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In order to stimulate innovation and sustainable development in a business environment that is always changing, it is important to support successful collaboration between academia, industry, and society. This project is focused on creating and implementing MDU's Creative Lab, an innovation lab based on the Quintuple Helix Model, in order to promote collaboration between students, academic institutions, and business partners and to meet the needs of all parties. The study was conducted utilizing a methodology that included a review of the literature, interviews, and data analysis. The primary issues that need to be resolved have been highlighted through the interviews as the absence of support systems, the requirement to create clear objectives at the outset of the project, and the significance of ensuring projects and authors maintain continuity.The results of the research have shown that better communication between parties, a clear connection between projects and courses, and opportunities for students to obtain knowledge and experience in the real world are all necessary. In addition, major motivators such as the desire for a social impact, interdisciplinary teamwork, and satisfaction in one's job have been highlighted. As a result, a business model has been developed that takes into account the information gleaned from the presentations and aims to facilitate collaboration and communication between academic institutions, students, and business partners, guarantee the quality of collaborations, offer mentoring and guidance to students, and plan workshops and events that encourage creativity, innovation, and entrepreneurship.This project provides a thorough analysis of how to create and implement a student-driven innovation lab that adheres to the Quintuple Helix Model and is geared toward sustainable growth in the direction of Industry 5.0. By encouraging student cooperation and contentment, it fosters an environment that fosters the development of creative ideas and the development of a team-oriented workforce that will progress society and business.

With the development towards Industry 5.0, manufacturing companies are developing towards smart production. In smart production, data is used as a resource to interconnect different elements in the production system to learn and adapt to changing production conditions. Common objectives include human-centricity, resource-efficiency, and sustainable production. To enable these desired benefits of smart production, there is a need to use digital technologies to create and manage the entire flow of data. To enable smart production, it is essential to deploy digital technologies in a way so that collected raw data is converted into useful data that can be applied by equipment or humans to generate value or reduce waste in production. This requires consideration to the data flow within the production system, i.e., the entire process of converting raw data into useful data which includes data management aspects such as the collection, analysis, and visualization of data. To enable a good data flow, there is a need to combine several digital technologies. However, many manufacturing companies are facing challenges when selecting suitable digital technologies for their specific production system. Common challenges are related to the overwhelming number of advanced digital technologies available on the market, and the complexity of production system and digital technologies. This makes it a complex task to understand what digital technologies to select and the recourses and actions needed to integrate them in the production system.

Against this background, the purpose of this licentiate thesis is to examine the selection and integration of digital technologies to enable smart production within manufacturing companies. More specifically, this licentiate thesis examines the challenges and critical factors of selecting and integrating digital technologies for smart production. This was accomplished by performing a qualitative-based multiple case study involving manufacturing companies within different industries and of different sizes. The findings show that identified challenges and critical factors are related to the different phases of the data value chain: data sources and collection, data communication, data processing and storage, and data visualisation and usage. General challenges and critical factors that were related to all phases of the data value chain were also identified. Moreover, the challenges and critical factors were related to people, process, and technology aspects. This shows that there is a need for holistic perspective on the entire data value chain and different production system elements when digital technologies are selected and integrated. Furthermore, there is a need to define a structured process for the selection and integration of digital technologies, where both management and operational level are involved. 

With the development towards Industry 5.0, manufacturing companies are developing towards Smart Production, i.e., using data as a resource to interconnect the elements in the production system to learn and adapt accordingly for a more resource-efficient and sustainable production. This requires selecting and integrating digital technologies for the entire data lifecycle, also referred to as the data value chain. However, manufacturing companies are facing many challenges related to building data value chains to achieve the desired benefits of Smart Production. Therefore, the purpose of this paper is to identify and analyze the critical factors of selecting and integrating digital technologies for efficiently benefiting data value chains for Smart Production. This paper employed a qualitative-based multiple case study design involving manufacturing companies within different industries and of different sizes. The paper also analyses two Smart Production cases in detail by mapping the data flow using a technology selection and integration framework to propose solutions to the existing challenges. By analyzing the two in-depth studies and additionally two reference cases, 13 themes of critical factors for selecting and integrating digital technologies were identified.

To stay competitive, manufacturing companies are developing towards Smart Production which requires the use of digital technologies. However, there is a lack of guidance supporting manufacturing companies in selecting and integrating a combination of suitable digital technologies, which is required for Smart Production. To address this gap, the purpose of this paper is twofold: (i) to identify the main challenges of selecting and integrating digital technologies for Smart Production, and (ii) to propose a holistic concept to support manufacturing companies in mitigating identified challenges in order to select and integrate a combination of digital technologies for Smart Production. This is accomplished by using a qualitative-based multiple case study design. This paper identifies current challenges related to selection and integration of digital technologies. To overcome these challenges and achieve Smart production, the concept of data value chain was proposed, i.e., a holistic approach to systematically map and improve data flows within the production system. © 2022, IFIP International Federation for Information Processing.

In the literature there is no consensus regarding the meaning of the term digital transformation. Therefore, the purpose of this paper is to explore the differing views of the meaning of digital transformation. A case study has been conducted in collaboration with four Swedish manufacturing companies. The results shows that digital transformation can have different meaning within a company and the main challenge when performing digital transformation is knowledge. This study is the first investigation in a research project focusing on coordination of digital transformation. Therefore, is the underlaying goal to identify how the participating companies in the research project describe digital transformation in comparison with the literature. The research intention is not to define digital transformation rather to explore differing views of digital transformation and highlighting similarities and difference in comparison with the literature reviewed. The findings are practically relevant for manufacturing companies by highlighting differing views of digital transformation and in the creation of a common language within a company. 

