Industrial Benchmarking through Information Visualization and Data Envelopment Analysis: A New Framework
Özlem Köse(ET Al)
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We present a benchmarking study on the companies in the Turkish food industry based on their financial data. Our aim is to develop a comprehensive benchmarking framework using Data Envelopment Analysis (DEA) and information visualization. Besides DEA, a traditional tool for financial benchmarking based on financial ratios is also incorporated. The consistency/inconsistency between the two methodologies is investigated using information visualization tools. In addition, k-means clustering, a fundamental method from machine learning, is applied to understand the relationship between k-means clustering and DEA.Finally, other relevant data, apart from the financial data, is introduced to the analysis through information visualization to discover new insights into DEA results. This study uses information visualization to both explore and reveal the relationships between the different methodologies of financial benchmarking and gain practical insights on the Turkish food industry. The results show that the framework developed is a comprehensive and effective strategy for benchmarking; it can be applied in other industries as well. As a result, our study contributes to the DEA benchmarking literature with a novel methodology that integrates the various benchmarking methods from the fields of operations research, machine learning, and financial analysis.