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Prediction of drivers' impact on green supply chain management using deep learning algorithm.

Anjibabu Merneedi1, Ramesh Palisetty2

  • 1Department of Mechanical Engineering, Aditya College of Engineering, Surampalem, Andhra Pradesh, 533437, India. anjiamiable@gmail.com.

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Summary

Green supply chain management (GSCM) deployment in the leather industry is analyzed using deep belief networks. Key drivers and dimensions impact GSCM implementation, leading to enhanced efficiency and reduced waste.

Keywords:
GSCMRestructuring GSCMRisk and security consciousnessUrbanization

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Area of Science:

  • Environmental Science
  • Industrial Engineering
  • Operations Management

Background:

  • Green Supply Chain Management (GSCM) is crucial for controlling environmental impact and enhancing industrial performance.
  • This study focuses on the deployment of GSCM systems within the leather industry.
  • It examines key dimensions like consumer intrusiveness and supply chain restructuring, alongside drivers such as effectiveness and limitations of GSCM.

Purpose of the Study:

  • To investigate the primary factors and drivers for deploying GSCM in the northern Tamil Nadu leather industry.
  • To analyze the impact of GSCM drivers on system implementation using advanced data analysis.
  • To evaluate the proposed GSCM framework's performance against existing methods.

Main Methods:

  • A survey was conducted among leather industry authorities in northern Tamil Nadu.
  • Data from three participating industries were analyzed using a Deep Belief Network (DBN).
  • The DBN model measured the impact of primary drivers on GSCM implementation effectiveness.

Main Results:

  • The study identified key drivers and dimensions influencing GSCM deployment in the leather sector.
  • Deep Belief Network analysis quantified the impact of these factors on implementation.
  • The proposed GSCM framework demonstrated superior performance in accuracy and precision.

Conclusions:

  • Implementing GSCM in the leather industry enhances organizational efficiency, reduces costs, and minimizes waste.
  • The study provides insights for policy development and the adoption of green innovation methods.
  • Findings support fostering a green culture within industrial personnel.