Ebook: SPS2022

This book of proceedings contains papers accepted for the 10th Swedish Production Symposium (SPS2022), hosted by the School of Engineering Science, University of Skövde, Sweden, and held at the ASSAR Industrial Innovation Arena in Skövde on April 26–29 2022. The overall theme of SPS2022 is “Industry 5.0 Transformation – Towards a Sustainable, Human-Centric, and Resilient Production”. This corresponds with the vision of the European Commission on the future of manufacturing, which focuses on stakeholder value, reinforces the contribution of industry to society, enhances the wellbeing of workers and respects planetary boundaries.

Behind the Swedish Production Symposium (SPS), stands the Swedish Production Academy (SPA). SPA was founded in 2006. The vision of SPA is: To drive and develop production research and higher education in Sweden and to increase national cooperation in research and education within the production area (see https://swedishproductionacademy.se). The Swedish Production Symposium (SPS) was held for the first time in August 2007, in Gothenburg, Sweden. Since that first symposium, the purpose of SPS has been to facilitate an event at which the affiliated institutions of SPA members and any participants from the industry and academia with an interest in topics related to production research and education can meet to exchange ideas. Based on the understanding that successful product realization requires collaboration between product development and production, SPS2022 is jointly organized in Sweden by SPA and the Product Development Academy (PDA) (see https://www.productdevelopmentacademy.se), following the practice begun at SPS2020, which was hosted by Jönköping University.

The book contains the 69 papers accepted for presentation at the symposium, and is divided into ten sections that reflect the topics of SPS2022:

- Resource-efficient production

- Flexible production

- Humans in the production system

- Circular production systems and maintenance

- Integrated product and production development

- Industrial optimization and decision-making

- Cyber-physical production systems and digital twins

- Innovative production processes and additive manufacturing

- Smart and resilient supply chains

- Linking research and education

Also included are three sections covering the Special Sessions at SPS2022, each of which is described below.

Special Session 1: Artificial Intelligence and Industrial Analytics in Industry 4.0. Chaired by: Sunith Bandaru and Amos H.C. Ng, School of Engineering Science, University of Skövde, Sweden. With the Industry 4.0 vision, the collection and comprehensive evaluation of data from many different sources – production equipment and systems as well as enterprise – and customer-management systems becomes standard to support real-time decision-making. The importance of sophisticated Industrial Analytics (IA) and Big Data will continue to increase in order to transform data into knowledge and insights. Artificial intelligence (AI) is a disruptive technology and one of the main drivers of advanced industrial developments. AI is at the centre of the smart factory, and efficient processing of the exponentially growing volume of data would not be possible without it. Over time, AI will gradually be implemented in almost every aspect of industrial operations. As we see it, AI will not replace humans, but will enhance them. By combining the power of the human brain and the artificial brain, we can create a real game-changer. This special session will collect the contributed and invited papers related to IA and AI in manufacturing. Presentations from any ongoing projects and/or Ph.D. studies associated with IA and AI are particularly welcome.

Special Session 2: Development of resilient and sustainable production systems. Chaired by: Kristina Säfsten and Kerstin Johansen, School of Engineering, Jönköping University, Sweden. Resilience and sustainability are essential requirements for competitive manufacturing in the future. Sustainability was identified as a megatrend more than a decade ago. We take a triple bottom-line perspective and include social, economic, and environmental aspects in relation to the production system. Furthermore, in the wake of the Covid 19 pandemic, the need for resilient production systems –meaning production systems with the ability to change or adapt during times of stress, disruption, or uncertainty – has become evident. To be competitive, the quest for profitability cannot be neglected but must be considered in parallel. It is well-known that it is during the development of a production system that we have the best opportunity to create the abilities requested of it. During this special session we will focus on the development of resilient and sustainable production systems; systems that contribute to profitability and a competitive position for the manufacturing company. We welcome papers that elaborate on how this can be done. Focus may be on the production system development process, the balance between the different requirements, the content and design of such a production system, required production technologies, relevant competitive priorities, and other related aspects.

