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Visual perception plays an important role for recognizing possible hazards. In the context of heavy machinery, relevant visual information can be obtained from the machine's surrounding and from the human-machine interface that exists inside the cabin. In this paper, we propose a method that classifies the occurring collisions by combining the data collected by the eye tracker and the automatic logging mechanism in the mixed reality simulation. Thirteen participants were asked to complete a test scenario in the mixed reality simulation, while wearing an eye tracker. The results demonstrate that we could classify the occurring collisions based on two visual perception conditions: (1) whether the colliding objects were visible from the participants' field of view and (2) whether the participants have seen the information presented on the human-machine interface before the collisions occurred. This approach enabled us to interpret the occurring collisions differently, compared to the traditional approach that uses the total number of collisions as the representation of participants' performance., This is the accepted version of the conference paper presented at HUCAPP 2021 (http://www.hucapp.visigrapp.org/?y=2021). The final published version is available at Scitepress via https://doi.org/10.5220/0010386702550262. Personal use of this material is permitted. Permission from Scitepress must be obtained for all other uses., ITS-EASY Post Graduate School for Embedded Software and Systems, Immersive Visual Technologies for Safety-critical Applications (ImmerSAFE)

Visual perception plays an important role for recognizing possible hazards. In the context of heavy machinery, relevant visual information can be obtained from the machine's surrounding and from the human-machine interface that exists inside the cabin. In this paper, we propose a method that classifies the occurring collisions by combining the data collected by the eye tracker and the automatic logging mechanism in the mixed reality simulation. Thirteen participants were asked to complete a test scenario in the mixed reality simulation, while wearing an eye tracker. The results demonstrate that we could classify the occurring collisions based on two visual perception conditions: (1) whether the colliding objects were visible from the participants' field of view and (2) whether the participants have seen the information presented on the human-machine interface before the collisions occurred. This approach enabled us to interpret the occurring collisions differently, compared to the traditional approach that uses the total number of collisions as the representation of participants' performance., This is the accepted version of the conference paper presented at HUCAPP 2021 (http://www.hucapp.visigrapp.org/?y=2021). The final published version is available at Scitepress via https://doi.org/10.5220/0010386702550262. Personal use of this material is permitted. Permission from Scitepress must be obtained for all other uses., ITS-EASY Post Graduate School for Embedded Software and Systems, Immersive Visual Technologies for Safety-critical Applications (ImmerSAFE)

The Internet of Things (IoT) will be used in increasingly complex and critical applications where heterogeneous devices will work together in connected systems. In this paper we address methods for log-analytics and process mining in order to support automatic problem detection and diagnosis in IoT. We introduce the idea of generating consistent event logs over various IoT devices in a particular format, and later a roadmap for it to be used in process mining. The paper also provides information about various statistics on process mining and its future prospects. Those methods are essential to provide a foundation for the future generation IoT systems that will be capable of self-healing.

The Internet of Things (IoT) will be used in increasingly complex and critical applications where heterogeneous devices will work together in connected systems. In this paper we address methods for log-analytics and process mining in order to support automatic problem detection and diagnosis in IoT. We introduce the idea of generating consistent event logs over various IoT devices in a particular format, and later a roadmap for it to be used in process mining. The paper also provides information about various statistics on process mining and its future prospects. Those methods are essential to provide a foundation for the future generation IoT systems that will be capable of self-healing.

Intelligent transport systems will be increasingly adopting the Internet of Things as a key enabling technology. LoRa WAntechnology is one of the most popular long-range wireless technologies with a huge potential in applications such as intelligent shipment tracking. This paper addresses the problem of roaming between multiple cooperative shipping companies and their LoRaWAN networks. The proposed roaming improvement is currently in the testing phase. This paper provides an overview of how to expand LoRa WAnroaming with the support of Artificial Intelligence for providing additional services.

The Internet of Things (IoT) will be used in increasingly complex and critical applications where heterogeneous devices will work together in connected systems. In this paper we address methods for log-analytics and process mining in order to support automatic problem detection and diagnosis in IoT. We introduce the idea of generating consistent event logs over various IoT devices in a particular format, and later a roadmap for it to be used in process mining. The paper also provides information about various statistics on process mining and its future prospects. Those methods are essential to provide a foundation for the future generation IoT systems that will be capable of self-healing.

The Internet of Things (IoT) will be used in increasingly complex and critical applications where heterogeneous devices will work together in connected systems. In this paper we address methods for log-analytics and process mining in order to support automatic problem detection and diagnosis in IoT. We introduce the idea of generating consistent event logs over various IoT devices in a particular format, and later a roadmap for it to be used in process mining. The paper also provides information about various statistics on process mining and its future prospects. Those methods are essential to provide a foundation for the future generation IoT systems that will be capable of self-healing.

Overall equipment effectiveness (OEE) is a common performance measure used in manufacturing industry to identify and prioritize losses to perform improvement work on in order to increase the effectiveness of equipment. There exist challenges though, both in implementing OEE as well as in running an OEE-program. Some of these challenges include lack of training and awareness, lack of focus, risk of misunderstanding the measure etc. This paper will deal with some of the possible misconceptions within the use of OEE that might arise during implementation or in continuously running an OEE-program. Some of the topics of misconceptions that will be discussed include: no financial issues are taken into consideration; that the factors of availability; performance and quality are not weighted; the connection to productivity is not always clear; the importance of cross-functionality of the measurement and work method; the issue of comparison of OEE results; and last but not least the view on and hunt for world class levels. The paper will discuss these (and some additional ones) theoretically and suggest some counter-Actions so that they may be avoided.

