Ebook: SPS2020

This book of proceedings contains papers accepted for the 9th Swedish Production Symposium (SPS2020), hosted by the School of Engineering, Jönköping University, Sweden. SPS2020 was arranged online for two days, 7–8 October 2020. The overall scope for SPS2020 was Knowledge Intensive Product Realisation in Cooperation for Future Sustainable Competitiveness. Knowledge intensive product realization implies embedded intelligence, that both theoretical and practical knowledge and understanding of a subject is integrated in production processes and in the design of products to considerably increase the added value.

Behind the Swedish Production Symposium (SPS) stands the Swedish Production Academy (SPA). SPA was founded in 2006 and the vision is to drive and develop the 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 arranged for the first time in August 2007, in Gothenburg, Sweden. The purpose with the Swedish Production Symposium (SPS) was originally to support publication and to create a meeting place for members in the Swedish Production Academy (SPA). Successful product realization requires collaboration between product development and production and the Product Development Academy – Sweden was invited as a coorganizer.

The book contains the 57 papers accepted for presentation at the conference, and is divided into nine sections which reflect the topics covered:

• Resource Efficient Production

• Flexible Production

• Virtual Production Development

• Humans in Production Systems

• Circular Production Systems and Maintenance

• Integrated Product and Production Development

• Advanced and Optimized Components, Materials and Manufacturing

• Digitalization for Smart Products and Services

• Responsive and Efficient Operations and Supply Chains

In addition, there are five sections with the special sessions, each of them described below.

Special Session 1: Development of Changeable and Reconfigurable Production Systems

This special session include research contributions on how to handle and benefit from variety, frequent product introduction, and customization in production, with focus on reconfigurable and flexible production systems, modularity and platform-based production, production systems for customized products, and virtual support for changeable production.

Chaired by: Ann-Louise Andersen, Aalborg University, Denmark.

Carin Rösiö, School of Engineering, Jönköping University, Sweden.

Alessia Napoleone, Politecnico di Milano, Italy.

Special Session 2: Smart Production System Design and Development

Smart production systems opens up for promising opportunities but managing its implications for design and implementation in production is immensely complex. Manufacturing companies need to build strategic capabilities to support the transition from the current production systems towards a smart production system and a factory. The specific focus of the special session is on the design and implementation of smart production systems.

Chaired by: Jessica Bruch, Mälardalen University, Sweden.

Anna Syberfeldt and Tehseen Aslan, University of Skövde, Sweden.

Koteshwar Chirumalla and Erik Flores, Mälardalen University, Sweden.

Special Session 3: Supply Chain Relocation

There is a need to start evaluating manufacturing location decisions from a holistic supply chain perspective in order to make decisions that are resilient over time. The aim of this special track is to capture new trends and issues in the field of supply chain relocation with a particular emphasis on decision support development.

Chaired by:Per Hilletofth, School of Engineering, Jönköping University, Sweden and Jan Olhager, Lund University, Sweden.

Special Session 4: Management of Manufacturing Digitalisation

This Special Session focuses on management aspects for companies implementing Industry 4.0. Session participants are invited to contribute with different views on opportunities and challenges of managing manufacturing digitalization; how it affects organization, work environment and business models, and how this can be addressed in engineering education and life-long learning for practising engineers.

Chaired by: Anna Öhrwall Rönnbäck, Luleå University of Technology, Sweden, and Monica Bellgran, KTH Royal Institute of Technology, Sweden.

Special Session 5: Additive Manufacturing in the Production System

This session address the state of art and highlight important research questions in additive manufacturing. The session is primarily concentrated on additive manufacturing as a production method and its role in the production system. Examples of topics are: quality control, process simulation, calculation methods for cost and sustainability and process planning.

Chaired by: Roland Stolt, School of Engineering, Jönköping University, Sweden.

The SPS2020 was supported by the Strategic Innovation Programme Production 2030. The goal for Production 2030 is to ensure that Sweden remains a competitive manufacturing nation. We hope that the state-of-the art research presented in this book, related to Knowledge-Intensive Product Realisation in Co-operation can contribute to fulfillment of the goal.

