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The purpose of this paper is to describe how emerging open standards are replacing traditional industrial networks. Current industrial Ethernet networks are not interoperable; thus, limiting the potential capabilities for the Industrial Internet of Things (IIoT). There is no forthcoming new generation fieldbus standard to integrate into the IIoT and Industry 4.0 revolution. The open platform communications unified architecture (OPC UA) time-sensitive networking (TSN) is a potential vendor-independent successor technology for the factory network. The OPC UA is a data exchange standard for industrial communication, and TSN is an Institute of Electrical and Electronics Engineers standard for Ethernet that supports real-time behaviour. The merging of these open standard solutions can facilitate cross-vendor interoperability for Industry 4.0 and IIoT products.

A brief review of the history of the fieldbus standards is presented, which highlights the shortcomings for current industrial systems in meeting converged traffic solutions. An experimental system for the OPC UA TSN is described to demonstrate an approach to developing a three-layer factory network system with an emphasis on the field layer.

From the multitude of existing industrial network schemes, there is a convergence pathway in solutions based on TSN Ethernet and OPC UA. At the field level, basic timing measurements in this paper show that the OPC UA TSN can meet the basic critical timing requirements for a fieldbus network.

This paper uniquely focuses on the specific fieldbus standards elements of industrial networks evolution and traces the developments from the early history to the current developing integration in IIoT context.

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.

Digital, Internet‐enabled assembly line and factory modeling is essential for creating hardware and software independent, model‐driven system designs that can be implemented in a variety of different ways on a global‐basis in different countries and industries based on constraints such as cost, quality, productivity, real time responsiveness, technological support, culture, climate, risk factors, and others. In order to make our model‐driven approach practical, we follow an analytical, quantitative and open‐source computational method and use IBM's Rational Rose, UML (unified modeling language). This is an industry‐standard, platform‐independent, object‐oriented software and system analysis/design and documentation method, as well as a language for specifying, constructing, visualizing and documenting complex systems. To illustrate our Internet‐enabled factory design and modeling approach we demonstrate in‐depth examples of our own Internet‐enabled factory system designs, including class diagrams, use case and activity diagrams, and even some automatically generated Java code.

Facing keen worldwide competition, it is not enough for companies to pursue customer satisfaction; they must actively pursue customer delight. This paper seeks to design a work‐in‐process (WIP) exception handling system (WIPEHS) not simply measuring on‐time delivery performance for managers to take necessary improvement activities. It helps managers detect abnormal WIP levels in advance, trigger rectifying actions and finally notify pertinent people to coordinate roots causes and preventive means.

The structure of WIPEHS is proposed and then constructed with a soft package, Vigilance. A typical semiconductor factory is built and production data are simulated to evaluate the effectiveness of WIPEHS.

Collecting and analyzing results from the simulated typical semiconductor factory, the paper finds that the proposed system can effectively improve on‐time delivery performance; and that durations from a WIP exception detected a WIP exception back to normal and durations between two successive WIP exceptions significantly.

It helps factories outperform due dates, achieving significantly higher performance than prior performance without the production exception handling system, which should greatly please customers.

The proposed WIPEHS provide a total solution for undesirable production variations potentially harmful to due‐date performance. It anticipates WIP exception, notifies pertinent recipients, tracks the progress of exception resolution, and provides a forum for discussion of the root causes.

The authors’ main objective is to examine the resource alteration underlying the digital manufacturing transformation. The authors rely on the adaptation aspect of dynamic capabilities (DC) theory and their analysis shows how and why a factory adapts its resources and capabilities during digital transformation.

To grasp the change, the authors apply the longitudinal case study method within a revelatory case setting. The digital transformation is detailed from the perspective of a subsidiary that has played a key role in the division's digital transformation.

Analysing the revealed four stages of the transformation through the lenses of the DC components of adaptation (sensing capability, absorptive capacity, integrative capability, relational capability), this study suggests a sequence with unbalanced characteristics. Each stage starts with sensing capability, each component appears during each stage and each stage is dominated by a different component. Relying on the path dependency concept, the authors also present that the interplay between lean as an old resource stock and digital manufacturing as a new resource stock is rather a necessity, especially at the beginning of the transformation (at a corporation that pursues lean for years).

Digital strategy development is rather an intermediate element of the transformation, since committed personnel (or maybe their network) start bottom-up and coordinate initiatives as they sense the opportunities in the environment. Top managers should rely on their accumulated knowledge and involve them into the transfer coalition in the top-down phase of digitalization. The authors’ case also underlines that starting to experiment with novel technologies requires a solid (and usually expensive) technological and human basis. Finally, process improvement focussed developments at a high-performing factory might be just enough to deal with ever-demanding customer expectations.

This study is among the firsts in operations management that relies on the DC theory to follow up the digital transformation of a factory. A further valuable contribution is that the adaptation process is examined in a longitudinal case study.

This study aims to investigate the relationship between building smart factories in manufacturing small- and medium-sized enterprises (SMEs) and firm performance and the moderating effect according to product complexity and company size.

Data were collected from 206 companies selected in the list of SMEs, which had built smart factories, provided by the Smart Manufacturing Innovation Center in Korea. The collected data were analyzed using structural equation modeling (SEM) technique.