The aims of this research are to enhance industry-university collaboration and to design learning experiences connecting the research front to practitioners. We present an empirical study with a qualitative approach involving teachers who gathered data from newly developed advanced level courses in artificial intelligence, energy, environmental, and systems engineering. The study is part of FutureE, an academic development project over 3 years involving 12 courses. The project, as well as this study, is part of a cross-disciplinary collaboration effort. Empirical data comes from course evaluations, course analysis, teacher workshops, and semi-structured interviews with selected students, who are also professionals. This paper will discuss course design and course implementation by presenting dilemmas and paradoxes. Flexibility is key for the completion of studies while working. Academia needs to develop new ways to offer flexible education for students from a professional context, but still fulfil high quality standards and regulations as an academic institution. Student-to-student interactions are often suggested as necessary for qualified learning, and students support this idea but will often not commit to it during courses. Other dilemmas are micro-sized learning versus vast knowledge, flexibility versus deadlines as motivating factors, and feedback hunger versus hesitation to share work. Furthermore, we present the challenges of providing equivalent online experience to practical in-person labs. On a structural level, dilemmas appear in the communication between university management and teachers. These dilemmas are often the result of a culture designed for traditional campus education. We suggest a user-oriented approach to solve these dilemmas, which involves changes in teacher roles, culture, and processes. The findings will be relevant for teachers designing and running courses aiming to attract professionals. They will also be relevant for university management, building a strategy for lifelong e-learning based on co-creation with industry.

AI methods referred to as interpretable are often discredited as inaccurate by supporters of the existence of a trade-off between interpretability and accuracy. In many problem contexts however this trade-off does not hold. This paper discusses a regression problem context to predict flight take-off delays where the most accurate data regression model was trained via the XGBoost implementation of gradient boosted decision trees. While building an XGB-CBR Twin and converting the XGBoost feature importance into global weights in the CBR model, the resultant CBR model alone provides the most accurate local prediction, maintains the global importance to provide a global explanation of the model, and offers the most interpretable representation for local explanations. This resultant CBR model becomes a benchmark of accuracy and interpretability for this problem context, and hence it is used to evaluate the two additive feature attribute methods SHAP and LIME to explain the XGBoost regression model. The results with respect to local accuracy and feature attribution lead to potentially valuable future work. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Proactive maintenance management of world-class standard is close to impossible without the support of a computerized management system. In order to reduce failures, and failure recurrence, the key information to log are failure causes. However, Computerized Maintenance Management System (CMMS) seems to be scarcely used for analysis for improvement initiatives. One part of this is due to the fact that many CMMS utilizes free-text fields which may be difficult to analyze statistically. The aim of this study is to apply Natural Language Processing (NPL), Ontology and Machine Learning (ML) as a means to analyze free-textual information from a CMMS. Through the initial steps of the study, it was concluded though that none of these methods were able to find any suitable hidden patterns with high-performance accuracy that could be related to recurring failures and their root causes. The main reason behind that was that the free-textual information was too unstructured, in terms of for instance: spelling- and grammar mistakes and use of slang. That is the quality of the data are not suitable for the analysis. However, several improvement potentials in reporting and to develop the CMMS further could be provided to the company so that they in the future more easily will be able to analyze its maintenance data.

Small-and medium-sized enterprises (SMEs) have inherent characteristics, which require specific solutions for improving the sustainability performance of their operations. The purpose of this paper is to increase the knowledge on barriers and enablers for the adoption of sustainable manufacturing by manufacturing SMEs and to provide insights into what enablers can be used to overcome existing barriers. Taking, as a starting point, a systematic literature review, this paper presents a categorization of barriers and enablers for the adoption of sustainable manufacturing by manufacturing SMEs. In total, seven categories for classifying the barriers and enablers for the adoption of sustainable manufacturing within SMEs were identified: organizational, managerial and attitudinal; informational; governmental; financial; training and skills development; market and business context; and technological. Additionally, this study elaborates on what barriers could be mitigated through the enablers. This study found specific enablers with the potential to mitigate a significantly higher number of barriers and referred to them as ‘critical enablers’. SMEs aiming to adopt sustainable manufacturing practices or improve their sustainability performance are encouraged to focus on the enablers in these categories. This paper synthesizes and facilitates interpretation of the existing body of evidence on barriers and enablers for adopting sustainable manufacturing in SMEs. 

 There is a growing trend to embrace the idea of public participation in the work of museums, from exhibition design to collections. To further develop participatory cultures in museums, these negotiations and emerging practices should be examined more closely. This paper explores a museum’s whole-hearted attempt to engage with the societal issue of climate change and work with a high degree of participationfrom civic society when staging a temporary exhibition. We investigate experiences inthe process of building, measuring, separating and transgressing during the collaboration. Based on these explorations the paper presents three emerging and interconnected territories in the staging of participatory temporary exhibitions, the territory of aesthetics, the territory of action (autonomy), and the territory of unpredictability. The result contributes to research on public participatory practices mainly in museum context

The industrial collaborative robot (ICR) is a promising technology for automating assembly systems in manufacturing industries. Yet, ICRs are not widely implemented in the manufacturing industry as there are challenges during its implementation. Furthermore, current research lacks real-world case studies on ICR implementation. Therefore, the purpose of this paper is to investigate the critical factors supporting the implementation of ICR applications in assembly systems. A multiple-case study with eight case companies is presented in this paper, consisting of thirteen interviews. Moreover, critical factors were identified that could mitigate challenges in the ICR implementation process. By this, the study contributes to the current body of research by identifying and structuring the critical factors using a newness perspective. These factors can support the mitigation of potential challenges when manufacturers implement technology with a high novelty into their assembly systems. Specifically, this paper suggests that manufacturing companies focus on relieving operators from unergonomic tasks rather than focusing on high financial and efficiency gains. This finding contradicts previous research suggesting that financial gains are the main goal for manufacturing companies when implementing ICR applications. Moreover, how manufacturers work with external actors might change when implementing ICR applications, compared to traditional robots. Finally, we suggest testing the critical factors in a real-world case study investigating the whole implementing process to see if these factors, in fact, mitigate challenges. 

Historically, cylinder locks’ quality has been tested manually by human operators after full assembly. The frequency and the characteristics of the testing procedure for these locks wear the operators’ wrists and lead to varying results of the quality control. The consistency in the quality control is an important factor for the expected lifetime of the locks which is why the industry seeks an automated solution. This study evaluates how consistently the operators can classify a collection of locks, using their tactile sense, compared to a more objective approach, using torque measurements and Machine Learning (ML). These locks were deliberately chosen because they are prone to get inconsistent classifications, which means that there is no ground truth of how to classify them. The ML algorithms were therefore evaluated with two different labeling approaches, one based on the results from the operators, using their tactile sense to classify into ‘working’ or ‘faulty’ locks, and a second approach by letting an unsupervised learner create two clusters of the data which were then labeled by an expert using visual inspection of the torque diagrams. The results show that an ML-solution, trained with the second approach, can classify mechanical anomalies, based on torque data, more consistently compared to operators, using their tactile sense. These findings are a crucial milestone for the further development of a fully automated test procedure that has the potential to increase the reliability of the quality control and remove an injury-prone task from the operators.

