Prediction of drivers’ impact on green supply chain management using deep learning algorithm - Merneedi, Anjibabu; Palisetty, Ramesh (Environmental science and pollution research international, 2023)
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.