We would like to thank:

• all authors of papers,

• the chairs of the five special sessions,

• the members of the Scientific Committee who assisted with the blind peer review of the papers submitted and presented at the symposium,

• the keynote speakers Lena Hagman from the Association of Swedish Engineering Industries, Claes Nord, AB Sandvik Coromant, Anders Wiberg Husqvarna AB, and finally Cecilia Warrol, the Strategic Innovation Programme Production 2030 for sharing their experiences,

• our sponsors Production 2030, SPARK and School of Engineering, Jönköping University for supporting the event,

• Yordan Atanasov for supporting us with the webpage,

• our Zoom-coordinator Simon Boldt,

• IOS Press and editor-in-chief Josip Stjepandić for accepting to publish the SPS2020 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 that you attended and contributed to a successful SPS2020.

The programme committee of SPS2020

Kristina Säfsten (chair)

Fredrik Elgh (co-chair)

Carin Rösiö (chair of the best paper committee)

Anders Jarfors, Kerstin Johansen, Roland Stolt, Linda Bergqvist and Jenny Bäckstrand, School of Engineering, Jönköping University.

Mats Jackson, Jönköping University/SPARK,

Joel Johansson, Thule Sweden, Hillerstorp,

Glenn Johansson, Lund University.

This paper describes the use of a flexible full-scale simulation environment for Lean Production training and education called “KLF Karlstad Lean Factory^®”. Instead of using the PDCA cycle as model for improvement cycles, the authors have developed a model that is more descriptive; it supports training transfer to the work environment in a more intuitive way. Recently, the authors have started to use the simulator as a testbed for innovative production solutions. Together with a company, the simulator is configured so as to emulate their envisaged future production solution. This participatory modelling & simulation process consists of three main stages: (i) creating a common view on aim and scope, (ii) configuration modelling, and (iii) simulations. After the simulations, participants tend to continue seeking improvements, which illustrates the effectiveness of the approach. Future work will include developing a model for measuring lean production maturity in SMEs.

There is a remaining need from both academia and practitioners, to gain further knowledge about the decision making process for automation of low volume production. This paper includes insights of drivers for automation, the development of a guide for low volume production and the outcome of using the guide. The research in this study is based on both empirical data and theoretical considerations. The empirical data was collected in five case studies and a questionnaire. This paper is part of a research project with the main objective to develop knowledge about how flexible automation may contribute to improvements in efficiency, ergonomics, quality and production economics in different industries with low volume production. One of the results in the project was a comprehensive guide, developed, refined and improved in an iterative collaborative process, where tools and parts of the guide were tested and verified by five manufacturing case companies. The paper describes briefly the development process of the guide and content. The requirements of the guide derived from literature, case companies, questionnaire as well as industrial experts. The resulting guide can be used in several ways, depending on the requirements of the application. The guide includes guiding principles, a decision model for the analysis of the company, choice of automation and facts about automation. In the end of the project, four companies had invested or decided to invest in different types of automation.

In a time of change focusing on the application of technology, there is a high risk of underestimating the compliance of internal needs and adaption to context. The research study employs a qualitative approach using the case study methodology. The source of data comes from five different manufacturing companies categorized as Small to Medium Size Enterprises (SMEs). A multidisciplinary team performed semi-structured interviews and fieldwork at each site, along with regular online meetings with the partners. The study employs five dimensions of the information quality perspective to assess information utilized to support deviation handling and connects the information quality deficiencies to the digital tools’ impact. The empirical findings indicate the need for the companies to perform a requirement analysis of information needs before the adoption of digital systems or digital tools, to assess their current state in terms of data and information. The paper discusses the impact digital tools may have on deviation management in SMEs and under which circumstances digital tools could improve deviation management. Lastly, this paper intends to shed light on the utilization of digital technologies for disturbance handling on the production shop floor.