Special Session 3: Boundary crossing and boundary objects in product and production development. Chaired by: Paraskeva Wlazlak, School of Engineering, Jönköping University, Sweden. Managing specialized knowledge across boundaries is a key challenge for today’s manufacturing companies. Companies must have the ability to adapt their products and production systems swiftly to new and quickly changing requirements. Integration and the boundaries between product and production development addressed from a knowledge perspective are the focus of this special session. This session aims to explore how engineers dealing with product and production development devise and use means and mediators to support boundary-crossing knowledge integration in product and production development. One way to support knowledge integration is through boundary objects; a potential concept still underdeveloped in product and production development. Boundary objects provide a means of representing, learning about or transforming knowledge to deal with the consequences of differences and dependencies at the boundary between two specialized knowledge domains. Boundary objects are contextual, i.e., they are not effective in all contexts and are dependent on the boundary that must be negotiated. It is well known that there is variable complexity at a boundary, and that this requires boundary objects with different characteristics to support knowledge integration across the boundary. We especially welcome contributions to this special session that delve into design and the use of boundary objects to support boundary crossing and knowledge integration between product and production development. Successful and less successful examples are both valuable. Furthermore, we welcome contributions which explore the contextual factors affecting the use of boundary objects in product and production development.

We would like to thank:

- the authors of all papers

- the members of the Scientific Committee who assisted with the review of the papers submitted and presented at the symposium

- the session chairs

- the keynote speakers: Jenny Elfsberg – Head of innovation management at Vinnova; Henric Johnson – Global Head of Science and Innovation at Business Sweden; Andie Zhang – Leading Collaborative Robotics at ABB; and Thomas Lezama – Vice President Digital Engineering at Volvo Group Trucks Technology for sharing their experiences

- Production 2030, Swedish Foundation for Strategic Research, and The Knowledge Foundation for their support

- IOS Press and editor-in-chief Josip Stjepandić for agreeing to publish the SPS2022 proceedings in the book series Advances in Transdisciplinary Engineering (ATDE)

- and last but not least, everyone who has contributed to the realization of the symposium.

We are grateful for your attendance and contribution to a successful SPS2022.

Organizing committee of SPS2022

Amos H.C. Ng (Chair)

Anna Syberfeldt (Co-chair)

Dan Högberg (Co-chair)

Magnus Holm (Co-chair, admin. and accounting)

Rena Ahmad (Admin. support)

Jill Elmshorn (Web design and management)

Pernilla Klingspor (Event organizer)

University of Skövde, Sweden

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

Recycling is an important area to improve to reduce negative impact on the environment. With increased material recovery, less virgin materials are needed to provide the same benefits for the society. Aluminium is an important metal in the efforts to reduce negative climate impact. Demand for wrought aluminium will heavily increase with electrification of vehicles. However, with today’s recycling, contamination of aluminium alloys results in significant losses where wrought aluminium products are downcycled to cast aluminium with lower value and performance. This paper review the state of the art of aluminium recycling and investigate the current knowledge on the recyclability of current important aluminium alloys and their alloying elements. Future implementations and research are explored to find possible road maps for a sustainable circular economy of aluminium products. The findings indicate that closed-loop recycling trough better developed sorting and separation processes are one of the primary improvement directions. Also, improve utilization of the alloys and their alloying elements in the making of new aluminium alloys.

Developing IIoT-enabled digital services is essential for facilitating human centered digital transformation and achieving resource-efficient production. IIoT-enabled digital services focus on providing the best possible value proposition to end users based on three main components including hardware, middleware, and visualization applications. An area of increasing interest is that of developing IIoT-enabled digital services in smart production logistics (SPL) that facilitate the delivery of material and information in manufacturing. Prior studies focusing on IIoT-enabled digital services give precedence to the location, energy consumption, and execution of material handling tasks in SPL. However, the literature neglects the importance of supporting staff responsible for maintenance of material handling equipment. Recent publications propose the use of Maintenance Opportunity Windows (MOW), yet this approach requires extensive calculations unsuitable to the dynamic environments of manufacturing. Addressing this need, the purpose of this study is to propose IIoT-enabled digital services for detecting MOW in material handling for the automotive industry. This study presents two contributions. Firstly, we draw extant knowledge about IIoT architectures in SPL to a novel context, namely that of MOW. Accordingly, this result reduces the time and resources for acquiring, processing, and identifying empty spots in MOW as compared to prior studies. Secondly, the study proposes IIoT-enabled digital services in material handling targeting maintenance staff including finding, filtering, and detecting the status of forklifts and their MOW. In doing so, the results complement existing literature about SPL targeting the autonomous coordination and scheduling of material handling. This is critical for offering digital services supporting the working needs of maintenance staff for a human centric industrial transformation.

