Abstract
The design and management of supply chains in today's competitive business environment presents one of the most important challenges to managers. The competitiveness of products and services in the global economy is increasingly measured not only by individual product or service characteristics but also by the efficiency and effectiveness of their underlying supply chain in catering to differentiated customer requirements. Facing shrinking product life cycles, differentiation and diversification of customer requirements, and cost transparency and accountability, managers are ever eager to pursue strategic, tactical and operational initiatives that will lead to supply chain configurations that afford greater competitive advantage. In fact, the pursuit of such competitive advantage might explain recent mergers such as those of Kmart and Sears in November 2004, Proctor & Gamble and Gillette in January 2005, Coors and Molson in January 2005, and Federated and May in April 2005. To what extent might the core strategic impetus of these mergers be more of capitalizing on potential supply chain synergies than stemming from other levers such as financial, market expansion, product line expansion or technology acquisition? In 2002, the S & P 500 Survey indicated that improvement of supply chain operations, such as a twenty percent reduction in inventory, or a one percent decrease in operational expense can increase a company's stock by as much as six percent. A company's logistics operations comprise a significant share of overall costs and their efficiency can favorably impact the company's supply chain performance and its valuation. A Bain & Company study concluded that the difference in the profitability of companies that have employed sophisticated supply chain methodologies can be as much as a factor of twelve. This dissertation investigates the potential of supply chain synergistic gains brought about by a merger or an acquisition using mathematical programming models. The models are used to assess the extent to which defining characteristics of the two companies such as product structure and cost, distribution network configuration, temporal market demand patterns, and spatial market dispersion, favorably impact a potential merger. The models are used in computational studies which quantify supply chain performance in terms of truck or fleet utilization, inventory levels, and logistics costs. The computational studies examine a variety of scenarios of two prospective companies seeking to merge, to reveal the impacts of different supply chain networks, product characteristics, clustering of market regions, and the granularity of time period that defines operations on potential synergistic gains.