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

The purpose of the research outlined in this paper is to explore the question of how the Lean concept evolves at a strategic management level in an international manufacturing company. The firm has set out to review its strategy and management system in a series of workshops to meet upcoming challenges in a business environment under transformation. Full access to the ongoing strategic work and related documents facilitates the execution of this longitudinal case study that started in March 2019. The empirical findings demonstrate concrete examples from the process of developing a management system that has its foundation in Lean production. One model comprising three types of co-existing conceptual management systems is presented, illustrating a scenario of how to handle the expected increasing industrial complexity. An opportunity to learn and further develop from the three types of management systems arise. The data further displays examples of the presence of co-existing corporate versions of the management system as a possible reaction to the different contexts and challenges at hand. The research suggests and further elaborates on the phenomenon of co-existing management systems and management systems development based on Lean.

Production equipment such as machines have crucial impact on the overall performance of production operations in manufacturing industries, since there is a strong correlation between the machines and working conditions and performance on the shop floor. Well designed production equipment has the potential to achieve economic gain by reducing the disturbances during the operational phase, to fulfill environmental commitment by reducing emissions and resources consumption and utility, and to increase employee satisfaction ensuring safety and good ergonomics. Therefore, when acquiring production equipment it is important to consider different sustainability aspects relevant to its usage during the operational phase. This study aims at exploring the critical features of production equipment to facilitate different practices in the context of sustainable production operational system, and how manufacturing companies are considering sustainability aspects when acquiring production equipment. The data has been collected based on a literature study, interviews conducted in different manufacturing companies located in Sweden, attending group discussion sessions, and reviewing machines’ technical regulation guidelines. Some of the critical features identified are error proofing, setup time, one-piece flow, automatic generation of required data, reduction of energy and resource consumption, together with worker’s health and safety, etc. The data indicates that companies specify different features of machines based on the requirements of operational performance and these features are aligned with different lean techniques, green practice, and safety issues. However, during acquisition process of production equipment the environmental issues are still not prioritized yet compared to lean and safety aspects. Budget constraint, insufficient information of the whole life cycle costing and lack of innovation from the equipment suppliersÂť side are exampled of major barriers for acquiring more environment-friendly production equipment.

Having project goals that are shared among project members are preconditions for resource efficient as well effective projects and operations. However, specifying and communicating project goals require an ability to identify goals that are indeed commonly shared. Rapid technological developments may require digitalization projects that lead to large portions of existing company staff being redundant, making it possible to assume that the quest of finding a commonly shared view of what is ‘resource-efficient’ will be increasingly challenging. Development of methods to specify project goals that are incentivizing for all project members and staff can hence be assumed to be important. One step in developing improved specification methods is to ask how the process to specify desired value from digitalization projects handles possible disagreements of what is ‘desired value’. The purpose of this study was to answer this question. We analyzed several digitalization projects, and how specifications of desired project results impacted project outcomes. We found that potential disagreements regarding desirable project outcomes generally are avoided by avoiding specification of what a desirable resource efficiency outcome is, and how actual project outcomes should be measured. However, we also found that this practice also led to unsatisfying project outcomes regarding resource-efficiency improvements, and that improved methods to specify desired value from digitalization projects should be developed. Our findings support earlier findings that the general failure rate of digitalization projects is high, often due to insufficient specification of desired projects outcomes before the projects are initiated. Our findings contribute to the understanding that despite this, there are also perceived benefits of spending limited resources on specification of desired outcomes. If attempts to improve the success rate of digitalization projects by improving specifications of desired project outcomes is to succeed, these perceived benefits must be considered.