Sales and Operations Planning (S&OP) is a process that aims to align dimensioning efforts in a company, based on one integrated plan and with clear decision milestones. The alignment is cross-functional and connects different operations functions with each other to set an overall delivery ability. There are always challenges connecting different functions in a company which most S&OP practitioners agree with, still, that is one of the things that the S&OP-process should bridge. Digital solutions such as Enterprise Resource Planning (ERP) and other more or less sophisticated tools have contributed to an improved cross functional communication over time. S&OP in an Engineer-to-order (ETO) context, especially where engineering is a major or an equal portion as e.g., make-to-stock (MTS) and make-to-order (MTO) contexts, may experience even further challenges. Technologies within Industry 4.0 are changing the way S&OP is carried out; one of the most relevant ones is Artificial Intelligence (AI), particularly, Machine Learning (ML) that analyses data collected during these processes to find patterns and extract knowledge. The intent with this paper is to, based on S&OP-challenges, see if ML can be used to improve these challenges.

In a brief literature review together with empiric data from a single industrial case (SIC), S&OP-challenges were defined and structured. Based on the challenges in several S&OP-sub-areas, classified into data quality, horizontal and vertical disconnects, specific tasks were specified and structured into anomaly detection, clustering and classification, and predictions. Which exact ML-method to use require further work and tests. Still, this is a good starting point to take the next step and the specified tasks could also be used for other practitioners that want to start using ML/AI in their daily activities.

Large costs and lead-time losses are created by returned aluminum products - to a great extent unnecessarily. Much of the metal product complaints are due to visual surface defects. Today, the aluminum industry relies on several non-standardized classification systems for surface quality assessments which provides far too much scope for subjective and non-repeatable surface estimations. To challenge this situation, a common toolbox to describe and define surface quality in a more objective way needs to be developed. A first step towards such standardization is to speak the same language, thus this study is based on a state-of-the-art survey covering terminology and descriptions of surface defects in literature, and a round-robin assessment collecting terms used by employees at seven companies within the aluminum industry. The literature study showed that most attempts to catalog and categorized various types of defects on commercial aluminum extrusions are based on the origin of defects and how to prevent and/or reduce them, thus the vocabulary is production-oriented and most terms are not useful from the customers’ nor the designers’ point of view when coming to describe desired surface effect, i.e. perceived surface quality. The round-robin assessment confirmed the large variation of terminology used, and that defects were judged differently also within the same company due to experience and field of work. A common vocabulary is suggested to be based on the relationships between used expressions; from general terms at stages linked to consumers, designers and sale, tracing towards more technical terms the closer the stage where the origin of the defect can be found. This structure, in combination with e.g. manufacturing cost, is expected to guide customers towards more sustainable surface quality choices that, together with more consistent surface assessments along the production chain, is expected to strongly reduce unnecessary scrapping.

The transition towards a circular economy (CE) is part of the solution to reduce the global consumption of natural resources and increase resource efficiency in society. Product-Service Systems (PSS) is seen as one of the effective ways of moving towards a CE. PSS leads to an increase in product use by sharing or renting, and by extending material and product lifecycles through repair, remanufacturing, reuse and recycling. Therefore, designing PSSs have great potential to facilitate the CE transition. Many SMEs show an increased interest in a CE transition; however, they fall short in taking the right path towards designing PSS. Designing PSSs involve a rearrangement of resources, and SMEs usually do not possess the same resources as larger firms. Previous research clarifies that the transition from traditional product design to designing PSS is challenging for SMEs. This paper adds insights to the PSS literature and industrial practices through a single-case study by identifying and describing the challenges an SME may face when intending to design PSS. The data is based on interviews, workshops, and internal archive documents. The findings show that an SME faces both internal and external challenges. The internal challenges related to time constraints, the current business model, lack of financial resources, organisational structure and internal processes, dedicated employees for business and service development, and competence. The external challenges relate to SMEs position in the value chain, customer interests in PSS solutions, and handling of reversed logistics.

The Internet of Things (IoT) offers potential for developing an intelligent and sustainable manufacturing system, allowing for better and more informed decisions that increase efficiency and cut down waste in production processes. The insights are generated from automatically collected data coming from machines and devices. While process data are already reported and support a close to real-time monitoring and evaluation of process efficiencies, data about resource consumption in manufacturing environments is more scarce but crucial for becoming more resource efficient. Through connected hardware and software applications, data from resource consumption of energy, water, and waste can be automatically collected. To achieve this, this study presents an IoT framework for monitoring resource efficiency in an automatic and frequent manner. Thus, the eco-efficiency and productivity of the process can be measured and integrated into the decision-making processes by sharing the data with shop floor and production management personnel via dashboards.

Nowadays customer needs are changing rapidly, resulting in shorter product life cycles and a need for a higher product introduction rate. This requires manufacturers to introduce new products whilst keeping production efficiency at a satisfactory level and production costs low. Based on these challenges, there is a need to consider both production efficiency and potential assembly line investment costs during the planning of new product introductions. Hence, this paper aims to support decision-making regarding whether to introduce and produce a new product in an already existing assembly line or to invest in a new assembly line. To its support, a tool which illustrates how to support manufacturing investment decisions through line balancing techniques has been developed. The tool was based on theoretical findings from two literature reviews, investigating assembly line balancing techniques and assembly line investment costs, and through data collected in a single case study, including how a company is currently supporting investment decisions and performing line balancing. The case study was conducted with a large Swedish company from the automotive industry. Data was collected through semi-structured interviews, document studies and a focus group. The proposed decision-supporting tool conducts line balancing for both combined and separate assembly lines, and converts the results into costs. These costs are then compared with the potential investment costs of either producing in an already existing assembly line or investing in a new assembly line. The final output is a summarization of the potential costs related to both alternatives which provides the user with the most economically beneficial alternative by taking both production efficiency and investment costs into consideration.

Globalization and mass customization are commonly translated into increased levels of complexity in manufacturing systems. One of the main reasons is the increased number of variables, parameters, and interrelations on the shop floor. This intrinsic complexity can grow exponentially when considering the manufacture of large-size products with high levels of variability and variants: the mass production of large recreational motorboats with high levels of customization and low production volumes, mass customization. With the increasing role of sustainability and concepts of Industry 5.0, focusing not just on improving production systems but also human wellbeing, quick decision making becomes essential. Data and digitalization are becoming the cornerstone for system improvement, and digital data availability and analysis can facilitate the utilization of computerized tools to support decision making and maximize the performance of complex systems.

For that purpose, simulation can be a powerful analytical tool to design, maintain, and improve complex manufacturing systems. Simulation techniques usually allow handling the size and complexity commonly associated with manufacturing systems. However, in systems with highly customized and large-size products, manual processes, and limited floor space, the implementation of simulation techniques is not straightforward, especially considering the aspects of variability, data collection, model validation, and system reconfiguration. With a particular focus on large-size products and limitations of a constrained existing facility layout, this paper presents the implementation of a simulation-based reconfiguration assessment considering manual production, assembly, and internal logistics requirements.

Going through an industrial case study of large recreational motorboats manufacturing, the paper analyses the system analysis, data collection, implementation, and validation of the methodology step by step. Considering different what-if scenarios, the focus is on the capacity reconfiguration using Discrete-Event Simulation. The results can serve as a guideline for decision-makers and stakeholders working with complex mass customization manufacturing systems and space-constrained facility layouts.

The key driving factors in using humans and robots in collaborative applications for assembly processes are to reduce assembly time, cost and to improve the human working environment from an ergonomic viewpoint. Currently, there are limited automated procedures in assembly operations in house construction because the traditional type of assembly process depends entirely on manpower. This is common in the assembly process in different industries since assembly is one of the most demanding and intense manufacturing processes, and it is difficult to automate. This paper presents a case study on the implementation of human-robot collaboration for window assembly by way of an offline robot programming simulation. A self-adaptive software architecture that runs on a real-time target machine is also proposed for robotic window assembly. The window assembly method that will be used in this study is called “Click-In” and is manufactured by Fixture System Sweden AB. Apart from robot simulations, detailed suggestions are given for building a pilot cell for robot window assembly. The case study presented in this paper has both economical and ergonomic goals. The economic goal is to reduce the assembly time which will lead to an increase in window production. By introducing human-robot collaboration, operators do not need to perform uncomfortable assembly operations—rather the robot will perform these un-ergonomic operations. The feasibility of both goals is verified with offline robot programming simulation.

This paper identifies challenges and proposes enablers for simultaneous exploration and exploitation in Operations (the production part of a manufacturing company). It also contributes with an empirical example of organizing dual operating systems through behavioral ambidexterity during the digital transformation journey. The main challenges related to achieving simultaneous exploration and exploitation are communication, involved resources, innovation process, collaboration, and implementation. One of the proposed enablers is to develop a more supportive culture through awareness and competence development of managers about the competing cultures of exploration and exploitation. Further on, the diagnostic of opportunities model (the readiness and maturity evaluation model) is an overall enabler and can be used as a supportive dialogue tool to address several of the challenges identified. In addition to this, there also needs to be a strong focus on overall continuous communication and follow-up, especially of the proposed enablers, to support the overall change approach to reach strategic legitimacy in the organization.

Multi-agent technology, used for implementing Plug & Produce systems have many proposed benefits for fast adaption of manufacturing systems. However, still today multi-agent technology is not ready for the industry, due to the lack of mature supporting tools and guidelines. The result is that today, multi-agent systems are more complicated and time-consuming to use than traditional approaches. This hides their true benefits. In this paper, a new method for configuring agents is presented that includes automated deployment to manufacturing systems and by its flexible design opens the possibility to connect many other supporting tools when needed. A configuration tool is also designed that works with the proposed method by connecting to an agent configuration database. The overall aim of the method is to simplify the steps taken for adapting a manufacturing system for new parts and resources.

The remanufacturing industry currently relies significantly on manual work when, for example, sorting and disassembling. Due to several issues, including process time and sequence, operations number, disassembly planning and scheduling, process cost, and performance measurement, it is challenging to stay competitive. Based on this, it is assumed that more extensive use of robots and automation in these industries can facilitate higher efficiency and better work conditions. This research paper aims to explore how remanufacturing of car components can be made automatic. The paper describes a case where a specific car component was selected and a specific step in its remanufacturing process explored from the perspective of automating that task. When conducting remanufacturing of the selected car component, some machines are used for the testing, cleaning, and grinding of materials. However, all assembly work is done manually. In collaboration with the case company, the process step of applying sealant for the assembling of a lid that covers electronic components was selected. The demonstrator shows that it is possible to apply sealant with a human-robot layout with a good result. One of the advantages of using a robot for this step is that a high-quality result was achieved.

During machining the accumulated bulk stresses induced by previous shape forming process steps, such as forging, casting or additive manufacturing and subsequent heat treatment, will be released and cause undesirable geometry errors on the final component. By considering the residual stresses during process planning a significant improvement in dimensional accuracy can be achieved. This paper presents experiences for prediction of residual stresses for components with complex geometries using the Contour method. Three sectioning procedures have been tested and a cutting strategi using Electric Discharge Machining with slow feed rate and cutting from two sides with final cut in the middle is proposed. Two Finite Element modelling strategies for 3D-models have been tested and a meshing strategy based on extrusion of the geometry from the cut plane is recommended. Further, a procedure to automate the Finite Element meshing of complex structures using the Alpha Shape algorithm is proposed. The ambition is to integrate this algorithm in procedures for automatization of the entire analysis.

