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Nowadays, there is a constant focus on implementing the net-zero emission (NZE) concept in the manufacturing supply chain (MSC). To reduce emissions and improve organisational efficiency, adopting the net-zero concept is a prevalent trend in today's highly competitive global business environment. Governments and stakeholders are pressuring the manufacturing sector to use natural resources efficiently and reduce environmental impacts. As a result, the manufacturing industry is focusing on cleaner production using net-zero practices. This study aims to identify and analyse the interaction among the drivers of net-zero adoption in the MSC. Through a systematic literature review (SLR), a list of drivers was recognised. To validate these drivers, we conducted an empirical study with 173 respondents from the Indian manufacturing industry. Further, we employed an artificial neural network (ANN) to weigh the nonlinear effect of drivers. Fuzzy interpretive structural modelling (F-ISM) was used to identify the interaction relationships among the drivers and construct a hierarchical structure among these identified drivers. The fuzzy matrix of cross-impact multiplications applied to the classification (F-MICMAC) method was used to categorise these drivers into driving and dependent categories. The outcomes of ANN show that Environmental predictors (100%) emerged as the most significant drivers, followed by Economic drivers (60.38%) and Technological drivers (59.05%). This study is a valuable resource for academia and industry professionals, providing essential insights into how adopting net zero facilitates the manufacturing industry's ability to achieve net zero across the supply chain. [ABSTRACT FROM AUTHOR]

Nowadays, industries across the globe are acknowledging the need for a Net Zero Supply Chain (NZSC) by 2050, particularly within the Manufacturing Supply Chain (MSC) due to its significant contribution to Greenhouse Gas (GHG) emissions. Government and regulatory bodies pressure the industry to reduce GHG emissions. Industries focus on cleaner and sustainable production by adopting net zero technique practices. Achieving the net zero goal needs technological integration, such as Caron Capture Technology (CCT) and Life Cycle Assessment (LCA). Are LCA and CCT unlocking the potential for net-zero practices in MSC? This still needs to be answered. Therefore, this study aims to identify and prioritize potential enablers of Life Cycle Assessment (LCA) to facilitate the NZSC. We identified potential enablers using a Systematic Literature Review (SLR) and expert opinion. An empirical study validated these enablers, followed by the application of the "Best-Worst Method (BWM)" to rank them and the "Fuzzy Decision-making trial and evaluation laboratory (F-DEMATEL)" to identify causal relationships. The outcomes of this study reveal that 'Energy Management and Emission Reduction' is the most critical category of enablers, followed by 'Governance and Collaboration', 'Assessment and Digitalization', and 'Sustainable Materials and Production'. Outcomes highlight the importance of integrating technological innovations and stakeholder engagement to achieve net-zero goals. The implications of this study provide valuable insights for policymakers and practitioners. By focusing on the prioritized enablers, the manufacturing industry can effectively implement LCA, develop the NZSC business model, and enhance competitiveness in the global market. This study contributes to the ongoing discourse on sustainable manufacturing practices and offers a strategic framework for achieving NZSC by 2050. [ABSTRACT FROM AUTHOR]

Accumulated heat input during layer deposition causes high residual stress in the Wire-Arc Additive Manufacturing (WAAM) components. The developed residual stress results in defects like distortion, delamination, cracks, and low fatigue life. To deal with such engineering problems, numerical methods have always been required. It gives an insight into the system that can be used for real-world applications. Consequently, a sequentially coupled finite element model has been developed to simulate the thermal-structural behavior of the feedstock during and after deposition in the WAAM process. Precisely, a novel multi-level layer-wise heat input approach characterized by four different stages is compared with the layer-wise single heat input strategy. The variation of thermal and residual stress distributions has been studied based on the different cases proposed related to layer-wise multi-level heat loading. A good agreement between predicted and experimentally observed temperature and residual stress values has been observed. The developed framework predicted thermal distribution with an average error of 9.71%, 9.13%, 7.57%, and 4.52% for case #1, case #2, case #3, and case #4, respectively. In addition to that, longitudinal stresses in the modeled component recorded a reduction of 17.94% for four-level heat input (case #4) compared to the respective value observed in case #1. Therefore, a multi-level heat input strategy is recommended over a single-level heat input approach for the components with small deposition lengths manufactured through the WAAM process. [ABSTRACT FROM AUTHOR]