On Finding the Maxima of a Set of Vectors
Journal of the ACM (JACM)
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It has been widely documented that disruptions and changes in the environment of manufacturing in a global enterprise impact the performance of the enabling supply network. The need exists for a methodology that can not only provide an objective and transparent evaluation of the efficiency and risks of a supply network configuration but that can also design supply network configurations with provably superior performance. Supply chain engineering SCE fundamentally applies the perspective and methodology of systems engineering to the domain of supply chains. SCE is the integrator of various corporate functions such as purchasing, production, distribution, and sustainment over the total product life cycle from design, through deployment, production, sustainment, and disposal. A supply chain is actually a network that consists of a diverse set of organizational and geographical components that typically change over time. The systematic design and management of such an inherently complex system requires the principles and tools of model-based systems engineering. A comprehensive and integrated model of the structure and the behavior of the supply network of a manufacturing enterprise has been developed. In this model the inherent uncertainty of the future environmental conditions in which the supply network will have to function is captured in a large number of scenarios. The variability of the profits achieved by the supply network for the various scenarios is used as a proxy for the supply network risk. The model allows for the explicit tradeoff of the systems life cycle profit with the variability of this profit. An efficient solution algorithm eliminates the vast majority of feasible supply network configurations from consideration and identifies all the Pareto-optimal configurations. However, the final selection of the network configuration depends on the risk preferences of the designer and the complexity of the enterprise. Selecting the supply network configuration with the appropriate robustness becomes one of the principal risk mitigation policies for the network and, in turn, the enterprise. The combination of the socio designer risk preferences and analysis and technical Pareto-optimization of all feasible supply network configurations creates a transparent engineering design process that finds the supply network configuration best matched to the objectives of the manufacturing enterprise.