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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.

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.

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.

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.

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.

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.

The role of industry 4.0 (I4.0) technologies for organizations to achieve a competitive advantage and mitigate disruptive emergency situations are well exhibited in literature. However, more light needs to be thrown into implementing I4.0 technologies to digitally transform organizations. This paper introduces a novel framework for formulating manufacturing strategy 4.0 (MS 4.0) that guides organizations to implement I4.0 successfully.

The experts working in I4.0 and technology management domains were interviewed to determine the definition, role and process for formulating MS 4.0. Text mining using VOSViewer© is performed on the experts' opinions to determine the key terms from the opinions through keyword analysis. The identified key terms are mapped together using the existing traditional manufacturing strategy formulation framework to develop the MS 4.0 framework. Finally, the proposed MS 4.0 framework is validated through a triangulation approach.

This study captured the role, definition and process to formulate MS 4.0 and proposed a framework to help practitioners implement I4.0 at manufacturing organizations to achieve competitiveness during normal and emergency situations.

The proposed MS 4.0 framework can assist industry practitioners in formulating the strategy for implementing the I4.0 technology/gies to digitally transform their manufacturing firm to retain the maximum manufacturing output and become market competent in normal and emergency situations.

This study is the first of its kind in the body of knowledge to formulate a digital transformation strategy, i.e. MS 4.0, to implement I4.0 technologies through a manufacturing strategic lens.

This paper demonstrates a multi-criteria analytic hierarchy process (AHP) framework for evaluating and benchmarking maintenance performance in the select agro-based industry.

Initially, 20 maintenance practices (criteria) have been identified after a detailed literature review and discussion with the agro-based industry (sugar, textile and dairy industry) executives. These are then grouped into six maintenance management areas referred to as group criteria. The multi-criteria methodology consists of three steps: criteria identification, hierarchical modeling and data collection and maintenance performance evaluation, and benchmarking. The multi-criteria methodology proposed in this work facilitates two ways of carrying out benchmarking: (1) within the agro-based industry and (2) between the agro-based industry. The methodology has been explained by taking a case example of 45 agro-based industries (18 dairy, 13 sugar and 14 textile) from the western region of India. The sensitivity analysis of the model has been performed to ascertain the robustness of the results.

There is a difference in the maintenance performance across the agro-based industries due to different maintenance practices perceived differently.

The outcome of the model is mainly given by the judgments of the agro-based industry executives. It is also sensitive to any change in the relative importance to the evaluation criteria or the perception about the maintenance performance.

The study contributes in identifying the weakness, if any, by comparing the agro-based industry under investigation with the benchmark factory at three levels, namely, overall performance (factory level), group criteria (maintenance management area level) and criteria (maintenance practice level) allowing further improvement.

The methodology assists in better decision-making and in improving maintenance performance.

Firms have been adopting lean manufacturing to improve their business performances. However, they are facing failures or less success in implementation, mainly due to lack of understanding in relating the lean practices (LPs) from the required performance measures perspective. In view of the lack of research and the importance of understanding them, the purpose of this paper is to prioritize LPs.

As LPs are scattered in the literature and a variety of performance measures are used, an extensive literature review is first carried out to identify the LPs and performance measures. The blend of interpretive structural modeling and interpretive ranking process interpretive tools is adopted in establishing the contextual relationship among LPs and then ranking them based on the performance measures. A three-dimensional priority matrix is proposed for better explanation of the results.

The proposed framework can help firms better understand LPs and their levels of importance in lean implementation.

The involvement of lean experts may produce some bias in evaluating the LPs.

The proposed framework can help practitioners to develop an industry-specific road-map for the result-oriented LP implementation. Based on the area of performance to be improved, practitioners can prioritize LPs for implementation.

This is the first study that provides a comprehensive review of LPs available in the literature and prioritizes them in accordance with performance with interpretive tools.

This paper aims to assess the feasibility of a hybrid manufacturing and remanufacturing system (HMRS) for essential commodities in the context of COVID-19. Specifically, it emphasises using HMRS based on costs associated with various manufacturing activities.

The combination of mathematical model and system dynamics is used to model the HMRS system. The model was tried on sanitiser bottle manufacturing to generalise the result.

The remanufacturing cost is higher because of reverse logistics, inspection and holding costs. Ultimately remanufacturing costs turn out to be lesser than the original manufacturing the moment system attains stability.

The study put forth the reason to encourage remanufacturing towards sustainability through government incentives.

The study put forth the feasibility of the HMRS system for an essential commodity in the context of a covid pandemic. The research implemented system dynamics for modelling and validation.

Companies have been implementing lean manufacturing to improve their business performances. However, many of them have difficulties in the implementation because of various barriers, thus encountering failures. This paper aims to prioritize and analyze the lean barriers for better understanding and interpretation for successful lean implementation.

Extensive literature review has been carried out to identify the lean barriers. Subsequently, total interpretive structural modeling (TISM) has been adopted where lean experts’ inputs have been sought to obtain the self-interaction and reachability matrix. Further, driving power and dependence of lean barriers have been derived, and TISM-based lean barrier model has been developed.

