Purpose The purpose of this paper is to identify various factors influencing additive manufacturing (AM) implementation from operational performance in the Indian manufacturing sector and to establish the hierarchical relationship among them. Design/methodology/approach The methodology includes three phases, namely, identification of factors through systematic literature review (SLR), interviews with experts to capture industry perspective of AM implementation factors and to develop the hierarchical model and classify it by deriving the interrelationship between the factors using interpretive structural modeling (ISM), followed with the fuzzy Matrice d’Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) analysis. Findings This research has identified 14 key factors that influence the successful AM implementation in the Indian manufacturing sector. Based on the analysis, top management commitment is an essential factor with high driving power, which exaggerates other factors. Factors, namely, manufacturing flexibility, operational excellence and firm competitiveness are placed at the top level of the model, which indicates that they have less driving power and organizations need to focus on those factors after implementing the bottom-level factors. Research limitations/implications Additional factors may be considered, which are important for AM implementation from different industry contexts. The variations from different industry contexts and geographical locations can foster the theoretical robustness of the model. Practical implications The proposed ISM model sets the directions for business managers in planning the operational strategies for addressing AM implementation issues in the Indian manufacturing sector. Also, competitive strategies may be framed by organizations based on the driving and dependence power of AM implementation factors. Originality/value This paper contributes by identification of AM implementation factors based on in-depth literature review as per SLR methodology and validation of these factors from a variety of industries and developing hierarchical model by integrative ISM-MICMAC approach.

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In the era of circular economy (CE), sustainability in the supply chain provides a competitive edge over other organizations in a globally competitive environment. There have been an increasing number of studies in sustainable supply chain practices in the recent past. Limited studies have been done to identify the key strategic factors for sustainable operations of automotive sector in context to developing countries like India. The primary goal of this research is to undertake a thorough assessment and give a brief understanding of the barriers of sustainability in the automotive supply chain. Various barriers are identified from experts' interviews and past academic literature. An integrated approach comprising of the “decision‐making trial and evaluation laboratory method” (DEMATEL) and “interpretive structural modelling” (ISM) method is employed to develop a contextual and hierarchical relationship among the identified barriers. The lack of awareness concerning reverse logistics adoption has the greatest causal effect. This is followed by a lack of information sharing on sustainable practices and complexity in measurement and monitoring of suppliers' environmental activities. To ensure the robustness of the model, sensitivity analysis has been also performed. The novelty of this work is that it identified several barriers towards a sustainable supply chain in automotive sector with a special focus on India. This work will help the practitioners to prioritize the strategic actions needed to be taken for developing sustainable supply chain management. This research has crucial policy implications for stakeholders working to build a sustainable CE by remanufacturing and reusing production waste.

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Additive manufacturing (AM) is one of the technologies that revolutionize production operations by developing digital competencies to satisfy changing market demands. The purpose of this paper is to explore essential AM implementation factors from an operational performance point of view. The methodology includes identification of AM implementation factors through past literature and semi-structured interviews with experts to capture industry perspectives. Initially, eighteen AM implementation factors have been identified and validated through nine industry experts from Indian manufacturing firms. We found various dimensions or key terms from transcripts and tried to map those dimensions across AM implementation factors. In the process, four factors found unrelated to the AM implementation process. The novelty of this paper lies in the identification of essential AM implementation factors and suggesting a conceptual model based on the linkage between AM implementation factors and firm competitiveness signifying the structured approach towards leveraging AM for competitiveness.