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A substantial body of research has already been dedicated to exploring the benefits of digital twins, particularly in the context of meeting the growing demands of contemporary production systems. However, prior research fails to provide specific insights into the decisions, KPIs, and users associated with various time horizons and the corresponding benefits of digital twins. This ambiguity poses significant risks, jeopardizing not only the effectiveness of developing digital twins but also the intended outcomes these systems are meant to achieve. It is imperative to consider the fundamental question of the purpose and intended beneficiaries of digital twin development from the preliminary stage in order to guide their development and increase their usability. The study focused on a co-developed digital twin with a Swedish global manufacturer, alongside broader reflections on purpose-driven requirements and potential applications based on post-development input from managers and shopfloor leaders, as well as additional proposed use cases. This publication offers a contribution by presenting an assessment matrix evaluating the strategic, operational, and tactical dimensions of digital twins in relation to cost, delivery, quality, and sustainability. In doing so, it asserts that digital twins transcend mere theoretical digital models, evolving into purpose-driven tools that deliver tangible value and impact in manufacturing.

The ability to quickly design and deliver a product is especially important within Engineer-to-Order (ETO) industry, and a successful product delivery within ETO requires significant amount of data exchange and integration amongst systems and stakeholders of the order fulfilment process. This necessitates that the systems and digital infrastructure supporting this process have the required amount of interoperability to exchange and use data from each other. However, despite being of strategic importance, interoperability has received limited attention in ETO literature. Therefore, the purpose of this article is to analyse the current body of academic literature and explore how interoperability has been approached within low-volume ETO industry. A literature review of 41 articles was conducted to provide an overview of how interoperability has been approached in existing ETO research, what technologies are enabled by it, and the most prominent approaches of improving interoperability within ETO industry. The literature review indicated that five categories of advanced technologies within ETO industry are enabled by interoperability: cloud computing, digital twins, cyber physical systems, blockchain and large scale collaborative data ecosystems. Furthermore, the review identified six prominent approaches to increasing interoperability, including knowledge management frameworks, data standardization and variation management, enterprise and manufacturing platforms, middleware, asset administration shells and semantic decision support systems.

With the development towards Industry 5.0, manufacturing companies are developing towards Smart Production - namely, using data as a resource to interconnect the elements in the production system for a more resource-efficient and sustainable production. Selection and integration of digital technologies are crucial steps to ensure that suitable technology is chosen and properly introduced in the production system. However, having one digital technology is not enough; rather there is a need to combine several synergising technologies for smart production. There are many challenges when selecting and integrating a combination of synergising digital technologies for smart production. Therefore, the purpose of this paper is to support manufacturing companies in systematically selecting and integrating suitable digital technologies for efficiently benefiting data value chains for smart production. This paper employed a multiple case study involving manufacturing companies within different industries and of different sizes. The paper analyses the current challenges related to the selection and integration of digital technologies and proposes a data flow framework with possible ways of combining digital technologies. The proposed framework shows alternative data flows between a combination of technologies depending on what digital technologies are selected and how they are integrated.

PurposeManufacturing companies still struggle to integrate additive manufacturing (AM) technologies with existing traditional manufacturing technologies. This paper explores AM technology integration into a global manufacturing company from an operational capability perspective.Design/methodology/approachThe research was conducted using a single case study in collaboration with a global heavy-duty vehicle manufacturer. Data were collected through a focus group and interviews representing management and engineering roles. Additional data were collected from meetings, company documents, field notes and observations. Subsequently, the collected data were analyzed thematically.FindingsThe findings reveal that, despite the company embarking on its AM technology integration journey, it encountered challenges, including cognitive fixation, manufacturing fixation, situational awareness, ambiguous ownership and the make-or-buy dilemma. Furthermore, the findings showed that the company developed operational capabilities - such as developing proficiency in AM know-how, continuous use of AM technology, operational practices for AM technology, cross-collaboration for AM initiatives and business cases for AM technology - to address these challenges. A facilitation model was developed, outlining essential actions prioritized for the short-term, mid-term and long-term. These actions leverage the operational capabilities to address challenges in AM technology integration.Originality/valueThis paper offers an in-depth exploration of AM technology integration in a global heavy-duty vehicle manufacturer. It introduces a novel application of operational capability theory and proposes a facilitation model for managers and academics in pursuit of achieving AM technology integration.

The purpose of this study is to examine the technical interoperability of a current product data infrastructure for an engineer-to-order (ETO) process and explore the implications of technical interoperability on design manufacturing integration in low-volume manufacturing companies.Design/methodology/approachThe results originate from a longitudinal case study of an ETO process within a large rolling stock manufacturing company.FindingsThe research identified theoretical and practical implications of low technical interoperability. The findings suggest that a lack of technical interoperability can negatively impact the efficiency of an ETO process. This lack of efficiency presents itself via an increase in manual administration and the risk of miscommunication, resulting in delayed lead times. This study also indicates that lacking technical interoperability can act as a barrier to innovation and the further implementation of advanced technologies.Research limitations/implicationsThis case study was limited to a single company, which in turn limits the generalizability of the findings. Future research should therefore focus on additional low-volume manufacturing companies.Originality/valueThe results from this study provide important insights for managing ETO processes within a low-volume manufacturing sector. The findings stress the importance of the technical interoperability of product data infrastructures for design manufacturing integration and, thus, the efficiency of ETO processes. Being aware of these implications can aid management in their decision-making process and support them in circumventing the negative effects of low technical interoperability.

