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A real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.

The architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.

The authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.

This paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.

The study aims to explore the overall growth trend, top publishing countries, co-authorship and author keywords in the field of Industry 5.0.

This study presents the outcomes of a bibliometric analysis conducted using VOSviewer software. The analysis retrieved data from the Scopus database, including citations, co-authors, keywords, bibliometric coupling and co-occurrence.

The findings reveal a significant increase in publications and citations related to Industry 5.0 in recent years. China, the USA and India emerge as the leading countries driving research in this field. The co-authorship analysis indicates limited collaboration among authors, with only 48 out of 354 authors being linked through co-authorship. Through co-occurrence analysis, the investigation identifies the most frequently occurring keywords in the research, with “Industry 5.0” and “Industry 4.0” being the most frequently co-occurring keywords. The bibliographic coupling analysis identifies six clusters of research themes.

The study solely relies on data gathered from the Scopus database for analysis on a specific date. Therefore, data from other databases collected at different times may yield different observations and findings.

This study enhances the knowledge of professionals and academia in Industry 5.0, enabling the professionals to efficiently and sustainably manage the sector.

The bibliometric analysis presented in this study provides valuable insights into the contributions made by authors, keywords and co-authors to the field of Industry 5.0. Additionally, the thematic analysis summarized in this study is a novel contribution to the field.

In the context of the Industry 4.0, this paper aims to investigate the state of the art of Italian manufacturing, focusing the attention on the implementation of intelligent predictive maintenance (IPdM) and 4.0 key enabling technologies (KETs), analyzing advantages and limitations encountered by companies.

A survey has been developed by the University of Parma in cooperation with the Italian Workers' Compensation Authority (INAIL) and was submitted to a sample of Italian companies. Overall, 70 answers were collected and analyzed.

Results show that the 54% of companies implemented smart technologies, increasing quality and safety, reducing the operating costs and sometimes improving the process' sustainability. However, IPdM was implemented only by the 37% of respondents: thanks to big data collection and analytics, Internet of Things, machine learning and collaborative robots, they reduced downtime and maintenance costs. These changes were implemented mainly by large companies, located in northern Italy. To spread the use of IPdM in Italian manufacturing, the high initial investment, lack of skilled labor and difficulties in the integration of new digital technologies with the existing infrastructure are the main obstacles to overcome.

The article gives an overview on the current state of the art of 4.0 technologies implementation in Italy: it is useful not only for companies that want to discover the implementations' advantages but also for institutions or research centres that could help them to solve the encountered obstacles.

The objective of this research is to investigate the barriers impacting the integration of Industry 5.0 (I5.0) in supply chain sustainability. By understanding these challenges, this study aims to provide valuable insights that can guide organizations in successfully implementing the transformative potential of I5.0. The ultimate aim is to improve operational efficiency and advocate for sustainable practices within supply chains.

Research has used industry expert interviews, a comprehensive literature review and the decision-making trial and evaluation laboratory approach for analysis. Industry expert interviews serve to capture first-hand insights from professionals well versed in the field, providing practical perspectives on the barriers to I5.0 adoption.

This study identifies technological challenges, organizational barriers, regulatory impediments and economic constraints as pivotal factors inhibiting the widespread adoption of I5.0 in supply chain sustainability.

This research serves as a foundation for future investigations into overcoming barriers to I5.0 adoption, guiding scholars and practitioners in refining strategies for successful implementation.

The findings offer practical insights for organizations aiming to adopt I5.0, informing decision-makers on key challenges and facilitating the development of targeted strategies to overcome them.

The social implications lie in fostering sustainable business practices through the adoption of I5.0, contributing to environmental responsibility and societal well-being.

This research contributes original insights from practitioners, policymakers and researchers in navigating the complex landscape of I5.0 adoption, ensuring meaningful contributions to both academia and industry.

Business Model Innovation is increasingly created by an ecosystem of related companies. This paper aims to investigate the transition of a manufacturing ecosystem toward electric vehicles from a business model perspective.

The authors investigate an automotive manufacturing ecosystem that is in transition toward electric and electrified vehicles, conducting semi-structured interviews with 46 informants from 27 ecosystem members.

The results reveal that the actions of several ecosystem members are driven by regulations relating to emissions. Novel requirements regarding components and complementary offers necessitate the entry of actors from other industries and the formation of new ecosystem members. While the newly emerged ecosystem has roots in an established ecosystem, it relies on new value offers. Further, the findings highlight the importance of ecosystem governance, while the necessary degree of change in the members' business models depends on their roles and positions in the ecosystem. Therefore, upstream suppliers of components must perform business model adaptation, whereas downstream providers must perform more complex business model innovation.

