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Industry 4.0 has put forward a smart perspective on managing supply chain networks and their operations. The current manufacturing system is primarily data-driven. Industries are deploying new emerging technologies in their operations to build a competitive edge in the business environment; however, the true potential of smart manufacturing has not yet been fully unveiled. This research aims to extensively analyse emerging technologies and their interconnection with smart manufacturing in developing smarter supply chains.

This research endeavours to establish a conceptual framework for a smart supply chain. A real case study on a smart factory is conducted to demonstrate the validity of this framework for building smarter supply chains. A comparative analysis is carried out between conventional and smart supply chains to ascertain the advantages of smart supply chains. In addition, a thorough investigation of the several factors needed to transition from smart to smarter supply chains is undertaken.

The integration of smart technology exemplifies the ability to improve the efficiency of supply chain operations. Research findings indicate that transitioning to a smart factory radically enhances productivity, quality assurance, data privacy and labour efficiency. The outcomes of this research will help academic and industrial sectors critically comprehend technological breakthroughs and their applications in smart supply chains.

This study highlights the implications of incorporating smart technologies into supply chain operations, specifically in smart purchasing, smart factory operations, smart warehousing and smart customer performance. A paradigm transition from conventional, smart to smarter supply chains offers a comprehensive perspective on the evolving dynamics in automation, optimisation and manufacturing technology domains, ultimately leading to the emergence of Industry 5.0.

The purpose of this paper is to explore and disseminate knowledge about quantum-inspired computing technology's potential to solve complex challenges faced by the operational agility capability in Industry 4.0 manufacturing and logistics operations.

A multi-case study approach is used to determine the impact of quantum-inspired computing technology in manufacturing and logistics processes from the supplier perspective. A literature review provides the basis for a framework to identify a set of flexibility and agility operational capabilities enabled by Industry 4.0 Information and Digital Technologies. The use cases are analyzed in depth, first individually and then jointly.

Study results suggest that quantum-inspired computing technology has the potential to harness and boost companies' operational flexibility to enhance operational agility in manufacturing and logistics operations management, particularly in the Industry 4.0 context. An exploratory model is proposed to explain the relationships between quantum-inspired computing technology and the deployment of operational agility capabilities.

This is study explores the use of quantum-inspired computing technology in Industry 4.0 operations management and contributes to understanding its potential to enable operational agility capability in manufacturing and logistics operations.

The purpose of this paper is to investigate, from a thorough review of the literature, the role of metaverse-based quality 4.0 (MV-based Q4.0) in achieving manufacturing resilience (MFGRES). Based on a categorization of MV-based Q4.0 enabler technologies and MFGRES antecedents, the paper provides a conceptual framework depicting the relationship between both areas while exploring existing knowledge in current literature.

The paper is structured as a comprehensive systematic literature review (SLR) at the intersection of MV-based Q4.0 and MFGRES fields. From the Scopus database up to 2023, a final sample of 182 papers is selected based on the inclusion/exclusion criteria that shape the knowledge base of the research.

In light of the classification of reviewed papers, the findings show that artificial intelligence is especially well-suited to enhancing MFGRES. Transparency and flexibility are the resilience enablers that gain most from the implementation of MV-based Q4.0. Through analysis and synthesis of the literature, the study reveals the lack of an integrated approach combining both MV-based Q4.0 and MFGRES. This is particularly clear during disruptions.

This study has a significant impact on managers and businesses. It also advances knowledge of the importance of MV-based Q4.0 in achieving MFGRES and gaining its full rewards.

This paper makes significant recommendations for academics, particularly those who are interested in the metaverse concept within MFGRES. The study also helps managers by illuminating a key area to concentrate on for the improvement of MFGRES within their organizations. In light of this, future research directions are suggested.

This study aims to analyze the determinants influencing Chinese manufacturing companies in implementing lean manufacturing (LM).

The determinants to be explored in this study consist of technological, organizational and environmental (TOE) dimensions. A questionnaire survey was conducted on Chinese manufacturing companies, and 208 samples were analyzed.

The findings show that the relative advantage of LM and organizational support have significantly positive effects on Chinese manufacturing firms’ adoption of LM. The complexity of LM, quality of human resources, organizational readiness, customer pressure, international situation, governmental support and environmental uncertainty do not have significant effects.

