Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures

The number of servitized manufacturers is growing, along with the number of product-service system (PSS) offerings. Digitally-enabled PSS offerings can be complex value constructs comprising multiple products and services organized and executed to create new value for the stakeholders. Recent and past literature have focused on developing and commercializing such PSS offerings. While the most recent literature focuses on the role and influence of digital servitization and PSS theory, very few papers have introduced the need for investigating the lifecycle management of PSS offerings. The early contributions focus on lifecycle management frameworks and are mainly based on academic data, thus needing proper industrial validation. Therefore, this paper explores practitioners’ challenges related to the three main types of PSS offerings: product-oriented, use-oriented, and result-oriented. The paper’s results are based on insights generated by expert interviews from different industries. The results show a need for a systematic approach to the lifecycle management of digitally-enabled PSS offerings, especially for use- and result-oriented offerings. It is becoming more critical due to sustainability aspects.

Digital-enabled product service systems (PSS) enable the overall improvement of multi-actor value streams by increasing the system efficiency and thus enabling a shared value for all actors. This work aims to provide a new approach to designing data models in digital-enabled PSS. The approach addresses key challenges in designing effective PSS by presenting the source target link matrix (STLM) concept. The STLM enables the identification of primary data sources and the determination of the minimal set of data transfers needed, ensuring that PSS are designed efficiently and effectively. The STLM approach can be fully integrated into the design process, enabling managers to validate the completeness and feasibility of their PSS designs. In summary, this approach provides a valuable tool for managers looking to design effectively and ensure collaboration and integration between different areas of expertise.

The paper discusses the need for a standardized process to phase out after-market services within a product-service system (PSS). The authors suggest that integrating data from different silos and creating a clear vision and change management process for phase-out decisions can help managers understand the impacts of their choices. Using a standard modular, upgradable design with multi-use consumables could also prolong the service life of the installed base and improve sustainability and margin generation. The authors highlight the need for more research to understand the phasing out of after-market services as part of the combined product lifecycle management (PLM) and service lifecycle management (SLM) process. They suggest that interactions between the product and service over the lifecycle must be better understood. New models must include ongoing upgrading/obsolescence and integration of new technologies. Further research is also required on the change management process for phase-out. The article concludes that there is currently no standardized service withdrawal process within a PSS context, and lifecycle management needs to evolve further to cope with complexity and deliver an actionable PLM/SLM model. The authors recommend creating a standard process for phase-out based on the interactions between the PLM and SLM perspectives and collecting decentralized data to make data-driven decisions.

The efficiency of operations in service-oriented organizations is amongst the primary objectives. Libraries are no exception in this context, and this goal becomes even more complex due to being a non-profitable entity. While recent technological improvements have favored organizations in various dimensions, there is a technological lag in library operations. Through adoption of input-transformation-output model to elaborate the library operations, and using Trondheim Public Library as the case study, material processing turns out as the dominant element of transformation processes. Given the solid link between material processing and intralogistics operations—and material handling—this study collects a list of technological and digital solutions that have contributed to this realm, by exploiting the principles of systematic literature review. According to four areas of material processing in library, i.e., moving, sorting, storing, and controlling, an exploratory study of the applicability of technologies is conducted at the Trondheim Public Library as the case study. Moreover, through focus group discussions with librarians and authors, the operational function of each identified solution is measured across four key performance indicators (KPIs). This leads to generating a first pragmatic profile for each technology that assists libraries in better decision making for adoption of digital solutions in favor of upbuilding their operations. The findings are, however, not conclusive due to the exploratory aspect of this study and to fast-paced advances in the current digital era, and in this regard, some of the significant considerations along with possible limitations and future research are put into discussions.

The paper emphasizes the importance of an iterative approach, flexible planning, and execution for successfully implementing a PSS. It also highlights the role of technology, such as the Internet of Things and sensor technology, in digital servitization. The document further discusses the challenges of quantifying value-in-context in a start-up environment due to the continuous development and iteration of the product or service. The paper suggests that start-ups must identify critical factors influencing value-in-context and focus on gathering feedback and information on these factors. The document also presents a case study that provides insights into successfully implementing a digitally-enabled PSS in a start-up environment. The case study emphasizes the importance of the Lean Start-up methodology, which prioritizes rapid prototyping, customer feedback, and continuous iteration. The document also provides a framework for planning sub-steps in line with the principles of action research, which allows for documenting the process, considerations, and insights in a scientific framework that is easy to understand. The paper suggests that implementing a digitally-enabled PSS can be complex, requiring flexible planning and execution. The case study highlighted the need for an iterative approach, where each phase is revisited and refined based on feedback and insights from previous steps. This iterative approach resulted in greater flexibility and adaptability, improving outcomes.

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.

