Supply Chain Network Performance Measurement and Improvement Using DEA and SCOR Models in Dynamic Conditions

Dynamic systems have always attracted much attention from researchers as a significant part of the various types of systems, and modeling of supply chain processes is considered as one of these due to the nature of its change over time, the volatility of customer demand is considered as one of these problems that have many effects on the system and its costs. In the present study, the SCOR Supply Chain is first modeled with the Dynamic Systems Approach (DSA) under specific parameters. We determine the control parameters of the studied policy using the DEA-SCOR model. We also Improvement the basic five-stage model to investigate models incorporating advanced demand information and evaluate the influence of demand variability on the system performance. Then, the evaluation and ranking of the supply chain network of several distribution companies have been analyzed using the indicators of information sharing, based on the opinion of managers and experts familiar with the subject and by a combination of the data envelopment analysis (DEA)-SCOR and stochastic frontier analysis (SFA). By calculating total efficiency according to DEA-SCOR model in SCOR supply chain supply network of oil products Distribution Companies, Falavarjan Branch and Tehran Office Branch, have the highest performance, and the lowest performance is observed in Lordegan, Shahriar branch. According to the results of the SFA method, Tehran Office Branch and Isfahan Branch had the highest performances, and Shahriyar and Tiran branches had the lowest performances. The performance level calculated using the SFA method is approximately the same as the performance level calculated using the DEA method. The performance calculated by the DEA method is less than that calculated by the SFA method in some cases. The average calculated performance in the DEA method equals 0.80, and the average for the SFA method is 0.82. Given the inadequacy of indicators and the improvement of these indices at each stage, the calculated efficiency in each dynamic period gradually improves, and the average total performance in the dynamic period is 0.90.