Mobile devices have become more common in recent years. A key feature is their location awareness. The absolute location as well as the relative location awareness related to an object can be exploited for location-based applications on mobile devices allowing location-based interactions. A common example are digital scavenger hunt apps that guide users to different locations. Social inclusion [1] and social interaction [2] are a common goal of location-based experiences. Location-based apps are also used for learning purposes, as they support a variety of learning concepts. In their literature review on mobile apps in general [3], the authors found situated learning, inquiry-based learning, sociocultural theory, scaffolding, communities of practice, and seamless learning, which can be taken as an indication of the manifold didactic options of location-based apps. 

The integration of artificial intelligence (AI) into the manufacturing sector introduces new challenges and demands for the engineering workforce in the evolving European economy. This paper investigates how advancements in AI tools, especially in manufacturing, necessitate a shift in engineering education to equip graduates with relevant skills and ethical understanding. While AI is not new to manufacturing, its ongoing development and increased accessibility bring forth fresh challenges related to required competencies and ethical considerations. Furthermore, this work explores the potential of incorporating recent AI tools, such as ChatGPT and other generative adversarial networks, into engineering education. This is illustrated through a case study of a master’s level digitalization course. In this course, AI tools aimed to help students bridge their programming knowledge gaps and educate them on ethical AI use, providing a model adaptable to lifelong learning courses in the field. This inquiry also addresses the broader concerns related to AI misuse in academic settings and the subsequent difficulties in plagiarism detection and accurate learning outcome assessment. The discussion does not argue against AI adoption but emphasizes managing its inadvertent impacts on the industry and society. By integrating emerging technologies and their ethical use into the curriculum, the engineering education system can better align with the shifting demands of the workforce in an increasingly digitalized manufacturing landscape.

The experiential learning principle has a long tradition in engineering education. Within production & supply chain management as well as logistics, a primarily learning goal is connected to the complexity of decision making and how the same decision may impact differently depending on the context. Such decisions are complex and difficult to understand, and serious games have proven to contribute to this understanding. Many of the games used for teaching the relevant topics are typically applied in a workshop setting and are often been specifically made for a specific course. However, not all educational institutions have the possibility to develop tailored games since the development requires multi-disciplinary knowledge, are costly and time consuming. The usage of commercial off-the-shelf games might be a solution. We know from existing work that this requires that the game can be modded or adapted to fit the intended learning outcomes in the course it may be used. This article takes previous work on the integration of commercial off-the-shelf games into logistics, engineering and supply management education one step further, and reports on the first results of full implementation.

Digital learning environments exhibit spatial dimensions, such as virtual labs, virtual worlds, or 360 degrees models. This usage increased during the pandemic, and heterogeneous motivation was found in the target audiences. Gamification is seen as enhancing the motivation of heterogeneous audiences to more homogeneous levels. Consequently, the question arises, which options are available for the gamification of spatial digital learning environments? This article presents the results of a rapid literature review addressing what is known regarding a) which mechanisms of gamification support engagement and motivation, b) which mechanisms of gamification support spatial learning, and c) why and when gamification should be used. The three databases ACM, IEEE and Scopus were examined, and 25 relevant articles were found. Qualitative results regarding the application domains, the type of digital environment, the gamification mechanisms, and the influence of gamification are compiled. Overall, the results reveal that gamification elements applied in spatial digital learning environments have so far been little adapted to spatiality.

Digital learning environments exhibiting spatial dimensions, such as VR experiences or virtual labs, have become increasingly common in recent years. At the same time, it is known that gamification, i.e., the application of game-like mechanisms, might support the motivational design of learning environments. However, so far, there seems to be no systematic overview on whether and how spatial dimensions are incorporated into gamification mechanisms. Accordingly, we conducted a systematic literature review to identify gamification mechanisms that use spatial dimensions. Out of 849 articles we finally included 11 articles found in the three databases ScienceDirect, IEEE, and ACM DL that conducted gamification in the context of spatial digital learning environments. For the most part, the gamification mechanisms used in these 11 articles did not relate to any spatial dimension. Conclusively, we state that there have been few approaches to gamification exploiting the spatial dimension. Given the limited findings in the literature, we propose developing a model to bridge existing gaps and support a structured approach to incorporating spatial dimensions into gamification mechanisms. Accordingly, we contribute to enhancing spatial digital learning environments with further motivational cues through spatial gamification mechanics.

