Continuous Performance Assessment using Data Envelopment Analysis Models: An Approach to Industrial Sustainability Measurement
Subject Areas : Research in operation and optimization of systems and processes
Mohammadreza Shahriari
1
(عضو هیات علمی گروه مدیریت صنعتی، واحد تهران، دانشگاه آزاد اسلامی، تهران، ایران)
Keywords: Data Envelopment Analysis, Relative Efficiency, Undesirable Output, Component Efficiency, Continuous Assessment,
Abstract :
This article examines a quantitative approach for assessing and continuously monitoring industrial performance using Data Envelopment Analysis (DEA) models, which account for undesirable outputs that negatively impact industrial sustainability. These models, as powerful tools, are capable of handling diverse data types and are used to analyze the complex relationships between various performance variables across different industries. This method enables the identification, evaluation, and monitoring of factors affecting industrial performance and examines changes in their performance over time. The use of DEA models offers numerous advantages. Among these benefits are the ability to determine and analyze key factors influencing performance, predict future trends, and create opportunities for performance improvement. Additionally, this method helps industries continuously monitor their performance, assess efficiency levels across different periods, achieve greater sustainability and productivity, and control undesirable outputs. By employing DEA models as a reliable approach, industrial performance improvement and sustainability can be precisely monitored. This approach, with its ability to evaluate the core performance of a business, management levels, processes, human resources, and various assets—including tangible and intangible assets—can ultimately provide an improved version of operations and help achieve more reliable results. This improved version reveals the company's potential to remain resilient in its competitive environment. As a case study, this research analyzed data from a bank across twenty provinces in the country. The results indicate that DEA models can effectively identify strengths and weaknesses in the bank's performance, assisting managers in making better decisions and improving overall performance. Thus, DEA models can be employed as effective tools to enhance performance and mitigate the negative impacts of undesirable outputs across various industries.
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