Purpose: The use of quality management (QM) to achieve the United Nations Sustainable Development Goals (UNSDGs) is a topic of growing interest in academia and industry. The IAQ (International Academy for Quality) established Quality Sustainability Award in 2020, a testament to this growing interest. This study aims to investigate how QM philosophies, methodologies and tools can be used to achieve sustainable development in organizations. Design/methodology/approach: Five large manufacturing organizations – three from India and two from China – who reported their achievements about using QM in achieving Sustainable Development Goals (SDGs) were studied using multiple sources of data collection. A detailed within-case and cross-case analysis were conducted to unearth this linkage's practical and theoretical aspects. Findings: The study finds that QM methodologies effectively met the five organizations' UNSDGs. These organizations successfully used OPEX (Operational Excellence) methodologies such as Lean, Kaizen and Six Sigma to meet UNSDGs 7, 11, 12 and 13. Moreover, UNSG 12 (Responsible Consumption and Production) is the most targeted goal across the case studies. A cross-case analysis revealed that the most frequently used quality tools were Design of Experiments (DoE), Measurement Systems Analysis (MSA), C&E analysis and Inferential statistics, among other essential tools. Research limitations/implications: The study's sample size was limited to large-scale manufacturing organizations in the two most populous countries in the world. This may limit the study's generalizability to other countries, continents, or micro-, small- and medium-sized enterprises (SMEs). Additionally, the study's conclusions would be strengthened if tested as hypotheses in a follow-up survey. Practical implications: This practical paper provides case studies on how to use QM to impact SDGs. It offers both descriptive and prescriptive solutions for practitioners. The study highlights the importance of using essential QM tools in a structured and systematic manner, with effective teams, to meet the SDGs of organizations. Social implications: The study shows how QM can be used to impact UNSDGs, and this is very important because the UNSDGs are a set of global objectives that aim to address a wide range of social and environmental issues. This study could motivate organizations to achieve the UNSDGs using essential QM tools and make the world a better place for the present and future generations. Originality/value: This case study is the first to investigate at a micro-level how QM can impact UNSDGs using live examples. It uses data from the IAQ to demonstrate how QM can be integrated into UNSDGs to ensure sustainable manufacturing.

Futures Literacy is the capability to imagine and understand potential futures to prepare ourselves to act and innovate in the present. This pilot study aims to understand how artistic methodologies and speculative design can support the collaborative exploration of futures in the context of work and contribute to developing peoples' capability of futures literacy. Our premise is that technologies such as Artificial Intelligence and the Internet of things can augment people and support their needs at work. To illustrate this process, we have presented a collaborative method that integrates an artistic intervention with speculative design activities. We tested the method in a full-day workshop with seventeen (17) participants from a Swedish academy responsible for enabling learning and competence development at work in the healthcare sector. The results indicate that the artistic intervention, combined with the speculative design activities, can challenge current participants' perspectives and offer them new ways of seeing futures with technologies. These new ways of seeing reveal underlying premises crucial in developing the capability of futures literacy. 

The availability of new research for IoT support and the human-centric perspective of industry 4.0 opens a gap to support operators in unleashing their creativity so they can provide improvements opportunities with IoT technology. This paper presents a case-study carried out in four Swedish manufacturing companies, where four different workshops were facilitated to support operators in the conceptualization of manufacturing improvements with IoT technologies. The empirical material gathered during these workshops has been analyzed in five different reflective sessions and discussed in light of previous research from industry 4.0, operators, and IoT support. Results indicate that operators can collaboratively create conceptual IoT solutions and that expressiveness in communicating their ideas and needs using IoT technology is more relevant than technical aspects and details of their proposed IoT solutions. This technological expressiveness is identified as a necessary skill to be cultivated on the shop floor and can potentially contribute to making a more effective and socially sustainable industrial landscape in the future.

Artificial Intelligence offers a wide variety of capabilities that can potentially address people's needs and desires in their specific contexts. This pilot study presents a collaborative method using a deck of AI cards tested with 58 production, AI, and information science students, and experts from an accessible media agency. The results suggest that, with the support of the method and AI cards, participants can ideate and reach conceptual AI solutions. Such conceptualisations can contribute to a more inclusive integration of AI solutions in society.

In energy production the characterization of the fuel is a key aspect for modelling and optimizing the operation of a power plant. Near-infrared spectroscopy is a wellestablished method for characterization of different fuels and is widely used both in laboratory environments and in power plants for real-time results. It can provide a fast and accurate estimate of key parameters of the fuel, which for the case of biomass can include moisture content, heating value, and ash content. These instruments provide a chemical fingerprint of the samples and require a calibration model to relate that to the parameters of interest.

A near-infrared spectrometer can provide point data whereas a hyperspectral imaging camera allows the simultaneous acquisition of spatial and spectral information from an object. As a result, an installation above a conveyor belt can provide a distribution of the spectral data on a plane. This results in a large amount of data that is difficult to handle with traditional statistical analysis. Furthermore, storage of the data becomes a key issue, therefore a model to predict the parameters of interest should be able to be updated continuously in an automated way. This makes hyperspectral imaging data a prime candidate for the application of machine learning techniques. This paper discusses the modelling approach for hyperspectral imaging, focusing on data analysis and assessment of machine learning approaches for the development of calibration models.

Denna forskningsstudie har skett inom ramen för ett projekt finansierat av Delegationen mot segregation (Delmos) under år 2021/2022 för att främja samverkan kring frågan om segregation och trygghet i stadsdelen Årby i Eskilstuna. Projektet har initierats och drivits av civilsamhällets (CS) aktörer, nämligen Studiefrämjandet i Sörmland, Sisters in business och Årbyföreningen, Årby M. Dessutom är Eskilstuna kommun en samverkanspart som stått bakom ansökan och deltagit i projektet. Eskilstuna är en av de 32 kommuner som lyfts fram som ett socioekonomiskt eftersatt område där särskilt stöd kan sökas för att minska och motverka social och ekonomisk segregation (Studiefrämjandet i Sörmland, 2021). Årby är en stadsdel i Eskilstuna med ca 4800 invånare av kommunens drygt 100 000. Området byggdes i samband med miljonprogrammet under 1960-talet och är ett av de områden som Eskilstuna kommun valt ut som strategiskt viktigt att fokusera på när det gäller inkludering, trygghetsskapande aktiviteter och att skapa levande mötesplatser i tillsammans med boende i området. Dessa mötesplatser finns även i några andra stadsdelar, och där finns en uttalad vilja och påbörjat samarbete med civilsamhället.