The paper is among the first to investigate an entire manufacturing ecosystem and analyze its transition toward electric vehicles and the implications for business model innovation.

We studied the relationship between job engagement and systematic problem solving (SPS) among shop-floor employees and how lean production (LP) and Internet of Things (IoT) systems moderate this relationship.

We collected data from a sample of 440 shop floor workers in 101 manufacturing work units across 33 plants. Because our data is nested, we employed a series of multilevel regression models to test the hypotheses. The application of IoT systems within work units was evaluated by our research team through direct observations from on-site visits.

Our findings indicate a positive association between job engagement and SPS. Additionally, we found that the adoption of lean bundles positively moderates this relationship, while, surprisingly, the adoption of IoT systems negatively moderates this relationship. Interestingly, we found that, when the adoption of IoT systems is complemented by a lean management system, workers tend to experience a higher effect on the SPS of their engagement.

One limitation of this research is the reliance on the self-reported data collected from both workers (job engagement, SPS and control variables) and supervisors (lean bundles). Furthermore, our study was conducted in a specific country, Italy, which might have limitations on the generalizability of the results since cross-cultural differences in job engagement and SPS have been documented.

Our findings highlight that employees’ strong engagement in SPS behaviors is shaped by the managerial and technological systems implemented on the shop floor. Specifically, we point out that implementing IoT systems without the appropriate managerial practices can pose challenges to fostering employee engagement and SPS.

This paper provides new insights on how lean and new technologies contribute to the development of learning-to-learn capabilities at the individual level by empirically analyzing the moderating effects of IoT systems and LP on the relationship between job engagement and SPS.

This research sought to identify the best strategy for avoiding corporate amnesia in the context of the Industry 5.0 and an aging society.

To achieve this goal, a multi-phase methodology based on analytic network process was proposed and tested in one of the biggest companies in the bakery industry.

The results highlight that online communities of practice and storytelling are the best way to avoid corporate amnesia. The most important factors are commitment, work satisfaction and organizational culture. Commitment and work satisfaction also enhance the use of online communities of practice, while work satisfaction and organizational culture foster the use of storytelling.

This article proposes a nexus between knowledge management and operations management. This research also presents a decision-making tool that can help managers determine the most appropriate strategy for avoiding corporate amnesia.

Being acquainted with both lean and action learning in theory and in practice, this study finds that the theoretical complementarity of these two research streams has traditionally been underexploited. In this conceptual paper, this study aims to advance the theoretical understanding of lean by exploring the complementarity of lean thinking and action learning leading to a proposed integrated theory of these two research streams. Target audience is the operations management research community.

By deliberately adopting a process of theorising, this paper explores, reflects upon and combines individual experiences of researching, teaching and engaging in lean and action learning as operations management scholars.

Having taken a gemba walk through the literature and practices of lean and action learning, this study views and notices a systematic and complementary relationship between the two domains. The overlapping theoretical and practical complementarities of lean and action learning suggest that these two research streams are ripe for synthesis into an integrated theory. This finding provides an opportunity to (1) progress towards an integrative design of interventions leading to more sustainable lean system adoptions and (2) add new depth to our theoretical explanation of the success and failures of lean system adoptions.

This paper contributes an original integrated theory perspective on lean and action learning.

Prior research has highlighted the pervasive importance of digital technologies in business and societal settings, but their enabling role in digital transformation, and effective forms of organization to address tensions that arise during attempts to promote it, have been insufficiently explored. Therefore, the purpose of this paper is to investigate how and why tensions affect clusters established to foster digital transformation.

Empirical data were acquired through a qualitative exploratory holistic single case study, focused on the Swedish Cluster of Forest Technology. This included interviews with informants, selected by homogeneous purposive sampling, and event observation to investigate the personal perspectives of representatives of every company engaged in the cluster, followed by a thematic analysis of their comments.

The case study revealed three major tensions, between knowledge flow, collaboration and competition, but also others that were interrelated with those major tensions, related to matters such as trust and protection of intellectual property, power equality and hierarchy, and networks that must be managed in digital transformation efforts.

The paper extends understanding of the tensions that arise, and their management, in digital transformation processes.

In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda.

Using the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda.

The results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative.

The study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities.

This research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models.

This study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda.