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The purpose of this paper is to identify actions and guidelines for enabling and fostering the Industry 4.0 adoption, as well as to understand the role of three ecosystem actors in these actions (i.e. companies, educational organizations and regional policy makers).

52 semi-structured expert interviews in the Tyrol-Veneto cross-border macro-region were carried out and interpreted using the innovation ecosystem concept. In particular, drawing from this latter, six ecosystem building blocks were identified and used to analyze the interviews' content.

The findings allow not only to build a comprehensive framework for action to support Industry 4.0 adoption, but also to confirm the importance of exploring Industry 4.0 through the lens of the ecosystem concept. Indeed, the authors show that R&D activities should be complemented with interorganizational actions, such as training and networking, and that all ecosystem actors should be involved in the Industry 4.0 adoption.

This is among the few studies that adopt the innovation ecosystem perspective to explore best practices for Industry 4.0 adoption, thus overcoming the weakness of existing papers based on a firm-level perspective. It also complements previous ecosystem-based research on Industry 4.0 by exploring the technology adoption side, rather than the technology provision one, and by considering the adoption of a wide set of technologies.

The purpose of this paper is to provide a methodological guidance for the practical use of the axiomatic designed production module template presented in a former publication. The objective is to accelerate the design process and increase the quality of results in the design of lean production systems.

Two case studies based on practical cases were presented to different test teams. A first test cycle helped to improve the user friendliness of the axiomatic designed tree of functional requirements and design parameters. The second test cycle served to prove the practicability of the template, comparing the teams' results with the realized solution.

Based on the teams' feedbacks, ten “easy‐to‐use” steps for the systematic design of lean production systems were developed. The guideline obtains the best results if used in combination with the value stream mapping concept.

Apart from one case study in injection moulding, practical evaluations were focused on applications in the field of manual, hybrid or automated assembly systems, which perhaps limits the applicability of the presented approach in some machining processes.

Several successful implementations demonstrated the validity of the presented method in terms of results, planning time and user friendliness. Even students with nearly no practical experiences in production system design were able to present astonishing results within short timeframes.

This paper fulfils an identified need of a methodological guidance in the design of lean production systems and offers practical help to shorten the design times and improve the quality of the design results.

The purpose of this paper is to develop and test a design approach based on the investigation of the sensitivity of assembly systems to volume fluctuations as part of the selection process of alternative design solutions for scalable assembly systems on the basis of a real industrial case study.

A conceptual approach for the (re‐)design of a scalable assembly system is developed on the basis of an industrial case research using axiomatic design (AD) for the top level structuring of the framework incorporating useful methods and insights obtained from a thorough literature review and from previous research work.

The findings of this research are limited due to the focused nature of a case study based research. However, the obtained results encourage assuming its transferability to similar problems.

Significant research has been done in the design of assembly systems for high product variety, but the review of literature in this field still identifies many opportunities for future research. This paper responds to the clearly identified research need of a methodological guidance regarding the design of scalable assembly systems and offers a practically proven help to improve the efficiency of the design process and the quality of the design results.

One of the achievements of the fourth industrial revolution is smart manufacturing, a manufacturing system based on Industry 4.0 technologies that will increase systems' reliability, efficiency and productivity. Despite the many benefits, some barriers obstruct the implementation of this manufacturing system. This study aims to analyze these barriers.

One of the measures that must be taken is to identify and try to remove these barriers, which involves identifying the stakeholders and components of technology associated with each barrier. As such, the primary purpose of this paper is to present a systematic literature review in the field of smart manufacturing with a focus on barriers to implementation related to the stakeholders and components of technology.

This research conducted a systematic literature review in Scopus and Web of Science databases and considered the studies published until 2021 were examined. The central question of this paper is answered based on this literature review, in which 133 related studies and 15 barriers were identified.

The significant gap observed in the literature review is that no research has been conducted to determine the stakeholders and components of technology related to the barriers, making it a potentially worthwhile subject for future research. In addition, the results of this study may help managers to implement smart manufacturing.

This study provides two main originalities. The former is helpful information for managers to make effective decisions when they face smart manufacturing barriers. The latter is related to identifying critical research gaps through systematic literature review.

The purpose of this paper is to develop a preliminary conceptual scale for the measurement of distributed manufacturing (DM) capacity of manufacturing companies operating in rubber and plastic sectors.

A two-step research methodology is employed. In first step, the dimensions of DM and different levels of each dimension have been defined. In second step, an empirical analysis (cluster analysis) of database firms is performed by collecting the data of 38 firms operating in Italian mould manufacturing sector. Application case studies are then analyzed to show the use of the proposed DM conceptual scale.

A hyperspace, composed of five dimensions of DM, i.e. manufacturing localization; manufacturing technologies; customization and personalization; digitalization; and democratization of design, is developed and a hierarchy is defined by listing the levels of each dimension in an ascending order. Based on this hyperspace, a conceptual scale is proposed to measure the positioning of a generic company in the DM continuum.

The empirical data are collected from Italian mould manufacturing companies operating in rubber and plastic sectors. It cannot be assumed that the industrial sectors in different parts of the world are operating under similar operational, regulatory and economic conditions. The results, therefore, might not be generalized to manufacturing companies operating in different countries (particularly developing countries) under different circumstances.

This is first preliminary scale of its kind to evaluate the positioning of companies with respect to their DM capacity. This scale is helpful for companies to compare their capacity with standard profiles and for decision making to convert the existing manufacturing operations into distributed operations.

Technology has disrupted many industries from the start of Industrialization era to the Industry 4.0 era. There has been an exponential growth in the technological front and people are talking about Industry 5.0. Digital transformation is a critical direction in which organizations will have to move toward in order to succeed in this competitive world. To make a smooth transition, firms must understand the basic building blocks of the digital transformation process and the key areas it touches upon namely customer experience, operational process and dynamic business models. Organizations will also have to identify the enablers of digital transformation which they can work on to smoothen the transformative process. Firms will also need the framework of digital transformation spelling out the roadmap for effective digital transformation. Firms on the urge to go for digital transformation will face numerous challenges in all the stages of implementation namely the initiation phase, the execution phase and the governance phase. A clear understanding of these challenges will help the firms to overcome or mitigate these challenges and be successful in their digital transformation process.

This chapter is designed to provide an overview of the challenges facing Industry 4.0, focussing on the manufacturing sector, and highlighting the specifics of small to medium-sized enterprises. Recent technologies for data science, analysts, robotics, and other smart manufacturing trends are discussed, and the opportunities, difficulties, and limitations for breakthrough development are highlighted.

There is a growing interest in the use of HR-based Industry 4.0 technologies for equality, diversity, and inclusion (EDI) issues yet the emerging trends of Industry 4.0 in EDI implementations and interventions are not fully covered. This chapter investigates the emerging themes regarding EDI and Industry 4.0 interaction through Google-based big data that show the actual interest in Industry 4.0 and EDI. Drawing on a web analytics method that tracks the real click behaviours of web users through querying combined sets of keywords, the study explores the trends and interactions between Industry 4.0 technologies and EDI-related HR practices. Our search engine results page (SERP) analyses find a high volume of queries and a significant interest between EDI elements and artificial intelligence (AI) only. In contrast to the suggestions of the extant literature, no significant user interest in other Industry 4.0 applications for EDI implementations was observed. The authors suggest that other Industry 4.0 technologies such as machine learning (ML) and natural language processing (NLP) for EDI implementations are in their early stages.

This chapter seeks to explain the manufacturing industries that have gone a lot of transformation in recent years. The changes were brought as a result of implementation of Industry 4.0 technologies. The aim of this chapter is to study Smart Manufacturing (SM) and to implement Industry 4.0 in a Sustainable Supply Chain. The study is qualitative which employs secondary sources of data. The data of the study were sourced from relevant published articles from 2017 to 23. Also the data were analyzed using thematic analysis. The results of the study revealed that, artificial intelligence (AI) Models, Cloud Connection, Smart Product, standard communication, cyber-physical system (CPS), virtual system builder among others are the requirements for adopting SM. While Augmented Reality (AR), 3D Printing, Big Data Analytics, AI, Internet of Things (IoT), among others are the major 14.0 technologies that enable Smart Manufacturing System (SMS). However, security issues, system integration, interoperability, multilingualism, standard interface, data quality, privacy concern, investment concern are the major challenges of implementing SMS for sustainable supply chain. This study concluded that, implementing SM in sustainable supply chain have significantly improved company's productivity, innovation, efficiency, effectiveness, cost-effective manufacturing operations as well as sustainable management.