För att kunna möta upp, och börja skapa lösningar på, komplexa samhällsutmaningar, såsom social och ekonomisk segregation, krävs ett stort mått av nytänkande och samverkan. Enligt regeringens långsiktiga strategi för att minska och motverka segregation (Regeringskansliet, 2018), har civilsamhället en viktig roll i detta arbete. Detta som komplementär aktör, brobyggare mellan invånare och kommunen och som en friare form av, och plattform för, samverkan och aktiviteter.

I ansökan om statsbidrag från Delegationen mot segregation (Studiefrämjandet, 2021) beskrivs syftet med arbetet inom ramen för projektet som tredelat: (1) kunskaps- och metodutveckling, (2) gemensamt agerande och strategisk samverkan kring segregation, samt (3) direkta insatser som tar utgångspunkt i lokala och regionala behov. Inom ramen för den första delen så lyfts behov om kunskap om dels segregationens nuläge, dels om vilka åtgärder som kan leda till att minska och motverka segregation. Inom ramen för del två så pekas på vikten att samverka mellan och inom aktörerna för att åstadkomma en förändring.

I skärningspunkten för dessa finns behov av att undersöka på vilket sätt samverkan sker idag, hur olika aktörer och individer ser på behov av samverkan, hur detta ska organiseras samt utmaningar och möjligheter med densamma. Allt i en framåtsyftande anda för att skapa en så god grund och plattform för att tillsammans åstadkomma en fortsatt positiv utveckling i Årby, Eskilstuna.

Därför har studien genomfört en avgränsad analys av utvecklingen gällande samverkan i projektet. Syftet var att undersöka samverkan mellan civilsamhällets aktörer och kommunen, men i undersökning och analys framkom även kunskap om samverkan mellan civilsamhällets aktörer samt internt mellan kommunala verksamheter. Studien är en empirisk undersökning på mikronivå. Mot denna bakgrund formulerades följande övergripande forskningsfrågor; 

RQ1: Vilka utmaningar och möjligheter finns när det gäller samverkan mellan offentliga och privata aktörer och civilsamhället för att utveckla mötesplatser för att bryta segregation och utanförskap? 

RQ2: Vad behöver förstärkas för att åstadkomma en mer utvecklad samverkan mellan offentliga aktörer och civilsamhället? 

I denna rapport kommer även respondenternas uppfattning av resultatet av projektet och forskarens analys av detta lyftas fram. 

Studiens resultat bidrar inte bara till kunskap hos de aktörer som var delaktiga i projektet utan har även relevans för andra aktörer och initiativ som arbetar mot samma mål och som söker utveckla former för samverkan mellan CS och kommunala verksamheter. Idén om civilsamhällets viktiga roll, och frågor kring dess samverkan med kommuner, lyfts även i andra sammanhang.  Eskilstuna kommun har exempelvis en långsiktig satsning som heter Trygga unga, där fokus ligger på främjande och förebyggande arbete för ungdomar i åldern 13–20 år, som befinner sig i riskzonen för kriminalitet och missbruk. I detta arbete uttrycks likaledes att man vill åstadkomma en ökad samverkan med civilsamhället och föreningslivet (Eskilstuna kommun, 2022). Frågan kring trygghet, minskad segregation och samhällsutveckling är även ett fokus för Samhällskontraktet, en gemensam samverkansarena där Mälardalens universitet, Eskilstuna kommun och Västerås stad arbetar för att utforska och hitta lösningar på komplexa samhällsutmaningar tillsammans med andra samhällsaktörer. För denna arena är studien och rapportens innehåll relevant, då ett av de fokusområden man arbetar med är något man benämner skolresultat, men detta ska ses i vid bemärkelse. Skolresultat ska inte enbart kopplas till klassrummet, utan man diskuterar det även som en del av samhällsutveckling, demokrati, integration och trygghet samt skärningspunkter som ligger utanför skolmiljön. Därför utifrån workshops med Eskilstuna kommun och Västerås stad intresserar man sig exempelvis för frågor som ’Hur motverkar vi effekter av segregation?’ samt ’Hur kan vi genom stärkt samverkan mellan myndigheter öka tryggheten som ytterst hjälper barn och ungdomar att gå ut skolan med godkända betyg?’ Här vill man undersöka detta ur ett trygghetsperspektiv (Samhällskontraktet, 2022). Forskaren i denna studie är en del av detta arbete. Studien är också ett bidrag till arbetet inom MDU Living Lab, inom vilket man fokuserar på metoder och modeller för samverkan och samproduktion

The Zero-Defect Manufacturing (ZDM) movement has received increasing interest from practitioners and academics. However, despite the academic development of the field, the adoption of ZDM enablers such as robotic in-line quality inspection applications has not increased as expected. This article explores the state of adoption of robotic in-line quality inspection at five global Swedish manufacturing companies. Results show that contrary to the case companies' beliefs, more people- and process-oriented challenges have been encountered compared with technological ones. Future work will focus on developing system design guidelines for robotic in-line quality inspection systems in the realm of ZDM.

As manufacturing companies are becoming more global, dynamic, and competitive, contradictory demands intensify. Flexibility is a key enabler for meeting the challenges of a global market if offered at mass production price and quality. Many companies have adopted Flexible Manufacturing System (FMS) together with new technologies. Nevertheless, despite the drastic increase in industrial robots adoption, industrial robot applications continue today as they were designed 50 years ago. To obtain a flexible and reliable production system, it takes more than technology as quality depends on equipment and manufacturing processes. Non-adaptive industrial robots autonomy may be disrupted by the geometrical deformations of the fixtures. This paper presents a comprehensive case study of adopting a robotic in-line quality inspection in an automotive Original Equipment Manufacturer (OEM) to aid the robot-fixture collision problem. The purpose is to examine errors that occur in production processes and how quality inspection can mitigate such errors. Empirical data collection was carried out in the form of (i) interviews, (ii) participant observations, (iii) documents, and (iv) video recording of robot cells. Results show that contrary to the case company beliefs, the manufacturing system does not follow the FMS standards; thus, to harmonise resources design and manufacturing processes, adding a robotic in-line quality inspection station is not enough. First, the robotic in-line quality inspection should follow a “preventive” control strategy to avoid deviated fixturing from entering the robot line. Second, the managers should address the beliefs of operators and their activities in solving the robot-fixture collision problem. Moreover third, the robot gripper design needs to be updated to an appropriate one.

