Paul Hong, University of Toledo, Paul.Hong@utoledo.edu

Yehua Chen, Yanshan University, chenyh@ysu.edu.cn

The growing importance of China’s economy in the world directs many researchers’ attention to the Chinese manufacturing sector which is becoming an engine of growth for China and global economy. Automobile industry in particular has become the backbone of Chinese economic growth. The explosive demand in the Chinese market for more vehicles makes it an irresistible attraction for many of global automakers and Chinese auto-manufacturers.
Yet, much still remains unknown in regard to Chinese auto-manufacturing industry. Chinese auto-industry represents challenges and opportunities of the larger Chinese manufacturing sector. This paper reviews the current issues and carefully considers the trends of Chinese automobile industry based on extensive data, published papers and case illustrations. Manufacturing and management implications are given in terms of global supply chain and policy issues from a Chinese market point of view.

Behnaz Saboonchi, University of Windsor, saboonc@uwindsor.ca

Guoqing Zhang, University of Windsor, gzhang@uwindsor.ca

This paper encompasses the design of a multi-stage global supply chain with stochastic demand. The network consists of manufacturing sites, distribution centers and retail zones situated at both domestic and international locations. Tactical level decisions to be made are the selection international outsourcing partners, transportation modes and the capacity of each facility. The existence of exchange rate variations, and the presence of economies of scale in production which lead to different capacity expansion and outsourcing policies, are considered to provide a practical decision support tool for the design of global supply chains. We formulate the problem as a mixed-integer nonlinear optimization model with the objective of minimizing the overall costs and maximizing the expected average service level. A two-stage stochastic programming method is used to handle the stochasticity in demand. The proposed model is finally applied to various cases to demonstrate its applicability in facilitating decision making for managers.