Collaborative robots, cobots can be an alternative to traditional industrial robots, but for small and medium-sized enterprises, SMEs, the adoption still is in an early stage. This study, a combination of literature study and interviews with staff at companies and reserachers, aims to identify the challenges for manufacturing SMEs when introducing cobots in the business so that future work in companies can be based on these finding facilitating a smooth implementation. The mainchallenges identified are related to safety, performance, strategy, involvement, and training. Safety aspects are crucial since human operators work closely with collaborative robots and risk serious injuries even though the managers and operators in the case study do not seem to worry since they perceive the current cobots as relatively slow and safe. Other high-prioritised challenges are related to performance and strategy, e.g., how to achieve cost-effectiveness with small production volumes and get the robotic investment to pay off in the long turn, but also to choose a proper cobot solution and a reliable supplier, find suitable work tasks and obtain quality if the cobot fails to recognize a defective product or skewed inputs on the production line. Employee involvement is another success factor since early involvement of the operators leads to better acceptance and understanding of the new technology and the changed work situation. There is a need for skilled, educated workers as well, although the case study shows that the SMEs highlight the importance of choosing a robot system that is easy to learn and easy to use for everyone. This paper will discuss challenges when introducing cobots in manufacturing SMEs.

To form an inclusive and sustainable society, workplace design that can be used by different individuals, regardless of sex, language, background, and body function variations is needed. Such workplaces can also give economic benefits to companies if they provide a more accessible, safer, more productive and error proofed working environment. This aim of this paper is to evaluate a universal design concept developed at a company aiming at providing an “easy job”–workplace design for manual industrial operations. The study investigated key factors from 8 interviews and compared it to theoretical constructs such as WHO’s ICIDH-2. A synthesis was formed that included the following factors: personal factors, environmental factors and outcomes of universal work. The study has resulted in new insights regarding universal workplace design and the vision is that the synthesis can be used by other production companies that want to increase the universal design in assembly work.

Digital transformation toward optimized production process planning, highlights new challenges for assembly process planning departments as we move toward fully virtual engineering processes. ESI Software Germany GmbH (ESI) wants to introduce an important project outcome generated by the MOSIM (Modular Simulation of Human Motions, www.mosim.eu) framework – an open framework for efficient, interactive simulation and analysis of realistic human motion of manual assembly-worker sequences for industrial applications. The ESI – IC.IDO solution is one of the proposed industry solutions aiming to re-use the project outcome to extend existing IC.IDO process validation capabilities and enhance human-centric assembly environment validations completed in entirely virtual try-outs. Currently, the impact of assembly process plans on the people, who produce, use, and maintain a proposed new product, is often left unquantified until late in the product lifecycle. In some cases, identification of issues comes too late to mitigate through product design change or improved assembly methods for the shop floor. The goal of MOSIM is to increase comprehension of, and provide capability to shift, manufacturing engineering paradigms through the integrated evaluation of products in processes with the people who build, use, and maintain those products throughout their lifecycle. There are no solutions on the market yet, which comprehensively address human-centric engineering factors during assembly process planning that includes on-board realistic human simulation capabilities. ESI intends to use the MOSIM project results to improve manual, human-performed, manufacturing assembly procedure validations and generate dynamic simulations of human-centric motion in production environments. This will lead to a major reduction of time and effort needed to conduct shopfloor worker assembly simulations compared to current industrial practice – compared to approaches using digital mock-up (DMU) solutions for worker simulation studies. Identifying ergonomic opportunities during the assembly process to improve worker productivity, safety, and training. MOSIM has enormous potential to influence numerous stages of production.

Small and medium-sized (SME) manufacturing enterprises have been described as a sector that traditionally has not been data-intensive, with low spending on IT and cybersecurity and employees with low cybersecurity awareness. SMEs have also been described as agile and under pressure to adopt new technology and embrace digitalization to gain a competitive advantage. Entering this data-intensive world also comes with new risks, making them extra vulnerable. Not much attention has been directed at how SMEs in the manufacturing sector are working with improving employees’ cybersecurity awareness. Especially not where cybersecurity training programs are in focus. To investigate these aspects, we opted for a set of five SMEs in the manufacturing industry where it was possible to perform in-depth semi-structured interviews with chief information security officers’ (CISO) and employees. The results show several interesting results, for example, regarding the view on contextualization of training material and the relevance of microlearning. The study also presents several practical implications, including recommendations for improving cybersecurity training measures for SMEs in the manufacturing sector.