Contemporary manufacturing must prioritise the sustainability of its manufacturing processes and systems. Zero Defect Manufacturing (ZDM) focusses on minimising waste of any kind using data-driven technology, hence enhancing the quality of all manufacturing aspects (product, process, service, etc.). Making things right on the first try is the central tenet of ZDM. In recent years, the application of automation for in-line quality inspection systems has begun to attract the interest of both practitioners and academics because of its capability to detect defects in real-time, and thus adapt the system to disturbances. In this work, we provide a systematic review of the literature on current trends in the application of automation for in-line quality inspection with the ultimate objective of achieving ZDM. Additionally, bibliometric and performance analyses have been performed to gain a complete picture of the field. In this work, we have collected bibliometric data from the most widely referred search engines for academic engineering papers, i.e. Scopus, Web of Science, and IEEE Explorer, involving a total of 145 academic publications from 2011 to 2021. Uniquely for this study, we used three research attributes for the analysis of the selected articles, that is, the level of automation, the condition for quality inspection, and the contribution to ZDM dimensions. The literature suggests that there is a lack of research on the use of in-line detection data for the prediction of defects or repair. Based on the results and our interpretation of the literature, an adapted framework of ZDM (Psarommatis et al., 2020a) and multi-layer quality inspection (Azamfirei et al., 2021a) is presented.

Manufacturing companies around the world are under constant pressure to perform effectively and sustainably. Incidental processes, such as Quality Inspection (QI), are needed to achieve Zero-Defects Manufacturing (ZDM). This study aimed to identify the Human Factors and Ergonomics (HF&E) in the design of advanced automation, QI systems, and ZDM through selected papers and empirical observations. Our presented model is built around the six main dimensions, i.e., top management, manager (project owner), designers, engineers (internal and suppliers), and operators. The commitment of top management, the openness of the manager, the design-friendly nature of the technological system, and the constant updating of knowledge by engineers are important for the success of ZDM. Researchers need to be familiar with cognitive and organisational human factors to align theory with specific cases. Operators face physical and cognitive challenges, and their environment and health must be considered for their successful contribution to the design of advanced QI systems.

An essential aspect of resilience within Cyber-Physical Systems stands in their capacity of early detection of faults before they generate failures. Faults can be of any origin, either natural or intentional. Detection of faults enables predictive maintenance, where faults are managed through diagnosis and prognosis. In this paper we focus on intelligent predictive maintenance based on a class of machine learning techniques, namely transfer learning, which overcomes some limitations of traditional approaches in terms of availability of appropriate training datasets and discrepancy of data distribution. We provide a conceptual approach and a reference architecture supporting transfer learning within intelligent predictive maintenance applications for cyber-physical systems. The approach is based on the emerging paradigms of Industry 4.0, the industrial Internet of Things, and Digital Twins hosting run-time models for providing the training data set for the target domain. Although we mainly focus on health monitoring and prognostics of industrial machinery as a reference application, the general approach is suitable to both physical- and cyber-threat detection, and to any combination of them within the same system, or even in complex systems-of-systems such as critical infrastructures. We show how transfer learning can aid predictive maintenance with intelligent fault detection, diagnosis and prognosis, and describe some the challenges that need to be addressed for its effective adoption in real industrial applications.

The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and digital technologies within industrial production and manufacturing systems. The objective of making industrial operations monitoring easier also implied the usage of more effective data-driven predictive maintenance approaches, including those based on machine learning. Although those approaches are becoming increasingly popular, most of the traditional machine learning and deep learning algorithms experience the following three major challenges: 1) lack of training data (especially faulty data), 2) incompatible computation power, and 3) discrepancy in data distribution. A new data-driven technique, such as transfer learning, can be developed to overcome the issues related to traditional machine learning and deep learning for predictive maintenance. Motivated by the recent big interest towards transfer learning within computer science and artificial intelligence, in this paper we provide a systematic literature review addressing related research with a focus on predictive maintenance. The review aims to define transfer learning in the context of predictive maintenance by introducing a specific taxonomy based on relevant perspectives. We also discuss current advances, challenges, open-source datasets, and future directions of transfer learning applications in predictive maintenance from both theoretical and practical viewpoints.

The 5G communication technology has the ability to create logical net-works, called network slices, which are specifically carved to serve particular application domains. Due to the mix of different application criticality, it becomes crucial to verify if the applications’ service level agreements are met. In this pa-per, we propose a novel framework for modeling and verifying 5G orchestration, considering simultaneous access and admission of new requests to slices as well as virtual network function scheduling and routing. By combining modeling in user-friendly UML, with UPPAAL model checking and satisfiability-modulo-theories-based model finding, our framework supports both modeling and formal verification of service orchestration. We demonstrate our approach on a e-health case study showing how a user, with no knowledge of formal methods, can model a system in UML and verify that the application meets its requirements. © 2023, ComSIS Consortium. All rights reserved.

 More and more meetings in working life are conducted remotely via digital media. How are creative meetings affected by participants being at a distance? Some experiment suggests group creativity to improve with remote work, other show challenges. How is it in real practice? Ten well performing leaders working from home have been interviewed. They reported several challenges. Some have been found in earlier research, the preparation phase is harder and the tools for remote communication aren’t yet good and simple enough for practitioners. But some might be new discoveries, the long time needed for creative meetings being too tiresome for the remote situation, and participants becoming disturbed by irrelevant activities in the home context.Most of the interviewed managers see a hybrid as the future, where some work will be performed from home and some at a shared workplace. Creative meetings are suggested to be performed with physical closeness.