In view of major social changes, such as the growing climate crisis, increased external expectations on the production sector demand an industrial transformation. Since transformations call for innovation, new lean practices will emerge locally at sites in production networks to cope with new challenges. But, how can new local lean practices be deployed for utilization by other parts of the company? Global production companies strive for broad over-all improvements within the network. This is often approached through a top-down deployment of a global lean framework, using various mechanisms. Lean standard development is a central mechanism for transferring best practices and lean knowledge within a corporate group. Anchored to well-established theories, such as innovation diffusion and plant network theory, prior lean transfer studies often take a cascading top-down perspective. In contrast, this study aims to explore lean practice diffusion through a bottom-up perspective. It explores the process of deploying new local lean practices to the corporate network. The empirical findings are based on a single case study at the pharmaceutical company AstraZeneca. The findings indicate that the bottom-up deployment process can be explained by four phases, ‘Piloting’, ‘Branding’, ‘Codifying Knowledge’ and ‘Making a Product’ that varies in degree of practice adaptation. The lean practice incorporation to a global lean framework is discussed around three conceptual deployment approaches called, ‘template’, ‘standard’ and ‘product’ deployment. The empirical insight contributes to the body of global lean literature by providing a more dynamic view of global lean frameworks, of which development depends on the underlying processes such as bottom-up practice incorporation. It also provides practitioners in global lean settings with valuable insight and a possibility to review internal global-local deployment processes within a corporate group to increase intra-organizational learning.

The increased demand for product variety has implied that many manufacturing companies are struggling with managing product complexity. This article suggests a framework for combined modeling of product variants and the process flow in production and assembly for customized products. The aim of the framework is to create a visual model that illustrates the product variety relative to the process flow and provides transparency of product variety in the different process steps. Literature has suggested various methods and techniques. These provide means for reducing complexity based on analysis of the end product, but do not pay much attention to understanding where in the production and assembly processes, this variance occurs. The suggested models form a basis for analyzing and reducing product complexity based on a visual model of the product variety in each process step. The models gave rise to a reduction of the SKUs with 33% without losing product variety offered to the end customer. The initial test of the framework and models in the case company showed that the models can provide new insight into the product variety, which forms a solid basis for making decisions on reducing product portfolio variety and adjusting the order decoupling point.

The increase in customization is pushing companies to use more advanced automation technologies in their production lines. Yet, assembly operations are predominantly performed by humans because of their ability to be flexible. The emergence of industrial collaborative robots provides an opportunity to have robots work alongside humans in a flexible and collaborative application. The aim of this study is to explore the industrial collaborative robot capabilities in a collaborative application compared to traditional robot applications. This interview study draws data from four companies with experience in industrial collaborative robot applications. The companies involved in this study experienced that there are several benefits of using an industrial collaborative robot but challenges still exist, in particular related to usability and the robot integration process.

When embarking a cost reduction strategy, it is important to know what causes the costs, how the costs are connected to value adding and to non-value adding activities, and thereby conduct a knowledge-intensive production development. This paper present a method on how to connect costs to production losses and how they can relate to different cost factor groups. The method uses a digital tool that was designed in collaboration with a medium-sized tool manufacturing company, using several manufacturing operations in sequence.

The tool is designed to be used for management monitoring and for strategic decisions. The method uses a performance-based cost model for discrete part manufacturing and incorporates an approximation when dividing the calculated loss costs. To ensure the accuracy of the model a sensitivity analysis was conducted. The result shows that only smaller errors occur due to this approximation and amount to a few percent when extremely high losses are in effect. The novelty of the paper is the variation of the cost model, ensuring that costs can be divided on each of the cost factor groups and investigated performance parameter. In addition, the designed layout of the result presentation in the digital tool, is a further development of the previous presented production performance matrix, which contribute to a comprehensive overview used for production monitoring.

This paper describes how a special assignment from the Swedish government was carried out to support small and medium sized enterprises in implementing automation and robotics. The focus of this paper is to investigate i) how insights and knowledge has been transferred and ii) how the ability of integrators and advisors has been increased. Within the project Pilot project automation challenge in the robot leap (PILAR), 84 pre-studies and 40 in-depth studies were carried out. In PILAR a methodology was developed, and a way of working was tested where coaches in four regionally defined nodes in Sweden visited companies and had advisor support through telephone and video. The project results indicate that insights and knowledge had been increased in several companies and that integrators and advisors have increased their ability to stimulate automation solutions. In addition, eight recommendations on how to successfully perform a nationwide dissemination of robotics is presented.

