Identifying Cost Drivers for Operating Expenditures

The objective of using a mathematical optimization model is to identify network design alternatives that best exploit structural cost differences between various locations and resolve trade-offs between different cost elements such as production cost advantages and additional transporta-tion/tariff costs. Therefore, relationships expressing the costs that will be incurred as a function of cost drivers (decision variables of the model) have to be established. A proper creation of these cost functions is a critical success factor of the overall analysis (cf. Shapiro 2001, p. 234 Vidal and Goetschalckx 1996, p. 13). 

The level of aggregation used in strategic planning makes obvious the need to focus on core cost items that actually determine the economic viability of alternative network designs. A two-step approach was used to prioritize cost items. 

In a first step the cost structure of a product representative for the value chain was analyzed both for a high-cost and a low-cost country (cf. Fig. 39). As a result, it was decided to explicitly model material costs, labor 

Cost structure of core value chain exemplary 

Unfortunately, most companies, including the industrial cooperation partner, do not use ABC. Instead, data had to be obtained from standard accounting systems. Cost functions linking each cost item to decision variables of the network design model had to be created both for existing and potential product-plant combinations. To do so, cost items were grouped into unit-related, batch-related, production line-related, plant-related and site-related costs and the cost functions described in Table 22 were estab- 

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