This paper contributes to the literature by using the TOE model to explore the factors influencing LM adoption in the Chinese manufacturing industry.

This study aims to investigate the implementation of lean human resource management (HRM) practices in manufacturing small- and medium-sized enterprises (SMEs) and explore how various factors interact to influence their successful adoption. By exploring the interplay among these factors, the research seeks to identify key drivers affecting the adoption of lean HRM in manufacturing SMEs. Ultimately, the research intends to provide insights that can guide organisations, practitioners and policymakers in effectively implementing lean HRM practices to enhance operational efficiency, workforce engagement and competitiveness within the manufacturing SME sector.

The study combined total interpretive structural modelling (TISM) and Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis. TISM helped in understanding the hierarchical relationship among different factors influencing lean HRM implementation, whereas MICMAC analysis provided insights into the level of influence and dependence of each factor on others.

The research revealed that “top management support” emerged as the most independent factor, indicating that strong support from top management is crucial for initiating and sustaining lean HRM practices in manufacturing SMEs. On the other hand, “employee involvement and empowerment” was identified as the most dependent factor, suggesting that fostering a culture of employee engagement and empowerment greatly relies on the successful implementation of lean HRM practices.

While the study provided valuable insights, it has certain limitations. The research was conducted within the specific context of manufacturing SMEs, which might limit the generalizability of the findings to other industries. Expert opinions introduce subjectivity in data collection. Additionally, the study may not cover all critical factors, allowing room for further exploration in future research.

The findings have practical implications for manufacturing SMEs aiming to implement lean HRM practices. Recognising the pivotal role of top management support, organisations should invest in cultivating a strong leadership commitment to lean HRM initiatives. Furthermore, enhancing employee involvement and empowerment can lead to better adoption of lean HRM practices, resulting in improved operational efficiency and overall competitiveness.

This research contributes to the field by offering a comprehensive exploration of the interplay among factors influencing lean HRM implementation. The use of TISM and MICMAC analysis provides a unique perspective on the relationship dynamics between these factors, allowing for a nuanced understanding of their roles in the adoption of lean HRM practices in manufacturing SMEs. The identification of “top management support” as the most independent and “employee involvement and empowerment” as the most dependent factors adds original insights to the existing literature.

The purpose of this paper is to examine the digitalization of operational processes and activities in lean manufacturing firms and explore the associated learning implications through the lens of cumulative capability theory.

Adopting a multiple-case design, we examine four cases of digitalization initiatives within lean manufacturing firms. We collected data through semi-structured interviews and direct observations during site visits.

The study uncovers the development of learning capabilities as a result of integrating lean and digitalization. We find that digitalization in lean manufacturing firms contributes to the development of both routinized and evolutionary learning capabilities in a cumulative fashion.

The study adds nuance to the limited theoretical understanding of the integration of lean and digitalization by showing how it cumulatively develops the learning capabilities of lean manufacturing firms. As such, the study supports the robustness of cumulative capability theory. We further contribute to research by offering empirical support for the cumulative nature of learning.

In response to mounting global concerns about climate change and scarcity of natural resources, manufacturers have been pressured to develop strategies and enhance their sustainability performance. The integration of sustainable lean manufacturing (SLM) during value chain processes could balance environmental, social and economic concerns into their decision-making, which not only ensures responsible practices but also drives efficiency and success. This paper aims to identify, measure and prioritize metrics to develop a performance measurement system that assesses the multi-dimensional performance of SLM.

Strategic decision-making has some conflicting criteria and objectives to be considered simultaneously. The Multi-Criteria Decision Making provides a foundation for selecting, sorting and prioritizing these strategies with the determination of drivers and indicator weight.

The performance model enables the decision-makers to consistently evaluate the level of sustainability through a multidimensional framework, which could support the assessment of the existing sustainability of a manufacturing process and analyze opportunities for improvement. This study divided the performance into five drivers: Quality, Operational, Finance, Environment, Safety and People and selected 17 KPIs for assessing the multi-dimensional performance of SLM organizations. The research results revealed an organization's perspective transition from strategies focused on operational and economic performance to a more sustainable ideal with greater importance for social and environmental directions.