Interactive learning espouses a “hands-on” approach, aiming to foster student engagement. In an interactive form of learning, serious games, and gamification have been used in the classrooms to engage students. Nowadays, many serious games and gamification with AR are used in classrooms as it encourages engagement in interactive learning. However, with the usage of such technologies, it remains challenging to engage students with certain impairments as the tools are not designed to accommodate the needs of students with certain impairments. In this poster paper, the focus is on challenges related to red-green color blindness, since this is regularly affecting the learning experience among our students. Making learning content accessible in an AR environment depending on the type and severity of this color blindness is challenging and requires a better understanding of the specific requirements in designing AR in serious games and gamification for red-green colorblindness. To accomplish this, a systematic literature review (SLR) is conducted with the aim of highlighting the existing challenges in designing AR: i) serious games and ii) gamification. The paper also examines the challenges of using AR in interactive learning in a broader context and identified challenges with the key findings discussed.

This study applies an intersectional approach to address concerns aboutdiversity of data acquisition when applying computer vision systems in humanorder picking. The study draws empirical data from a single case study conductedat an automotive manufacturer. It identifies critical factors of intersectionality forthe use of vision systems to enrich data collection in human order picking at fourlevels including form and function, experience and services, systems and infrastructure,and paradigm and purpose. These findings are helpful for mitigating biasand ensuring accurate representation of the target population in training datasets.The results of our study are indispensable for enhancing human-centricity whenapplying computer vision systems, and facilitating the acquisition of unstructureddata in human order picking. The study contributes to enhancing diversity in humanorder picking, a situation that is highly relevant because of the variations in age,gender, cultural background, and language of staff. The study discusses theoreticalandmanagerial implications of findings, alongside suggestions for future research.

Recent publications underscore the critical implications of adapting to dynamic environments forenhancing the performance of material and information flows. This study presents a systematicreview of literature that explores the technological capabilities of smart production logistics (SPL)when applying machine learning (ML) to enhance logistics capabilities in dynamic environments.This study applies inductive theory building and extends existing knowledge about SPL in threeways. First, it describes the role of ML in advancing the logistics capabilities of SPL across variousdimensions, such as time, quality, sustainability, and cost. Second, this study demonstrates the applicationof the component technologies of ML (i.e. scanning, storing, interpreting, executing, andlearning) to attain superior performance in SPL. Third, it outlines how manufacturing companiescan cultivate the technological capabilities of SPL to effectively apply ML. In particular, the studyintroduces a comprehensive framework that establishes the technological foundations of SPL, thusfacilitating the successful integration of ML, and the improvement of logistics capabilities. Finally,the study outlines practical implications for managers and staff responsible for the planning andexecution of tasks, including the movement of materials and information in factories.

Order picking in manufacturing warehouses is a labor intensive activity with critical implications to the well-being of staff and operational performance of companies. This study addresses the need for applying digital technologies that lead to enhancing a human-centered approach in order picking. It proposes the use of artificial intelligence (AI)-enabled vision systems to facilitate the generation and analysis of information about tasks in manufacturing warehouses. We present the results of a collaborative project between academic and industrial partners from a case in automotive manufacturing. This consists of the development of a pilot study in a laboratory environment and includes two findings. First, we show the steps of implementing an AI-enabled vision system in order picking. This findings is important for automatically generating and analyzing information of tasks in order picking such as setup, travel, search, and picking of parts, which directly affect staff performance. Second, we discuss the implications of this findings for manufacturing companies and its contribution a future in order picking with improved human-centricity.

In the data-rich but knowledge-poor domain of production management systems, the utilization of machine learning (ML) for lead-time prediction has gained increasing attention. Despite several efforts focusing on ML and regression techniques, the selection of features for lead-time prediction remains a challenge. The purpose of this study is to explore the socio-technical challenges and benefits of applying ML to predict lead-time in manually executed tasks in the biopharmaceutical industry, with a particular emphasis on the quality control of raw materials and semi-finished products. Through a case study and empirical analysis, the research identifies critical factors affecting lead-time prediction in manual tasks and evaluates the socio-technical implications of implementing ML-based solutions. Moreover, the study provides valuable insights into the practical challenges and potential advantages of adopting ML techniques for lead-time prediction in the biopharmaceutical sector, offering a comprehensive understanding of the complex interplay between technology and human factors. Finally, we discuss the implications of the findings for managers and staff responsible for the planning of manual tasks, providing actionable recommendations to improve production efficiency and lead-time prediction accuracy. This research contributes to the growing body of knowledge on the integration of ML in production management systems and highlights the need for further investigation to harness the full potential of ML in addressing the unique challenges of the biopharmaceutical industry.