Complex capital goods such as aircraft engines are stressed by the environment and actual operation. These influences, e.g. air pollution, lead to wear of the capital goods. Therefore, capital goods are maintained, repaired or overhauled (MRO). Regeneration is carried out by MRO service providers and enables an elongation of the utilization period within the product life cycle. Within the regeneration supply chain, spare parts demands arise for MRO service providers. However, at the beginning of the regeneration supply chain, the precise spare parts demand is uncertain in terms of the required type, quantity and quality of the spare parts. This kind of information is only available after the capital good has been inspected. Often long replenishment times for spare parts lead to the challenge of providing upcoming spare parts demand for assembly in time. This paper presents an approach to dimension spare parts inventory levels forecast- and model-based that addresses the underlying uncertainty related to aircraft spare parts demand. The forecasting model is based on historical data of past regeneration orders and takes relevant features into account. Based on this, a procedure is developed for the structured evaluation of procurement opportunities with regard to their logistical potential and financial risk. The approach addresses shortcomings of existing approaches related to repairable and serviceable aero engine components that can be stocked in pool levels. Consequently, the approach supports the achievement of short delivery times and high schedule reliability in the MRO industry. In addition to a literature review identifying shortcomings in existing approaches in this field, this paper includes a case study on forecasting spare parts demand focusing on the explorative data analysis to define forecasting model requirements.

Industry 5.0 represents a new strategy for manufacturing firms in the 21st century. Additionally, the new concepts of Industry 5.0 are developed based on Industry 4.0 and its obstacles in implementation. The existence of Digital Servitization is the result of the relationship between Industry 4.0 and Traditional Servitization. However, the relationship between Industry 5.0 concepts and Servitization is still neglected in the research. This paper has the aim of showing the relationship between Industry 5.0 concepts and Servitization. The data for this research were obtained through the Serbian dataset from the European Manufacturing Survey conducted in 2022. The results of the research show the levels of implementation of Industry 5.0 concepts (i.e., human-centricity, sustainability, and resilience). Additional results show the level of Servitization implementation (i.e., traditional and digital services). Finally, the results show the correlation analysis between Industry 5.0 and the implementation of the Servitization concepts.

Crisis situations can lead to extreme consequences for society and the economy, such as the disruption of supply chains and the collapse of critical infrastructure. The challenge for optimal crisis preparation lies in the unpredictability of causes, duration and scope, and severity. AI-based resilience services can aid in crisis preparation by providing software-based warnings, recommendations, and countermeasures. The aim of this paper is to present a method for evaluating such services in terms of their usefulness and acceptance. A questionnaire is presented, and the results of its piloting phase are disseminated. With these results, existing and projected AI-based services for crisis prevention can be evaluated.

In the recent past, machine manufacturers’ focus has shifted from sales towards the usage of their products, and new business models with recurring revenues have been developed. These trends underline the need to support customers in the after-sales to best utilize the products and services. One data-driven key performance indicator to support this interaction between companies and their customers by measuring the holistic well-being of the customer is the customer health score (CHS). We adapt such a CHS to sheet metal processing, using the machine tool manufacturer TRUMPF as a case study, aiming to identify potentials in the customers’ production processes and monitor the relationship with the customers. First, we derive requirements for our CHS from both literature and practice. Then—applying the CRISP-DM process—we map these requirements to the available data, covering the areas 1) machine usage, 2) service, 3) strategic alignment, and 4) customer relationship. Subsequently, we develop an equation for the calculation of our CHS. For the evaluation, we implement the CHS exemplary for a highly automated 2D laser cutting machine. Finally, we distribute our CHS dashboards in the form of self-service reports to our internal stakeholders.

The Circular Economy (CE) paradigm, accelerated within the context of Industry 4.0, has been increasingly applied both inside and outside the manufacturing domain. The set of new capabilities and skills developed through I4.0 need to be augmented and enhanced to conform to the Triple Bottom Line perspective, initially affecting supply chains management and then expanding towards entrepreneurship, business model development, innovation management and societal development. Research shows that there is a significant potential to address the complex challenges towards a more sustainable and resilient society by cross-fertilizing these disciplines through the CE perspective, via provisioning new courses and practical cases. For this reason, the joint CE-I4.0 evolution needs to be grounded on a new set of knowledge and best practices. The CERES project recognizes the need to shift the restricted focus of CE from firms to a more extended and system-level view that considers skills, competences and knowledge needs. This would catalyse the embracement of CE under a social development perspective from the preliminary stage of training and education. This paper presents the CERES’ new innovation ecosystem, the Circular Economy Digital Innovation Hub (CE-DIH), aimed to promote connectedness among its stakeholders and to generate a systematized set of services, skills, competences and knowledge able to support the multi-faceted CE domain. The CE-DIH not only can raise awareness but can also provide the most suitable and complete set of services able to support the circular enrichment and transition of both companies on the market and individuals in society.

