چارچوبی برای طراحی قوی زنجیره های تامین مواد غذایی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|922||2012||14 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 10719 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 137, Issue 1, May 2012, Pages 176–189
After years of emphasis on leanness and responsiveness businesses are now experiencing their vulnerability to supply chain disturbances. Although more literature is appearing on this subject, there is a need for an integrated framework to support the analysis and design of robust food supply chains. In this paper we present such a framework. We define the concept of robustness and classify supply chain disturbances, sources of food supply chain vulnerability, and adequate redesign principles and strategies to achieve robust supply chain performances. To test and illustrate its applicability, the research framework is applied to a meat supply chain.
Today's business environment has become an international playing field in which companies have to excel in logistics performance, i.e. markets require full responsiveness, high quality products and high reliability of supply in small time windows at the lowest cost. As a consequence, supply chains have eliminated most non-value adding activities and have become leaner. However, lean supply chains without much inventory are more vulnerable to disturbances in logistic processes, which mean that they might be less consistent in their performance, i.e. are less robust (cf. Kleindorfer and Saad, 2005 and Dong, 2006). Consequently, the competitive power of vulnerable supply chains in the market may diminish. In practice, in recent years there have been reported many events that have led to disturbances in supply chains processes (e.g. supplier failures caused by natural disasters or fires in the warehouses, delivery delays due to traffic accidents, product recalls due to lack of fulfilment of quality or safety requirements, etc.). Because of that, there is increasing interest by practitioners and academics to reduce supply chain vulnerability and design robust supply chains. This holds especially for food supply chains as these chains have specific characteristics that increase its vulnerability, such as seasonality in supply and demand and a limited shelf-life of products. In supply chain management theory, robustness and vulnerability are perceived as opposite though not mature concepts (Asbjørnslett and Rausand, 1999 and Wagner and Bode, 2006). As a term, robustness has a broad meaning and it is often couched in different settings (Qiang et al., 2009). However, despite its frequent use, there is no general, widely accepted definition (Vlajic et al., 2010). In a supply chain literature robustness is mainly considered as the ability of the system to continue to function well in the event of a disruption (Dong, 2006, Tang, 2006a and Waters, 2007) i.e. an unexpected event that severely impacts performance. Here, three points get attention. First, if the system functions well depends on what is measured and how it is measured and it varies from application to application (Snyder, 2003). Second, robustness of the supply chain could be jeopardized by various kinds of unexpected events: accidental events (e.g. a fire in the facility, a machine failure, flood or a traffic accident), and events that result from or belong to the systems characteristics (e.g. poor communication or decision making processes). Third, consequences of unexpected events could be measured at process or at system's (company or supply chain) level and the severity depends on system's design. The severity of the consequences determine the level of supply chain robustness, or it is opposite—supply chain vulnerability. In this paper we focus on (process) disturbances, i.e. any consequences of unexpected events at the process level and their impact to the robustness of the supply chain performance. A literature review on supply chain robustness (Vlajic et al., 2010) shows that there is a lack of an integral framework that guides companies in managing disturbances and designing robust supply chains. With this paper, we aim to contribute to supply chain management theory by developing such an integrated framework for the design of robust (food) supply chains. To develop this framework we have conducted an extended literature review, participated in a number of workshops and conducted several interviews with field experts to get insight into practical issues in food industry relevant for the framework. To test it, we applied it to a case in the meat supply chain, as one of the main chains in food industry. The data collection is based on observations, historical data and semi-structured interviews. This paper is organized as follows. Section 2 discusses what supply chain robustness is. Section 3 presents the framework for designing robust food supply chains, and here we focus on the following elements: supply chain disturbances, sources of vulnerability and redesign strategies. Section 4 presents the application of the research framework in the case study. We conclude the paper with a discussion and issues for further research.
نتیجه گیری انگلیسی
In this paper an integrated framework is developed that guides food companies in managing disturbances and in designing robust supply chains. We defined supply chain robustness as the degree to which a supply chain shows an acceptable performance in (each of) its KPIs at various levels of disturbances. More particular, a supply chain is robust with respect to a KPI if the value of that KPI, adequately measured over an observation period, is sustained in a predefined desired range, even in the presence of disturbances. Our framework consists of the following elements: (1) the description of the supply chain scenario, and the identification of KPIs; (2) the identification and characterization of unexpected events and disturbances in processes that impact the performance robustness; (3) the assessment of performance robustness; (4) the identification of sources of vulnerability; and (5) the identification of appropriate redesign principles and strategies. We have discussed the relationship between the elements of the framework and we have applied and tested the framework in an explorative case study. The results confirm that by analyzing the performance robustness of specific scenarios we can detect and typify disturbances. For each disturbance found, we identified a set of vulnerability sources that can represent a direct or indirect cause of the disturbance. Then, per vulnerability source a set of redesign principles and strategies were identified to prevent the disturbance itself. Alternatively, if that is not possible or cost effective, a set of redesign principles and strategies can be used to reduce its impact of disturbance to other processes in the company or supply chain members (domino effect). The paper contributes to a better understanding of the concepts of vulnerability and robustness and of related issues in food supply chains. Moreover, here we synthesize and integrate relevant papers on supply chain vulnerability and supply chain robustness. From a practical point of view, the involved managers of the company concluded that the research framework supports the analysis of supply chain's robustness and vulnerability, and helps in finding and categorizing disturbances, vulnerability sources and appropriate redesign principles and strategies. However, more research is needed to extend and validate our findings. On the one hand more case studies could be done within the food industry to be able to construct generic overviews of sources of vulnerability and redesign strategies. On the other hand, in order to select the most appropriate redesign strategies for sources of vulnerability and disturbances identified in the case, more research is needed that models and quantifies the impact on key supply chain performance indicators for alternative supply chain scenarios.