In the last decade, sustainability skills have become critical for preparing the future workforce and efforts have been made to implement sustainability principles across various disciples. In this context, serious games have been promoted as a viable approach that can be used to reshape the learning environments and facilitate the development of sustainability skills and competencies. Serious games are known to make learning interesting and fun, and they have been employed to create engaging spaces for learning, spaces where students have more freedom and take more responsibility. This paper seeks to explore how serious games can foster the acquisition of key sustainability skills and competencies, frequently needed on the labour market. Three games have been analysed starting from their specificity such as the narrative and educational context, the target group, the game mechanics used, up to the identification of sustainability skills and competencies that can be acquired during the game play and the pedagogical methodologies used to acquire certain skills and competencies.

This paper introduces a framework that integrates smart production logistics (SPL) with network diagrams. This integration enhances visibility in the material and information flow within the manufacturing sector, thereby adding value through data visualization. Drawing from a detailed case study in the automotive industry, we outline the essential components of network diagrams that are tailored to depict spatial-temporal data linked with material handling processes in an SPL context. This integrated approach presents managers with a new tool for optimizing planning and executing tasks related to the transport of materials and information. Furthermore, while the framework brings about significant technological progress, it also emphasizes the managerial implications of SPL data visualization. In particular, it highlights its potential to foster informed decision-making, resource optimization, and strategic forecasting. The paper also discusses prospective research avenues, stressing the importance of dynamic diagrams that decode complex patterns from digital data and the incorporation of sustainability metrics in SPL assessments.

This study proposes a model describing the functions and procedures for configuring the transmission of data of Digital Twins (DT) in humancenteredCyber-Physical Systems (CPS). We draw data from two cases including aircraft manufacturing and production logistics and presenttwo contributions. First, we derive a procedure identifying the steps and resources of data processing for DT in human-centered CPS. Second,we propose a systematic procedure for configuring bi-directional data transmission depending on the desired system utilization. These resultscontribute to realizing data processing activities supporting the implementation of DTs for human-centered CPS in manufacturing.

This paper introduces a framework that integrates smart production logistics(SPL) with network diagrams. This integration enhances visibility in the materialand information flow within the manufacturing sector, thereby adding valuethrough data visualization. Drawing from a detailed case study in the automotiveindustry, we outline the essential components of network diagrams that are tailoredto depict spatial-temporal data linked with material handling processes in an SPLcontext. This integrated approach presents managers with a new tool for optimizingplanning and executing tasks related to the transport of materials and information.Furthermore, while the framework brings about significant technological progress,it also emphasizes the managerial implications of SPL data visualization. In particular,it highlights its potential to foster informed decision-making, resource optimization,and strategic forecasting. The paper also discusses prospective researchavenues, stressing the importance of dynamic diagrams that decode complex patternsfrom digital data and the incorporation of sustainability metrics in SPL assessments.

Manual assembly task analysis is essential for optimizing work instructions, improving tasks, and scheduling assembly lines in the context of Industry 5.0’s emphasis on human-centric, sustainable, and resilient manufacturing processes. The current paper outlines a comprehensive approach for data preparation for AI-assisted video analysis, aiming to simplify manual assembly task analysis, alleviate the workload of assembly operators and time setting experts, and advance Industry 5.0 principles. The paper focuses on setting up processes for recording videos of assembly tasks and converting the operator movements into skeleton models for subsequent analysis. Landmark points extracted from these models provide a numerical basis for task analysis. This data preparation process prepares the ground for future machine learning-based time setting prediction, considering companies’ unique time settings. The paper also addresses the ethical implications of video recording and data anonymization. Future work will delve into machine learning applications for time setting prediction and task-to-landmark correlations.

This paper explores the development of digital systems to identify and reduce cognitive stresses in contemporary manufacturing environments with increasing numbers of robots and smart machines. To achieve this, the study attempts to answer the following research question: How can technology-driven advancements in engineering psychology be leveraged to foster more productive, ethical, and psychologically supportive collaboration between humans and robots in the context of modern manufacturing environments? The study explores relevant literature to gain deeper insights into the subject succeeded by the development of a prototype composed of two digital solutions. By improving cognitive ergonomics through the detection and recognition of non-verbal cues, as well as reducing cognitive stress by providing real-time information on the positions of mobile robots, this study offers potential solutions to the social and psychological challenges of human-robot collaboration. The paper concludes with an analysis of the final prototype, a discussion on sustainability implications, and recommendations for future research. Overall, this research aims to bridge the gap between human workers and technology in the manufacturing sector, facilitating a harmonious and productive collaboration that aligns with the goals of Industry 5.0.

