https://www.mdu.se/

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. 

In order to support manufacturing companies in their digital transformation, challenges and views of the term 'digital transformation' need to be identified since digital transformation is considered a source of competitive advantages. Therefore, this paper aims to explore the challenges and differing views of digital transformation in the manufacturing industry. A case study was conducted in collaboration with four Swedish manufacturing companies. The results were then mapped into categories of three dimensions (people, process and technology), indicating that digital transformation can have different meanings within a company. We conclude that the term 'digitalisation' is more frequently used in the manufacturing industry than 'digital transformation' and identified challenges relate to lack of best practice for digital transformation, degree of standardisation and therefore affects the workload and limits the possibilities of transferring technical solutions between factories. Our findings are relevant to operations managers and other interested in digital transformation. 

Digital transformation of production systems is a challenging task that demands radical responses from existing organizations. During the digital transformation of productions systems tensions occur that need to be managed and the purpose of this paper is to identify paradoxes in the digital transformation of production systems. Paradox theory has been applied as an analytical framework when identifying digital transformation paradoxes and tensions. A case study has been conducted and two manufacturing companies’ digitalization projects have been studied and analyzed in combination with data from workshops around digital transformation. The results were mapped into four types of paradoxes: organizing, performing, belonging, and learning. We conclude that the identified tensions are intertwined, and a major tension is the degree of standardization of technologies (standardization vs customization) and a more agile way of working (learning by doing vs learning before) doing is a trend within the digital transformation of production system. Our findings are relevant to operations managers and others interested in tensions during the digital transformation of production systems.

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.

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.

Aim: This thesis aims to investigate how businesses within traffic safety and temporary traffic management can apply visual artificial intelligence (AI) to achieve safer, more environmentally friendly, and cost-effective operations.

Research questions: “How can the application of visual artificial intelligence be used for identifying temporary protective devices on road construction sites?”; “How can the application of visual artificial intelligence be used for identifying personal protective equipment on road construction sites?”; and “What changes can occur regarding the company's operational safety, costs, and carbon emissions with the application of visual artificial intelligence on road construction sites?”.

Methodology: This study was conducted using an abductive approach and a mixed research design. A theoretical framework based on scientific articles and an empirical data collection consisting of a case study, interviews with employees, and calculations based on data from the case company were analyzed to answer the study’s research questions.

Conclusion: By addressing the challenges and leveraging the opportunities that technology offers, AI cameras can be a valuable tool in promoting a safer and more sustainable work environment on road construction sites, as well as for more favorable operations for companies. By applying visual artificial intelligence to identify correct usage of personal protective equipment on road construction sites and combining it with manual inspections, worker safety can be improved, and proper use of personal protective equipment (PPE) can be ensured. Finally, the study indicates that the application of visual artificial intelligence on road construction sites has the potential to improve the safety of workers, road users, and civilians nearby, reduce costs for companies and decrease environmental impacts by reducing carbon emissions.

 Produktnedläggningsbeslut kan gynna företag på flera områden, inklusive organisatoriska, ekonomiska och strategiska aspekter. Trots dessa fördelar uppstår utmaningar såsom bedömning av finansiell prestation, påverkan på intressenter och behovet av effektiv resursallokering. Detta gör beslutet mer komplext och krävande, vilket ofta möter motstånd från chefer. Trots ämnets vikt finns det en brist på forskning inom området. Av den anledningen undersöks vilka utmaningar, åtgärder och möjligheter som är förknippade med beslutet att lägga ned en produkt i denna studie. För denna studie användes en mixed-method approach, vilket innebar att både kvantitativa och kvalitativa metoder användes för att samla in empiriska data. Både en enkätundersökning och intervjuer med produktchefer inom industriföretag genomfördes för att möjliggöra en holistisk och omfattande undersökning av det studerade ämnesområdet. Den empiriska datan jämfördes sedan med den teori som sammanställts.  PNB kan medföra olika utmaningar relaterade till kunder, externa partners, strategi, ekonomi och organisation. För att hantera dessa utmaningar finns olika åtgärder som chefer inom industriella företag bör vidta, men den viktigaste av dessa är tydlig kommunikation. Det finns också möjligheter som detta beslut kan medföra. Genom att förstå dessa möjligheter kan företag skapa strategier för att maximera de positiva effekterna av PNB och stärka sin position på marknaden. 

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. 

Cloud computing is a game-changer model that opens new directions for modern manufacturing. It enables services and solutions that help improve the productivity and efficiency of smart production plants. The main objective of the paper is to provide a summary of the various cloud-based manufacturing services currently being offered to manufacturers or that could be offered in the future. Additionally, the paper aims to discuss the various enabling technologies used to support the integration of cloud manufacturing in the manufacturing industry. Furthermore, the paper categorizes the different services based on their functionalities and maps them to four levels of production such as plant level, production line level, machine level, and process level. The categorization of services and mapping them to appropriate levels in production can enhance efficiency and productivity in the manufacturing industry. The study advances the discussion on cloud-based manufacturing from the types of services and enabling technologies perspective.

