A deep learning-based approach for performance assessment and prediction: A case study of pulp and paper industries

Sunil Kumar Jauhar (), Praveen Vijaya Raj Pushpa Raj (), Sachin Kamble (), Saurabh Pratap (), Shivam Gupta () and Amine Belhadi ()
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Sunil Kumar Jauhar: Indian Institute of Management Kashipur
Praveen Vijaya Raj Pushpa Raj: Indian Institute of Management Raipur
Sachin Kamble: Strategy (Operations and Supply Chain Management), EDHEC Business School
Saurabh Pratap: Indian Institute of Technology (BHU)
Shivam Gupta: NEOMA Business School
Amine Belhadi: Cadi Ayyad University

Annals of Operations Research, 2024, vol. 332, issue 1, No 16, 405-431

Abstract: Abstract The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers.

Keywords: Performance measurement; Data envelopment analysis; Deep learning; Artificial neural networks; Pulp and paper industry (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-022-04528-3

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