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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.

The complexity of manufacturing systems, on-going production and existing constraints on the shop floor remain among the main challenges for the analysis, design and development of the models in product, process and factory domains. The potential of different virtual factory (VF) tools and approaches to support simultaneous engineering for the design, and development of these domains has been addressed in the literature. To fulfil this potential, there is a need for an approach which integrates the product, process and production systems for designing and developing VF and its validation in real-life cases. This paper aims to present an integrated design approach for VF design and development, as well as a demonstration implemented in a wind turbine manufacturing plant.

As the research calls for instrumental knowledge to discover the effects of intervention on the operations of an enterprise, design science research methodology is considered to be a well-suited methodology for exploring practical usefulness of a generic design to close the theory–practice gap. The study was planned as an exploratory research activity which encompassed the simultaneous design and development of artefacts and retrospective analysis of the design and implementation processes. The extended VF concept, architecture, a demonstration and procedures followed during the research work are presented and evaluated.

The artefacts (models and methods) and the VF demonstrator, which was evaluated by industry experts and scholars based on the role of the VF in improving the performance in the evaluation and reconfiguration of new or existing factories, reduce the ramp-up and design times, supporting management decisions. Preliminary results are presented and discussed.

The concept VF model, its architecture and general methodology as an integrated design and development approach, can be adopted and used for VF design and development both for discrete and continuous manufacturing plants. The development and demonstration were limited, however, because real-time synchronisation, 3D laser scanning data and a commonly shared data model, to enable the integration of different VF tools, were not achievable.

The paper presents a novel VF concept and architecture, which integrates product, process and production systems. Moreover, design and development methods of the concept and its demonstration for a wind turbine manufacturing plant are presented. The paper, therefore, contributes to the information systems and manufacturing engineering field by identifying a novel concept and approach to the effective design and development of a VF and its function in the analysis, design and development of manufacturing systems.

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.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

This study aims to advance a behavioural approach towards understanding how managerial perception impacts the enactment of responses to risk management during the implementation of digital technologies in industrial operations and supply chains. The purpose is to investigate the influence of (digital) technology and task uncertainty on the risk perception of managers and how this impacts risk responses adopted by managers.

Following an exploratory theory elaboration approach, the authors collected more than 80 h of interview material from 53 expert interviews. These interviews were conducted with representatives of 46 German companies that have adopted digital technologies for different industrial applications within manufacturing, assembly and logistics processes.

The findings provide nuanced insights on how individual and combined sources of uncertainty (technology and task uncertainty) impact the perception of decision makers and the resulting managerial responses adopted. The authors uncover the important role played by the interaction between digital technology and human being in the context of industrial operations. The exploratory study shows that the joint collaboration between humans and technologies has negative implications for managerial risk responses regardless of positive or negative perception, and therefore, requires significant attention in future studies.

The empirical base for this study is limited to German companies (mainly small and medium size). Moreover, German culture can be characterised by a high uncertainty avoidance and this may also limit the generalizability of the findings.

Managers should critically revise their perception of different types of digital technologies and be aware of the impact of human-machine interaction. Thereby, they should investigate more systematic approaches of risk identification and assessment.

This paper focuses on the managerial risk responses in the context of digitalisation projects with practical insights of 53 expert interviews.

The purpose of this paper is to present a conceptual model that defines the essential components shaping the new Digital Supply Chains (DSCs) through the implementation and acceleration of Industry 4.0.

The scope of the present work exposes a conceptual approach and review of the key literature from 1989 to 2019, concerning the evolution and transformation of the actors and constructs in logistics and Supply Chain Management (SCM) by means of examining different conceptual models and a state-of-the-art review of Industry 4.0’s concepts and elements, with a focus on digitization in supply chain (SC) processes. A detailed study of the constructs and components of SCM, as defined by their authors, resulted in the development of a referential and systematic model that fuses the inherent concepts and roles of SCM, with the new technological trends directed toward digitization, automation, and the increasing use of information and communication technologies across logistics global value chains.

Having achieved an exploration of the different conceptual frameworks, there is no compelling evidence of the existence of a conceptual SCM that incorporates the basic theoretical constructs and the new roles and elements of Industry 4.0. Therefore, the main components of Industry 4.0 and their impact on DSC Management are described, driving the proposal for a new conceptual model which addresses and accelerates a vision of the future of the interconnectivity between different DSCs, grouped in clusters in order to add value, through new forms of cooperation and digital integration.

This research explores the gap in the current SCM models leading into Industry 4.0. The proposed model provides a novel and comprehensive overview of the new concepts and components driving the nascent and current DSCs. This conceptual framework will further aid researchers in the exploration of knowledge regarding the variables and components presented, as well as the verification of the newly revealed roles and constructs to understand the new forms of cooperation and implementation of Industry 4.0 in digitalized SCs.

The purpose of this paper is to introduce coherent Industry 4.0 definition via a rigorous analysis framework, and provide a holistic view of technological, organizational and other key aspects (variables) of Industry 4.0 along with the identification of interdependencies that co-occur between them.

The study conducts a systematic literature review using Preferred Reporting Items for Systematic Review and Meta-Analysis methodology, and includes 675 papers analyzed both quantitatively and qualitatively. The former utilizes TIBCO Statistica. Furthermore, to define Industry 4.0, the authors reviewed 52 publications.

Industry 4.0 is a multidimensional system of value creation that includes 42 groups of terms in management, organizational and business-related variables, 30 technological and manufacturing-related variables – classified into seven categories – and several interdependencies that co-occur between them.

The analyses’ outcomes are of high importance both for academia and industry practitioners, as the findings elucidate the meaning of Industry 4.0 and may be used as the basis of future research in management, production management, industrial organizations and other Industry 4.0-related disciplines. Regarding industrial companies, the publication serves as a compendium, and should support industrial businesses in the transition from traditional manufacturing into the Industry 4.0 era.

This work’s novelty and value is threefold: first, the paper introduces an Industry 4.0 definition framework based on the most popular publications in the field. Second, the paper identifies and presents Industry 4.0’s common technologies and organizational variables via a systematic and current literature review. Finally, the paper extends the ongoing discourse on Industry 4.0. For the first time in this discipline, interdependences between identified Industry 4.0 variables are presented and discussed.

This research aims to outline the key factors responsible for industry 4.0 (I4.0) application in industries and establish a factor stratification model.

This article identifies the factor pool responsible for I4.0 from the extant literature. It aims to identify the set of key factors for the I4.0 application in the manufacturing industry and validate, classify factor pool using appropriate statistical tools, for example, factor analysis, principal component analysis and item analysis.

This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries from the factor pool. This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries. Strategy, leadership and culture are found key elements of transformation in the journey of I4.0. Additionally, design and development in the digital twin, virtual testing and simulations were also important factors to consider by manufacturing firms.

The proposed I4.0 factor stratification model will act as a starting point while designing strategy, adopting readiness index for I4.0 and creating a roadmap for I4.0 application in manufacturing. The I4.0 factors identified and validated in this paper will act as a guide for policymakers, researchers, academicians and practitioners working on the implementation of Industry 4.0. This work establishes a solid groundwork for developing an I4.0 maturity model for manufacturing industries.

The existing I4.0 literature is critically examined for creating a factor pool that further presented to experts to ensure sufficient rigor and comprehensiveness, particularly checking the relevance of subfactors for the manufacturing sector. This work is an attempt to identify and validate major I4.0 factors that can impact its mass adoption that is further empirically tested for factor stratification.