Reducing lead times and quickly introducing new products to the market is becoming increasingly more important for low-volume manufacturing. This has led to management principles such as manufacturing and design integration to accelerate the time to market of new products, which puts immense pressure on the new product development (NPD) process. Since it is both time-intensive and a critical interface between manufacturing and design, requiring a significant amount of data to be transferred between these two functions and their systems. Successful integration between manufacturing and design requires a robust product data infrastructure, with high levels of technical interoperability to facilitate effective data flow between systems. However, research on the topic of technical interoperability and its impact on manufacturing and design integration within NPD processes is limited. A longitudinal case study was conducted at a low-volume development site, wherein an NPD process, and its product data infrastructure were analysed from a technical interoperability perspective. The purpose of this article is to explore the potential of technical interoperability within manufacturing and design integration, by identifying bottlenecks within a NPD process. The findings from this case study identified two systems lacking technical interoperability, the product data management (PDM) system and the customer requirements database. Causing negative impacts such as increased manual administrative load, late design revisions and extended lead times. From the discussion it can be derived that by increasing the technical interoperability of said systems, the aforementioned issues can be mitigated, enhancing the overall manufacturing design integration.

Cyber-physical production systems (CPPS) are the backbone of Smart Production. Digital transformation often starts from a hierarchical systems landscape based on the automation pyramid with legacy systems and low data availability. CPPS's are characterized by a decentralized structure with the ability to share data and services across the value chain, to gain benefits in line with Industry 5.0 demands. Literature proposes different models, which in theory should enable companies with individual starting points and legacy systems to implement a holistic CPPS architecture. Nevertheless, companies struggle with its implementation. Therefore, the purpose of this article is to analyze challenges when implementing a holistic CPPS architecture. A case study assesses the transformation of a global manufacturing company towards a holistic CPPS architecture with input from stakeholders at management level to operators. This paper provides examples and insights into current challenges and gives recommendations for next steps towards a holistic CPPS architecture.

Zero Defect Manufacturing (ZDM), driven by Artificial Intelligence (AI), has the potential to transform industry in the digital age, utilizing data from various production stages to achieve zero defects and minimize waste, resulting in superior customer value. However, extensive empirical studies are required to validate its effectiveness across various production settings and enhance its implementation by improving technological components, seamlessly integrating with existing processes, and clearly defining human roles. The purpose of this paper is to investigate how to facilitate the adoption of AI-driven ZDM in production systems. This research highlights challenges and recommendations associated when implementing AI-driven ZDM within a production system and suggests a framework to facilitate this process.Using a longitudinal case study in the heavy-duty automotive industry, four AI-driven ZDM applications were examined to determine adoption dynamics. Leveraging the Technology-Organization-Environment (TOE) theory, we identified ten application challenges and made recommendations for how to address them. The analysis highlights the importance of adhering to agile principles with continuous adaptation during AI implementation, stressing the need for feedback collection from stakeholders to assess adoption factors. This study underscores the significance of explainable AI in empowering operators and emphasizes the necessity of human-centered design principles and feedback loops to ensure AI systems truly serve their users. Building on these findings, a framework is proposed aiming for incremental value creation, continuous learning, and adaptation while considering human feedback through various channels, similar to the Plan-Do-Check-Act cycle in lean management. This iterative approach, encompassing continuous improvement loops, aims to ensure successful adoption and maximize the potential of AI-driven ZDM in order to achieve operational excellence.This study contributes to the existing literature on AI adoption in production systems by offering a comprehensive understanding of the challenges and recommendations associated with implementing AI-driven ZDM. Additionally, the proposed framework provides a practical roadmap for organizations seeking to leverage the power of AI to achieve zero-defect manufacturing and enhance their competitive advantage.

Digital transformation is complicated in International Manufacturing Networks (IMNs), where factories are spread globally, and actions are interdependent. Adapting organizational structures is one way to handle the demands and expectations posed by digital transformation. However, research covering the adaptations to the IT organization is limited, and this paper's purpose is to explore adaptations of IT organizations within IMNs during digital transformation. A multiple case study was performed, studying two manufacturing companies’ adaptations of the IT organization. The result reveals three phases of adaptations of the IT organization: the foundation phase, the mapping phase, and the coordinating phase. The phases are accompanied by the setup of a new operational IT function that works with digital transformation in the IMNs. Also, the interfaces and collaboration between the operational IT function and the factories in the IMNs are identified. Lastly, it highlights remaining challenges with the adaptations of the IT organization that need further research. 

PurposeThe purpose of this paper is to advance the understanding of paradoxes, underlying tensions and potential management strategies when integrating digital technologies into existing lean-based production systems (LPSs), with the aim of achieving synergies and fostering the development of production systems.Design/methodology/approachThis study adopts a collaborative management research (CMR) approach to identify patterns of organisational tensions and paradoxes and explore management strategies to overcome them. The data were collected through interviews and focus group interviews with experts on lean and/or digital technologies from the companies, from documents and from workshops with the in-case researchers.FindingsThe findings of this paper provide insights into the salient organisational paradoxes embraced in the integration of digital technologies in LPS by identifying different aspects of the performing, organising, learning and belonging paradoxes. Furthermore, the findings demonstrate the intricacies and relatedness between different paradoxes and their resolutions, and more specifically, how a resolution strategy adopted to manage one paradox might unintentionally generate new tensions. This, in turn, calls for either re-contextualising actions to counteract the drift or the adoption of new resolution strategies.Originality/valueThis paper adds perspective to operations management (OM) research through the use of paradox theory, and we (1) provide a fine-grained perspective on why integration sometimes "fails" and label the forces of internal drift as mechanisms of imbalances and (2) provide detailed insights into how different management and resolution strategies are adopted, especially by identifying re-contextualising actions as a key to rebalancing organisational paradoxes in favour of the integration of digital technologies in LPSs.