The Internet of Things (IoT) will be used in increasingly complex and critical applications where heterogeneous devices will work together in connected systems. In this paper we address methods for log-analytics and process mining in order to support automatic problem detection and diagnosis in IoT. We introduce the idea of generating consistent event logs over various IoT devices in a particular format, and later a roadmap for it to be used in process mining. The paper also provides information about various statistics on process mining and its future prospects. Those methods are essential to provide a foundation for the future generation IoT systems that will be capable of self-healing.

Today, many services, such as ticket purchase and cash withdrawal, are provided by stationary machines such as ATM and ticket machines placed in train stations and travel centers. Personalized service is not always available, and users are referred to machines and automats. Previous research on ATM and ticket machines mostly focus on user interface and do not take the contextual conditions into consideration. A well-designed interface may not work under conditions such as poor lighting and time limitations of the service which affects the usability. The focus of this paper is not the physical or digital design of ATM and ticket automats as such, but rather how they function in their environment, and how that will affect users; especially old people with some sensory limitations such as limited eyesight and partial motor skills. The empirical study indicates that the physical environment of these machines and automats is crucial for how the service works. Identified parameters that affect usability are light conditions, auditory conditions, the liveliness of the setting and time limitations of services. If the contextual conditions surrounding the automat or machine does not work, it will be inaccessible to many users, however deliberate the design of their physical and digital interface. © Springer Nature Switzerland AG 2020., Conference code: 242409; Conference Paper; Correspondence Address: Uggla, K.; Division of Information Design, Mälardalen UniversitySweden; email: karolina.uggla@mdh.se

Project portfolio management (PPM) is central for project-based firms to achieve structure and prioritization among multiple projects. Competitive pressures, emergence of new technology and constantly changing customer demand imply a dynamic nature of PPM that calls for adjustments to different situations. This paper investigates PPM challenges that a company in the lighting industry face during a technology transformation from fluorescent technology to LED technology. The technology transformation resembles a modular innovation and the question asked is: How does technology transformation influence the project selection process? The findings rest upon an in-depth case study where data was collected via narrative interviews with representatives having detailed insights into the company's PPM activities and decisions. The key findings from the study are: (1) When a company faces a technology, which involves most products in the portfolio to be converted to a new technology, the project selection focus shifts from “what products should be developed” to “in what order should the products be developed”. (2) The technology transformation might lead to that the planned order for carrying out projects can be frequently changed due to reprioritizations during project execution phase. Based on the key findings it can be concluded that PPM selection seems more dynamic than postulated in the literature. This paper contributes with increased insights into the dynamic nature of project selection, specifically related to a technology transformation situations characterized by modular innovation. Further studies are needed regarding effects of other kinds of technology transformations on project selection activities and decisions as well as other factors contributing to the dynamics of PPM.

The Internet of Things (IoT) will be used in increasingly complex and critical applications where heterogeneous devices will work together in connected systems. In this paper we address methods for log-analytics and process mining in order to support automatic problem detection and diagnosis in IoT. We introduce the idea of generating consistent event logs over various IoT devices in a particular format, and later a roadmap for it to be used in process mining. The paper also provides information about various statistics on process mining and its future prospects. Those methods are essential to provide a foundation for the future generation IoT systems that will be capable of self-healing.

Major changes of production systems having strategic impacts on the organizations have occasionally been done in agile-like ways. However, they have been practiced with little theoretical foundation. This paper is one of the first attempts to identify similarities between Lean Startup (LS) and the phenomena in question as an effort to establish theoretical foundation to the phenomena. To identify the similarities, the phenomena observed in three previous studies are examined and compared with LS. As a result, similarities are found in key characteristics of LS and those of the phenomena observed in the studies. Similarities are also found in their contexts. Later in this paper, implications drawn from the identified similarities are discussed in terms of strengthening the theoretical foundation of the phenomena.

This paper describes the use of blended learning approaches in designing two courses in the two-years international master's program in innovation and design education. The paper discusses the benefits and challenges encountered during the implementation of the courses in a blended way. The guiding research questions for this study are: How can blended learning approaches be applied in innovation and design education? what are key benefits and challenges in designing and implementing blended courses in this education field? The paper is based on our three years' experiences in designing and implementing blended courses between 2016-2018. The Blackboard and Yammer micro-blog were used as the main course tools. Data was collected throughout the course periods between 2016-2018 from 55 students. The study observed following benefits with designing and implementing courses in a "blended" way: useful for learning different insights and perspectives, enables reflection and provide a good summary of face-to-face lectures, and fosters engagement and collaboration even outside the classroom. The identified challenges during the implementation of blended courses are students' limited engagement, errors due to technical, connectivity, and interfaces, new to the course management systems and blended courses, and difficulties to keep track and to follow-up over time. Moreover, the paper discussed six lessons learned in designing and implementing the blended courses in innovation and design education. The paper contributes to the blended learning research in soft-applied fields like innovation and design, where the blended studies are limited. In addition, the paper contributes to the discussion of using micro-blogs for designing blended courses in innovation and design education.

