Kharazmi University International Journal of Supply and Operations Management 2383-1359 11 1 2024 02 01 Credit Rating of Commercial Companies Using Data Envelopment Analysis (DEA) Model: A Case Study of 100 Iranian Active Commercial Companies in Import 100 113 2924 10.22034/ijsom.2023.108137.1572 EN Mehdi Fesanghari Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran Mariam Ameli Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran Hamidreza Izadbakhsh Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran Journal Article 2019 10 25 One of the important issues in Iran is to monitor imports and business processes as well as exchange rates. Given the government’s supportive policies for traders, how to implement these policies is challenging. An strategy to implement targeted oversight and policies is to control the actions of active traders based on their background. Data Envelopment Analysis (DEA) is one of the key tools to achieve this goal. The present research is based on Slack-Based Measure DEA Model (SBM-DEA) with an output-based approach which rates traders using real data as well as their background. In this model, 30 input indicators and six output indicators were first considered. Subsequently, given the correlation between them, the number was reduced to five input and four output indicators. After the extraction of effective input and output indicators, traders’ efficiency was measured and rated using DEA model. Then, well-respected traders would receive facilities and various supports. To evaluate the performance of the model, the traders also were ranked using the Best Worst Method (BWM and after results shows better performance of the DEA model. Another result and application is the use of reference decision-making units, who indicate traders that are expected to have good performance by the market knowledge. Recognizing these units allows policy-makers to reduce other traders’ risk by disseminating their behavior. Another important application is traders’ classifications. By knowing the traders, the policy-maker reference can make a good classification of them, which is necessary for different resource allocation or facilitating policies. http://www.ijsom.com/article_2924_3953630da28e5181cffca1278517e3cf.pdf