A multi-objective SCOR-based decision alignment for supply chain performance management

A dynamic integrated solution to three main problems through integrating all metrics using SCOR is proposed in this research. This dynamic solution comprises strategic decisions in high level, operational decisions in low level, and alignment of these two decision levels. In this regard, a human intelligence-based process for high-level decisions and machine-intelligence based Decision Support Systems (DSSs) for low-level decisions are proposed using a novel approach. The presented operational model considers important supply chain features thoroughly, such as different echelons, several suppliers, several manufacturers, and several products, during multiple periods. A multi-objective mathematical programming model is then developed to yield the operational decisions with Pareto efficient performance values and solved using a well-known meta-heuristic algorithm, i.e., NSGAII, the parameters of which are tuned using Taguchi method. Afterwards, an intermediate machine-intelligence module is used to determine the best operational solution based on the strategic idea of the decision maker. The efficiency of the proposed framework is shown through numerical example and then, a sensitivity analysis is conducted for the obtained results so as to show the impact of the strategic scenario planning on the performance of the considered supply chain.