First, production automation and big data utilization are associated positively with productivity, but not significantly with export performance. Second, supply chain integration is associated positively with both productivity and export performance. Third, product complexity moderates negatively the relationship of productivity with each of production automation, big data utilization and supply chain integration while moderating positively the relationship between supply chain integration and export performance. Finally, company size does not moderate significantly the relationship between productivity or export performance with any of production automation, big data utilization and supply chain integration.

This study contributes theoretically to literature by demonstrating the usefulness of building smart factories and suggesting how SMEs build a smart factory to enhance productivity and export performance from a business perspective. Moreover, this study contributes practically by proposing that SMEs should put priority on supply chain integration over production automation and big data utilization and execute different strategies of building smart factories depending on product complexity.

Industry 4.0 implies that global challenges exist within the manufacturing sector. Both theoretical and empirical research has been developed to support these transformations and assist companies in the process of changing. The purpose of this paper is to gather previous articles through an updated review and defines a research agenda for future investigation based on the most recent studies published in the field.

Key articles on the subject are analysed. The articles were published in 39 journals from which 107 papers dating from 2005 to 2018 have been selected.

The main findings imply the definition of a research agenda where: a common terminology should be created; the levels of implementation of Industry 4.0 should be defined; the stages of the development of Industry 4.0 should be identified; a lean approach for this industry is defined and the implications of Industry 4.0 in either a sustainable or circular economy should be understood; the consequences of human resources should be analysed; and the effects of the smart factory in the organisation are the areas identified and studied in the mentioned research agenda.

This review has some limitations. First, a number of grey literature, such as reports from non-governmental organisations and front-line practitioners’ reflections, were not included. Second, only research studies in English and Spanish were reviewed.

This review helps practitioners in their implementation of Industry 4.0. Moreover, the identified future research areas may help to define priorities in this implementation.

After examining previous research, this paper proposes a research agenda covering issues about Industry 4.0. This research agenda should guide future investigations in the smart industry.

This paper aims to develop a conceptual framework of the implementation of the contact points (CPs) between Lean Six Sigma practices and Industry 4.0 technologies.

A systematic literature review was carried out based on two samples. A first sample containing 78 articles was analyzed through bibliometric indicators. After that, a second sample of 33 articles was analyzed in-depth according to research questions.

The conceptual framework involves 13 CPs between Lean Six Sigma (LSS) practices and I4.0 technologies (what), going through the technical requirements needed (how), categorized as information technology (IT), automation and competence requirements, to finally present the main results reported in the literature (why).

This paper presents an innovative perspective of interactions between digital technologies and LSS practices, expanding knowledge about Digital LSS. Such perspective gives emphasis to the importance of technical requirements, such as communication and connectivity protocols, network topology, machine-to-machine communication (M2M), human–machine interfaces (HMI), as well as analytical and digital skills.

The managerial implications regarding the digitalization of LSS practices address the investments required for the acquisition and maintenance of cyber-physical systems (CPS). Moreover, there is a need for the development of skills so that operators can successfully use the new technologies in a context of continuous improvement.

This paper presents a conceptual framework covering 13 CPs between LSS practices and Industry 4.0 technologies, the technical requirements and the expected results. It is hoped that this framework can assist future research and operational excellence projects towards digitalization.

The purpose of this paper is to identify the relationships between process modeling and Industry 4.0, the strategic themes and the most used process modeling language in smart factories. The study also presents the growth of the field of study worldwide, the perspectives, main challenges, trends and suggestions for future works.

To do this, a science mapping was performed using the software SciMAT, supported by VOS viewer.

The results show that the Business Process Model and Notation (BPMN), Unified Modelling Language (UML) and Petri Net are the most relevant languages to smart manufacturing. The authors also highlighted the need to develop new languages or extensions capable of representing the dynamism, interoperability and multiple technologies of smart factories.

It was possible to identify the most used process modeling languages in smart environments and understand how these languages assist control and manage smart processes. Besides, the authors highlighted challenges, new perspectives and the need for future works in the field.

The purpose of this paper is to examine (1) how the recovery speed using promotional investment and (2) distributed production using additive manufacturing (AM) improve the resilience of the supply chain to manage any disruptions in the diffusion of green products.

The environmental performance, service level performance and economic performance are the measures of interest. These measures are studied through the integration of inventory and production planning (I&PP) of the reverse logistics system and consumer behavior using Bass (1969) model of diffusion of innovation under the paradigm of Industry 4.0 architecture. The Taguchi experimental design framework was used for the simulation analysis.

The adoption patterns based on the Bass model in conjunction with recovery speed and production on AM during the disruption period suggest that there exist tradeoff decisions between various combinations of information-sharing and I&PP policies.

The extensive sensitivity analyses provide real-time support for managerial decisions. Besides the potentials of Industry 4.0 capabilities, the present research suggests paying close attention to the recovery speed in conjunction with the inventory management system.

The integration of consumers' behavior (Bass model) to digital technologies is an additional contribution of the present research toward sustainability issues from the social perspective.

Previous research studies have discussed resilience to manage the ripple effect. However, none of them have addressed the changing scope of resilience to manage the ripple effect caused by the disruption in the diffusion of green products in a reverse logistics setup.