To boost revenues and create a lasting competitive advantage in the present global market, an increasing number of manufacturing companies are experimenting with shifting from product-centric offerings to service solutions leveraging digital technologies according to the Industry 4.0 paradigm. This (digital) transformation, known as “Digital Servitization”, aims to provide new (digital) services and/or enhance existing ones. Yet, this transformation is challenging and manufacturing companies frequently have trouble meeting their expectations. To shed light on the current state of the Digital Servitization trend in the global manufacturing sector, researchers involved in the ASAP Service Management Forum and the IFIP WG5.7 Special Interest Group on “Service Systems Design, Engineering and Management” have conducted an international survey targeting manufacturing SME managers. The main survey objectives are twofold: (i) to analyse how manufacturing companies are implementing digital technologies to support their Digital Servitization transformation from traditional business models based on product sales to models focused on service delivery, and (ii) to identify which critical issues and best practices are characterizing the Digital Servitization transformation of manufacturing companies. Survey results have demonstrated a rising trend in the global manufacturing sector towards the use of digital technologies for service delivery, but more mature servitization strategies, data management activities, coordination efforts at the ecosystem level, and supporting tools for conscious decisions in the delivery of (digital) services are still required to succeed in the new Digital Servitization arena.

The industrial journey towards digitalization and sustainability requires an understanding of companies’ status and desired state. New business models, like digital servitization, can support the transition from pilots to full implementations on the shop floor and the innovation of the end-to-end value chains. This paper aims to identify the requirements for industrial companies to undergo the transition towards digital servitization, where sustainability is at the core. The proposition is that Sustainability-as-a-Service can enable industrial companies, like manufacturers, to deliver sustainability values through digital servitization. This study presents a framework that contributes to the body of knowledge of the digitalization and servitization domains by describing the phenomenon of digital servitization and narrating its relationship to the sustainability domain. The research method of this work includes a review of relevant literature, and the analysis of four use cases, where challenges from real companies were identified and transformed into requirements. The results are summarized in a list of requirements for companies to follow towards a digital servitization business model, which includes technological enablers, organizational skills, knowledge of influential external factors, understanding of sustainability principles, and the contextual situation of the value chain. This study can guide industrial companies to advance towards Sustainability-as-a-Service by providing them with concrete measures on how to prepare for this journey.

In the current economic environment, there is a growing trend in exploring the adoption of “as-a-service” business models to cope with competition, customer focus on cash conservation and reduced capital expenditures and exploit opportunities offered by new digital technologies. Also in the manufacturing sector, the “Everything-as-a-Service” paradigm is getting more attention. The adoption of such models, in fact, attracts companies’ interest in the possibility of establishing long-term partnerships with customers, providing solutions on an ongoing basis in exchange for recurring payments. This emerging trend, however, finds limited application in practice as it is poorly understood. To fill this gap, this paper proposes an empirical analysis involving 7 manufacturing companies to investigate the opportunities and challenges of this transformation. Evidence from case studies allows researchers also to identify the main elements that manufacturing companies must consider in order to adopt as-a-service models. In particular, the case analysis shows that the presence of strategic partnerships, particularly for financial risk management, and technology are relevant aspects in order to adopt an as-a-service model in manufacturing.

More and more companies in the mechanical and plant engineering industry are transforming their business model and evolving from product to solution providers. Subscription business models play a key role in this development. They enable companies to enter long-term collaborative relationships with customers and thus monetize the potential of Industry 4.0. However, this development is not easy for many companies and is associated with numerous hurdles. One of these hurdles is the development of a suitable range of services tailored to customer needs. In this context, the bundling of individual services to service modules plays a key role in realizing new value propositions. In practice, however, companies often lack an understanding of which services need to be combined in what way to be able to realize new value propositions. Accordingly, the goal of this work is to identify relevant services for subscription business models, to cluster them into meaningful value-adding bundles, and to derive new value propositions accordingly. The new value propositions in turn enable mechanical and plant engineering companies to strengthen customer loyalty and thus achieve long-term economic success.

The mechanical and plant engineering industry faces a stagnation in the new machinery market and is relying on innovative business models such as subscription to overcome these. In this business model, individually customized solution packages are offered. The success of these models depends directly on the future success of the customer, making the selection of the right customers crucial. The aim of this paper is to identify the criteria that indicate the suitability of customers for subscription models. While there are individual descriptions of suitability criteria in the existing literature, there is a lack of comprehensive consideration of customer relationship, customer company, and customer market, as the extensive consideration was not necessary in the transactional sale of machines until now. Therefore, in this study, expert interviews are conducted with companies in mechanical and plant engineering that offer subscription models. The results show criteria that are used to evaluate customers in the six main categories of creditworthiness, market potential, benefit potential, feasibility, relationship, and sales effort. In total, 24 criteria can provide insight into the suitability of the customer for a successful subscription relationship. These criteria are intended to develop target systems that meet the requirements of different stakeholders in the customer and thus support the economic viability of these business models.

