دانلود مقاله ISI انگلیسی شماره 896
عنوان فارسی مقاله

مدل دو هدفه برای طراحی زنجیره تامین تولید پراکنده در چین

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
896 2012 11 صفحه PDF سفارش دهید 9020 کلمه
خرید مقاله
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عنوان انگلیسی
A bi-objective model for supply chain design of dispersed manufacturing in China
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Journal of Production Economics, Available online 20 December 2012

کلمات کلیدی
مدیریت زنجیره تامین - استراتژی زنجیره تامین - تولید پراکنده - شبکه تولید - چین - برنامه ریزی با اعداد صحیح
پیش نمایش مقاله
پیش نمایش مقاله مدل دو هدفه برای طراحی زنجیره تامین تولید پراکنده در چین

چکیده انگلیسی

Dispersed manufacturing achieves the greatest cumulative competitive advantage by dissecting a supply chain and assigning each process to an optimal location. Dispersed manufacturing has been an integral part of global manufacturing in China. This paper presents a bi-objective model for the supply chain design of dispersed manufacturing in the context of rising business operating costs in coastal China. It considers essential trade-offs between supply chain cost and lead time to determine optimal facility locations of manufacturing steps. The model is applied to a representative case to illustrate the cost benefits of dispersed manufacturing as opposed to performing all manufacturing steps of a product at a single facility location. It provides explanations in several factors that have benefited manufacturing growth in China, and offers insights in the emerging global manufacturing trends.

مقدمه انگلیسی

Manufacturing activities have become more spatially fragmented in the past few decades (Ferdows, 1997, Lee and Lau, 1999, Ronald et al., 2005 and Christopher et al., 2011). Manufacturers nowadays do not necessarily perform all manufacturing steps of a product at a single facility location. Instead, they often ship semi-finished products to a different location for further processing or sales (Fawcett, 1992, Ferdows, 1997 and Feng and Wu, 2009). The rapid advancement of information technologies, especially the wide adoption of e-business platforms and enterprise information systems (Li, 2011b), has been a key enabler behind the trend. It allows facilities at distant locations to coordinate product design and development (Fritzsche et al., 2012, Li and Liu, 2012, Liu and Wang, 2012 and Ren et al., 2012), and production activities (Tan et al., 2010 and Wang and Xu, 2012) efficiently at an affordable cost. This paper defines dispersed manufacturing as the practice of dissecting the manufacturing process into multiple stages, and assigning them to geographically dispersed locations to achieve a competitive edge (Magretta and Fung, 1998). Dispersed manufacturing exploits comparative advantages of multiple locations, however, dramatically increases the complexity in supply chain design. According to the seminal work of Fisher (1997), a typical challenge of supply chain design is the management of trade-offs between efficiency and responsiveness, which are measured by cost and lead time, respectively. Locating labor-intensive manufacturing steps in proximity to cheap labor is able to lower production costs, but lengthens the supply chain and increases logistics costs. Global manufacturers need to define business priorities, design their supply chains, and review facility location decisions when there are major changes in global and regional business environments (Skinner, 1996). Dispersed manufacturing has been an integral part of global manufacturing in China. It has allowed the country to participate in global supply chains to realize its labor cost advantage and skill competence. Dispersed manufacturing is what is behind the boom in intra-Asia trade as China rises as the “Factory of the World” (Magretta and Fung, 1998). Tens of thousands of global manufacturers in China import raw materials and semi-finished products from Asian countries, perform labor-intensive assembly operations, and then export end-products to developed countries (GPRD Business Council, 2007). As the traditional gateway to China, Hong Kong has played a pivotal role to support manufacturing growth in China, especially in the southern regions. Hong Kong traders typically obtain overseas orders and organize manufacturing in a dispersed network of factories in the Pearl River Delta (PRD) region (Fung et al., 2008 and HKTDC, 2008). A great example is Li & Fung (Hamid and Lee, 2006), which dissects the supply chain to assign a manufacturing step to an optimal location. Li & Fung synchronizes a network of thousands of factories around the globe, to minimize total costs and shorten order lead times (Magretta and Fung, 1998 and Hagel, 2002). Its business model has attracted high profile retailers including The GAP, Target Corp. and Marks & Spencers Plc. In 2010, giant retailer Wal-Mart also signed a multi-billion dollar deal to source through Li & Fung and expected “significant” savings across its supply chain (Cheng, 2010 and Talley and O’Keeffe, 2010). Inspired by Li & Fung’s success (Magretta and Fung, 1998, Joanna, 1999 and Hagel, 2002), several studies advocated dispersed manufacturing from a strategic viewpoint (Chung et al., 2004 and Hamid and Lee, 2006). However, quantitative studies on dispersed manufacturing have been scare. In recent years, new trends have emerged as some global manufacturing activities are moving away from coastal China because of rising production costs and the hike in oil prices (Trunick, 2008, Kumar et al., 2009, Zhang and Huang, 2010 and Zhang et al., 2012). However, they assumed that all manufacturing steps of a product are performed at a single facility location, although dispersed manufacturing has been a business reality in China. There is an urgent need to perform quantitative studies in the supply chain design of dispersed manufacturing in China in light of the emerging global manufacturing trends. In a broader scope of supply chain design, many mathematical models have been built to aid manufacturing facility location decisions. A recent review of these models can be found in Melo et al. (2009). However, there are considerable challenges to adapt these models for Chinese manufacturing due to very different business environments, for example, North American Free Trade Agreement (NAFTA) (Wilhelm et al., 2005 and Robinson and Bookbinder, 2007). The Chinese manufacturing and its business environment are unique in many ways. Many Chinese factories are export oriented and their major markets are faraway developed countries (GPRD Business Council, 2007). Their supply chain costs are sensitive to oil price fluctuations due to a long transport distance. In terms of business environment, China is still far from being a free market. The Chinese central government controls the exchange rate of its currency renminbi (RMB), which is very influential on the cost competitiveness of Chinese manufacturers. It offers export value-added tax (VAT) rebates by product types to encourage certain industries. Geographically, China has a large continent and there are significant cost disparities between its coastal and inland regions. To mitigate rising cost pressure in coastal regions, Chinese manufacturers has the alternative of relocating to inland regions besides the option of moving overseas. This paper aims to narrow the research gap by developing a bi-objective model for the supply chain design of dispersed manufacturing in China. The work is inspired by a supply chain optimization project that Li & Fung implemented for a major US client. The client achieved substantial cost savings by switching to a dispersed manufacturing network. The bi-objective model captures the distinctive attribute of dispersed manufacturing by defining multiple production stages. It considers essential trade-offs between supply chain cost and lead time (Fisher, 1997) to determine optimal facility locations of manufacturing steps. The measurement of supply chain lead time is particularly relevant to dispersed manufacturing as it may consume considerable transport lead times if manufacturing facilities are far from each other or at different countries. The model is tailored for the unique Chinese manufacturing environment and it includes parameters such as currency exchange rate and export VAT rate. The model application with a representative case illustrates the cost benefits of dispersed manufacturing as opposed to performing all manufacturing steps of a product at a single facility location. It provides explanations in several factors that have benefited manufacturing growth in China in the past few decades. It also offers managerial insights on the future developments of global manufacturing trends. The rest of this paper is organized as follows. Section 2 reviews relevant literature. Section 3 develops a bi-objective model. Section 4 applies the model for a case study. Section 5 presents results and analysis. Section 6 discusses findings and managerial implications. Section 7 concludes the research.

