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

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

An integrated supply chain model for new products with imprecise production and supply under scenario dependent fuzzy random demand

In the present day business scenario, instant changes in market demand, different source of materials and manufacturing technologies force many companies to change their supply chain planning in order to tackle the real-world uncertainty. The purpose of this paper is to develop a multi-objective two-stage stochastic programming supply chain model that incorporates imprecise production rate and supplier capacity under scenario dependent fuzzy random demand associated with new product supply chains. The objectives are to maximise the supply chain profit, achieve desired service level and minimise financial risk. The proposed model allows simultaneous determination of optimum supply chain design, procurement and production quantities across the different plants, and trade-offs between inventory and transportation modes for both inbound and outbound logistics. Analogous to chance constraints, we have used the possibility measure to quantify the demand uncertainties and the model is solved using fuzzy linear programming approach. An illustration is presented to demonstrate the effectiveness of the proposed model. Sensitivity analysis is performed for maximisation of the supply chain profit with respect to different confidence level of service, risk and possibility measure. It is found that when one considers the service level and risk as robustness measure the variability in profit reduces