To monetize the potential of digitalization in times of saturated markets, increased machinery and plant engineering companies are starting to transform the transaction-based business model into a customer- and service-oriented subscription business. Even though subscription offerings can create win-win situations for providers and customers, companies encounter significant difficulties in acquiring customers for this innovative business model. Historically linear acquisition processes focused on transactional product sales impede success. To identify key challenges and targeted coping strategies for customer acquisition we conducted in-depth interviews with 18 subscription managers and sales representatives from seven machinery and plant engineering case studies. In our research we uncovered four challenge dimensions: (1) lack of motivation, (2) missing skills and competences, (3) insufficient customer confidence and (4) transaction-oriented sales approach. Beyond that we derived four appropriate coping strategies (1) steering mechanisms, (2) human resource management, (3) trust building instruments and (4) systematic methodology to address them. These insights highlight the key challenges at the management level for customer acquisition that companies face when trying to initiate and sustain the transition from a purely transactional product and service business to subscription-oriented growth. Furthermore, they provide guidance how to cope with these challenges.

Currently, there are many advanced tools supporting work in various phases of the product life cycle, but most often these tools are not integrated well enough. This results in the need to work in distributed processes and difficulties in communication and compromises the harmonization of the process. Furthermore, the growing complexity of mechatronic systems and their interdisciplinary nature forced the need to develop new methodologies for the development and implementation of products based on these systems. The article presents the concept called the Requirements-Functional-Logical-Physical methodology that allows to include Systems Engineering in the development process of complex products and to ensure full consistency of data architecture. The paper also presents a description of a prototype version of the system that is based on the digital thread concept and implements OASIS Open Services for Lifecycle Collaboration specification to overcome these difficulties and gives an overview at the impact of this new approach on the product development process.

The rapid development of information and communication technology (ICT) and digitalization in the Industry 5.0 era have opened up new opportunities for reverse logistics management to become digitalized, smarter, more sustainable, and simplified by incorporating disruptive technologies, e.g., Internet-of-things (IoT), artificial intelligence (AI), big data analysis, simulation, blockchain, etc. Digital twin is one of the most promising concepts in Industry 5.0, which can re-create a physical object or system in the digital world. In this paper, different from the widely practiced product-based definitions, we extend this concept to a system-oriented digital reverse logistics twin. Based on a conceptual framework allowing for a high level of system integration, we present the key enabling elements for a digital reverse logistics twin that can support decisions in a complex and uncertain environment. Through an illustrative example of a remanufacturing network design problem in Norway, the initial proof-of-concept illustrates how different systems and models can be combined in a digital reverse logistics twin in order to support different decisions.

Leveraging the potential of digital twins is of utmost importance to support smart production. Digital twin research has principally focused on defining digital twin concepts and applications and proposing various frameworks for their implementation. Less is known about using simplified models to overcome many challenges related to digital twin models. Based on a longitudinal case study at a multinational manufacturing company engaged in digital twins in manufacturing efforts, this paper identifies the main challenges encountered related to people, processes, and technology, as well as requirements placed on a digital twin. This study also presents the opportunities of applying simplified models for digital twins to overcome the identified challenges and fulfill the defined requirements. The present study provides theoretical and practical implications of the development of digital twins in manufacturing, focusing attention on the challenges and requirements that affect the outcome of the manufacturing company to drive digital twin efforts.

What are the design criteria, requirements, and scope that a Digital Service Twin must fulfill to be an efficient tool in service management/service development? Product-service systems and solution offerings with new business models are becoming increasingly important in the economy. Competencies and tools for their engineering, as well as the management of development and operations, require further development. A Digital Service Twin (DST) should optimize the recurring development and operation of services as a development tool for the simulation and validation of services. In service engineering it is expected that services can be developed faster and more customer-specific with the help of a DST. This can help identify and optimize risks and hazards in the current service experience. A DST is supposed to be a tool for service co-creation along the entire service life cycle. In contrast to product-based businesses, the use of Digital Twins (DT) for service products is largely unexplored in both science and practice. There is a lack of simulation capabilities to test and evaluate a service without prior integration. For this reason, a research project was initiated to build a Digital Service Twin (DST). A DST prototype is intended to be created for general use in the service life cycle. For the creation of the prototype, presentation properties were defined to represent the simulation as simply but effectively as possible. Additionally, these were refined by configuration options. The morphological box was developed as a decision matrix.

This paper presents a timely opportunity for manufacturing researchers to ontologize a digital twin framework for manufacturing by using recently published international standards. An ISO/IEC 21838 series of standards was released recently to address ‘top-level’ ontologies. These standards have been used by industrial consortia to release standards called ‘mid-level’ ontologies. One such ‘mid-level’ ontology standards is the Industrial Ontologies Foundry (IOF) Core. Around the same time, an ISO 23247 series of standards was released to standardize a digital twin framework, specifically for the manufacturing domain. This paper proposes to apply existing top-level and mid-level ontologies to create a ‘domain-level’ ontology for a digital twin framework for manufacturing. This domain-level ontology can then be used to create a manufacturing sector-specific digital twin ontology, called the ‘application-level’ ontology. In this paper, that application-level is the biomanufacturing sector. This paper also calls for a collaborative effort to create and deploy these two bottom-level ontologies and the digital twin standards associated with them.