The main focus of this paper is how a manufacturing organization can secure the capabilities of effectively executing a series of improvements of various sizes and innovativeness from small to large and incremental to more radical at the manufacturing plant, so that the speed of improvement as a whole at the plant can be kept high. A recent study on a Toyota plant in Japan has shown that a group of production engineers located at the plant plays an important role in ensuring the mentioned kind of capabilities. Those engineers are considered trusted experts in improvement of various sizes and innovativeness. They also coordinate those improvements so that the speed of improvement at the plant level can be kept high. This study raises a question of how other manufacturing companies do to ensure these capabilities, given that the group of the production engineers playing the role is likely a specific organization design to this specific company. For the question, three manufacturing plants producing vehicles in Swedish are studied. The paper discusses implications drawn from a cross-case analysis of those plants including the Toyota plant, in terms of the main focus of this paper. © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

The aim of this paper is to provide new perspectives on the implementation of new operations management practices by applying three different but interrelated frameworks: Human Interaction Dynamics, Normalization Process Theory, and Professional competence as ways of being. The empirical material in this paper is based on a case study within a global manufacturing company, and more specific the development and implementation of a new OM practice for knowledge sharing at one of the sites in Sweden. A mixed-method approach is used, and the empirical material is collected through analysis of a database, two group interviews, and a survey., COPE: Core Plant Excellence

Researchers has been studied and applied lean thinking in different industries and contexts, such as product development, production systems, and sustainability. However, there is a limited knowledge on the application of lean thinking to the servitization context. In particular, there is a lack of theoretical and practical understanding on how to apply lean methods and tools for the benefit of servitization of manufacturing. Based on a systematic literature review, the paper reports specific lean methods and tools that are applied in servitization of manufacturing. Further, the paper discusses enablers and barriers, and opportunities and challenges with the application of lean methods and tools in servitization. Finally, based on the literature analysis, the paper pin points five future research directions to achieve a systematic lean servitization in manufacturing companies., XPRES

In this paper, we analyze the challenges in data acquisition for simulation models of production systems based on two cases from the robotics and aerospace industries. Unlike prior research, we focus not only on the challenges of data acquisition but also on how these challenges affect decisions in production systems. We examine this linkage using the concepts of strategic objectives, decision areas, and internal fit from operations management literature. Empirical findings show that for data acquisition to lead to improved production system performance it is necessary to develop standards. Standards should consider ownership of data by different functions within a manufacturing company, alignment of data to performance measurements, and the connection between data, information, and production decisions. Using these concepts, this paper proposes a set of guidelines that facilitate the standardization of data acquisition for simulation models in production systems. We conclude by discussing the managerial implications of our findings., INNOFACTURE - innovative manufacturing development

The purpose of this study is to explore the current available practices of feedback loops at different phases in the industrialization process. Although literature highlights the impact of feedback loops for both product and production systems development, there is limited research about how firms utilize the concept of feedback loops in the industrialization process. Based on a case study at a railway component manufacturing company, the paper presents the identified feedback loops and mechanisms that are working well and not working well within the industrialization process. Further, it explains a practical method to improve the current or establish new feedback loops. The paper contributes to the discussion on the application of lean and agile approaches to the industrialization process where feedback loops act as enablers.

The global world over the years, witnessed significant transformations, where innovative technologies are shaping lifestyles, lifting millions from poverty, providing alternative and sustainable sources of income and stretching the reach of quality life to rural and remote areas. Africa is not an exception, and in this regard, Nigeria is witnessing a new page in its socioeconomic development, where the country is leveraging innovative technologies to unlock its technological potentials. Using desk and document reviews, coupled with online interviews with some young Nigerian innovators, the results showcase several Nigerian home-grown technology companies that have achieved global recognition. The companies include ccHub, Andela, PagaTech, BudgIT, Hotels.ng, Konga, Wakanow and MainOne among others as the country’s success stories, making immense opportunities for technological growth and job creation. The paper further sees the Facebook founder’s visit to Nigeria as validation for betting big on the countries innovative technology industry. The paper however found that the scientific narrative on Nigeria’s technology and innovative industry is under-appreciated across the world due to lack of information and support systems. This scientific contribution is a page turner to reposition Nigeria’s innovative technological ecosystem on the global map. The paper hereby recommends a public–private support to this young Nigeria’s innovative technological ecosystem to accelerate the socioeconomic development of the country.

Although scholars and practitioners are actively discussing the potential benefits of introducing Internet of Thing (IoT) in production, IoT is still as an expensive solution in terms of investment and high technological threshold. Manufacturing companies seek a simpler and lower-cost approach to adopting IoT technologies in production, allowing companies to take advantage of the knowledge and innovation capabilities of people close to shop floor operations. This paper introduces the concept of “Karakuri IoT” – simple and low-cost IoT-aided improvements driven by the people close to shop floor operations. A pre-study is conducted to examine the feasibility of the concept. This paper presents the results of the pre-study., Karakuri IoT, Karakuri IoT step 2

In this paper we report on an attempt to let students, companies, and organisations themselves discover the kinds of technologies that could be useful when co-producing knowledge in a Master’s-level course in innovation and design. Traditionally, and for various good reasons such as security and stability, universities have had certain online tools and systems for collaboration, while companies and municipalities have had others. These systems support internal communication within organisations but do not necessarily enhance communication with external contacts. This use of different systems creates barriers to the iterative, recurring, convenient, non-hierarchical, and open online collaboration needed in an innovative design process involving multiple stakeholders. During a ten-week Master’s-level course in innovation and design in 2016 and 2017 the 38 students divided into five project groups established contact with five companies and organisations and could choose their own online tools in dialogue with them. This paper presents the students’ and organisations’ emerging practices during the process based on observations and reflective evaluations conducted during and after the course. The results are discussed in light of how socio-material assemblages formed in this special setting and how the results might be used to improve the teaching of online literacy in design collaboration. The result indicates that for co-production of knowledge in innovation and design projects, three new social media literacies would be useful: meta communication, peak performance, and design awareness.