This paper presents a solution that integrates a smart textiles system with virtual reality to assess the design of workstations from an ergonomics point of view. By using the system, ergonomists, designers, engineers, and operators, can test design proposals of workstations in an immersive virtual environment while they see their ergonomics evaluation results displayed in real-time. The system allows its users to evaluate the ergonomics of the workplace in a pre-production phase. The workstation design can be modified, enabling workstation designers to better understand, test and evaluate how to create successful workstation designs, eventually to be used by the operators in production. This approach uses motion capture together with virtual reality and is aimed to complement and integrate with the use of digital human modelling (DHM) software at virtual stages of the production development process.

Increasing manufacturing efficiency has been a constant challenge since the First Industrial Revolution. What started as mechanization and turned into electricity-driven operations has experienced the power of digitalization. Currently, the manufacturing industry is experiencing an exponential increase in data availability, but it is essential to deal with the complexity and dynamics involved to improve manufacturing indicators. The aim of this study is to identify and allow an understanding of the unfilled gaps and the opportunities regarding production scheduling using machine learning and data science processes. In order to accomplish these goals, the current study was based on the Knowledge Development Process – Constructivist (ProKnow-C) methodology. Firstly, selecting 30 articles from 3608 published articles across five databases between 2015 and 2019 created a bibliographic portfolio. Secondly, a bibliometric analysis, which generated comparative charts of the journals’ relevance regarding its impact factor, scientific recognition of the articles, publishing year, highlighted authors and keywords was carried out. Thirdly, the selected articles were read thoroughly through a systemic analysis in order to identify research problems, proposed solutions, and unfilled gaps. Then, research opportunities identified were: (i) Big data and associated analytics; (ii) Collaboration between different disciplines; (iii) Solution Customization; and (iv) Digital twin development.

The world is striving for a sustainable future as United Nations proposed the 17 Sustainable Development Goals to reduce the environmental impact and increase societal wellbeing by 2030. In this endeavor, eco-efficiency is considered as one of the key concept to facilitate the successful transition to the sustainable development with the focus to reduce the ecological impact of industry through efficiency improvements. The shipping industry is largely involved in this challenge with a target set by International Maritime Organization to cut emissions from individual ships by 40% from 2008 levels by 2030. The ship loading process is believed to have great impact to the overall eco-efficiency as it is not only a time consuming process but also determines the fuel consumption of the transportation. In this study, we aim to incorporate virtual reality (VR) technology and gamification theories to raise the eco-efficiency awareness in the shipping loading process. A VR application for ship loading process was developed using a real world case in the Baltic sea region. Eco-efficiency concept is introduced in different levels based on the gamification theories. Maritime professionals tested the VR application and provided their feedback. The results are positive that combining VR and gamification can be useful to train operators with eco-efficiency in the ship loading operations. It also shows a huge potential to support the shipping industry in this transition towards a more sustainable future.

This paper conceptually introduces VF-KDO (Virtual Factories with Knowledge-Driven Optimization, a research profile of the University of Skövde, Sweden, which is underway from 2018-2026. The goal of this research profile is to deliver radical innovations in manufacturing research essential to the design and operation of next-generation manufacturing systems. A unique concept proposed in VF-KDO is: knowledge extracted for decision support is achieved through systematically exploring, e.g., using advanced, interactive data analytics techniques on optimal solutions generated via many-objective optimizations on virtual factory models. As the word “driven” means “motivated” or “manipulated”, so does KDO have some two-fold meanings: (1) optimizations that aim at generating knowledge, not only mathematically optimal solutions; (2) knowledge-controlled optimizations, instead of some blind/black-box processes. It is this concept of KDO, combining with modular, virtual factory models at different levels, which distinguishes VF-KDO from other related research efforts found internationally and in Sweden. The cutting-edge research topics involved in the research profile and their synergy with the digitalization efforts of the 7 partner companies, in form of the development of an intelligent decision support system, can be used to improve the competiveness of the Swedish manufacturing industry by supporting their holistic, optimal and sustainable decision making.

