Managing the trade-offs between financial performance and credit solvency in the optimal design of supply chain networks under economic uncertainty

Inherent uncertainties and risks in the economic environment are diffused to all vital operations of a supply chain network (SCN). However, the great impact of this contagion is on the financial operation due to its interdependence with financial markets and business conditions. Economic uncertainty poses uncertainty in the financial status of SCNs and this in turns leads to sustainability and growth risks. Financial performance and credit solvency are two essential pillars of financial status capable of providing the necessary capitals to a SCN. As each of these pillars focuses on a different aspect of investment attractiveness, underlined trade-offs exist, under various economic conditions, and challenge further investigation. This paper aims to enrich the SCN design literature by introducing a mathematical model that integrates financial performance and credit solvency modelling with SCN design decisions under economic uncertainty. The proposed multi-objective mixed integer non linear programming (moMINLP) model enchases financial performance through economic value added (EVA™) and credit solvency through a valid credit scoring model (Altman's Z-score). The applicability of the model is illustrated by using a real case study. The model could be used as an effective strategic decision tool by managers responsible for strategic SCN design.

► We develop a multi-objective mixed integer non linear programming (moMINLP) model for the optimal design of supply chain networks. ► We incorporate the EVA™ Index and the Altman's Z-score within the model. ► We incorporate economic uncertainty within the model. ► We evaluate the model with a real case study and we communicate the results. ► We provide managerial implications and future research directions.