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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.

Integrating additive manufacturing (AM) tools in traditional mold-making provides complex yet affordable sand molds and cores. AM processes such as selective laser sintering (SLS) and Binder jetting three-dimensional printing (BJ3DP) are widely used for patternless sand mold and core production. This study aims to perform an in-depth literature review to understand the current status, determine research gaps and propose future research directions. In addition, obtain valuable insights into authors, organizations, countries, keywords, documents, sources and cited references, sources and authors.

This study followed the systematic literature review (SLR) to gather relevant rapid sand casting (RSC) documents via Scopus, Web of Science and EBSCO databases. Furthermore, bibliometrics was performed via the Visualization of Similarities (VOSviewer) software.

An evaluation of 116 documents focused primarily on commercial AM setups and process optimization of the SLS. Process optimization studies the effects of AM processes, their input parameters, scanning approaches, sand types and the integration of computer-aided design in AM on the properties of sample. The authors performed detailed bibliometrics of 80 out of 120 documents via VOSviewer software.

This review focuses primarily on the SLS AM process.

A SLR and bibliometrics using VOSviewer software for patternless sand mold and core production via the AM process.

This study aims to improve the acceleration in the additive manufacturing (AM) process. AM tools, such as extrusion heads, jets, electric arcs, lasers and electron beams (EB), experience negligible forces. However, their speeds are limited by the positioning systems. In addition, a thin tool must travel several kilometers in tiny motions with several turns while realizing the AM part. Hence, acceleration is a more significant limiting factor than the velocity or precision for all except EB.

The sawtooth (ST) scanning strategy presented in this paper minimizes the time by combining three motion features: zigzag scan, 45º or 135º rotation for successive layers in G00 to avoid the CNC interpolation, and modifying these movements along 45º or 135º into sawtooth to halve the turns.

Sawtooth effectiveness is tested using an in-house developed Sand AM (SaAM) apparatus based on the laser–powder bed fusion AM technique. For a simple rectangle layer, the sawtooth achieved a path length reduction of 0.19%–1.49% and reduced the overall time by 3.508–4.889 times, proving that sawtooth uses increased acceleration more effectively than the other three scans. The complex layer study reduced calculated time by 69.80%–139.96% and manufacturing time by 47.35%–86.85%. Sawtooth samples also exhibited less dimensional variation (0.88%) than zigzag 45° (12.94%) along the build direction.

Sawtooth is limited to flying optics AM process.

Development of scanning strategy for flying optics AM process to reduce the warpage by improving the acceleration.

The study aims to explore the available academic literature on the decision-making frameworks used in additive manufacturing management (AMM).

This research formulates a systematic literature review to determine the research trend of the decision-making framework in AMM. Further, the theory, context, characteristics, and methodology (TCCM) framework is used to identify the research gaps and suggest future research directions.

The systematic literature review (SLR) delves into overarching research themes within decision-making frameworks in AMM. Additionally, it uncovers trends in article publication, geographical distribution, methodologies utilized, and industry applications. This review not only reveals research gaps but also proposes directions for future exploration.

The key novelty of this research lies in revealing the five most contributing themes of decision-making frameworks in AMM, with the highest contributing theme being AM process selection, followed by part selection for AM. This finding enables decision-makers to make informed decisions to address similar problems while exploring AM technology. Moreover, this research introduces an AM part fabrication roadmap inspired by the literature review. Lastly, the paper highlights key research gaps for future research.