• Energy Research

Nowadays, managing clean and green supply chain activities by reducing carbon footprint is a prominent aspect of the research. This study presents a dual supply chain (DSC) model under the cap-and-trade (CAT) emission reduction for the manufacturer-led five different DSC network structures, namely vertically integrated, decentralized, partially integrated retail, partially integrated direct, and horizontally integrated to minimize the carbon footprint. It uses a backward induction method to find optimum decision variables namely wholesale price, selling price, demand, and emission reduction rate. It presents a mathematical analysis to evaluate how the permitted emission quota and emission level of channel members affect the decision variables of various network structures. Further, it conducts a sensitivity analysis to measure how consumers’ channel preferences affect price and emission reduction rates. This study illustrates that (1) the emission reduction investment can increase demand and improve the emission reduction rate with a considerable drop in price. Also, (2) the additional purchased carbon quota can help maintain emission levels up to a certain extent only because any additional buying will be considered a short-term salutation. As a result, channel members are encouraged to invest in emission reduction technology instead of buying additional permits. These results conclude that promoting CAT can help achieve targets to reduce emissions and foster economic and social development.

Nowadays, managing clean and green supply chain activities by reducing carbon footprint is a prominent aspect of the research. This study presents a dual supply chain (DSC) model under the cap-and-trade (CAT) emission reduction for the manufacturer-led five different DSC network structures, namely vertically integrated, decentralized, partially integrated retail, partially integrated direct, and horizontally integrated to minimize the carbon footprint. It uses a backward induction method to find optimum decision variables namely wholesale price, selling price, demand, and emission reduction rate. It presents a mathematical analysis to evaluate how the permitted emission quota and emission level of channel members affect the decision variables of various network structures. Further, it conducts a sensitivity analysis to measure how consumers’ channel preferences affect price and emission reduction rates. This study illustrates that (1) the emission reduction investment can increase demand and improve the emission reduction rate with a considerable drop in price. Also, (2) the additional purchased carbon quota can help maintain emission levels up to a certain extent only because any additional buying will be considered a short-term salutation. As a result, channel members are encouraged to invest in emission reduction technology instead of buying additional permits. These results conclude that promoting CAT can help achieve targets to reduce emissions and foster economic and social development.

PurposeThis research aims to identify and prioritize the circular economy (CE) benefits (CEBs) due to the adoption of CE enablers (CEEs) in the Indian manufacturing organization context.Design/methodology/approachThis research proposes a hybrid framework of Pythagorean fuzzy analytic hierarchy process (PF-AHP) and Pythagorean fuzzy TODIM (an acronym in Portuguese for Interactive Multicriteria Decision-Making) techniques. It identifies the CEEs and CEBs based on literature review and validated through industrial experts. Further, this research conducts an empirical case study to demonstrate the applicability of the proposed framework.FindingsThe result shows that CE enabler SE1 (clear vision, support and commitment from top management for CE adoption) is the most critical enabler for CE implementation. The CE benefit CEB1 (improves the value chain of products and mitigating environmental damage during product life cycle phase) is the most significant benefit derived from the adoption of CEEs. The proposed framework will provide a more accurate, structural and systematic approach to the business organizations for achieving the CEBs in a stepwise manner through the effective adoption of CEEs.Research limitations/implicationsThe findings of this research are nation-specific and based on a case study of single manufacturing industry. Thus, the result obtained can vary from case to case and nation to nation.Originality/valueA deep understanding of each CEEs and CEBs would help build confidence among decision-makers and industrial practitioners to eliminate the risks associated with CE implementation.

PurposeThis research aims to identify and prioritize the circular economy (CE) benefits (CEBs) due to the adoption of CE enablers (CEEs) in the Indian manufacturing organization context.Design/methodology/approachThis research proposes a hybrid framework of Pythagorean fuzzy analytic hierarchy process (PF-AHP) and Pythagorean fuzzy TODIM (an acronym in Portuguese for Interactive Multicriteria Decision-Making) techniques. It identifies the CEEs and CEBs based on literature review and validated through industrial experts. Further, this research conducts an empirical case study to demonstrate the applicability of the proposed framework.FindingsThe result shows that CE enabler SE1 (clear vision, support and commitment from top management for CE adoption) is the most critical enabler for CE implementation. The CE benefit CEB1 (improves the value chain of products and mitigating environmental damage during product life cycle phase) is the most significant benefit derived from the adoption of CEEs. The proposed framework will provide a more accurate, structural and systematic approach to the business organizations for achieving the CEBs in a stepwise manner through the effective adoption of CEEs.Research limitations/implicationsThe findings of this research are nation-specific and based on a case study of single manufacturing industry. Thus, the result obtained can vary from case to case and nation to nation.Originality/valueA deep understanding of each CEEs and CEBs would help build confidence among decision-makers and industrial practitioners to eliminate the risks associated with CE implementation.

The objective of this research is to identify and rank the solutions to mitigate the sustainable remanufacturing supply chain (RSC) risks effectively. Risks and the solutions are identified based on a literature survey and discussion with the expert’s panel. A hybrid multi-criteria decision making (MCDM) framework using fuzzy step-wise weight assessment ratio analysis (SWARA) and fuzzy complex proportional assessment of alternatives (COPRAS) is applied to analyse the risks and the solutions. Fuzzy SWARA is used to obtain relative weights of each risk and fuzzy COPRAS is used to rank and select the most efficient solution to manage the risks. The proposed framework was numerically applied to a case organisation involved in manufacturing/remanufacturing activities. The results indicate that initiatives from the management and modification in organisation goals is the top-ranked solution that would mitigate the majority of risks. Government, manufacturer and distributor collaboration to develop action plans and setting up international standards are also prominent solutions to overcome the risks. Further, this research performs a sensitivity analysis to check the robustness of the proposed fuzzy SWARA-fuzzy COPRAS framework. The findings and implications of this research will help the practitioners and researchers for decision-making in RSC.

The objective of this research is to identify and rank the solutions to mitigate the sustainable remanufacturing supply chain (RSC) risks effectively. Risks and the solutions are identified based on a literature survey and discussion with the expert’s panel. A hybrid multi-criteria decision making (MCDM) framework using fuzzy step-wise weight assessment ratio analysis (SWARA) and fuzzy complex proportional assessment of alternatives (COPRAS) is applied to analyse the risks and the solutions. Fuzzy SWARA is used to obtain relative weights of each risk and fuzzy COPRAS is used to rank and select the most efficient solution to manage the risks. The proposed framework was numerically applied to a case organisation involved in manufacturing/remanufacturing activities. The results indicate that initiatives from the management and modification in organisation goals is the top-ranked solution that would mitigate the majority of risks. Government, manufacturer and distributor collaboration to develop action plans and setting up international standards are also prominent solutions to overcome the risks. Further, this research performs a sensitivity analysis to check the robustness of the proposed fuzzy SWARA-fuzzy COPRAS framework. The findings and implications of this research will help the practitioners and researchers for decision-making in RSC.