Industry 4.0, additive manufacturing, digitalization, and the emergence of the circular economy are changing the face of manufacturing. New pedagogical approaches, including web-based delivery, are changing the face of teaching and learning. The major findings of a survey carried out by the IE3 Erasmus Plus project on the ‘knowledge demand’ identified by companies and the ‘knowledge supply’ in master level programs in Industrial Engineering and Management offered from European Universities are presented. Soft skills, digital abilities and experiential learning based programs are identified as the most sought after requirements of industry which are not satisfactorily addressed by many existing university master programs. An integrated model of competence, already available in literature, is suggested as a reference framework for designing new manufacturing or industrial engineering master programs.

Serious games have been effectively used to understand the complex dynamics of supply chains and acquire practical operational skills. However, previous studies on serious supply chain games have not adequately addressed specific issues related to food products, such as supply-side fluctuations, dynamic pricing to balance fluctuating supply and demand, perishability of goods, dependence of the value of goods on the remaining life, delivery and sell-by deadlines, life-based discounting, and food loss reduction. In this study, we developed a new serious game for supply chains called the Milky Chain Game, which incorporates these food-product-specific issues. The game was tested on university students, and its educational effectiveness was examined. The results showed that the participants who were conscious of reducing food loss experienced a persistent undersupply of milk cartons in the game experiments, highlighting the challenge of balancing food loss reduction and consumer satisfaction. In the post-hoc questionnaire, six out of 10 participants expressed a desire to play the game again, indicating they had a positive impression of the Milky Chain Game.

Traditional pedagogical methods in engineering programmes of most universities are not always effective. Various studies underline that active learning is generally more effective and can facilitate understanding of concepts and assimilation of content. In this context, “serious games” are widely used in various fields and prove their effectiveness. This article addresses the structure and provides some evidence of the effectiveness of a serious game developed in a Lean Manufacturing course held at the University of Applied Science and Arts of Southern Switzerland: the Lean LEGO™ Game. After a concise literature review reporting existing serious games covering industrial engineering, operations and lean management, this paper will report on the Lean LEGO™ Game objectives, mechanics and measured results.

Along with the growing popularity of game-based teaching, research on how to design serious games has gained momentum. While some prior work discusses the creation of learning experiences, the role of player emotions and their potential in enhancing learning has largely been overlooked. This paper discusses the design and development of Aalto Manufacturing Game, a board game on manufacturing and service operations dynamics for students and practitioners. We elaborate on the process of crafting a learning experience, with a specific emphasis on emotional engagement. We argue that learning experiences can be crafted through constructing framing events. Based on our experience in game design, we arrive at design propositions for creating and controlling emotional engagement during a serious game. Further, we discuss the effectiveness of our design based on feedback provided through a learning protocol, filled by students that participated in the game. Through our design propositions we make the case for “emotional engineering” in serious game design.

Learning factories offer hands-on practice for industrial environments, covering many areas, from product design to end-of-life phases. They are an effective testing ground for new products and processes, helping businesses to increase efficiency, productivity, and sustainability while providing workforce training. Using information coming from the major scientific databases, this paper provides a new classification framework for Industry 4.0 learning factories, to analyse the most relevant ones globally, with a focus on Europe and Italy and to give researchers and industrial stakeholders an overview of the activities that can be performed with them, particularly concerning learning contents and implemented technologies. The findings of the proposed analysis provide valuable insights into the more comprehensively covered topics, the overarching themes, and the existing gaps in the product lifecycle areas. The paper underlines that there is high coherence between the analysed laboratories and the Industry 4.0 requirements, with particular emphasis on the areas of production, logistics and R&D.

Serious games (SGs) in engineering education are a proven topic, whose implementation has been significantly growing in the last decades. They are recognized as effective tools to teach and learn subjects like Operations and Supply Chain Management. The research on SGs, however, is primarily focused on displaying applications and teaching results of particular games to achieve given purposes. In this paper, we provide an exploratory research and a stress test of a new SG on a specific target group in the field of food operations and supply chain management. We provide an overview of the SG and detail its mechanics. Also, we explain how the mechanics has been implemented, by means of a set of parameters and indicators that better explain the roles available to players in the game. We conclude by reporting and discussing the results of a game session played by a class of Vocational Education and Training students under stress conditions generated by an accelerated game time.

Smart Manufacturing and Industry 4.0 are driving the digital transformation of industrial processes. Barriers to entry provide challenges for researching these topics in academia. Due to the limited analysis of and case studies regarding these challenges, they are not well understood. This paper aims to improve that understanding through the first-hand case study of a lab in a R1 research institution, comparing the case study to related work, generalizing underlying drivers of challenges, and identifying research gaps which limit research acceleration. Through the lens of practical applicability, this paper provides insights which aid researchers in avoiding and overcoming similar challenges.

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.