This framework will be facilitated by the selection of the most significant drivers and the development of strategic plans for the successful adoption of sustainable manufacturing. The practices support implementation, pursue competitive advantages and sustain manufacturing, meeting strategic requirements of suitable and lean performance. With the limited resources of the organizations, the framework proposed will guide the priorities and actions to be taken toward the SLM.

The purpose of this study is to understand how manufacturers (both discrete and process) are managing disruptions amid the COVID-19 pandemic outbreak, using UAE as an empirical context.

This research uses a multiple case study approach and undertakes 36 semi-structured interviews with senior management of four discrete and four process manufacturing firms that outsource products/components from overseas and domestic suppliers.

Results reveal that manufacturing firms are using six distinct actions to mitigate the effects of the COVID-19 pandemic. For instance, they are relying on the automation process, transferring new and updated knowledge to the current and new suppliers, managing workforce diversity, understanding the impact of demand’s disruption, managing the ecosystem and finally using digital technologies to mitigate the impact of the COVID-19 pandemic.

This study has some limitations. Firstly, the results of this study cannot be generalized to a broader population as it attempts to build an initial theory in manufacturing supply chains within the context of a pandemic outbreak. Second, the study uses a cross-sectional approach to explore the actions used by manufacturing firms to mitigate the effects of the COVID-19 pandemic.

Manufacturing firms can replicate the actions proposed in this study to lessen the effect of the COVID-19 pandemic and emerge stronger in the post-COVID-era.

This study contributes to the manufacturing supply chain literature within the context of pandemic outbreaks by exploring the steps taken by manufacturing firms to minimize the effects of the COVID-19 pandemic. Particularly, it explores such steps by considering both the discrete and process manufacturing industries within the United Arab Emirates.

The purpose of this study is to discuss the construction of a structural measurement model utilizing structural equation modelling (SEM) to confirm the link between Industry 4.0 technologies, sustainable manufacturing practices and organizational sustainable performance. Relationship among the paradigm has yet to be fully investigated, necessitating a more conceptual and empirical examination on what impact they have on organizational sustainable performance when used together.

Industry 4.0 and sustainable production practices aim to progress a company's business competitiveness, forming sustainable development that benefits manufacturing companies. The aim of the study is to analyze the relationship between constructs that lead to operational excellence in firms that use Industry 4.0 technologies and sustainable manufacturing techniques. Experts from diverse automotive industries, who are applying both Industry 4.0 and sustainable manufacturing practices, provided data for the study.

Statistical estimations (hypotheses) are created to substantiate the measurement model that has been developed. The structural model was analysed, and the findings were discussed. The statistical estimate is either approved or rejected based on the findings. According to the conclusions of this study, strong link exists between Industry 4.0 technologies and sustainable manufacturing practices that affect organizational sustainable performance environmentally, economically and socially.

The research was conducted in the framework of automobile component manufacturing companies in India. The outcomes of the study are practically feasible.

The authors' novel contribution is the construction of a structural model with Industry 4.0 technologies and sustainable manufacturing practices into account.

This study aims to investigate the manufacturing reshoring decision-making content from an Eclectic Paradigm perspective.

Data were collected through a six-step systematic literature review on factors influencing manufacturing reshoring decision-making. The review is based on 100 peer-reviewed journal papers discussing reshoring decision-making contents published from 2009 to 2022.

In total, 80 decision factors were extracted and then categorized into resource-seeking (8%), market-seeking (11%), efficiency-seeking (41%) and strategic asset-seeking (16%) advantages. Additionally, 24% of these were identified as hybrid, which means that they were classified into multiple categories. Some decision factors were further identified as reshoring influencing factors (i.e. drivers, enablers and barriers).

Scholars need to consider what other theories can be used or developed to identify and evaluate the decision factors (determinants) of manufacturing reshoring as well as how currently adopted theory can be further advanced to create clearer and comprehensive theoretical frameworks.

This research underscores the importance of developing clearer and more comprehensive theoretical frameworks. For practitioners, understanding the multifaceted nature of decision factors could enhance strategic decision-making regarding reshoring initiatives.

To the best of the authors’ knowledge, this is the first study to investigate the value and practicality of the Eclectic Paradigm in categorizing factors in manufacturing reshoring decision-making content and presents in-depth theoretical classifications. In addition, it bridges the gap between decision factors and influencing factors in the decision-making content research realm.