Evaluating Supply Chain Efficiency Under Uncertainty: An Integration of Rough Set Theory and Data Envelopment Analysis
Journal Title: Journal of Intelligent Management Decision - Year 2024, Vol 3, Issue 4
Abstract
The evaluation of supply chain (SC) efficiency in the presence of uncertainty presents significant challenges due to the multi-criteria nature of SC performance and the inherent ambiguities in both input and output data. This study proposes an innovative framework that combines Rough Set Theory (RST) with Data Envelopment Analysis (DEA) to address these challenges. By employing rough variables, the framework captures uncertainty in the measurement of inputs and outputs, defining efficiency intervals that reflect the imprecision of real-world data. In this approach, rough sets are used to model the vagueness and granularity of the data, while DEA is applied to assess the relative efficiency of decision-making units (DMUs) within the SC. The effectiveness of the proposed model is demonstrated through case studies that highlight its capacity to handle ambiguous and incomplete data. The results reveal the model’s superiority in providing actionable insights for identifying inefficiencies and areas for improvement within the SC, thus offering a more robust and flexible evaluation framework compared to traditional methods. Moreover, this integrated approach allows decision-makers to assess the efficiency of SC more effectively, taking into account the uncertainty and complexity inherent in the data. These findings contribute significantly to the field of supply chain management (SCM) by offering an enhanced tool for performance assessment that is both comprehensive and adaptable to varying operational contexts.
Authors and Affiliations
Lorenzo Cevallos-Torres, Fatemeh Zahra Montazeri, Fatemeh Rasoulpour
Keywords
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- EP ID EP753273
- DOI https://doi.org/10.56578/jimd030404
- Views 35
- Downloads 1
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