This paper critically examines the potential benefits and drawbacks of merging supply chain and social deduction game mechanics. From a practical standpoint, designing and implementing such a game requires careful calibration to ensure a balanced and engaging experience for all participants. Ethically, concerns arise regarding the potential for collusion and its impact on fairness and trust within the game. Additionally, legal implications may arise if the game inadvertently promotes or facilitates unethical behaviour or violates anti-competition laws. Ultimately, our aim is to encourage further exploration in this area while promoting responsible game design and fostering a conducive learning environment.

Entrepreneurship has seen a significant growth in recent years as a topic taught within engineering curricula. The growth can be attributed to the continued progress of technology, which drives innovation and economic advancement. Today’s engineers now need to be entrepreneurial in their thinking and actions to effectively contribute to the advancement of technological innovations. The purpose of this paper is to showcase the creation, execution, and assessment of a summer school program dedicated to fostering innovation and entrepreneurship for engineers. The program was designed with the objective of promoting the growth of innovation and entrepreneurial abilities within an international class. Some lessons learnt at the end are derived with a twofold aim: to show the main added values based on this summer school assessment and to identify guidelines to design and deliver similar initiatives.

High-quality healthcare prevents disease and improves the quality of life by utilizing doctors, nurses, drugs, medical support, and other services. The primary goal of the healthcare supply chain is to deliver products on time to satisfy the needs of healthcare service providers. Due to numerous relevant barriers, it is challenging for the health sector to achieve sustainable consumption and production trends in supply chains. The future agendas of the transition to a circular economy include a dual mission of profitability and sustainability. A transition towards a circular economy (CE) represents a shift from a take-make-dispose economy toward a regenerative economy that matches the targets for all the 17 Sustainable Development Goals (SDGs). This study aims to define the barriers to circular transition and to highlight the role of manufacturing in minimizing barriers in the health sector. Manufacturing's role in the health sector, particularly at the product development stage, could be one of the long-term solutions to sustainable and circular development. The manufacturing and modern technology roles were highlighted as accomplishments of sustainable development in the circular transition in the conceptual framework that was developed based on overcoming barriers. The conceptual framework was created to make health sector managers, practitioners, and supply chain actors aware of the hurdles to circular economy implementation.

Elderly care services are increasingly becoming more technology-supported due to the changing socio-demographics globally. In this paper, we study the use of privately owned technology to deliver more personalized elderly care services. The introduction of technology into existing elderly care service models presupposes new forms of organizing these services which in turn challenges the performance goals of elderly care services, and yet little empirical research can be found on this issue. In this paper, we examine the organizational changes that happen in the elderly care service models when new technologies are introduced and how those changes affect the performance of those services. In addition, we want to explore how service level agreements can be used to align the performance of the different actors involved in the delivery of technology-enabled elderly care services. We argue that technologically driven changes in traditional elderly care service models have performance consequences (e.g., on quality of services) which influence the cost and resource sensitivity of business models of most public organizations. To validate the arguments made, the study draws empirics from an ongoing project on technology development and deployment of ambient assisted living technologies in Lillehammer municipality in Norway.

While medical research continuously develops new and innovative analysis methods, investing in new competence and resources is costly and challenging with the rigid budgeting of healthcare systems. This, combined with an increasing demand for healthcare services, implies an increasing need for efficient utilization of existing resources, while maintaining the quality of service. Being organized by medical skills rather than by processes, the different departments may carry out similar processes, and, thus, require similar resources. This study aims to investigate the effects of resource sharing on medical laboratory performance. Medical laboratories are often the first part of the diagnostics and treatment processes, contributing with information obtained from analyzing patients’ biological samples, such as blood, urine, or tissue. A case study, including a quantitative simulation analysis, has been performed at a large Scandinavian medical laboratory, where the current situation of each department having their own DNA isolation machine has been compared to a hypothetical future situation where the machines are shared by centralizing the process. Results suggest that resource sharing can reduce time and cost but may reduce the mix flexibility and the quality of the analyses carried out. Resource sharing will also increase the complexity of operations. This study contributes to increased knowledge on the effects of machine sharing in medical laboratories and demonstrates how simulation may be used to justify investments.

Distributed Decentralised Additive Manufacturing (DDAM) networks are considered a complementary method to mass/batch production paradigms providing ramp-up, robust and responsive global-local manufacturing capability. One implementation is to use Artificially Intelligent (AI) agents that represent jobs and machines, and broker on their behalf. Fundamental to the brokering process is the sharing of information about the jobs and machines such that machines are able to select appropriate jobs for which they have the capabilities and resources to complete. Here a challenge may exist, in that different jobs and machines may be unable (i.e. due to partial information) or unwilling (i.e. due to IP concerns) to share, then potentially limiting decision-making capability and system performance. This paper examines the nature of information sharing on the performance of a brokered DDAM network. To do so, AnyLogic was used to create a multi-agent simulation. The simulation modelled a DDAM network where the characteristic information shared to the machines by the jobs could be varied. The results of the simulation showed that in general more information sharing boosted system performance, but that different types of information shared by jobs and machines created a varying impact on the performance benefit that is realised and should be prioritised to maximise system throughput.