The manufacturing industry is facing a transformation, driven by an increasing technological development. This leads to major challenges at various levels in companies and organisations, not least increased demands on production staff, to handle digitalisation and new technology. Work content is changing, as are roles, and it is becoming increasingly important for organisations to take advantage of and develop the skills of their employees.

With growing rate of change, human factors such as motivation and learning become increasingly important. For sustainable production and a sustainable working life, the work environment needs to ensure development and learning, from the perspective of both individuals and companies.

With this background, the aim of this paper is to better understand how learning climate and continuous improvement processes affect learning and motivation in production teams. Four Swedish industrial companies were included in the study. Observations and interviews were used for data collection.

The results from the study show that continuous improvement processes have the potential to increase learning and motivation but are not always utilised in this way. We could see a focus on short term gains in productivity, rather than on long term reflection, development, and learning. Training and dedicated time needed were not prioritised enough to actually reach the potential of such processes.

We find that there is a substantial potential for development of these factors, which can aid industry in meeting the challenges that companies face in the rapid technological development. Examples of areas to improve are structure and processes for continuous improvement, as well as enhancing the learning climate within teams and organisations.

The type of ergonomics assessment methods typically used in digital human modelling (DHM) tools and automated assessment processes were rather developed to be used by ergonomists to assess ergonomics by observing the characteristics of the work. Direct measurement methods complement observation methods. Direct measurement methods have a design that suits being implemented into DHM tools. A drawback of direct measurement methods is that they traditionally do not include action levels. However, action levels in direct measurement methods have recently been suggested. The aim of this paper is to illustrate how these recent physical load exposure calculations and recommendations can be integrated in a DHM tool and in an automated assessment process. A demonstrator solution was developed that inputs exposure data from simulations in the DHM tool IPS IMMA as well as exposure data that originate from tracking real workers’ motions, using the motion capture system Xsens MVN. The demonstrator was applied in two use cases: one based on predicted human motions and one based on captured human motions. In the demonstrator, head posture, upper left and right arm posture and velocity, as well as left and right wrist velocity were calculated. Exposure data were compared with action levels, and extreme action levels were indicated by colouring the information. The results are promising, and the demonstrator illustrates that it is possible to follow the trends in Industry 4.0 and Industry 5.0 to automate and digitalize ergonomics assessment processes in industry.

In a Cyber-physical system, the information flow from the cyber part to the physical part plays a crucial role. This paper presents the work of development and initial testing of an augmented reality approach to provide a user interface for operators that could be a part of a robotic production system. The solution is distributed and includes a communication hub that allows the exchange of data and information between multiple clients e.g. robot controllers, an optimization platform, and visualization devices. The main contributions of the presented work are visualization of optimization results and visualization of information obtained from the robot controller and the integrated communication framework. The paper also presents challenges faced during the development work and opportunities related to the presented approach. The implemented interface uses HoloLens 2 mixed reality device to visualize in real-time information obtained from a robot controller as well as from simulation. Information regarding the placement of work objects and targets or currently executed lines of code can be useful for robotic cell programmers and commissioning teams to validate robot programs and to select more optimal solutions toward sustainable manufacturing. The operator can simulate the execution of the robot program and visualize it by overlying the robot cell with the 3D model of the simulated robot. Moreover, visualization of future robot motion could support human-robot collaboration. Furthermore, the interface allows providing the user with details from multi-objective optimization performed on a digital twin of the robotic cell with the aim to reduce cycle time and energy consumption. It allows visualizing selected scenarios to support decision-making by allowing comparison of proposed solutions and the initial one. The visualization includes cell layout, robot path, cycle time, robot energy consumption. The presented approach is demonstrated in industry-inspired cases and with the use of an industrial ABB robot.