Simulation software is used in the production development process to simulate production and predict behaviors, calculate times, and plan production in advance. Digital human modeling (DHM) software is used to simulate humans working in production and assess whether workstation designs offer appropriate ergonomic conditions for the workers. However, these human simulations are usually carried out by human factors engineers or ergonomists, whereas the production simulations are carried out by production engineers. Lack of integration of these two simulations can lead to suboptimal solutions when the factory is not optimized to improve both productivity and ergonomics. To tackle this problem, a platform has been developed that connects production flow simulation software data and DHM software data and integrates them in a generic software for data treatment and visualization. Production flow simulation software data and DHM software data are organized in a hierarchical structure that allows synchronization between the production data and the ergonomic data on the target simulation software. The platform is generic and can be connected to any production flow simulation software and any DHM software by creating specific links for each software. The platform requires only the models of the production line, workstations, and tasks in order to perform the simulations in the target simulation software and collect the simulation results to present the results to the user of the platform.

Working in groups is beneficial for many complex production jobs as groups can have the cognitive and physical capacity that lacks from individuals. The group learning process is complicated when, in addition to individual learning by doing, the number of workers and knowledge transfer have their effects. Production managers need tools for analyzing and predicting group performance and learning over future production periods. Mathematical learning curve models are one of those tools that managers use, with a few are available for groups. This paper reviews potential group learning curve models for production environments. The models are fitted to data from an assembly experiment consisting of different group sizes and repetitions. The results show that more parameters improve the fit. A qualitative evaluation has been performed to answer how well the models reflect group learning and support decision making in production and how their prediction of data could be improved. The results suggest that the S-shaped model performed the best making it a potential one for describing learning in groups in production environments. The paper also suggests future directions along with this line of research.

Although the automation level is high within the automotive industry, there are still a large number of manual tasks, especially is the final assembly of the vehicle. Overhead assembly operations is an example of a problematic manual task that can cause workers to develop musculoskeletal disorders in the shoulder complex. Exoskeletons may be a solution to reduce the risk for developing musculoskeletal disorders from the work tasks. This study evaluates and compares how the use of three different passive upper body exoskeletons affects the range of motion (ROM) of workers at overhead assembly tasks. An experiment consisting of three tasks was set up in order to analyze the differences between the models. Seventeen subjects were involved in the study. Interviews, observations, videos and motion capture recordings were the methods of collecting data. The results show agreement from all the subjects that the exoskeletons help the worker at this specific assembly operation. The results also show that different exoskeleton models cause different levels of ROM reductions. The subjects’ opinions about how the different exoskeletons influence the ROM corresponds with the analysis of the motion capture data. Positive and negative aspects of each exoskeleton from a ROM and an implementation point of view are discussed. In general, the results indicate that the exoskeleton models can be applicable for the type of work tasks studied. However, the exoskeletons would benefit from further development in order to decrease ROM limitations and therefore cover a larger number of different manual assembly tasks.

The manufacturing industry is becoming increasingly more complex as the paradigm of mass-production moves, via mass-customization, towards personalized production and Industry 4.0. This increased complexity in the production system also makes everyday work for shop-floor operators more complex. To take advantage of this complexity, shop-floor operators need to be properly supported in order to perform their important work. The shop-floor operators in this future complex manufacturing industry, the Operator 4.0, need to be supported with the implementation of new cognitive automation solutions. These automation solutions, together with the innovativeness of new processes and organizations will increase the competitiveness of the manufacturing industry. This paper discusses three different aspects of production innovation in the context of the needs and preferences of information for Operator 4.0. Conclusively, product innovations can be applied in the manufacturing processes, and thus becoming process innovations, but the implementation of such innovations require organizational innovations.