Designing a Collaboration Model among Supply Chain Members and Measuring the Technical Efficiency of Supplier Companies Using Data Envelopment Analysis in the Oil and Gas Industry
Keywords:
Supply chain, technical efficiency measurement , supplier companies, oil and gas industry , data coverage analysisAbstract
Given the increasing competition among companies and suppliers in the oil and petrochemical industry in the modern era, it has become essential for all active companies in this field to have a thorough understanding of their efficiency levels. They must examine the various causes of efficiency and inefficiency in their units and systematically plan and execute reforms in inefficient units. By enhancing the efficiency of underperforming units, it is expected that national interests will be better and more effectively served, improving the efficiency of the oil and petrochemical distribution and development systems in the country. This study focuses on designing a collaboration model among supply chain members and measuring the technical efficiency of supplier companies using Data Envelopment Analysis (DEA). By examining influential data and outputs, DEA is employed to calculate the technical efficiency of the country’s supplier companies under two assumptions: constant returns to scale (CRS) and variable returns to scale (VRS). The statistical sample consists of 15 experts with academic and practical experience in financial management and stock market operations. The research findings indicate that technical efficiency and pure technical efficiency (PTE) are directly derived from the computation of CCR and BCC models. Under the CRS assumption, the average efficiency of the companies under study is 88.45%. From a geometrical perspective, under this assumption, the production frontier is represented as a straight line on which efficient units are located, forming the frontier. Under the VRS assumption, the average efficiency is 90.88%. This indicates that companies have produced 9.12% less than the optimal amount given their current input levels. Furthermore, under this assumption, the production frontier is concave, and each efficient production unit is positioned on the frontier. The average scale efficiency is 97.17%, implying that the actual production scale deviates by 2.83% from the most productive scale. When pure technical efficiency (PTE) exceeds scale efficiency, inefficiency is due to scale inefficiency. Conversely, when this is not the case, the major portion of inefficiency is attributed to pure technical inefficiency or management (operational) inefficiency.
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