نتیجه گیری انگلیسی

This paper deals with supply chain design of dispersed manufacturing in China under changing business environments. Dispersed manufacturing dissects the supply chain to assign each process to an optimal location to achieve the greatest cumulative competitive advantage. It has been an integral part of global manufacturing in China as the nation rises as the “Factory of the World”. Supply chain design of dispersed manufacturing is a challenging task, because it needs to consider essential trade-offs between supply chain efficiency and responsiveness. Optimal supply chain design also needs to be reviewed when there are major changes in global and regional business environments. This paper presents a bi-objective model for the supply chain design of dispersed manufacturing. It incorporates major business environment variables that have been impacting the landscape of global manufacturing in China in recent years. The bi-objective model is then applied for a case study to illustrate the benefits of dispersed manufacturing. It provides explanations of business environment variables that have benefited manufacturing growth in China. It also offers managerial insights on the emerging global manufacturing trends. This paper makes several key contributions. First, it studies the supply chain design of global manufacturing in China in the context of dispersed manufacturing. Existing studies on manufacturing in China assumed all manufacturing steps of a product are performed at a single facility location. Second, it develops a bi-objective model for the supply chain design of dispersed manufacturing. Both supply chain cost and lead time are considered for the optimal location decisions of three production stages, namely component manufacturing, subassembly manufacturing and end-product manufacturing. Third, the bi-objective model is applied for a case study of manufacturing in coastal China amid rising business operating costs. The model application shows that supply chain strategies have primary influence on optimal facility location decisions. Fourth, the modeling results illustrate the cost benefits of dispersed manufacturing, and suggest significant impacts of business environment variables. Besides low labor cost, favorable Chinese currency policies and VAT policies have helped China to grow its manufacturing industries in the past few decades. Last but not least, it offers insights on the emerging global manufacturing trends in China. As business operating costs rise rapidly in coastal China, labor-intensive manufacturing steps are likely to move away, but time-sensitive production may stay because of its efficient logistics services and industrial clustering. It strikes a good balance between supply chain efficiency and responsiveness to relocate labor-intensive manufacturing steps to inland China and retain other manufacturing steps in coastal China. The work presented in this paper is probably first of its kind in the area of supply chain design of dispersed manufacturing in China. It will be beneficial to conduct further experimentations for different product sectors and extend the bi-objective model for a multiple-product case. Capacity constraints could also be added. In addition, the model could be extended to incorporate stochastic elements to consider risk factors that exist in global supply chains.

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