This study is a bachelor thesis in the field of information design with focus on informative illustration. The purpose of the work was to create an easy-to-understand and easily accessible overview map of Stockholm Central Station in order to strengthen the spatial understanding of the location and thereby increase navigation.

The study is based on methods and theories relevant to information design and informative illustration. The methods used in the work were observations/site analysis, interviews with tourists and unaccustomed travelers, a customer journey, and an environmental analysis. The results show a need in wayshowing systems at Stockholm Central Station and simplify the reading of the overview map. The design proposal focuses on reducing the cognitive load through, among other things, information reduction, the use of clear color coding and hierarchy, and a perspective that is adapted to the viewer´s location. This can contribute to viewers being able to read the information and understand the structure of the station, thereby creating a mental map more easily. The design proposal aims to facilitate navigation for all visitors regardless of previous experience.

Novel production paradigms aim at increasing the efficiency and flexibility of production systems. Nonetheless, traditional industrial infrastructures lack the mechanisms needed to support these new paradigms. One of the main limiting factors is the architecture, which follows the automation pyramid in which subsystems are divided in layers depending on their functionalities. This allowed to meet the timing and dependability requirements of the production subsystems, however at the cost of limiting the exchange of information required to provide increased flexibility to the system. For this reason, in this paper we propose a new industrial architecture with a single network infrastructure to connect all the devices that conform to the industrial systems. On top of that, we design an Automatic Network Configurator to support the automatic configuration of the system. To assess the feasibility of our design and evaluate its performance, we implement the first instance of the architecture capable of supporting changes in the traffic requirements during run-time, i.e., without stopping or disrupting the system's operation. Furthermore, we use the implemented instance to measure the time required for reconfigurations.

 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. 

Artificial intelligence in manufacturing systems is currently most used for quality control and predictive maintenance. In the lock industry, quality control of final assembled cylinder lock is still done by hand, wearing out the operators' wrists and introducing subjectivity which negatively affects reliability. Studies have shown that quality control can be automated using machine-learning to analyse torque measurements from the locks. The resulting performance of the approach depends on the dimensionality and size of the training dataset but unfortunately, the process of gathering data can be expensive so the amount collected data should therefore be minimized with respect to an acceptable performance measure. The dimensionality can be reduced with a method called Principal Component Analysis and the training dataset size can be estimated by repeated testing of the algorithms with smaller datasets of different sizes, which then can be used to extrapolate the expected performance for larger datasets. The purpose of this study is to evaluate the state-of-the-art methods to predict and minimize the needed sample size for commonly used machine-learning algorithms to reach an acceptable anomaly detection accuracy using torque measurements from locks. The results show that the learning curve with the best fit to the training data does not always give the best predictions. Instead, performance depends on the amount of data used to create the curve and the particular machine-learning algorithm used. Overall, the exponential and power-law functions gave the most reliable predictions and the use of principal component analysis greatly reduced the learning effort for the machine-learning algorithms. With torque measurements from 50-150 locks, we predicted a detection accuracy of over 95% while the current method of using the human tactile sense gives only 16% accuracy.

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.

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.

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.

PurposeIn a highly competitive and globalised era, agile organisations proactively steer towards sustainability. This situation persuaded the organisations to align Quality Management (QM) initiatives to achieve sustainable outcomes. This study aims to explore quality-sustainability linkage, explicitly focusing on attaining the prestigious IAQ Quality Sustainability Award. Further it investigates, the impact of QM as a strategy for promoting sustainability to meet sustainable development goals (SDGs).Design/methodology/approachDue to the lack of substantial literature connecting QM to sustainability, the current research adopted an explanatory multiple-case study. Six cases were purposively chosen for the study. Three cases of those who have achieved the prestigious IAQ Quality Sustainability Award and remaining have been selected that have fallen short of receiving the award. A detailed within-case and cross-case examinations involving six cases that reported their QM achievements aligned with SDGs.FindingsThe findings demonstrate the significant role of QM adoption in achieving positive results from the perspective of SDGs, such as reduced environmental impacts, improved operational efficiency and enhanced quality of life. Effective stakeholder collaboration, proficiency in analytical tools and strategic alignment with SDGs emerged as critical success factors. Conversely, weak linkage with sustainability and unclear approaches were crucial challenges in attaining the IAQ Quality Sustainability Award.Research limitations/implicationsThis paper outlines essential commandments for organisations actively seeking to promote sustainability. It offers valuable insights for decision-makers, facilitating a profound understanding of the challenges and opportunities in pursuing sustainable performance.Originality/valueThe distinctive nature of this study lies in its dedicated exploration of the intricate relationship between QM deployment and its true impact on the achievement of the SDGs.

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.

