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Globally, chemical industry realized revenues of 1,776 billion Euros in 2004. The European Union is the world’s largest producer of chemical products with a 33% share of global production and with revenues of approximately 140 billion Euros. Germany, where chemical industry represents approximately 10% of total industrial output, holds the third rank surpassed only by the United States and Japan. Additionally, both the world’s largest chemical company BASF and the world’s largest specialty chemicals company Degussa are headquartered in Germany. While worldwide chemical production is concentrated to a few countries (the top 10 countries represent more than 70% of global production), the industry is nevertheless truly global from an operations perspective. On the one hand, international trade represents more than 40% of global revenues. On the other hand, major chemical companies typically operate numerous production sites in all major economic regions of the world. With 124 billion Euros in 2004, the revenues generated by the international subsidiaries of German chemical companies almost equal the revenues generated from domestic operations.

Many different definitions of the term exist in literature (cf. Ganeshan et al. 1999, p. 842). Christopher (2005, p. 17) defines the supply chain as a “...network of organizations that are involved, through upstream and downstream linkages, in the different processes and activities that produce value in the form of products and services in the hands of the ultimate consumer”. Typically, a supply chain consists of suppliers, production sites, storage facilities, distribution facilities and customers linked by material, information and financial flows. As shown in Figure 2, a supply chain can be spread across several facilities located in different countries that might belong to different companies. At the same time, depending on the product portfolio, a company is usually part of numerous supply chains (cf. Lambert and Cooper 2000, p. 69).

The production network design process developed in Chapter 2.4 is split into a global network optimization phase at the country level and a site selection phase focusing on the evaluation of individual production sites within a country. In this chapter the mathematical optimization model required to support the global network optimization phase of specialty chemicals production networks is developed. To this end Chapter 3.1 briefly introduces general research in the field of quantitative location analysis. The literature specifically focusing on production network design is reviewed in greater detail in Chapter 3.2. Alternative approaches to model major elements of production networks and their applicability to specialty chemicals industry are discussed in Chapter 3.3 and tailored modeling approaches are developed. Chapter 3.4 contains the resulting Mixed-Integer Linear Programming (MILP) model and possible extensions to include features that were not required in the pilot application underlying this work (cf. Chap. 5). Finally, Chapter 3.5 presents the results of numerical performance tests to demonstrate the applicability of the model to problem instances of realistic size.

The need to evaluate production sites can arise in different situations. Most obviously, a network optimization project resulting in the decision to establish new production capacity may require a site selection phase in one or more countries (cf. Chap. 2.4.4). Depending on the status quo, the task either is to identify and evaluate potential production sites or to choose the most suitable one from a set of existing sites. Conversely, if capacity is to be reduced potential closure candidates might have to be assessed to identify the one least suitable for future use. Additionally, as pointed out in Chapter 2.4.5, a regular evaluation of all production sites is also required in the context of site controlling. Here the objective is to rank a company’s entire portfolio of existing sites to identify action needs.

Modeling production systems from industry initially appears to be a straightforward exercise once the model to be used has been customized to reflect the peculiarities of the industry considered. Unfortunately, as also pointed out by many authors such as Pooley (1994, p. 116), Klingman et al. (1986, p. 11) or Brown et al. (1986, p. 227), collecting, preparing and validating the required data is often the greatest challenge. This issue is especially prominent in strategic applications because most of the data has to be forecasted for a long planning horizon and hence cannot be obtained directly from accounting or ERP systems. As also noted by Billington and Davis (1992, p. 587), insufficient data availability is probably a main reason for the reluctance of practitioners from industry to employ optimization models to support strategic planning. The complexity inherent in setting up the data structure for strategic supply network design models is also illustrated by the few publications actually describing the data compilation process (e.g., Geoffrion et al. 1978).

Major players in specialty chemicals industry operate global production networks with numerous sites located in every economic area. If properly managed, these global production networks can by themselves be a source of competitive advantages e.g., via exploitation of factor cost advantages or providing operational hedging against currency fluctuations. However, in most cases today’s production networks still lack the design required to do so. Among the historical reasons responsible for this gap are that: