A bi-objective model for supply chain design of dispersed manufacturing in China

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Abstract

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.

Highlights

► Dispersed manufacturing dissects the supply chain to optimize process locations. ► Trade-offs between cost and lead time affect facility location decisions. ► Favorable government policies have benefited manufacturing growth in China. ► Labor-intensive manufacturing steps are likely to move away from coastal China. ► Time-sensitive production may stay for efficient logistics and industrial clustering.

Introduction

Manufacturing activities have become more spatially fragmented in the past few decades (Ferdows, 1997, Lee and Lau, 1999, Ronald et al., 2005, 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, 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, Ren et al., 2012), and production activities (Tan et al., 2010, 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, 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, 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, Talley and O’Keeffe, 2010).

Inspired by Li & Fung’s success (Magretta and Fung, 1998, Joanna, 1999, Hagel, 2002), several studies advocated dispersed manufacturing from a strategic viewpoint (Chung et al., 2004, 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, 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, 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.

Section snippets

Literature review

Dispersed manufacturing, multi-plant manufacturing, and manufacturing network all involve multiple manufacturing facilities and need advanced information technologies to support process integration (Li et al., 2012, Tao et al., 2012). However, they are of key distinctions. Dispersed manufacturing and multi-plant manufacturing are a manufacturing practice or strategy (Schmenner, 1982), while manufacturing network is referred to as a network of manufacturing facilities (Boone et al., 1996). To be

A bi-objective model

This section presents a bi-objective model for the supply chain design of dispersed manufacturing in China. The bi-objective model incorporates major business environment variables that have been affecting labor-intensive global manufacturers in China in recent years. These factors include currency exchange rate, production cost, transportation cost, and export VAT rate. We consider a geographically dispersed manufacturing network as depicted in Fig. 1 (Li and O’Brien, 1999, Meixell and

Case description

This section applies the model for a case study. The characteristics of manufacturing operations are adapted from Zhao (2006) case study of a leading footwear manufacturer in the PRD. The model application considers a family of low-end labor-intensive footwear products. A unit of end-product (E) is formed by one unit of upper subassembly (S1) and one unit of sole subassembly (S2). A subassembly S1 and S2 is made from one set of upper components (C1) and one set of sole components (C2),

Base case results

Table 4 shows optimal supply chain design of dispersed manufacturing in the base case scenario. The measurements of unit supply chain cost and supply chain lead time correspond to the two objectives of the bi-objective model. To be specific, unit supply chain cost is obtained from the division of total yearly cost by demand volume. Supply chain lead time is calculated as the sum of longest lead time at each supply chain echelon, assuming raw materials and semi-finished products are ordered

Discussion

Previous studies of Chinese manufacturers suggested that their competitiveness were derived from marketing and human resource competencies (Li, 2000), manufacturing control (Li, 2005), favourable foreign exchange rate, cheap labor (Adams et al., 2006), and supply chain collaboration (Li, 2012). This research sheds new light to offer explanations why China’s manufacturing sector has been growing fast in the past three decades. It also suggests future trends of manufacturing growth in China in a

Conclusions

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

Acknowledgements

The authors would like to thank guest editors and two anonymous reviewers for their constructive comments. The authors are grateful for partial financial supports from HKU research committee and UDF, HKSAR RGC GRF, and the Guangdong Department of Science and Technology (2010B050100023, 2010B050400005).

References (105)

  • C. Lee et al.

    On integrating theories of international economics in the strategic planning of global supply chains and facility location

    International Journal of Production Economics

    (2010)
  • W.B. Lee et al.

    Multi-agent modeling of dispersed manufacturing networks

    Expert Systems with Applications

    (1999)
  • D. Li et al.

    Integrated decision modelling of supply chain efficiency

    International Journal of Production Economics

    (1999)
  • L. Li

    Assessing the relational benefits of logistics services perceived by manufacturers in supply chain

    International Journal of Production Economics

    (2011)
  • R.H. Lowson

    Analysing the effectiveness of European retail sourcing strategies

    European Management Journal

    (2001)
  • R. Mason-Jones et al.

    Total cycle time compression and the agile supply chain

    International Journal of Production Economics, 62

    (1999)
  • M.J. Meixell et al.

    Global supply chain design: a literature review and critique

    Transportation Research Part E: Logistics and Transportation Review

    (2005)
  • E. Melachrinoudis et al.