Purpose

The study objectives were to (1) identify if providing solution-focused interaction training enables managers and employees to develop and implement actions to improve their psychosocial work environment and (2) test a recontextualization of the psychosocial work environment as social structures affecting members of the workplace and verify if social interactions effectively change the local psychosocial work environment.

Design/methodology/approach

The intervention involved training managers, supervisors and employees in solution-focused interaction. This study used a controlled interrupted time-series design, with an intervention and control group (CG) and pre- and post-measurements.

Findings

The psychosocial work environment improved, indicating that the training led to better social interactions, contributing to changes in the social structures within the intervention group (IG). Collective reflection between participants in the take action phase was the key to success. The recontextualization uncovered these mechanisms.

Research limitations/implications

The present study supports a recontextualization of the psychosocial work environment as primarily decided by social structures that emerge in recurrent interactions within work teams. The same social structures also seem to be important for other features of the production system, like job performance.

Practical implications

Training designed to enable high-quality social interactions, like dialogue and collective reflection, has proven to be effective in changing social structures. Moreover, managers may need training in facilitating the collective reflection between participants. Increased focus on social interactions within work teams is suggested for future study of organizational change processes, psychosocial work environment and practical psychosocial work environment management.

Originality/value

The intervention was delivered in the preparation phase to enable an effective take action phase. Both phases are less studied in psychosocial risk assessments research. The recontextualization has never been fully used in psychosocial research.

The digital transformation of International Manufacturing Networks (IMNs) has been identified as a complex undertaking that requires coordination. However, knowledge about coordination in this context is lacking.  The purpose of this paper is to explore enablers for the coordination of digital transformation in IMNs. To this effect, a case study was conducted with a manufacturing company in the heavy vehicle industry. The most prominent enabler is identified as establishing an organisational structure around the digital transformation that ensures coordination and promotes collaboration across the IMN. Future research could moreover compare enablers across multiple cases.

Coordinating the digital transformation of globally dispersed factories within international manufacturing networks has become a critical issue for competitiveness, yet there has been limited attention paid to this issue in previous research. Therefore, the purpose of this paper is to, from an organizational perspective, explore the challenges in coordinating the digital transformation in an international manufacturing network and the coping mechanisms to overcome those challenges. A case study is conducted in a manufacturing company within the heavy vehicle industry, thus contributing to the limited empirical research covering coordination of digital transformation. The data is analyzed through organizational structure and design theory, and the findings are mapped into four core dimensions: differentiation, integration, centralization, and formalization. The results show 15 challenges and 11 coping mechanisms for coordination of digital transformation in international manufacturing networks, identifying the significance of the coordination uncertainty within the formalization dimension that is particularly exposed to the changes induced by digital transformation. The findings include the need for a coordination-oriented organizational structure that incorporates how and where coordination can be actualized. The research implications contribute with new insights by providing a detailed description of the created organizational structure and, in contrast to previous research, focuses specifically on the coordination aspect of digital transformation in an international manufacturing network. 

Manufacturing companies have adapted to globalisation and collected their globally dispersed factories into factory networks, known as international manufacturing networks (IMNs). This requires coordination to access the associated competitive advantages, synchronise the factories, and exploit the capabilities inherent in those individual factories. However, when IMNs introduce digital transformation into the factories, the coordination is often hindered by uncertainty and complexity, as digital transformation requires an extensive adjustment that affects everything from the organisational structure to ways of working. Different parts of the network that are dependent on one another, for example, may be difficult to track and foresee the consequences of digital transformation. Hence, due to the respective challenges, complexities, and uncertainties, the coordination of digital transformation in IMN is paramount to examine. 

This licentiate thesis begins with exploring coordination mechanisms to increase the understanding of existing research and shape it into a comprehensive overview. Moreover, through case studies, this thesis explores the challenges in and enablers for coordinating digital transformation in IMNs. Altogether, the challenges and enablers are analysed within the dimensions and categories of coordination mechanisms, i.e., they are collected into an overview and described within the case study context. The most significant findings of this thesis are related to the organisational structure, which enables the coordination of digital transformation. However, plenty of challenges remain, foremost connected to the formalisation of the organisational structure, e.g., balancing the rules and guidelines for digital transformation but providing space for people performing the activities to explore and investigate digital technologies. 

This thesis further contributes to the existing body of research by untangling the complexity of coordination itself and, specifically, the coordination of digital transformation in IMNs, explicitly adding to the operations management field and the management of global operations. Practical contributions are also made, i.e., this thesis promotes practitioners’ increased understanding by providing specific examples and descriptions of the findings within their context. Based on the findings, this thesis offers multiple paths for future research, keeping in mind that coordination is not static but evolving, depending on the context in which it is performed. 

Factories within International Manufacturing Networks (IMNs) need coordination to harness globalization’s potential. Simultaneously, adding additional complexity, they seek to coordinate digital transformation across the IMNs, requiring a holistic view encompassing the organizational structure. The purpose of this paper is to identify how manufacturing companies adapt the organizational structures of their IMNs to coordinate digital transformation. Through a multiple case study of four manufacturing companies, the results show new or adapted functional units, roles and interfaces. The findings describe and exemplify global and local functional units, boundary spanner roles, and formal and informal interfaces created for coordinating digital transformation in IMNs.

Requirements engineering is a time-consuming process, and it can benefit significantly from automated tool support. Ambiguity detection in natural language requirements is a challenging problem in the requirements engineering community. Several Natural Language Processing tools and techniques have been developed to improve and solve the problem of ambiguity detection in natural language requirements. However, there is a lack of empirical evaluation of these tools. We aim to contribute the understanding of the empirical performance of such solutions by evaluating four tools using the dataset of 180 system requirements from the electric train propulsion system provided to us by our industrial partner Alstom. The tools that were selected for this study are Automated Requirements Measurement (ARM), Quality Analyzer for Requirement Specifications (QuARS), REquirements Template Analyzer (RETA), and Requirements Complexity Measurement (RCM). Our analysis showed that selected tools could achieve high recall. Two of them had the recall of 0.85 and 0.98. But they struggled to achieve high precision. The RCM, an in-house developed tool by our industrial partner Alstom, achieved the highest precision in our study of 0.68. 