There has been a request for more studies on how public sector organizations apply Strategic Management (SM) in practice. This paper is our attempt to meet this request by presenting a qualitative case study of how a digitalization strategy was used to apply SM in the Swedish Transport Administration, a central government agency in Sweden. The empirical data contains 67 interviews at different hierarchical levels, focus group interviews and document studies. Eight of the interviews specifically targeted the work on the digitalization strategy. To analyse the empirical data, we aligned with the strategizing perspective focusing on possible tensions in strategy work. Thus, the aim of the paper is to further develop our knowledge of strategy work in the public sector by enhancing our understanding of what tensions strategy work brings about in the public sector and how these tensions potentially affect strategy making. By analysing strategizing micro processes, our results indicate that when working on digitalization, public sector organizations need to be aware of at least four possible tensions that can enable or constrain strategy making. These tensions are: legitimacy vs. practicality, administrative management vs. innovative approaches, accessibility vs. security and planned vs. emergent strategies.

The main purpose this paper is to discuss a research potential in the area of understanding how a company can enhance innovation at the early phases of production system development. Due to this purpose, the structure of this paper deviates from the conventional ones that include methods, findings, and contributions. The chain of logic in this paper is structured as follows. Firstly, the importance of the early phases in terms of constantly developing new production systems is mentioned. Then, the early phases are defined and described in theoretical frameworks. Later, two industrial cases are introduced to emphases the aforementioned importance. Finally, the research potential is discussed based on the theories and practices introduced in the earlier parts of the paper., Accelerating Innovation at the Fuzzy Front-End of Production System Development

Simulations are often used for training novice operators to avoid accidents, while they are still polishing their skills. To ensure the experience gained in the simulation be applicable in real-world scenarios, the simulation has to be made as realistic as possible. This paper investigated how to make the lifting capacity of a virtual mobile crane behave similarly like its real counterpart. We initially planned to use information from the load charts, which document how the lifting capacity of a mobile crane works, but the data in the load charts were very limited. To mitigate this issue, we trained an artificial neural network (ANN) using 90% of random data from two official load charts of a real mobile crane. The trained model could predict the lifting capacity based on the real-time states of the boom length, the load radius, and the counterweight of the virtual mobile crane. To evaluate the accuracy of the ANN predictions, we conducted a real-time experiment inside the simulation, where we compared the lifting capacity predicted by the ANN and the remaining 10% of the data from the load charts. The results showed that the ANN could predict the lifting capacity with small deviation rates. The deviation rates also had no significant impact on the lifting capacity, except when both boom length and load radius were approaching their maximum states. Therefore, the predicted lifting capacity generated by the ANN could be assumed to be close enough to the values in the load charts., This is an Author Accepted Manuscript version of the following chapter: S. Roysson, T. A. Sitompul, and R. Lindell, Using Artificial Neural Network to Provide Realistic Lifting Capacity in the Mobile Crane Simulation, published in Proceedings of the 22nd Engineering Applications of Neural Networks Conference, edited by L. Iliadis, J. Macintyre, C. Jayne, and E. Pimenidis, 2021, Springer reproduced with permission of Springer. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-80568-5 37., ImmerSafe - Immersive Visual Technologies for Safety-critical Applications

This paper seeks to identify the contributions of artificial intelligence (AI) to supply chain management (SCM) through a systematic review of the existing literature. To address the current scientific gap of AI in SCM, this study aimed to determine the current and potential AI techniques that can enhance both the study and practice of SCM. Gaps in the literature that need to be addressed through scientific research were also identified. More specifically, the following four aspects were covered: (1) the most prevalent AI techniques in SCM; (2) the potential AI techniques for employment in SCM; (3) the current AI-improved SCM subfields; and (4) the subfields that have high potential to be enhanced by AI. A specific set of inclusion and exclusion criteria are used to identify and examine papers from four SCM fields: logistics, marketing, supply chain and production. This paper provides insights through systematic analysis and synthesis.

Simulations are often used for training novice operators to avoid accidents, while they are still polishing their skills. To ensure the experience gained in the simulation be applicable in real-world scenarios, the simulation has to be made as realistic as possible. This paper investigated how to make the lifting capacity of a virtual mobile crane behave similarly like its real counterpart. We initially planned to use information from the load charts, which document how the lifting capacity of a mobile crane works, but the data in the load charts were very limited. To mitigate this issue, we trained an artificial neural network (ANN) using 90% of random data from two official load charts of a real mobile crane. The trained model could predict the lifting capacity based on the real-time states of the boom length, the load radius, and the counterweight of the virtual mobile crane. To evaluate the accuracy of the ANN predictions, we conducted a real-time experiment inside the simulation, where we compared the lifting capacity predicted by the ANN and the remaining 10% of the data from the load charts. The results showed that the ANN could predict the lifting capacity with small deviation rates. The deviation rates also had no significant impact on the lifting capacity, except when both boom length and load radius were approaching their maximum states. Therefore, the predicted lifting capacity generated by the ANN could be assumed to be close enough to the values in the load charts., This is an Author Accepted Manuscript version of the following chapter: S. Roysson, T. A. Sitompul, and R. Lindell, Using Artificial Neural Network to Provide Realistic Lifting Capacity in the Mobile Crane Simulation, published in Proceedings of the 22nd Engineering Applications of Neural Networks Conference, edited by L. Iliadis, J. Macintyre, C. Jayne, and E. Pimenidis, 2021, Springer reproduced with permission of Springer. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-80568-5 37., ImmerSafe - Immersive Visual Technologies for Safety-critical Applications

