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Industry 4.0 has received significant attention in today's competitive business market, necessitating a restructuring of functional domains in nearly every manufacturing organization. A comprehensive strategy to improve performance in preparation for Industry 4.0 implementation necessitates several steps, one of which is the establishment of performance outcomes (POs). The aim of this paper is to identify and rank the POs realized due to the adoption of Industry 4.0 enablers.

Based on an extensive literature review and inputs received from experts, a comprehensive list of enablers and the POs was prepared and finalized. This paper proposes a framework based on hybrid solution methodology, namely Neutrosophic Analytical Hierarchy Process (N-AHP) and Neutrosophic Combined Compromise Solution (N-CoCoSo), to rank the POs realized due to the adoption of Industry 4.0 enablers. The N-AHP methodology has been adopted to calculate the relative weights of the Industry 4.0 enablers. In comparison, the N-CoCoSo method has been adopted to rank the POs of Industry 4.0.

The proposed framework is applied to an Indian manufacturing organization to test the organization's practical applicability. Additionally, sensitivity analysis is also carried out to check the steadiness of the proposed framework. The findings of this study revealed that “Improved responsiveness to market conditions in today's competitive business environment” is the top-ranked PO of Industry 4.0, followed by “Enhanced competitiveness and better market share”, “Better product quality, through smart management of production process” and “Reduction in manufacturing waste and environmental sustainability” which could be realized due to adoption of its enablers.

This research would aid practitioners by enhancing the practitioners' capacity to understand and prioritize the various POs resulting from implementing Industry 4.0 enablers. Embracing a clear strategic plan will further assist practitioners in improving the efficiency of Industry 4.0 implementation.

Previous literature has only addressed the relationship between Industry 4.0 enablers and POs in a limited way. This paper attempts to compile a comprehensive list of Industry 4.0 enablers relevant to manufacturing organizations in order to fill this knowledge and research gap.

This research work has developed an integrated fuzzy Delphi and neutrosophic best–worst framework for selecting the sustailient (sustainable and resilient) supplier for an additive manufacturing (AM)-enabled industry.

An integrated fuzzy Delphi method (FDM) and neutrosophic best–worst method (N-BWM) approach is developed. 34 supplier evaluation criteria falling under 4 groups, that is, traditional, sustainable, resilient, and AM specific, are identified and validated using the FDM. Afterward, the weights of each criterion are measured by N-BWM. Later on, the performance evaluation is carried out to determine the best-suited supplier. Finally, sensitivity analysis is performed to know the stability and robustness of the proposed framework.

The outcome indicates the high performance of the suggested decision-making framework. The analysis reveals that supplier 4 (S4) is selected as the most appropriate for a given firm based on the FDM and N-BWM method.

The applicability of this framework is demonstrated through an industrial case of a 3D-printed trinket manufacturer. The proposed research helps AM decision-makers better understand resiliency, sustainability, and AM-related attributes. With this, the practitioners working in AM business can prioritize the supplier selection criteria.

This is the primitive study to undertake the most critical aspect of supplier selection for AM-enabled firms. Apart from this, an integrated FDM-N-BWM framework is a novel contribution to the literature on supplier selection.

This study aims to address the critical challenge of implementing lean manufacturing (LM) in emerging economies, where sustainability complexities on the production floor hinder production efficiency and the transition towards a circular economy (CE). Addressing a gap in existing research, the paper introduces a path analysis model to systematically identify, prioritize and overcome LM implementation barriers, aiming to enhance performance through strategic removal.

The authors used a mixed-method approach, combining empirical survey data with literature reviews to pinpoint key LM barriers. Using the grey-based Decision-Making Trial and Evaluation Laboratory (DEMATEL) along with the Network Knowledge (NK) method, they mapped causal relationships and barrier intensities. This formed the basis for developing a path simulation algorithm, integrating heuristic considerations for practical decision-making.

This analysis reveals that the primary barriers to LM adoption is the negative perception and inadequate understanding of lean tools and CE principles. The study provides a strategic framework for managers, offering new insights into barrier prioritization and overcoming strategies to facilitate successful LM adoption.

This research provides a strategic pathway for overcoming LM implementation barriers, empowering managers in emerging economies to enhance sustainability and competitive advantage through LM and CE integration. It emphasizes the significance of structured barrier management in the manufacturing sector.

This research pioneers a systematic exploration of LM implementation barriers in the CE context, making a significant contribution to the literature. It identifies, evaluates barriers and proposes a practical model for overcoming them, enriching sustainable manufacturing practices in emerging markets.