[Context and Motivation] Requirements in tender documents are often mixed with other supporting information. Identifying requirements in large tender documents could aid the bidding process and help estimate the risk associated with the project. [Question/problem] Manual identification of requirements in large documents is a resource-intensive activity that is prone to human error and limits scalability. This study compares various state-of-the-art approaches for requirements identification in an industrial context. For generalizability, we also present an evaluation on a real-world public dataset. [Principal ideas/results] We formulate the requirement identification problem as a binary text classification problem. Various state-of-the-art classifiers based on traditional machine learning, deep learning, and few-shot learning are evaluated for requirements identification based on accuracy, precision, recall, and F1 score. Results from the evaluation show that the transformer-based BERT classifier performs the best, with an average F1 score of 0.82 and 0.87 on industrial and public datasets, respectively. Our results also confirm that few-shot classifiers can achieve comparable results with an average F1 score of 0.76 on significantly lower samples, i.e., only 20% of the data. [Contribution] There is little empirical evidence on the use of large language models and few-shots classifiers for requirements identification. This paper fills this gap by presenting an industrial empirical evaluation of the state-of-the-art approaches for requirements identification in large tender documents. We also provide a running tool and a replication package for further experimentation to support future research in this area.

Measuring overall equipment effectiveness can be rather difficult. Particularly to capture all chronic losses, those losses that occur frequently, often on a daily basis, and often with a rather quick and easy fix without involvement of other support functions. Sporadic losses, on the other hand, such as breakdowns, lack of material or manpower is quite easily logged as it gets noticed. This issue is clearly a bigger one when discussing manual or semi-automatic OEE measurement systems. As a complement to this and as a way of visualizing effects of chronic versus sporadic losses a tool has been developed and tested in a case study in an industrial setting. 

Model-based methods and techniques continuously evolve to meet the increasing challenges of modern-day technical landscapes. Parallel to Model-based methods, other paradigms are similarly maturing and being integrated, and one such paradigm is DevOps. Model-based methods and DevOps are perceived to provide benefits when viewed in isolation. Recently, there has been an increased interest in matching the two paradigms, with various proposals and early adoption results. However, little focus is put on the practitioners' view. In this paper, we propose a methodology that aims to utilise Model-driven engineering and DevOps practices in conjunction. Together with the methodology, we present an early evaluation of it from a practitioner's perspective. In particular, we study a large and long-running student project aiming to build a solar vehicle, by presenting the current integration and potential future directions. In this paper we limit the observation to the development phase. Early feedback from the case study indicates significant benefits for several identified project pain points, and it's expected that more benefits will emerge when more advanced DevOps aspects are integrated with model-based methods, and the project matures. © 2022 Owner/Author.

In general, trains are referred to as environment-friendly transportation means when compared e.g. to cars, busses, or aircraft, being modern trains electrified systems. Unfortunately, the costs due to creation and maintenance of railway infrastructures, notably the overhead lines to power the trains, impose boundaries to their expansion potentials. In this respect, the advances in battery technologies are disclosing new opportunities, like serving partially electrified tracks. In particular, on board batteries can be used as backup energy where overhead lines are not available. In such scenarios, analysing battery requirements and evaluating possible solutions is of critical importance.This paper proposes a model-based systems engineering methodology for evaluating the feasibility of heterogeneous battery systems in the railway domain. The methodology leverages separation of concerns to reduce the complexity of the problem and abstracts the different railway system components by means of corresponding simulation models. The methodology is illustrated through a study performed at an industrial partner; in particular, the paper discusses how simulation models have been conceived, refined, validated, and integrated to analyse the properties of various battery configurations for several passenger trains operating on commercial lines in France. Interestingly, the results demonstrate that heterogeneous battery systems provide a suitable trade-off alternative when compared to homogeneous batteries.

There is an increasing interest in research on the combination of AI techniques and methods with MDE. However, there is a gap between AI and MDE practices, as well as between researchers and practitioners. This paper tackles this gap by reporting on industrial requirements in this field. In the AIDOaRt research project, practitioners and researchers collaborate on AI-Augmented automation supporting modeling, coding, testing, monitoring, and continuous development in cyber-physical systems. The project specifically lies at the intersection of industry and academia collaboration with several industrial use cases. Through a process of elicitation and refinement, 78 high-level requirements were defined, and generalized into 30 generic requirements by the AIDOaRt partners. The main contribution of this paper is the set of generic requirements from the project for enhancing the development of cyber-physical systems with artificial intelligence, DevOps, and model-driven engineering, identifying the hot spots of industry needs in the interactions of MDE and AI. Future work will refine, implement and evaluate solutions toward these requirements in industry contexts. 

With the rapid development and increasing utilisation of eXtended Reality (XR), sustainability considerations have become an important factor in the design process. Despite the many advantages of XR, existing studies covering design education research show a fragmented early phase application use primarily focusing on local use-cases rather than global interconnected alternatives. While many universities are developing their digital capabilities using XR for various educational purposes, fewexamples exist of interceded activities using multiple variations of XR (i.e., VR, AR, or MR).Establishing a more interconnected set of use-cases is important to fully realise the potential of this technology. Past methodological considerations have been presented through guided design steps when working with elements of XR. However, to position early attempts to work on design imperatives using XR this paper presents a literature review and content analysis to examine the current state of design principles for XR. By utilizing the Design Society's knowledge repository, it employs a categorization process and charts valuable insights of existing XR pedagogical practices. Ultimately, this paper highlights the importance of considering sustainability in digitally enabled prototyping steps and practices using XR and its relevance for the E&PDE community. It also emphasises the need for more research and attention on how XR is practised and how it can be used to advance sustainability efforts.

Never before has restrictions at a global scale due to the Covid-19 pandemic put industry at such unprecedented transformation need. Sustainability by using resources with greater consideration in order to continue to stay resilient, and to thrive post-pandemic has put attention on how benefit the most out of emerging technologies. The advent of extended reality (XR) is today reshaping the way people experience the physical and the virtual environments, from observation to immersion. Due to the turmoil caused by the pandemic technologies that minimize equipment threshold has increased in popularity. Today XR enable experts to be more productive to detect and debug problems and resolve production issues remotely. Currently, both clear advantages and potential disadvantages may exist based on degree of the maturity level of integration. XR provides opportunities for immersive learning, upskilling and renewal. Still, it involves several uncertainties in benefits. XR implementation therefore remain very dispersed among companies. Implementation of these technologies has been considered more to the level of having an inhouse highly sophisticated playground, with potential for later consideration. However, what is missing in many instances is a clear understanding of the impact and transformative mechanisms that AR, VR and MR could provide if only equipped with learnings of how to introduce and adopt knowledge embedded in various use-cases. With a growing experience in testing these technologies the possibilities and requirements needed for successful decision-making are also starting to emerge. Consequently, by understand learning adoption and value drivers in industrial settings, skills practiced in academia can be sharpened not only from a contextual understanding but also to new academic domains and to incentivize implementation initiatives. This paper is looking at distinct value indicators for onboarding as a way for industry professionals to learn new XR skills. 