This paper presents a collaborative solution developed to enable people without prior Internet of Things (IoT) knowledge to ideate, conceptualise, role-play and prototype potential improvements to their work processes and environments. The solution, called the Karakuri IoT toolkit and method, was tested in two workshops with eight production leaders at a Swedish manufacturing company. Outcomes were analysed from the perspectives of materials interaction and instruments of inquiry. Results indicate the solution can help people conceive and prototype improvement ideas at early design stages.

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.

With a new emerging public administration approach, Public Value Governance, the local public authorities struggle with reinventing and developing their organization to become more innovative and collaborative. In trying to solve increasingly more complex challenges, while at best keeping their resources and budget constant or declining, they are turning to civil actors to involve them in different arenas of innovative co-creation. However, this collaboration is complex and challenging. Both the public authorities and civil society are struggling to find a prosperous and professional collaboration. This empirical paper, based on interactive qualitative research, position itself within public innovation and due to its context and focus contributes to the development of knowledge on social and collaborative innovation. In trying to understand how the innovation system works on a micro-level the case study follows a one-year project focusing on working against segregation and creating safety among children and youth in a socio-economic vulnerable area in a Mid-Swedish municipality. The research focuses on challenges with collaboration when a municipality and civil society actors are working together trying to solve a societal challenge. The results are presented through eight themes, including challenges regarding; the concept collaboration, who is collaborating with whom, a poor understanding of and knowledge about each other’s entities, organization and leadership, power relationships and competition, who is included, confusions on direction and who is doing what, as well as collaborative formats. In addition, the paper presents five overriding suggestions on what needs to be strengthened to achieve collaborative agency.

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

Strategic management in the public sector has become quite popular in practice and several studies have shown that it has an impact on organizational performance. However, we still know little of how strategic management unfolds in practice. Overall, our message is that there are specific challenges that appear when applying strategic management in a public sector context that need to be handled to enhance the possibility of succeeding with strategy work. We present these challenges in terms of four tensions emerging in strategy work: planned versus emergent strategies, legitimacy versus practicality, administrative management versus innovative approach, and accessibility versus security. Based on these identified tensions, we suggest four propositions that not only have implications for further research, but also practical implications.

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.

The current manufacturing climate is characterised by the offering of customisable or one-of-a-kind products at mass production prices while complying with sustainability demands. Consequently, modern manufacturing companies are employing more and more industrial robots to increase flexibility and productivity. Nonetheless, for manufacturing companies to achieve true sustainability, a Zero-Defect Manufacturing (ZDM) approach is needed. Such an approach implies applying emerging technologies for quality inspection and improvement, as no system is perfect or free from deviations. Science and Technology (S&T) roadmaps are employed as decision aids to improve the coordination of activities and resources in increasingly complex and uncertain environments. This paper presents a S&T roadmap to guide all-size companies towards ZDM through advanced robotics for in-line quality inspection. The Key Performance Indicators (KPIs) and progress metrics for robotic in-line quality inspection towards ZDM are outlined.

No system is perfect or free of errors. For companies to reach zero defects in a highly volatile and uncertain environment, emerging technologies, as well as human involvement, are needed. The tenant is to measurement-assist the manufacturing system to predict and prevent deviations in dynamic conditions. This article reviews the Measurement-assisted manufacturing (MAM) literature with the aim to (i) reveal key technologies and processes for MAM, (ii) identify current practices and their weaknesses, and (iii) propose future directions. Results show that despite MAM and Zero-Defect Manufacturing (ZDM) being treated separately, they are deeply interrelated and combining both strategies can lead to true sustainability. The literature indicated that future work must be placed in 'hardware' as instrument operation and equipment maintenance, and 'software', as data analytics and geometry assurance strategies.

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 result of reinforcement learning is often obtained in the form of a q-table mapping actions to future rewards. We propose to use SMT solvers and strategy trees to generate a representation of a learned strategy in a format which is understandable for a human. We present the methodology and demonstrate it on a small game. 

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.

This is a bachelor’s thesis in information design with a focus on text design. In this work, I have examined the information experience and information needs of the target group withcomers. The purpose of the thesis is to investigate how an integration service and information provider for newcomers better can meet the information needs of the sub-group withcomers. And how information about the service can be designed and mediated to the target group. The methods used to conduct this study are participant observations, a focus group, and qualitative interviews. The results of the study support previous research on the information needs and information behaviors of newcomers, as well as how social networks serve a function as an information source and a way for newcomers to access and mediate information. The results also demonstrate the contributing role of information in social inclusion and that the lack of, or insufficient information can lead to a sense of- or actual social exclusion. My study also shows that information that can enable access to the social network that the withcomer service can provide is considered a particularly important and vital information need by the withcomers, and that this is not currently being met or communicated about. As a result of the findings, I have developed a design proposal based on the principles of text and image interaction to create new informational material about the withcomer service.

 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.