During the COVID-19 pandemic, many supply chains were disrupted significantly, which led to various severe impacts on supply chain performance. When infection rate increased, especially the supply of medical and hygiene products was disrupted. This caused a critical shortage and asked for an active response from the society. Among others additive manufacturing (AM) was utilized for closing this gap. Established as well as spontaneously formed additive manufacturing networks contributed a fast and valuable support during the pandemic scenario. Despite this huge benefit, the applicability, performance and efficiency of these networks depend on multiple factors including but not limited to its working principles, situation, human and social factors. This paper aims to provide a close look into the challenges of AM networks as well as onto the requirements for applying these networks to gain a high performance by two complementary empirical analyses: (1) interviews with AM network participants including producers and cooperators of existing and ad-hoc networks during the pandemic, and (2) a detailed analysis of different commercial AM platforms. The result of the research is supposed to contribute to the further development in this field, especially for constructing a supporting AM network platform in the future.

Effective spare part management can increase the competitiveness of supply chains, but the intrinsic characteristics of spare parts (e.g., intermittent demands, dependence on suppliers) make their effective management complicated. In recent years, additive manufacturing (AM) has emerged as a possible way to overcome these issues and received significant research attention, especially the topic of supply chain configuration. AM enables the easy production of parts close to the point of use, thus favoring the decentralization of supply chains (i.e., on-site production), but while this topic has been studied extensively from an economic perspective, its environmental implications remain unexplored. The literature is limited merely to mentions of the reduced transportation emissions associated with on-site production strategies, without, for example, a lifecycle perspective in which the production phase is considered. It is common knowledge that different countries adopt different energy mixes, thus generating different carbon dioxide–equivalent emissions during the production phase. A lifecycle perspective therefore casts doubt on whether on-site production strategies are always environmentally preferable over strategies in which spare parts are produced far from the point of use and then shipped (i.e., off-site production or centralized supply chains). In this paper, we aim to resolve this doubt by developing a decision-support system that can assist managers and practitioners in determining the most environmentally friendly AM spare part production strategy, considering both the transportation and production phases.

Metal additive manufacturing has revolutionized the way we design and produce complex metallic components, enabling the creation of parts with intricate geometries and tailored material properties. Among various metal additive manufacturing techniques, metal powder bed fusion technology has emerged as a leading candidate, offering a high degree of geometric flexibility and material properties control to produce high-performance components. Assessing technology development is essential as it enables the identification of emerging trends, advancements, and innovations in the focus field. Specifically, tracing technology trajectories and pinpointing the latest advances in manufacturing technologies allows for a better understanding of the evolutionary process and regularities within the technological domain, which in turn informs policy and drives sustainable economic and social growth. This paper investigates the technological development trajectories of the powder bed fusion technology domain, employing a Genetic Knowledge Persistence-Based Main Path methodology to trace and analyse its evolutionary progress and key advancements. Results show that the recent innovations within the powder bed fusion technology domain are heavily focused on improving process monitoring and control, and materials and structure development. Key advancements demonstrate the ongoing efforts to enhance the manufacturing process's efficiency, quality, and versatility. Furthermore, innovations in heat exchangers, cooling systems, and manufacturing tooling and fixtures drive greater efficiency and flexibility in the powder bed fusion domain. This comprehensive analysis of technological development trajectories in the powder bed fusion technology domain provides valuable insights for industry stakeholders, researchers, and policymakers to support strategic decision-making and foster sustainable growth in additive manufacturing.

The purpose of this paper is to explore changes in operational capabilities and their impact when integrating additive manufacturing into a traditional manufacturing company. This was exemplified based on a sub-system component (gears and shafts in a gearbox) for automotive applications that are developed and manufactured at a manufacturing company in the commercial vehicle sector. The research was set up based on a case study consisting of semi-structured interviews, informal meetings, observations, company documents, reports, presentations, and field notes. The literature and findings from empirical data converged into 57 themes of operational capabilities that were categorized into six aggregated dimensions. The results suggest two emerging dimensions, namely stakeholders and strategy, building upon an existing theoretical framework. Integrating AM into traditional industrial settings requires several changes in operational capabilities. It is essential to consider the following priorities, 1) gain understanding and knowledge in design for AM, 2) build robust AM infrastructure as an integral part of existing infrastructure, 3) evaluate AM process changes in product development and production, 4) establish an internal AM team with at least one person working full time with AM, 5) establish collaborations with suitable AM research partners, and 6) conduct AM management and evaluate the business impact not only short-term but also long-term. Future research should investigate operational capabilities from other use cases, dynamic capabilities in the same or similar contexts, and transform the required operational capabilities into guidelines and best practices for managers and other decision-makers in the manufacturing industry.