The advent of augmented reality (AR) is reshaping the way people experience physical and virtual environments, from observation to immersion. Growing interest in adopting AR provides opportunities for immersive learning, upskilling, and renewal. However, uncertainties exist in how to maneuver a transition toward making use of this technology through systematic integration. Due to the turmoil caused by the global pandemic health crisis, implementation of AR now faces urgency in minimizing adoption thresholds and establishing a more systematic escalation approach. This paper investigates the characteristics of such learning approaches and examines the integration of AR with customized progression. Two solution suppliers were investigated to uncover the integration process of AR, which, to the best of our knowledge, is scarcely explored in existing research. This study reveals that a balanced escalation of user-centric learning activities, i.e., an onboarding process, harmonizes anticipated cognition levels for a designated AR application tool.

Augmented Reality (AR) has developed rapidly inrecent years and it is about to become a mainstream technology.We are witnessing how emerging technologies such as AR has been introduced and today widely applied in engineering education.The turmoil caused by the COVID-19 pandemic has in many ways highlighted the importance of AR technology for collaboration and remote assistance of frontline workers. Enabling experts to be much more productive in helping to debug problems and resolve production issues remotely. This kind of hands-on support and tutoring opportunities play well into the possibilities embedded in a more digitalized approach to engineering education. Still, both industries and universities are exploring ways to enhance the value-added credentials that come along with an integration and investment of AR. This paper set out to understand what type of assessment that are used to drive learning performance amongstudents in engineering education.

There is a pressure for the public sector to promote innovation but the research-based insights into how public sector organizations innovate is still low. Managers are in a constant balancing act between the conflicting demands of exploit and explore where the public sector are challenged by the disruptive nature of innovation. This paper explores the embedded innovation capabilities in a municipal organization by identifying enablers/barriers which affect the readiness to innovate.

The study is a qualitative, instrumental case study based on interviews with managers in a Swedish municipality which investigates the antecedents to innovation, which in turn contributes to knowledge on the managerial balancing act between exploit/explore in an empirical setting. The paper identifies a balancing act which risks defaulting towards optimization rather than innovation because of barriers for innovation being seen existing while enablers are assumed. There is a contested meaning behind innovation, making the balancing act between the known and the unknown which leads to a low readiness to innovate.

Middle managers have an important, but rarely recognized role in supporting innovation. This paper follows a group of middle-managers in a Swedish municipality over a five-year period in an attempt to understand middle managers role in innovation support, what they do and what they need. The study identifies that the creation and maintenance of innovation space as crucial for supporting innovation by others. Innovation support in this case where trough trust-based management rather than conscious promotion of innovation. This study also identified potential problems concerning the relationships with other parts of the municipality external to the innovation space. The results highlight a need to look further into concepts such as co-governance and the continuous management which supports employee driven innovation.

There is a growing pressure for innovation in the public sector. Research on innovation suggests a new role for organizations, from a producer of predefined goods and services to a facilitator of co-creation and new forms of network-based governance. Previous research is mostly focused on the need for organizations to collaborate across sectors (inter-organizational) to promote innovation, less on the need for collaboration across local governments different departments (intra-organizational) but also across smaller units (inter-departmental). The aim is to explore how internal conditions affect a municipality’s innovation capability. 

This is done through a study based on interviews with departmental managers. The study reveals a heterogenic organizational environment where managing is situated and intra-departmental on the expense of inter-departmental and intra-organizational collaborations. The study also reveals that the situation is created by established practices, processes and structures which do not promote inter-departmental collaborations and thus does not build intra-organizational innovation capabilities.

Many knowledge workers in the manufacturing sector from IT, research and development, sales, finance, customer care and management transitioned to work from home because of COVID-19 (Eurofound, 2020). We investigated how such an abrupt transition affected productivity. Data were collected from 138 white-collar workers using a survey shared on social media. Most of the respondents stated that working from home affected their productivity positively due to fewer interruptions and more efficient meetings. Some employees reported decreased productivity due to problems with the organisational, social and physical working conditions. The work environment should be actively managed when working from home.

 Employee-driven innovation involves all employees at all levels in improvement work in the organisation. Rather than only accessing the capabilities of research and development employees – the unique competence, insights and ideas of all staff can be activated. We use a longitudinal design to test the impact of leader training on third-level EDI as part of a psychosocial risk assessment process. Leaders are trained to listen to employees and work together to solve work environment problems. We found that idea generation and time to take a break to test new ideas increases after only six months, and this increase is sustained at 18 months compared to a control group. However, 'keenness to share ideas' and 'being listened to' were not significant compared to the control group. These findings and their implications for future research are discussed.

Managements working conditions during the pandemic are important since they are the ones who had to motivate, encourage, and navigate their team during this unique time in history. Managers are vital in forming the working environment conditions for others. When they feel stressed and overwhelmed, this acts as a barrier to caring for and supporting their team members. Our study aimed to investigate managers’ organisational and social work environment conditions and health when working from home during the pandemic.This study has a cross-sectional design. Survey data were collected one year into the COVID-19 pandemic from April-May 2021. We used a survey with fixed questions to collect data from four different organisations and through an open online survey. Data on the 161 managers out of a total of 888 respondents is included in the present study. We compare the manager’s data to pre-pandemic reference values.The respondents report on average a PSC-score of 14. Quantitative demands, work pace, social support from colleagues & supervisors and work life balance were better for managers during the pandemic. There was no difference in burnout scores compared to pre-pandemic reference values. Male managers reported significantly lower levels of work demands compared to female managers and better work life balance. Male managers also reported lower social support from their supervisor.Workplaces should pay attention to differences in working conditions provided for male and female managers going forward.The way the data is presented in this paper could be useful for practitioners to use when introducing PSC and COPSOQ to the workplace.