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The imperative to conserve resources and minimize operational expenses has spurred a notable increase in the adoption of lean manufacturing within the context of the circular economy across diverse industries in recent years. However, a notable gap exists in the research landscape, particularly concerning the implementation of lean practices within the pharmaceutical industry to enhance circular economy performance. Addressing this void, this study endeavors to identify and prioritize the pivotal drivers influencing lean manufacturing within the pharmaceutical sector.

The outcome of this rigorous examination highlights that “Continuous Monitoring Process for Sustainable Lean Implementation,” “Management Involvement for Sustainable Implementation” and “Training and Education” emerge as the most consequential drivers. These factors are deemed crucial for augmenting circular economy performance, underscoring the significance of management engagement, training initiatives and a continuous monitoring process in fostering a closed-loop practice within the pharmaceutical industry.

The findings contribute valuable insights for decision-makers aiming to adopt lean practices within a circular economy framework. Specifically, by streamlining the process of developing a robust action plan tailored to the unique needs of the pharmaceutical sector, our study provides actionable guidance for enhancing overall sustainability in the manufacturing processes.

This study represents one of the initial efforts to systematically identify and assess the drivers to LM implementation within the pharmaceutical industry, contributing to the emerging body of knowledge in this area.

The purpose of this paper is to identify the constituent parts of learning in the manufacturing work context and understand why these parts are key in the learning of the employees.

The data was collected from two sources: a literature review of the Information Systems literature to establish an initial picture of what learning in relation to digital technologies entails and in-depth interviews with engineers in the automotive industry whose knowledge-intensive work is exposed to substantial digital transformation.

The authors first identified three constituent parts for learning: change, reflection and deliberation. When the authors cross-checked the initial findings through in-depth interviews with the engineers, it was found that these three themes trigger learning through three different mechanisms, that is, balancing newness, finding point of reference and organizing actively. Thus, the findings of this paper extend beyond a categorical identification of what constitutes learning to also illustrate why learning entails these constituent parts.

This paper implies that progressive learning requires active organizing of learning stages. The data is limited to the review of the Information Systems field. The authors have also only focused on the automotive industry as the representative sector in the manufacturing industry.

Applying the model of progressive learning can be a primary way to actively plan and organize learning opportunities for employees. This is key for supporting learning culture in organizations that are exposed to continuous and disruptive changes.

A significant part of social sustainability is based on sustainable employability and feelings of contentment at work. This paper is an attempt to highlight how sustainable employability can be achieved by providing effective learning opportunities at work.

The originality of this paper emerges from two sources. First, the authors conducted the literature review and in-depth interviews by devising innovative methods because of the challenges of identifying when (informal) learning has occurred at work. Second, the authors owe the in-depth interviews to the first author’s extensive familiarity with the automotive industry and the knowledge and rapport acquired through her prior longitudinal research on the engineers’ work. It was this background that allowed the authors to find out when these engineers were about to leave the firm because of discontent about their competence development.

The purpose of this paper is to discover how Hungarian manufacturing companies interpret technology and human resources as driving forces and barriers in terms of Industry 4.0 implementation.

The authors conducted 23 semi-structured interviews with corporate leaders and applied qualitative content analysis using Atlas.ti software.

The authors formulated a new definition of Industry 4.0 which emphasises the role of human factors. The authors identified driving forces (efficiency with speed/information flow/precision) and barriers (technology compatibility, human fears and lack of digital skills) in terms of Industry 4.0 implementation and developed the DIGI-TEcH performance management dimensions.

Comparison with other countries is limited. Given the exploratory and qualitative nature, further quantitative research would be needed to generalise results. Finally, only manufacturing companies are examined.

It provides empirical evidence to practitioners to understand concerns about technology and human resource in terms of Industry 4.0 implementation. In addition, corporate performance management can be extended by the developed DIGI-TEcH dimensions.

This paper reveals key evidence for the uptake of technology and human factors in terms of Industry 4.0 implementation and their impacts on corporate operation and performance. It also provides an insight into a specific country context, which can be a useful benchmark for other Central and Eastern European countries.

Information-facilitated product recovery system (IFPRS) has captivated industry attention and has developed into a matter of consideration among the researchers because of enhanced climate concerns, jurisdictive logics and societal liabilities. Although IFPRS implementation has become an essential aspect in manufacturing industries functional in the developed nations, still, limited consideration has been given in the literature to analyze the issues to IFPRS implementation for a circular economy (CE) in emerging and developing nations. Therefore, the objective of this study is to recognize issues to implementing IFPRS for a CE in context of select manufacturing industries in India.

In this study, 24 potential issues are established from the literature and from suggestions from the experts. The issues are clubbed under five different perspectives of technical, government, organization, policy and knowledge. Further, fuzzy VIKOR technique is applied on the results obtained to prioritize the identified issues. A sensitivity analysis has been carried out to check the robustness of the framework.

