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

Nowadays, for controlling the organizations’ environmental impact and attaining performance enhancement in industries, green supply chain management (GSCM) plays a significant role. This work considers four major drivers and five primary dimensions for the deployment of GSCM systems in industries. T...

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Veröffentlicht in:Environmental science and pollution research international Jg. 30; H. 3; S. 8062 - 8079
Hauptverfasser: Merneedi, Anjibabu, Palisetty, Ramesh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2023
Springer Nature B.V
Schlagworte:
Algorithms
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Belief networks
consciousness
Consumers
Cost reduction
Deep Learning
Development policy
Earth and Environmental Science
Ecotoxicology
Energy consumption
Environment
Environmental Chemistry
Environmental Health
Environmental impact
Genetic algorithms
human resources
India
Industry
Knowledge management
Leather
Leather & leather products
leather industry
Machine learning
Manufacturing
Optimization techniques
Pareto optimum
Performance enhancement
Pollution
prediction
Product life cycle
Research Article
risk
supply chain
Supply chain management
Supply chain sustainability
Supply chains
Surveys
Sustainability
sustainable technology
Urbanization
Waste Water Technology
wastes
Water Management
Water Pollution Control
India
Tamil Nadu India
Urbanization
Risk and security consciousness
GSCM
Restructuring GSCM
ISSN:0944-1344, 1614-7499, 1614-7499
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Zusammenfassung:Nowadays, for controlling the organizations’ environmental impact and attaining performance enhancement in industries, green supply chain management (GSCM) plays a significant role. This work considers four major drivers and five primary dimensions for the deployment of GSCM systems in industries. The five primary dimensions are consumer intrusiveness, restructuring quality of supply chain, risk and security consciousness, and urbanization in the region leather industry, and the four major drivers are the effectiveness, features, significance, and limitations of the restructured GSCM system. Additionally, this paper investigates the primary factors that are used for deploying the GSCM system in leather industries located in the northern part of Tamil Nadu. Initially, the survey questions are prepared based on the concept and distributed to the authorities of industries. For the survey questions provided to various industries, three leather industries properly answered all the questions. Each question is mandatory. Then, the collected data were analyzed using the deep belief network (DBN) to measure the impact of primary drivers in implementing the GSCM system in industries. The performance of the proposed system is analyzed with various existing methods in terms of accuracy, precision, f-measure, etc. The proposed framework’s implementation has implications for increasing organizational efficiency, lowering costs, minimizing waste, and encouraging green culture among personnel. This study will aid in the development of policies and a better knowledge of how to implement green innovation methods.
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ISSN:0944-1344
1614-7499
1614-7499
DOI:10.1007/s11356-022-22499-7