This paper seeks to identify the contributions of artificial intelligence (AI) to supply chain management (SCM) through a systematic review of the existing literature. To address the current scientific gap of AI in SCM, this study aimed to determine the current and potential AI techniques that can enhance both the study and practice of SCM. Gaps in the literature that need to be addressed through scientific research were also identified. More specifically, the following four aspects were covered: (1) the most prevalent AI techniques in SCM; (2) the potential AI techniques for employment in SCM; (3) the current AI-improved SCM subfields; and (4) the subfields that have high potential to be enhanced by AI. A specific set of inclusion and exclusion criteria are used to identify and examine papers from four SCM fields: logistics, marketing, supply chain and production. This paper provides insights through systematic analysis and synthesis.

The energy generation landscape is changing, pushed by stricter regulations for emissions control and green energy generation. The limitations of renewable energy sources, however, require flexible energy production sources to supplement them. Micro gas turbine based combined heat and power plants, which are used for domestic applications, can fill this gap if they become more reliable. This can be achieved with the use of an engine monitoring and diagnostics system: real-time engine condition monitoring and fault diagnostics results in reduced operating and maintenance costs and increased component and engine life. In order to allow the step change in the connection of small engines to the grid, a fleet monitoring system for micro gas turbines is required. A proposed framework combines a physics-based model and a data-driven model with machine learning capabilities for predicting system behavior, and includes a purpose-developed diagnostic tool for anomaly detection and classification for a multitude of engines. The framework has been implemented on a fleet of micro gas turbines and some of the lessons learned from the demonstration of the concept as well as key takeaways from the general literature are presented in this paper. The extension of fleet monitoring to optimal operation and production planning in relation to the needs of the grid will allow the micro gas turbines to fit in the future green energy system, connect to the grid, and trade in the energy market. The requirements on the system level for the widespread use of micro gas turbines in the energy system are addressed in the paper. A review of the current solutions in fleet monitoring and diagnostics, generally developed for larger engines, is included, with an outlook into a sustainable future.

The increased need for constant connectivity and complete automation of existing systems fuels the popularity of Cyber Physical Systems (CPS) worldwide. Increasingly more, these systems are subjected to cyber attacks. In recent years, many major cyber‐attack incidents on CPS have been recorded and, in turn, have been raising concerns in their users' minds. Unlike in traditional IT systems, the complex architecture of CPS consisting of embedded systems integrated with the Internet of Things (IoT) requires rather extensive planning, implementation, and monitoring of security requirements. One crucial step to planning, implementing, and monitoring of these requirements in CPS is the integration of the risk management process in the CPS development life cycle. Existing studies do not clearly portray the extent of damage that the unattended security issues in CPS can cause or have caused, in the incidents recorded. An overview of the possible risk management techniques that could be integrated into the development and maintenance of CPS contributing to improving its security level in its actual environment is missing. In this paper, we are set out to highlight the security requirements and issues specific to CPS that are discussed in scientific literature and to identify the state‐of‐the‐art risk management processes adopted to identify, monitor, and control those security issues in CPS. For that, we conducted a systematic mapping study on the data collected from 312 papers published between 2000 and 2020, focused on the security requirements, challenges, and the risk management processes of CPS. Our work aims to form an overview of the security requirements and risks in CPS today and of those published contributions that have been made until now, towards improving the reliability of CPS. The results of this mapping study reveal (i) integrity authentication and confidentiality as the most targeted security attributes in CPS, (ii) model‐based techniques as the most used r

Purpose – The purpose of this paper is to clarify the methodological logic of coproductive research approaches like action research, collaborative research, interactive research and participatory research to clarify its effectiveness and scientific qualities in high quality knowledge production, and show a way that it can be integrated with institutionalized textbook science. Design/methodology/approach – The paper clarifies the character of coproduction as research methodology concept, the logic of coproductive research approaches, and its characteristics compared to quantitative and qualitative methodology. A model for characterizing research approaches from leading textbook social science is developed to specify the character of coproductive research approaches and support integration in mainstream research methodology discussions. Findings –The paper develops a research methodology framework for coproductive logic and approaches to research, as well as a model to support the integration of these approaches in mainstream research methodology. Research limitations/implications – The developed model of coproductive research approach is not empirically described. Therefore, researchers are encouraged to test and further develop the framework and model in relation to cases and designs of research projects. Practical implications –The paper is helpful for guiding the design of coproductive research in practice, i.e., in research project development or in research methodology education. Social implications –The development of coproductive research approaches supports making science relevant and useful for solving pressing problems and improving social conditions. It also is enabling stakeholders to participate in research and development processes, thus the democratization of research and knowledge production. Originality/value – The paper contributes to integration of the family of coproductive approaches in mainstream research methodology discussion

This paper seeks to identify the contributions of artificial intelligence (AI) to supply chain management (SCM) through a systematic review of the existing literature. To address the current scientific gap of AI in SCM, this study aimed to determine the current and potential AI techniques that can enhance both the study and practice of SCM. Gaps in the literature that need to be addressed through scientific research were also identified. More specifically, the following four aspects were covered: (1) the most prevalent AI techniques in SCM; (2) the potential AI techniques for employment in SCM; (3) the current AI-improved SCM subfields; and (4) the subfields that have high potential to be enhanced by AI. A specific set of inclusion and exclusion criteria are used to identify and examine papers from four SCM fields: logistics, marketing, supply chain and production. This paper provides insights through systematic analysis and synthesis.

