Network design models

The objective function of a supply network design model can either minimize costs or maximize profits. In practice the production function is often required to assume that all forecasted demands have to be met. In this constellation cost minimization and profit maximization lead to identical results and consequently cost minimization models are used. From an economic perspective this simplification can be justified in cases where a high share of fixed costs allows the assumption that any product sale con-... 

The end-of-horizon problem can be resolved in different ways. The simplest option is to accept that network design models with a cash flow objective function cannot properly evaluate investment and restructuring decisions at the end of the planning horizon if their dynamic payback exceeds the remaining periods of the planning horizon. Alternative ap-... 

Business valuation literature provides various other methods for estimating terminal values (for an overview see Koller et al. 2005, pp. 271-290). Unfortunately, as cash flows cannot be allocated to individual decisions in a network design model, a cash flow-based estimate is not possible. Instead, book value or liquidation value at the end of the planning horizon could be used. For example, Fong and Srinivasan (1981, p. 790) include a terminal value function in the unit capacity acquisition cost function. However, they do not specify how this function can be quantified in real-world applications. The major disadvantages are that it is difficult to justify the assumptions underling the terminal value estimate and that restructuring expenditures cannot be properly evaluated. 

Fixed cost effects are included in most production network design models but scale and scope effects related to variable costs and learning curve effects lead to concave cost functions (cf. Cohen and Moon 1990, p. 274). While these can be converted into piecewise linear cost functions, model complexity increases significantly both from a data preparation perspective (see Anderson (1995) for an approach to measure the impact on manufacturing overhead costs) and the mathematical solution process. Hence, most production network design models assume linear cost functions ignoring scale and scope effects related to variable costs. 

The basic model presented in Chapter 3.4.2 distinguishes between internally manufactured intermediates and externally procured raw materials without considering make or buy options for intermediates. For some application cases it might however be required to include make or buy - decisions in the network design model. The decision can be made either for the entire production network or individually for each site. In order to incorporate make or buy - decisions (and possibly vendor selection), suppliers have to be modeled as an additional network node. Table 11 contains the additional indices, parameters and decision variables required to implement a make or buy formulation for intermediates. 

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-... 

An interesting level of complexity is added if the network design model is required to provide precise data on currency exposure. As shown in Figure 42 various currencies can be involved in a simple transport of a product from a site to another site or a market. 

For instance, consider the logistics network design model discussed previously. A DSS is often used to assist in optimizing the number of warehouses required as well as their size and customer allocation to each warehouse. The DSS uses information about the distribution system to calculate the various costs related to the site selection and customer allocation. The data required for this problem involve the manufacturers, warehouses, and customers and the transportation between them. Since this is a long-term planning tool, yearly demand data and costs are typically used, but sometimes the user may need to determine how to account for seasonality. In addition, in order for this kind of DSS to be utilized successfully, the user needs to break the products into product families... 

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