Business enterprises such as small and medium-sized enterprises (SMEs) play a significant role in economic development but struggle for sustainability. A business enterprise such as a manufacturing unit tries many technological innovations and strategic initiatives to accomplish sustainability in the manufacturing system. Lean manufacturing implementation is one such initiative that helps SMEs manufacture value-added products with increased profitability and waste minimization. However, lean implementation in SMEs is challenging. Hence, it is essential to follow a systematic framework and control the critical success factors (CSFs) in attempting lean implementation. The purpose of this research is to find, evaluate, and rank the CSFs of lean implementation of SMEs so that they may be controlled to accomplish successful lean implementation. The CSFs of lean implementation found by an in-depth assessment of the literature are modeled using the interpretative structural modeling (ISM) approach. MICMAC analysis is also used in classifying and understanding the significance of each lean implementation CSF. ISM and MICMAC provide the relationship modeling to reveal the inter-relationships of each lean implementation CSF. Subsequently, the ISM model is validated using the Delphi technique. The interpretative ranking process (IRP) has been applied to rank the CSFs of lean implementations. The results show that sustainability in a manufacturing system, financial capability, and employee involvement hold significant importance in lean implementations in manufacturing SMEs. Practicing managers may benefit from revisiting their lean implementation plans and respective aligned strategies. They will also be in a position to identify and focus on the scarce resources required for the subsequent lean implementations.

Past research reveals that many lean implementation barriers hinder lean implementation in small and medium-sized enterprises (SMEs). Among many sectors, the manufacturing sector suffers more as it generates more waste while carrying out manufacturing processes. Many manufacturing units make unsuccessful attempts to implement lean principles in their manufacturing systems. Hence, such units must eliminate the prevailing lean barriers to accomplish successful lean implementation. Moreover, the contextual relationship of lean barriers must be studied to understand the effect of such barriers. This paper uses interpretive structural modeling (ISM) to explore lean barriers, their relationships, and their influence on other lean barriers. The present research also reveals the most significant classification of lean barriers into various categories of independent, dependent, autonomous, and linkage using the (MICMAC) Matrice d’Impacts Croisés Multiplication Appliquée á un Classement analysis. ISM and MICMAC together provide relationship modeling and reveal the interrelationship between each lean implementation barrier and its categories, respectively. The ISM model is validated using the Delphi technique. The interpretative ranking process (IRP) is used to rank the barriers. The three significant lean implementation barriers revealed through the IRP include “lack of lean understanding”, “lack of strong quality policy”, and “risk of sustainable practice implementation”. The present research will help practicing managers of SMEs in the manufacturing sector to understand the mutual influence of lean barriers before introducing lean implementation. It is suggested that SMEs work on independent barriers so that dependent barriers can also be overcome with the least amount of resources and effort.

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.

The construction sector is garnering increasing concern as an industry that generates significant pollution while suffering from a lack of adequate environmental performance evaluations. The purpose of this study is to evaluate the environmental performance of construction sector industries using the ISO 14031 standard in three dimensions: planning, using data and information, and reviewing and improving environmental performance evaluations. Using a descriptive and correlational approach, this study described and analyzed the level of implementation using a 7-point Likert scale questionnaire distributed to 1000 organizations in Saudi Arabia. The questionnaires were completed between May and December of 2018 and the results were measured against similar studies. The mean positive percentage responses were 31.36 and expressed the implementation levels of the three dimensions of environmental performance. The corresponding values for the three dimensions’ items were 33.36, 29.83, and 30.95 respectively. The minimum Kendall correlation coefficients for each item in a dimension were 0.79, 0.79, and 0.83, respectively. The maximum correlation values between an item and a dimension to which the item did not belong were 0.25. This study answers a fundamental question regarding the implementation level values of environmental performance in construction organizations based on the ISO 14031 standard model and analyzes the pairwise correlation among the descriptor variables of the model. Further studies are needed to investigate the model structure using the principle component, factor analysis, and cluster analysis.

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.