IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v332y2024i1d10.1007_s10479-022-04528-3.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sunil Kumar Jauhar & Praveen Vijaya Raj Pushpa Raj & Sachin Kamble & Saurabh Pratap & Shivam Gupta & Amine Belhadi, 2024. "A deep learning-based approach for performance assessment and prediction: A case study of pulp and paper industries," Annals of Operations Research, Springer, vol. 332(1), pages 405-431, January.
    • Handle: RePEc:spr:annopr:v:332:y:2024:i:1:d:10.1007_s10479-022-04528-3
    DOI: 10.1007/s10479-022-04528-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-022-04528-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-022-04528-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:332:y:2024:i:1:d:10.1007_s10479-022-04528-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.