Evaluation of Supply Chain Performance Based on SCOR Criteria (Case Study: Zanjan Zinc Refinery Company)

In today's era, the supply chain has taken a large share of human activities. In the field of SCM, to achieve better performance and focus on core capabilities, supplier performance monitoring is very important. On the one hand, the issue of globalization and transcontinental outsourcing, and on the other hand, the issue of viability has imposed high importance to choosing the right approach to the supplier based on the strategic importance and supply risk. Therefore, the success of organizations in the global competition environment is highly dependent on formulating the type of relationship with suppliers, including affiliation, cooperation, long-term rewards, and a multi-source approach. In this paper, by using adaptive neuro-fuzzy inference, the performance of the SC has been evaluated based on the components of reliability, responsiveness, agility, costs and efficiency of asset management in the case study of " Zanjan Zinc Refinery Company". The output of examining the indicators related to each component in this method will lead to predicting the performance of the supplier and formulating a strategy to achieve the desired situation. In addition, the comparison of clustering models showed that the fuzzy mean clustering method with the number of 4 clusters has the lowest prediction error. The results of this research make it possible to measure the performance of the SC from different perspectives of the SCOR model. One can also provide information that helps the system expert to analyze the gap between the expected and the current performance level in terms of the mentioned components.