Supply chain visibility has become a key priority for manufacturing companies to handle disruptions and improve supply chain performance. IoT technologies have been recognised to enable higher levels of real-time supply chain visibility. However, there is a need for empirical research focusing on testing IoT technologies to improve supply chain visibility of incoming goods from suppliers. This study presents the findings from a pilot study conducted at a global manufacturer of transport solutions testing IoT technologies in real operations to improve supply chain visibility of incoming goods in their inbound logistics flow. In particular, the opportunities and challenges of applying IoT technologies to increase supply chain visibility in real operations are identified. The study provides guidance for manufacturing companies aiming to improve supply chain visibility and performance in their inbound flow and potentially other areas of their supply chain using IoT technologies.

Cities are growing and sustainable urban mobility planning (SUMP) is gaining in importance with it. The problems in the domain often involve multiple stakeholders with conflicting or competing objectives. The stakeholders and objectives can be local to certain neighborhoods or apply to the global city-wide scale. We present a methodology to address such problems with the help of modern simulators and multi-objective evolutionary algorithms. The methodology brings all stakeholders to the table and presents to them a near optimal set of alternatives to choose from. As an example, we consider the problem of minimizing vehicular noise in a particular neighborhood while also minimizing city-wide emission for heavy vehicles. We describe the requirements and capabilities of the simulator and the optimization algorithm in detail and present a methodology to model both local (noise reduction) and global (emissions) objectives simultaneously. We apply our methodology on two large city scale case studies and present our findings.

Implementing an industrial collaborative robot for Human-Robot Collaboration (HRC) in the automotive manufacturing industry is an emerging technology-driven solution aiming to increase production efficiency and reduce the human operator's ergonomic load. Successful implementation of innovative technology depends on technical feasibility and on the acceptance by the affected actors. Many studies exist that focus on the technical aspects of HRC, however, research that focuses on understanding the multi-actor concerns of HRC adoption is rare. In an effort to support the successful adoption of industrial collaborative robots, this study aims to un-derstand the concerns of the various actors who work at the operational and management levels influencing future HRC adoption in the heavy automotive manufacturing industry.A literature review was conducted to understand the HRC implementation challenges and the methods used to investigate multi-actor involvement in advance of, and during, the implementation stage. After reviewing existing studies, the actor analysis method was selected to present the actors' perceptions using the action, factor, and goal (AFG) list to understand different actors' opinions of HRC adoption, using a Swedish heavy vehicle manufacturing company case study.The case study results showed that the actors from the same organization had different concerns but mostly positive expectations for future HRC adoption. The actors' perception map shows the details pertaining to Ac-tions, Concerns, and Goals as well as the logical flow between these elements in regards to HRC future adoption. The involvement of different actor groups prior to new solution implementation contributes to a holistic view of potential implementation influences and challenges in the organization. Actor analysis can provide a set of analysis processes that comply with multi-actor perceptions to understand future adoption challenges from different perspectives. In the next step, safety-related issues and under-development standardization are the key challenges of HRC implementation.

This paper addresses the impact of Smart Product-Service Systems (Smart PSS) on the economic and environmental sustainability of capital goods. With a global focus on sustainable development, manufacturing companies are under pressure to improve the sustainability of their products and production while still meeting individual customer demands. One strategy being used is the implementation of PSS with the added use of digital technologies for more sustainable value and business models. The paper involves a review of existing literature on the topic to determine the definition of Smart PSS and its elements in relationship to sustainability. The paper concludes that Smart PSS can offer personalized customer-specific solutions that improve resource efficiency, but manufacturing companies need to balance conflicting goals and assess multiple criteria to ensure that these systems are economically and environmentally sustainable. The study highlights the need for further research in this area for a multi-criteria decision framework and deepen the understanding of Smart PSS’s potential to increase sustainability in manufacturing.

Recent technological advances in capturing real-time and large amounts of data provide an opportunity for production logistics systems to enhance their performance. Transitioning to a data-driven system is a complex and multifaceted process. While many articles highlight the benefits of data-driven applications, there are few empirical studies on how production logistics are influenced at the system level. This paper examines the potential benefits of transitioning to a data-driven production logistics system through two case studies. The first case involves an internal material flow between two departments in a large Swedish heavy automotive manufacturer with complex multi-product internal logistic flows. The second case involves the cross-docking flow of pallets in one terminal of a major courier industry player in the Nordic region with diverse customers. The current situation in each case was compared to a smart data-driven state through discrete event simulation, expert interviews, observations, and document review. The analysis shows that a seamless data flow can improve the overall performance of the systems in terms of lead-time, resource utilization efficiency, responsiveness, and space efficiency. The study provides insights towards creating a roadmap for the transition to a data-driven production logistics system.