and fitness of the SN are achieved when the firms in the SN adopt the long-term collaboration strategy rather than the short-term strategy. Finally, a China case is explored which validates the self-organization evolution of SNs.
A supply network (SN) is referred to as a complex network of organizations that synchronizes a series of inter-related business processes, such as procurement, manufacturing and distribution, to create values to final customers in the form of one or more families of related products or services (Christopher, 1992 and Min and Zhou, 2002). In spite of growing interest in the management of supply networks (Choi et al., 2001, Lamming et al., 2000 and Stuart et al., 1998), researchers are still in an early stage of understanding how a SN behaves and evolves. Some studies investigated the factors that influence the SN evolution in different environments. For example, Hur, Hartley, and Hahn (2004) identified six factors that have influence on the structure of SNs in different industries. Choi and Hong (2002) found that the SN of Honda was controlled centrally by the final assembler, while the SN of DaimlerChrysler was decentralized. Choi et al. (2001) and Chung, Yam, and Chan (2004) proposed that a SN emerged and evolved in a way of its own. The emergence of cooperation networks was largely an endogenous process driven by the complex and dynamic interplay between institutions, products, technologies, markets and innovative actors (Bruce, 2000). What are the salient factors and the general principles that shape a SN? No one can answer this question with any degree of certainty (Harland, Zheng, Lamming, & Johnsen, 2002). This is due to the lack of our understanding of evolutionary aspects of SNs (Surana, Kumara, Greaves, & Raghavan, 2005). This study will investigate the general principles involved in the evolution of SNs. It proposes a SN evolution model based on CAS (complex adaptive system) (Holland, 1995) and fitness landscape theory to model the dynamic behavior of the SN evolution with the dynamic interaction among the firms and the environment. This approach underscores the importance of the model in which different entities in the SN operate subject to their own local strategies, constraints and objectives. With the simulation of the evolution model based on multi-agent, the dynamic behavior of the firms and the SN can be analyzed from a variety of organizational perspectives. It finds that the evolution of SNs is a self-organization, and identifies the salient factors that control the evolution. Also, a case study which explores the evolution of a SN in China for more than 30 years validates the findings. Finally, some managerial insights are proposed in the paper. The remainder of the paper is organized as follows: Section 2 reviews the relevant literature. Section 3 presents the system model and simulation of SN evolution based on CAS and fitness landscape theory. The principles and some salient factors that influence the SN evolution are discussed. In Section 4, a China case study is presented to validate the findings in Section 3. In Section 5, we present some propositions and managerial implications about the evolution of SN. Finally, concluding remarks and future research directions are pointed out in Section 6.
نتیجه گیری انگلیسی
The review on supply chain management identified a gap in the study of the evolution of supply networks due to the absence of knowledge of the dynamics of the network structure, collaboration mechanism and the environment. This paper proposed an evolution model of SN based on CAS and fitness landscape theory to gain understanding of the evolution of the SNs. The simulation of the evolution model and the case study indicated that the evolution of SN is self-organizing. An SN emerges from the dynamic interactions among the firms and evolves over time. The evolution is self-reinforcing and path-dependent. Slight perturbation of the environment and the internal mechanism of firms can drive the evolution into chaos and it is difficult to predict. The environment and the internal mechanisms of the firms are the origins of the self-organization evolution of a SN. Several aspects of the evolution of SN warrant further investigation. Our present research directions include (1) incorporation of more adaptive firms that are capable of modifying their collaboration strategies during simulation based on the evolving environment; (2) incorporation of the dynamics of the environment that are capable of changing Fc to model the dramatic changes of the environment. For instance, as a disaster happens, Fc may be changed greatly; and (3) development of an evolution model of service supply chain, which is of great difference to the traditional supply networks.