Supply Chain Balance

Research on balancing closed-loop supply chain networks with two-type suppliers, risk-averse manufacturers, and capacity constraints on remanufacturing and closed-loop supply chain management (CLSC) can not only protect the environment, promote sustainable development, but also make enterprises earn more profits (Guide & Wassenhove, 2006; Atasu, Guide & Wassenhove, 2008). Therefore, they have received great attention from entrepreneurs in recent years. Many well-known companies, such as BMW, IBM, Kodak, and Fuji Xerox, just to name a few, have established effective end-of-life product (EOL) collection and remanufacturing systems alongside their traditional production and distribution facilities. Correspondingly, in academia, many scholars have also undertaken a great deal of research on CLSC management to guide practice. In general, most of the literature deals with tactical and operational issues, such as reverse channel structure, new product and remanufacturing pricing strategies, inventory control, and logistics network design. Savasan, Bhattacharya and Wassenhove (2004) analyzed the reverse channel choice problem in CLSC and proved that the retailer collection mode is optimal. Shi, Zhang and Sha (2011) developed a mathematical model to maximize the overall profit of a CLSC system by simultaneously determining selling prices, production quantities for new products and remanufactured products. Golinska and Kawa (2011) provide a framework for the management of material feedback flows in the automotive industry, and utilize agent-based technologies to find optimal solutions for dynamic network configurations. Zhu and Xu (2012) built an integrated optimization model for CLSC networks under uncertainty, and solved the model with a hybrid genetic algorithm. But they fail to recognize CLSC as a business process and ignore the important strategic issue of competition. Under a competitive environment, Majumder and Groenevelt (2001) presented a two-period remanufacturing model in the face of competition between an original equipment manufacturer (OEM) and a local manufacturer. Savakan and Wassenhove (2006) investigated the optimal reverse channel design in CLSC with one manufacturer and two competing retailers. Research by (Webster & Partners 2007; Partners & Webster, 2008) examines the impact of recall laws and government subsidies respectively on OEM/remanufacturing competition frameworks. Other related studies include Debo, Toktay and Wassenhove (2005), Ferguson and Toktay (2006), Chen and Chang (2012), rsdemir, Kemahlıoğlu-Ziya and Parlaktürk (2014) and so on. Although the literature lays a solid foundation for future research, they only address competition between OEMs and remanufacturers or between two retailers dealing with a single demand market. In fact, as we know, CLSCs tend to have more diverse channel members and complex competitive relationships. For example, a complete CLSC might have several competing suppliers, competing manufacturers, competitive retailers, competitive collectors, and several demand markets. As noted in Nagurney, Dong and Zhang (2002), this type of competition can be a key driver of supply chain profitability and, therefore, deserves more in-depth analysis. There are several papers studying competition among different types of channel members of reverse supply chain networks or CLSC networks. Nagurney and Toyasaki (2005) constructed a multitiered network balance framework for e-waste recycling using quality variation methods. Hammond and Beullens (2007) extend Nagurney and Toyasaki’s (2005) model to construct an oligopolistic CLSC network model including producers and demand markets under the WEEE law. Yang, Wang and Li (2009) used the variational inequality method to model a five-echelon CLSC network including suppliers, manufacturers, retailers, recovery centers, and consumer markets. Qiang, Ke, Anderson and Dong (2013) built a CLSC network model by considering competition, distribution channel investment, and uncertainty. Wakolbinger, Toyasaki, Nowak and Nagurney (2014) formulated a flow model of e-wastenetwork as an inequality of variation problem to analyze how technical, market, and legislative factors affect the total amount of e-waste that is collected, recycled, exported and (legally and illegally) thrown away. However, the network models mentioned above do not consider the multi-supplier case.