Custom manufacturing benchmarking

The second path will also characterize the case of a customer seeking to consolidate its supplier base and outsource some of its own operations. In this case, the customer will seek fewer partners offering a broader range of services. A good example is a manufacturer looking to distributors to supply an increased share of its material requirements. The company will evaluate its own costs as a benchmark for make-or-buy decisions on specific services. It will also use its cost information to design the package of services needed from selected partners. 

The intangible benefits may provide more compeffing justification for software project within the supply chain. Often, key performance indicators such as market share, customer satisfaction scores, or industry benchmarks can quantify these intangible benefits. For example, a manufacturer might decrease the time taken to supply a customer there may be no direct cost saving but without it there may be no customer. 

The benchmark case is of greater stand-alone interest when the network exhibits complexities such as stochastic demand that is filtered through the inventory policies implemented at the successive echelons, intricate cost structures, lead times, and multiple time periods of activity. This is the theme of the bulk of multi-echelon inventory theory, dating back to the serial supply chain analysis of Clark and Scarf (1960). This literature also considers system architectures that move product from a manufacturer to end customers along, multiple parallel paths, such as a 1-depot N-warehouse network. 

In order to test the effect of changed parameters of the model on the total cost of the supply chain at each stage in plan period T, we analyze the sensitivity of the various parameters for suppliers, manufacturers and customers respectively. Taking the values of the parameters in experiment 1 (Fig. 4.5) as a benchmark, increase the parameters value of suppliers, manufacturers and customers respectively from the benchmark by 5, 10 and 15 % in turn (Minus sign implies decrease). As one parameter changes, all the other parameters value keep the same. The simulation results are shown in Tables 4.29, 4.30 and 4.31. 

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