This paper seeks to identify the contributions of artificial intelligence (AI) to supply chain management (SCM) through a systematic review of the existing literature. To address the current scientific gap of AI in SCM, this study aimed to determine the current and potential AI techniques that can enhance both the study and practice of SCM. Gaps in the literature that need to be addressed through scientific research were also identified. More specifically, the following four aspects were covered: (1) the most prevalent AI techniques in SCM; (2) the potential AI techniques for employment in SCM; (3) the current AI-improved SCM subfields; and (4) the subfields that have high potential to be enhanced by AI. A specific set of inclusion and exclusion criteria are used to identify and examine papers from four SCM fields: logistics, marketing, supply chain and production. This paper provides insights through systematic analysis and synthesis.

Purpose The purpose of this paper is to explore the role and function of visuals, visual communication and information design as they relate to management control systems and visual management (VM) in lean-inspired organisations. This paper helps expand knowledge on how visual and design studies can contribute to research on VM as part of a management control system. Design/methodology/approach A study is outlined, which was conducted at a multinational manufacturing company to investigate employees' perceptions and use of visual devices on the shop floor, including their related reactions and behaviour. The study is delimited to operation management, lean manufacturing and lean boards (i.e. daily management boards and performance measurement boards). Findings The findings point out the persuasive purpose of lean boards, as well as the metaphoric and persuasive functions of the visuals and information design in management control systems. Originality/value Visual research and design research are rare within studies of management control systems. There is a need to perform research that takes into account the role and function of visual communication and information design in VM. The proposed areas for future research can provide design principles, as well as insights into the complexity of visual communication and information design in VM and management control studies.

This paper seeks to identify the contributions of artificial intelligence (AI) to supply chain management (SCM) through a systematic review of the existing literature. To address the current scientific gap of AI in SCM, this study aimed to determine the current and potential AI techniques that can enhance both the study and practice of SCM. Gaps in the literature that need to be addressed through scientific research were also identified. More specifically, the following four aspects were covered: (1) the most prevalent AI techniques in SCM; (2) the potential AI techniques for employment in SCM; (3) the current AI-improved SCM subfields; and (4) the subfields that have high potential to be enhanced by AI. A specific set of inclusion and exclusion criteria are used to identify and examine papers from four SCM fields: logistics, marketing, supply chain and production. This paper provides insights through systematic analysis and synthesis.

The development of successful production systems is affected by conflicting forces, that is, initiatives seemingly conducive for one line of work can be a constraint for another. Consequently, this paper presents an alternative perspective on how these issues could be managed in organisations. There are a number of key challenges in terms of the involved dichotomies for future innovative quality improvements in operations. These dichotomies are part of four interrelated processes that are the central elements of a production system. As such, aiming for stability or change is a production process dilemma in terms of the production and distribution of offerings and solutions. Control and creativity are the main dilemmas of the innovation process, that is, the creation and implementation of new offerings and solutions, while exploitation and exploration are the dilemmas of the knowledge creation process and efficiency and effectiveness of the value creation process. As the simultaneous existence of both parts of the dichotomy seems to be a paradox, this paper suggests the emergent quality management paradigm as an alternative perspective providing the guidance, examples, and practical solutions necessary to solve these dilemmas by recognising the dichotomies as mutually dependent., XPRES - Excellence in Production Research

The main outcomes of an engineering course should be for the students to achieve the educational goals, enhance their problem solving capabilities and develop essential skills for their future career. In that context, it is important to understand what motivates the students and what helps them develop an engineering mindset. This paper discusses the improvement of a course with the use of student feedback to motivate students and help them develop essential skills. The purpose of the paper is to provide insight into how different aspects of the course are linked to the students’ growth. Different activities have been integrated in the course over the past years. The effect these have on the student motivation to follow the course and develop skills, knowledge and interest in the subject is discussed through the analysis of student performance, student feedback and the experience of the lecturers. The improvements in the course based on the student feedback were received positively by the students, whose learning experience improved, even though the workload of the course was high. Their motivation to successfully complete the course has also increased through the changes in the delivery of the course and the support by the teachers. The combination of student feedback and teacher experience is key for the improvement of a course, while ensuring that the students develop their engineering knowledge. Therefore, the teachers should strike a balance between helping the students find the solution and encouraging them to think on their own in order to develop essential skills.

This paper seeks to identify the contributions of artificial intelligence (AI) to supply chain management (SCM) through a systematic review of the existing literature. To address the current scientific gap of AI in SCM, this study aimed to determine the current and potential AI techniques that can enhance both the study and practice of SCM. Gaps in the literature that need to be addressed through scientific research were also identified. More specifically, the following four aspects were covered: (1) the most prevalent AI techniques in SCM; (2) the potential AI techniques for employment in SCM; (3) the current AI-improved SCM subfields; and (4) the subfields that have high potential to be enhanced by AI. A specific set of inclusion and exclusion criteria are used to identify and examine papers from four SCM fields: logistics, marketing, supply chain and production. This paper provides insights through systematic analysis and synthesis.