Additive Manufacturing (AM) allows for on-demand production of items. From a logistics perspective, AM of spare parts may represent a safeguard against manufacturing down-time caused by inventory stockouts or delayed deliveries from suppliers. AM of spare parts may lead to reduced inventories and reduced supply chain emissions. This study explores the implementation of AM of spare parts in a manufacturing company within the metal industry. Based on experience using AM on non-critical parts in-house, the respondents in the study emphasize several advantages by using AM for this purpose. The five key findings for when AM are advantageous were when: 1) certain parts are unavailable, 2) some parts can-not be bought separately from their assembly, 3) the costs of spare parts are high, 4) lead times are long, and 5) costs of parts produced by AM are lower than purchased parts. The evolution of AM technology, the AM cost and required competence for utilizing AM technology will be decisive for the configuration of AM supply chains in the future.

This study explores the heterogeneous patterns of companies in terms of their AI capabilities by analyzing various combinations of AI-specific resources. Drawing on the resource-based theory of the firm, we develop an analytical framework comprising two key dimensions: AI infrastructure and AI competencies, and employ two scores to quantify these dimensions. We apply this approach to a dataset of 215 companies and categorize them into four distinct groups: beginners, followers with strong AI-infrastructure, followers with strong AI-specific human resource, and leaders in terms of AI capabilities. Our analysis provides insights into the companies’ sectoral affiliation, size classes, fields of usage of AI, and make or buy decisions regarding the uptake of AI solutions. Our findings suggest that the manufacturing and construction industry had the highest proportion of beginner companies with low AI capabilities, while the services and IT industry had the largest share of leader companies with strong AI capabilities. The study also shows that companies with different levels of AI capabilities have distinct motives for adopting AI technologies, and leading companies are more likely to use AI for product innovation purposes. Overall, the study provides a comprehensive analysis of the various AI-specific resources that contribute to a company’s AI capabilities and sheds more light on configurations of AI-specific resources. Our analytical framework can help organizations better understand their AI capabilities and identify areas for improvement.

Artificial Intelligence (AI) has proven effective in assisting manufacturing companies to achieve Zero Defect Manufacturing. However, certain products may have quality characteristics that are challenging to verify in a manufacturing facility. This could be due to several factors, including the product’s complexity, a lack of available data or information, or the need for specialized testing or analysis. Prior research on using AI for challenging quality detection is limited. Therefore, the purpose of this article is to identify the enablers that contributed to the development of an AI-based defect detection approach in an industrial setting. A case study was conducted at a transmission axle assembly factory where an end-of-line defect detection test was being developed with the help of vibration sensors. This study demonstrates that it was possible to rapidly acquire domain expertise by experimenting, which contributed to the identification of important features to characterize defects. A regression model simulating the normal vibration behavior of transmission axles was created and could be used to detect anomalies by evaluating the deviation of new products compared to the model. The approach could be validated by creating an axle with a built-in defect. Five enablers were considered key to this development.

Artificial Intelligence (AI) in manufacturing has received significant attention in recent years due to its potential to assist manufacturing teams in monitoring projects, identifying defects and potential risks, and improving complex workflows and processes. Traditionally, manufacturing projects have been highly knowledge intensive, involving security-conscious processes, and internal and external actors. As AI solutions become more cost-effective and are deployed as assistive tools to support teams in projects, their deployment in manufacturing can be realized. This paper presents a conceptual framework that introduces how AI can enhance the efficiency of manufacturing projects and be successfully applied to projects. A review of extant literature on AI in manufacturing projects is presented and an empirical investigation with 10 manufacturing project managers and AI subject matter experts from engineering organizations in the UK is given. The proposed framework outlines how AI can be applied to manufacturing projects, encompassing both the necessities of projects for the application of AI, and the necessities of AI for their in manufacturing projects. Finally, managerial implications are provided for manufacturing leaders and project managers.

Industry 4.0 presents companies with an opportunity to embrace circular models that are increasingly pressing. This study examines how Artificial Intelligence contributes to enhancing key indicators of the circular economy, such as material, energy, and water consumption, through a multi-case analysis. The findings demonstrate that Artificial Intelligence can significantly impact resource efficiency and provide a competitive edge to organizations, primarily by reducing energy and material consumption. However, the potential effects of artificial intelligence vary depending on the type of technology and activity to which it is applied. Furthermore, it emphasizes the importance of ongoing research into the novel effects generated by Artificial Intelligence, both in the business sector, through the development of new applications, and in the public sector, through the integration of Artificial Intelligence in the formulation of public policies.

This study presents the development of a predictive model for the health monitoring of power transmitters in a packaging robot using machine learning techniques. The model is based on a Discrete Bayesian Filter (DBF) and is compared to a model based on a Naïve Bayes Filter (NBF). Data preprocessing techniques are applied to select suitable descriptors for the predictive model. The results show that the DBF model outperforms the NBF model in terms of predictive power. The model can be used to estimate the current state of the power transmitter and predict its degradation over time. This can lead to improved maintenance planning and cost savings in the context of Industry 4.0.