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Virtual Reality (VR) within manufacturing industry has continued to be of large interest, especially when seeking to reduce time-to-market for development of new products. To reduce time-to-market (TTM), VR technology must be implemented and used in the development process in a way that enables increased performance. Therefore, the purpose of this study is to identify key decisions and implementation activities that impact VR adoption in NPD processes. This paper explores the VR adoption and usage to shed light on key decisions and implementation activities. A retrospective multiple case study has been conducted. One case has successfully implemented and used VR while two other cases have not. The results describe the decisions and implementation activities performed. Based on the results a framework for VR adoption is proposed. The framework identifies attributes which are key to VR adoption and describes them among three adoption stages.

In today’s competitive industrial environment, characterized by diversecustomer demands and an increasing need for rapid adaptation, the ability toquickly and efficiently relocate manufacturing capacity to different locations hasbecome crucial. This paper introduces the concept of Cloneable AutomationSystems which are automation systems specifically designed for easy and efficientduplication of both functionality and physical structure. These systems aredistinguished by their capability to swiftly embed and relocate automationcapacity within a corporation, with minimal undesirable effects in terms of time,effort, cost, or performance. The research presented in this paper has beenconducted within the framework of the Mälardalen University AutomationResearch Center (MARC), which is committed to advancing digitalization andautomation in industry through a comprehensive, multidisciplinary approach andstrong industrial partnerships. The expected scientific contributions of theresearch include the development of innovative solutions in smart and portableautomation, robotics and control, as well as digital infrastructure. The maincontribution of this paper is the framing of a Cloneable Automation Systemconcept based on a case study within a global manufacturing company. The paperidentifies industrial needs and development areas related to enhancing thepossibilities for reusability and relocation of automation systems.

Traditional industrial robot automation is often fixed, and they have enabled increased competitiveness, but often lack the agility required for dynamic manufacturing environments. Building on research projects such as Factory-in-a-Box and Lean robotics, the purpose of the paper is to introduce the concept of smart and portable automation systems, a vision for modern manufacturing that emphasizes the need for scalable, movable, and reconfigurable automation solutions. The concept of smart and portable automation systems refers to advanced robotic systems that combine intelligent, data-driven capabilities with enhanced mobility and adaptability, allowing for flexible and reconfigurable operations. A collaborative and interactive research approach has been applied in a research project with three participating manufacturing companies. Based on the manufacturing companies’ automation challenges and proposed use cases for smart and portable automation the goal with the research project is to develop demonstrators that show cases smart and portable automation. In this paper a smart and portable automation system refers to “an automation system with a low degree of perceived complexity that can be easily moved and easily meet dynamic manufacturing needs.

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.

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. 

This paper presents an interpretable machine-learning model for anomaly detection in door locks using torque data. The model aims to replace the human tactile sense in the quality control process, reducing repetitive tasks and improving reliability. The model achieved an accuracy of 96%, however, to gain social acceptance and operators' trust, interpretability of the model is crucial. The purpose of this study was to evaluate an approach that can improve interpretability of anomalous classifications obtained from an anomaly detection model. We evaluate four instance-based counterfactual explanators, three of which, employ optimization techniques and one uses, a less complex, weighted nearest neighbor approach, which serve as our baseline. The former approaches, leverage a latent representation of the data, using a weighted principal component analysis, improving plausibility of the counter factual explanations and reduces computational cost. The explanations are presented together with the 5-50-95th percentile range of the training data, acting as a frame of reference to improve interpretability. All approaches successfully presented valid and plausible counterfactual explanations. However, instance-based approaches employing optimization techniques yielded explanations with greater similarity to the observations and was therefore concluded to be preferable despite the higher execution times (4-16s) compared to the baseline approach (0.1s). The findings of this study hold significant value for the lock industry and can potentially be extended to other industrial settings using timeseries data, serving as a valuable point of departure for further research.

This paper presents an interpretable machinelearning model for anomaly detection in door locks using torque data. The model aims to replace the human tactile sense in the quality control process, reducing repetitive tasks and improving reliability. The model achieved an accuracy of 96%, however, to gain social acceptance and operators' trust, interpretability of the model is crucial. The purpose of this study was to evaluate anapproach that can improve interpretability of anomalousclassifications obtained from an anomaly detection model. Weevaluate four instance-based counterfactual explanators, three of which, employ optimization techniques and one uses, a less complex, weighted nearest neighbor approach, which serve as ourbaseline. The former approaches, leverage a latent representation of the data, using a weighted principal component analysis, improving plausibility of the counter factual explanations andreduces computational cost. The explanations are presentedtogether with the 5-50-95th percentile range of the training data, acting as a frame of reference to improve interpretability. All approaches successfully presented valid and plausible counterfactual explanations. However, instance-based approachesemploying optimization techniques yielded explanations withgreater similarity to the observations and was therefore concluded to be preferable despite the higher execution times (4-16s) compared to the baseline approach (0.1s). The findings of this study hold significant value for the lock industry and can potentially be extended to other industrial settings using timeseries data, serving as a valuable point of departure for further research.

PurposeCoordination of digital transformation within globally dispersed factories belonging to international manufacturing networks (IMNs) is essential for competitiveness. This paper explores how digital transformation necessitates changes in the coordination of IMNs.Design/methodology/approachA case study is conducted with three Swedish manufacturing companies, thus adding to the limited empirical research covering the examined research field. Data analysis uses the technology-organization-environment (TOE) framework.FindingsThe results highlight 15 digital transformation attributes linked by intermediate themes to 13 changes in the coordination of IMNs and provide concrete industry examples. Four major themes emerged as significant in the coordination of IMNs: increased speed of technology development and rollout, amplified emphasis on a global mindset, increased need to adapt the organizational structures to enable collaboration and a higher degree of uncertainty.Originality/valueAlthough coordination of IMNs is acknowledged as directly related to competitive advantage, the ways digital transformation necessitates changes in the coordination of IMNs have been missing in contemporary research. This research decreases this omission.