In the realm of Industry 4.0, measurement systems play an important role in adapting industrial robots to dynamic environments. Real-time control techniques such as Measurement Assisted Assembly (MAA) can exploit the digital measurements for operation process corrections. Likewise, the propagation of defects can be avoided with in-line measurement conditions. The purpose of this paper is to first understand the capability of robotic in-line measurement assisted assembly in the industrial case of peg-in-hole assembly and second, record the encountered challenges and their enablers. A proof of concept - formed by two 6DoF industrial robots, an in-line Linear Laser (LL), and an on-machine force sensor - have been designed and tested in a lab environment. The experimental results show that robotic in-line measurement assisted assembly can be performed within the tight tolerances of (i) 0, 071 ° to 0, 154° angular deviation between X and Y axes, (ii) applying minimum (near 0) Newton forces in X and Y axes when performing the peg-in-hole robotic assembly of two parts with only 50μm clearance, and (iii) within the company's cycle time. Further, for the effectiveness and practicality of robotic measurement assisted assembly systems, we recorded the encountered challenges and key enablers.

The aim of this paper is to review existing publicly available and open artificial intelligence (AI) oriented datasets in different domains and subdomains of the railway sector. The contribution of this paper is an overview of AI-oriented railway data published under Creative Commons (CC) or any other copyright type that entails public availability and freedom of use. These data are of great value for open research and publications related to the application of AI in the railway sector. This paper includes insights on the public railway data: we distinguish different subdomains, including maintenance and inspection, traffic planning and management, safety and security and type of data including numerical, string, image and other. The datasets reviewed cover the last three decades, from January 1990 to January 2021. The study revealed that the number of open datasets is very small in comparison with the available literature related to AI applications in the railway industry. Another shortcoming is the lack of documentation and metadata on public datasets, including information related to missing data, collection schemes and other limitations. This study also presents quantitative data, such as the number of available open datasets divided by railway application, type of data and year of publication. This review also reveals that there are openly available APIs—maintained by government organizations and train operating companies (TOCs)—that can be of great use for data harvesting and can facilitate the creation of large public datasets. These data are usually well-curated real-time data that can greatly contribute to the accuracy of AI models. Furthermore, we conclude that the extension of AI applications in the railway sector merits a centralized hub for publicly available datasets and open APIs.

Cybersecurity has become an emerging challenge for business information management and critical infrastructure protection in recent years. Artificial Intelligence (AI) has been widely used in different fields, but it is still relatively new in the area of Cyber-Physical Systems (CPS) security. In this paper, we provide an approach based on Machine Learning (ML) to intelligent threat recognition to enable run-time risk assessment for superior situation awareness in CPS security monitoring. With the aim of classifying malicious activity, several machine learning methods, such as k-nearest neighbours (kNN), Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF), have been applied and compared using two different publicly available real-world testbeds. The results show that RF allowed for the best classification performance. When used in reference industrial applications, the approach allows security control room operators to get notified of threats only when classification confidence will be above a threshold, hence reducing the stress of security managers and effectively supporting their decisions.

The current travel demand in railways requires the adoption of novel approaches and technologies in order to increase network capacity. Virtual Coupling is considered one of the most innovative solutions to increase railway capacity by drastically reducing train headway. The aim of this paper is to provide an approach to investigate the potential of Virtual Coupling in railways by composing stochastic activity networks model templates. The paper starts describing the Virtual Coupling paradigm with a focus on standard European railway traffic controllers. Based on stochastic activity network model templates, we provide an approach to perform quantitative evaluation of capacity increase in reference Virtual Coupling scenarios. The approach can be used to estimate system capacity over a modelled track portion, accounting for the scheduled service as well as possible failures. Due to its modularity, the approach can be extended towards the inclusion of safety model components. The contribution of this paper is a preliminary result of the PERFORMINGRAIL (PERformance-based Formal modelling and Optimal tRaffic Management for movING-block RAILway signalling) project funded by the European Shift2Rail Joint Undertaking.

The industrial collaborative robot (ICR) application is a promising automation technology that combines human abilities with the repeatability and accuracy of an industrial robot. Yet, industrial challenges have prevented ICR applications from being implemented extensively in industry. Therefore, the purpose of the presented work is to deepen the knowledge of the key challenges that manufacturers experience during the implementation of ICR applications. In this study, a case study approach was used with eight companies to identify those challenges. The analysis of the qualitative data was conducted based on thirteen interviews with respondents from the industry to identify their challenges when implementing ICR applications. In this paper, a defined implementation process is presented that is combined with three significant areas of challenges relevant for the implementation of ICR applications, i.e., safety, knowledge, and functionality. Then, these areas are used as a basis to identify the corresponding challenges during the early implementation phases. The findings of the study point to an insufficient understanding of safety assessment and a lack of operator involvement in the pre-study phase that was propagated into the later implementation phases. The application design phase was identified to have several ad-hoc approaches due to a lack of knowledge concerning the application of ICR. In the factory installation phase, the challenges included increasing flexibility and ensuring standardised ways of working. This paper makes three distinct contributions to the research community. First, it provides rich data to the research concerning the implementation of applications of ICR, and it focuses on three areas, i.e., safety, knowledge, and functionality, and the challenges associated with their respective implementations. Second, contributions are made to the literature on implementing new technology, and they are focused on the early phases. Third, the results of