The present study shows that lack of skills and expertise in IFPRS implementation for a CE (I2), deficient capital to implement a CE in IFPRS (I₉), inadequate in adopting recent IT technology (I₁₈), feasibility of IFPRS employment for a CE (I₆) and no efficient training and program to CE adoption (I₂₁) are the top five potential issues in implementing IFPRS practices for a CE in Indian manufacturing industries.

In literature, limited study has been observed on determining issues to implementation of IFPRS for a CE. A more systematic method and statistical confirmation is necessary to establish further new confronting issues. This study is limited to Indian manufacturing industries.

The main contribution of this study includes identification of issues and later prioritizing them to reflect their severity. This would help the industry practitioners to formulate strategies for handling the issues conveniently.

This research aims to identify and organise the conditions of organisational readiness for digital transformation.

This qualitative study comprises three case studies within manufacturing companies from different sizes and industries located in Sweden. Plant visits and in-depth interviews bring to light companies' experiences with initial steps towards digital transformation. A set of conditions for digital organisational readiness was translated into a questionnaire and tested with one of the studied companies.

This paper organises and tests digital organisational readiness conditions to support companies' initial steps on digital transformation. The results are put in perspective of established change management theory and previous studies about digital transformation. The findings will conclude in a questionnaire to support dialogue and digital organisational readiness assessments.

Additional conditions for the initial phase of digital transformation could possibly be found if more cases had been included in the study.

The article identifies a set of conditions translated into a questionnaire that should be used as a dialogue tool to create strategic alignment and support companies in their initial discussions. If this process can be faster and more efficient, the company can achieve a competitive advantage against competitors.

This research's relevance relies on the fact that companies are advancing in adopting digital technologies without being ready from an organisational perspective. This gap creates barriers for companies' digital maturing processes, stopping them from having full access to digital technologies' benefits.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

The aim of this paper is to explore whether open innovation is emerging in the Italian fashion industry.

Based on available studies on innovation and open innovation, we first identified the main facets of open innovation within the industry investigated, such as the process of searching for new ideas, the involvement of external partners in the new product development process and the use of collaboration mechanisms between supply chain partners. Starting from these findings, the authors designed a semi-structured questionnaire that was used as a guideline for 15 case studies, carried out in the Italian fashion industry.

The outcomes from the case studies allow drawing some conclusions about the emergency of open innovation in the fashion industry and the related patterns.

Given its exploratory nature, this study is expected to start a debate about open innovation in the fashion industry, as well as to encourage future studies in this field.

This article aims to contribute to the digital servitisation literature by investigating the interrelations amongst Industry 4.0 technologies, servitisation and the performance of manufacturing small and medium-sized enterprises (SMEs).

The research uses survey data drawn from 200 manufacturing SMEs operating in the metals and machinery sector in Italy.

The study shows that Industry 4.0 technologies – Internet of Things (IoT), advanced simulation, cloud computing and Big Data Analytics (BDA) – positively moderate the relationship between servitisation and the performance of SMEs.

The study supports the need for firm managers of manufacturing SMEs to align servitisation and technological investments, suggesting that the synergic deployment of Industry 4.0 technologies supports servitisation performance.

The study supports the need for firm managers operating in business-to-business contexts to align their technological investments and servitisation strategies, suggesting that the synergic deployment of these Industry 4.0 technologies empower the effectiveness of servitisation strategies in terms of performance achieved.

The study highlights the moderating role played by specific Industry 4.0 technologies in the servitisation–performance relationship, opening avenues for future research exploring the mechanisms that underpin this complex relationship.

Industry 4.0 is assumed to yield extensive industry-spanning opportunities. However, exploiting these opportunities requires a targeted implementation of Industry 4.0. The purpose of this paper is to generate a deeper understanding of relevant implementation action. Existing recommendations are mostly general, highly aggregated and difficult to grasp. Yet, specific and concrete actions that need to be taken to accelerate the realization of Industry 4.0 are essential.

The article uses 13 semi-structured in-depth expert interviews as the source of empirical data. The interviews were conducted with managers from Industry 4.0-experienced German manufacturing companies. All interviews are analyzed using qualitative content analysis.

The study reveals relevant and targeted aspects for Industry 4.0 implementation: the development of Industry 4.0-specific know-how, securing financial resources, integrating employees into the implementation process and establishing an open-minded and flexible corporate culture. Further aspects include comprehensive planning processes, cooperation with external partners, proper handling of data interfaces, interdisciplinary communication, an adaptable organizational structure and data security.

The paper is limited to German manufacturing enterprises and should be transferred to other industries and countries.

The study supports managers to effectively implement Industry 4.0 within their organizations and consequently benefit from Industry 4.0 and derives recommendations for future research.

The paper is among the first to give specific and concrete examples for lessons learned from Industry 4.0 implementation, directly obtained from industrial application.