Evaluation of SCOR KPIs using a predictive MILP model under fuzzy parameters.

Abstract

The Supply Chain Operations Reference (SCOR) model is a well-recognized process reference model in the supply chain management field. Based on the literature, there is no research work that proposes a method to estimate and predict SCOR key performance indicators (KPIs) of a company. The objective of this paper is to propose a methodology to assess the SCOR KPIs under uncertainties based on level 2 of the SCOR-Make process metric, including nine KPIs. The proposed methodology consists of predictive MILP models with fuzzy parameters and some algorithms to assess the KPIs related to agility. A case study of a bottled-water factory is conducted to demonstrate the application of the proposed methodology. From the fact that some parameters are fuzzy numbers, the obtained SCOR KPIs are fuzzy numbers, which provide more information than constant values. The findings indicate that the proposed methodology is capable of developing the relationship between the manufacturing parameters and the SCOR KPIs, which enable the effective prediction process especially when the manufacturing parameters are changed or improved.