    The dynamic relocation and phase-out of a hybrid, two-echelon plant/warehousing facility: a multiple objective approach

    European Journal of Operational Research

    (2000)
  • M.T. Melo et al.

    Facility location and supply chain management—a review

    European Journal of Operational Research

    (2009)
  • Z.M. Mohamed

    An integrated production–distribution model for a multi-national company operating under varying exchange rates

    International Journal of Production Economics

    (1999)
  • M. Rudberg et al.

    Manufacturing networks and supply chains: an operations strategy perspective

    Omega

    (2003)
  • R.W. Schmenner

    Multiplant manufacturing strategies among the fortune 500

    Journal of Operations Management

    (1982)
  • Y. Shi et al.

    International manufacturing networks—to develop global competitive capabilities

    Journal of Operations Management

    (1998)
  • P. Thanh et al.

    A dynamic model for facility location in the design of complex supply chains

    International Journal of Production Economics

    (2008)
  • D. Vila et al.

    Designing logistics networks in divergent process industries: a methodology and its application to the lumber industry

    International Journal of Production Economics

    (2006)
  • L. Whicker et al.

    Understanding the relationships between time and cost to improve supply chain performance

    International Journal of Production Economics

    (2009)
  • W. Wilhelm et al.

    Design of international assembly systems and their supply chains under NAFTA

    Transportation Research Part E: Logistics and Transportation Review

    (2005)
  • F.G. Adams et al.

    Why is China so competitive? Measuring and explaining China’s competitiveness

    World Economy

    (2006)
  • G. Appa et al.

    A branch & cut algorithm for a four-index assignment problem

    The Journal of the Operational Research Society

    (2004)
  • B.C. Arntzen et al.

    Global supply chain management at Digital Equipment Corporation

    Interfaces

    (1995)
  • W.-G. Cheng

    Li & Fung Signs Walmart Deal That May Generate $2 Billion Sales

    (2010)
  • S. Chopra et al.

    Supply Chain Management: Strategy, Planning and Operation

    (2007)
  • M. Christopher et al.

    Approaches to managing global sourcing risk

    Supply Chain Management: An International Journal

    (2011)
  • M.A. Cohen et al.

    Impact of production scale economies, manufacturing complexity and transportation costs on supply chain facility networks

    Journal of Manufacturing and Operations Management

    (1990)
  • J. Collin et al.

    How to design the right supply chains for your customers

    Supply Chain Management: An International Journal

    (2009)
  • J.J. Coyle et al.

    Supply Chain Management: A Logistics Perspective

    (2009)
  • N.-E. Dahel

    Vendor selection and order quantity allocation in volume discount environments

    Supply Chain Management: An International Journal

    (2003)
  • S.E. Fawcett

    Strategic logistics in co-ordinated global manufacturing success

    International Journal of Production Research

    (1992)
  • K. Ferdows

    Made in the world: the global spread of production

    Production and Operations Management

    (1997)
  • M.L. Fisher

    What is the right supply chain for your product?

    Harvard Business Review

    (1997)
  • M. Fritzsche et al.

    Multidisciplinary design optimisation of a recurve bow based on applications of the autogenetic design theory and distributed computing

    Enterprise Information Systems

    (2012)
  • V. Fung et al.

    Competing in a Flat World: Building Enterprises for a Borderless World

    (2008)
  • Gantz, D.A., 1999. Maximizing the regional benefits of North American economic integration: rules of origin under...
  • A.K. Goel et al.

    Time to rethink offshoring?

    McKinsey Quarterly

    (2008)
  • GPRD Business Council

    Implications of Mainland Processing Trade Policy on Hong Kong

    (2007)
  • J. Hagel

    Leveraged growth—expanding sales without sacrificing profits

    Harvard Business Review

    (2002)
  • N. Hamid et al.

    Dispersed network manufacturing: adapting SMEs to compete on the global scale

    Journal of Manufacturing Technology Management

    (2006)
  • R.H. Hayes et al.

    How should you organize manufacturing?

    Harvard Business Review

    (1978)
  • F.S. Hillier et al.

    Introduction to Operations Research

    (2005)
  • HKTDC

    Cost escalation and Trends for Export Price Increase—A Look at the Rising Production Costs in the PRD

    (2007)
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