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 mana, FuturE

The presence of uncertainties associated to the introduction of novelty and significant change challenge manufacturing competitiveness. Addressing this issue, the purpose of this qualitative case study is to examine the uncertainties reduced by Discrete Event Simulation (DES) use during the design of a production system when significant changes are introduced at a manufacturing company. The results of this paper, based on empirical findings from a Swedish manufacturing company, reveal the presence of eight different uncertainties affecting the design of a production system including state, technical, environmental, systemic, temporal, structural, epistemic, and definitional. Empirical results also show how DES contributed to reducing technical, structural, epistemic, and definitional uncertainties. This paper contributes to existing knowledge by proposing a model that aids decision makers anticipate the type of uncertainty faced and the suitability of DES use as an uncertainty reducing activity during significant change introduction in the design of a production system., INNOFACTURE - innovative manufacturing development

From a theoretical perspective, this paper problematizes the future role of teachers in higher education, especially in the Swedish context, placing opportunities and raised challenges by blended learning in a historical context of distance education. Distance education was introduced in the late 19th century and has been offered by two main actors in Sweden: the correspondence school Hermods and universities. It has been viewed as a part of life-long learning, a concept introduced in the 1960s. The correspondence schools offered elementary education courses, and in-service training for various professions, while universities largely focused on higher education but also provided education or training commissioned by other organizations. Recently, the teaching requirements and role of the teacher in distance education have changed dramatically, from formulating exercises and commenting on students' work to giving videotaped lectures in English for an open audience. However, there is still a lack of appropriate guidance for teachers on effective pedagogical practice in the new settings. Specifically, there is an increasing need to support teachers in designing and creating effective videotaped lectures that are accessible for a dispersed audience. The TED talks seem to provide a role model for performance as a lecturer, but the average teacher hardly has time to both prepare regular face-to-face lectures and distance lectures, e.g. extensively rehearse before recording. The paper discusses how the challenges of future roles of teachers can be met when lecturing in front of a camera.

The overall research focus in the study is how photographs can be used in workplace innovation processes. This work-in-progress paper discusses photo-supported group discussions on innovation as an approach to incorporate employees in the development of a radically innovative culture. The method involves managers and engineers in a process that transforms their conceptions of innovation into visuals and words, and provides a possibility for collective reflection based on these formulations. Enabling all employees to use their experiences and knowledge in workplace innovation is an opportunity being pursued in Sweden. The paper is a starting point to discuss whether or not the method of photo supported discussion on innovation could be helpful to support a shift to a radically innovative culture. The concepts of downloading or presencing are introduced to analyse the method., Möjliggörande av praktikbaserad innovation: Utforskande och utveckling av den pedagogiska effekten av innovation management verktyg

The overall purpose of this paper is to explore the prerequisites that support the fuzzy front end of manufacturing technology development. An exploratory multiple embedded case study has been conducted at a Swedish manufacturing company in the manufacturing industry. By studying four pilot plants’ organisational structure and way of working, this paper contributes with an increased understanding regarding how the fuzzy front end of manufacturing technology development can be supported. This paper describes how a technology roadmap, the usage of master processes and a supportive organisational structure can support the fuzzy front end of new manufacturing technology development., INNOFACTURE - innovative manufacturing development

Assembly systems require to be designed considering flexibility from a holistic perspective to produce the variety of current and future product generations. Reactive ad hoc changes after realizing both the product and assembly system designs require considerable effort and may even be impossible. A systematic collaborative approach that concurrently considers the early phases of product and assembly system development appears to be essential for working with long-term changes. To this end, a greater understanding of the working procedures and design activities concerning flexibility is required. In this paper, this is investigated based on a theoretical framework and a multiple case study at a world-leading manufacturer of heavy-duty vehicles. As a result, a developed early phase design process to enable long-term flexibility in assembly systems is presented. The theoretical implications provided and the findings are also relevant to those involved in the design process of flexible assembly systems.

Purpose: The purpose of this paper is to provide insight into the competence in use when working with interactive research, which is a continuation and elaboration of action research. Design/methodology/approach: A qualitative research approach was adopted for the study. The main body of empirical material in this paper is based on two sources of data – a survey comprising open-ended questions, followed by a series of focus group interviews. The respondents were researchers with varying degrees of experience in using interactive research. Findings: The findings provide illustrations of what characterises interactive research as work and identify an additional set of activities that go beyond traditional research activities. Some activities are relatively easy to describe, while others exist in the gaps between other activities – e.g. boundary spanning – and are harder to explicitly define in terms of implications for the involved researchers’ competence. The work activities reaching beyond the traditional research boundaries are implicit and are not a common shared practice. From a competence point of view, this implies that the competence in use for these implicit tasks of interactive research becomes individually carried. Based on these findings, a number of individual aspects of what constitutes competence in use are suggested. Research limitations/implications: In future studies, it would be valuable to use a mixed-method approach that also includes longitudinal observations of the actual work of conducting interactive research. Practical implications: The findings and suggestions for how to understand the competence of interactive researchers can be used as guidance for training in research education. Originality/value: This study contributes to previous research by describing important requirements and critical elements of competence in use when conducting interactive research.