Optimal design of a multi-echelon supply chain in a system thinking framework: An integrated financial-operational approach

摘要

The purpose of this study is to design a four-echelon supply chain network in which operational and financial dimensions have been considered by a holistic, comprehensive and rigorous viewpoint within tactical and strategic decision-making levels. In this process, the aforementioned dimensions are extended and integrated in mathematical modeling framework. The research modeling is compared with traditional approaches, which merely rely on operational dimension to optimize profit and due to problems in profit, the research objectives changed to the multiple objectives of corporate value, change in equity and economic value added. Within this framework, a comparison is primarily made between profit and each of corporate value, change in equity and economic value added two by two (in the form of traditional approach (scenario A) and new approach (scenario B)). Then the objectives are simultaneously assessed and a compromise is gained among them through fuzzy goal programming. In this process, the effectiveness and efficiency of the scenario B is analyzed and assessed. Furthermore, a sensitivity analysis is performed on such factors as demand, return of equity rate, tax rate, production capacity and supplier capacity while the impacts of their changes on multi-objective function and satisfaction coefficient are simultaneously calculated. The mathematical model in the study is multi-product and multi-period while the values of parameters are determined under definite circumstances. For modeling and solving the mathematical model, GAMS 24 software and CPLEX solver are employed. To test the model, data of an Iranian petrochemical company are used. Besides highlighting the significance of the financial dimension and its integration with the operational dimension in gaining sustainable competitive advantage, the research results revealed that CVM -in contrast to change in equity and EVA- is much more favorable than the other objectives. Similarly, changes in demand had more favorable effects on multi-objective analysis.