Insufficient management time, insufficient supervisory skills and insufficient senior management skills are the significant barriers with highest driving power and lowest dependence. With low driving power, cost- and funding-related barriers such as cost of the investment, internal funding and external funding are found to be less important barriers.

This model provides a more realistic approach to the problems faced by practitioners during lean implementation. Thus, it provides a roadmap to implement lean by focusing on reducing or eliminating important barriers.

The paper not only provides a TISM-based model of contextual relationships among lean barriers but also describes the validation of this model.

This study aims at reviewing the articles on the themes of manufacturing strategy (MS) published in “Benchmarking: An International Journal (BIJ)” and investigating the trends of publication for future research.

Five-stage methodology to conduct a literature review is adopted comprising: (1) article collection, (2) inclusion/exclusion criteria, (3) reviewing the articles, (4) analyzing the articles and (5) future research directions. A total of 57 articles specific to MS domain published in BIJ are reviewed. Further, a bibliometric analysis comprising keywords co-occurrence, citation and co-citation using a VOSviewer© software followed by content analysis to analyze the type of research, type of industry and type of tool/method used is carried out.

The study helps to find the scope of the journal and research gaps in the MS domain to provide future research directions. Most of the work found is survey-based or case-based in nature. However, there is a need for empirical research to be done in the field of MS.

The study facilitates researchers willing to publish in BIJ to understand different themes of accepted papers concerning MS domain. The identified research gaps and future research direction can motivate researchers and practitioners to coin new approaches in the MS domain.

A comprehensive review and analysis of the MS literature published in BIJ has been provided. To the best of authors' knowledge, the current study is the only review study in MS domain focusing on one specific journal.

The advent of Internet of Things, cloud computing and advanced computing has endowed smart manufacturing environments with resilience, reconfigurability and intelligence, resulting in the emergence of novel capabilities. These capabilities have significantly reshaped the manufacturing ecosystem, enabling it to effectively navigate uncertainties. The purpose of this study is to assess the operational transformations resulting from the implementation of smart manufacturing, which distinguish it from conventional systems.

A list of qualitative and quantitative smart manufacturing performance metrics (SMPMs) are initially suggested and categorized into strategic, tactical and operational levels. The SMPMs resemble the capabilities of smart manufacturing systems to manage disruptions due to uncertainties. Then, industry and academia experts validate the SMPMs through the utilization of the Delphi method, enabling the ranking of the SMPMs.

The proposition of the SMPMs serves as a metric to assess the digital transformation capabilities of smart manufacturing systems. In addition, the ranking of the proposed SMPMs shows a degree of relevance of the measures in smart manufacturing deployment and managing the disruptions caused due to the COVID-19 pandemic

The findings benefit managers, consultants, policymakers and researchers in making appropriate decisions for deploying and operationalizing smart manufacturing systems by focusing on critical SMPMs.

The research provides a metric to assess the operational transformations during the deployment of smart manufacturing systems. Also, it states the role of the metric in managing the potential disruptions that can alter the performance of the business due to the COVID-19 pandemic.

This study provides a systematic review of the literature within the manufacturing strategy (MS) domain focusing on the congruence aspect of different strategic functions to determine the state-of-the-art research progression and the trend of publications.

We have adopted a five-stage review methodology consisting - 1) Article Identification; 2) Inclusion/Exclusion; 3) Review of the Articles; 4) Literature Analysis; 5) Future research directions. 121 articles focusing on congruence aspects and specific to the MS domain are identified and reviewed. Bibliometric analysis comprising keyword co-occurrence using a VOSviewer© software, and citation analysis is performed. Further, content analysis is carried out to categorize articles based on the type of research methodology, type of tool/method used, and aspects considered for congruence study.

Based on the research gaps identified in the existing literature on the congruence aspect within the MS domain, this study offers future research directions. Majorly, the work found is an empirical survey. Literature scants to develop a framework that helps to quantify the congruence between two strategic functions.

This study facilitates researchers and practitioners to understand the congruence between different strategic aspects studied in the literature and the level of fit between them. Further, the identified research directions can encourage researchers and practitioners to conceive novel approaches to conduct future works on congruence theme.

The unicity of the current review lies in its theme, i.e. congruence aspect within MS. To the best of author's knowledge, no comparable study is observed to review the congruence aspect in any other domain.

The purpose of this paper is to present an empirical investigation of maintenance performance (MP) management practices from the sugar industry in India.

Empirical data for this study were collected through field visits, interviews and published reports. Statistical methods including correlation, multiple regression and cluster analysis are utilized to accomplish the objective of the study.

Explanation with multiple regression analysis showed that the sugar industry MP is significantly and positively related to maintenance approach (MA), continuous improvement (CI), financial approach and spare part management (SPM). Cluster analysis showed that sugar industries focusing on MA, CI and policy development and organization are having higher MP. The cluster analysis also pointed out that there is a substantial variation in MP due to the type of ownership (private and cooperative) while no variation has been observed due to installed capacity (low and high).

The generalization of the results obtained in this work for the sugar industry can be possible through a larger sample size.

The study contributes to the better understanding of maintenance measures in the sugar industry and provides insights on the role of maintenance managerial practices in enhancing the MP.

The findings provide empirical evidence that maintenance practices across the sugar industry are important to improve MP.