Digitalization is forcing the industry to rethink current practices in all business domains, pushing for a digital transformation of business and operations at a high rate and, thus, paving the way for new business models and making others redundant. For small and medium-sized companies (SME), in particular, it is an enormous challenge to keep up with the pace of technological development. Several initiatives have argued the industry’s need for continuous digitalization, innovation, transformation ability, and future skills and competencies development. However, the advancement of the Swedish industry in this area has been uneven, where larger organizations have begun their digital transformation journey to some extent, but SMEs risk falling behind. In addition to the technological transformation, the challenges regarding the industries’ skills supply need to be solved, where a workforce with the right competencies, knowledge, and skill sets are equally, if not more, important for remaining competitive. One of the key elements to face these challenges in the companies will be to recruit knowledgeable employees or re-skill the existing ones. Efficient access to relevant knowledge and skills is still a major concern for companies that will surely affect their competitiveness for a long time to come. This paper elaborates on the opportunities and challenges that Swedish universities face in the context of lifelong learning and education for industry professionals. The paper presents results and experiences gained from a lifelong learning project for industry professionals at the University of Skövde in collaboration with ten industry partners. The results from the project show that in addition to pedagogical methods, current structures and policies within academia need to be further developed to effectively serve industry professionals. The paper also presents a concept of education for industry professionals in the lifelong learning context based on the results and experience gained from the project.
European higher education institutions (HEIs) are facing increasing demands for more flexible learning and flexibility in learning paths.
This report from a 2023 European University Association Learning & Teaching Thematic Peer Group on “Flexible learning and teaching” explores the complexity of implementing flexible learning at HEIs, starting by defining what it means and entails for the institution, and its members and entities (staff, students, leadership, faculties). With the view that the development of flexible learning is an essential condition for the future of learning at universities, the group identified challenges and examples of practice, and offered recommendations for institutions to reflect on their strategy and build capacity for flexible learning.
Lean is and will still be one of the most popular management philosophies in the Industry 4.0 context and simulation is one of its key technologies. Many authors discuss about the benefits of combining Lean and simulation to better support decision makers in system design and improvement. However, there is a lack of reviews in the domain. Therefore, this paper presents a four-stage comprehensive review and analysis of existing literature on their combination. The aim is to identify the state of the art, existing methods and frameworks for combining Lean and simulation, while also identifying key research perspectives and challenges. The main trends identified are the increased interest in the combination of Lean and simulation in the Industry 4.0 context and in their combination with optimisation, Six Sigma, as well as sustainability. The number of articles in these areas is likely to continue to grow. On the other hand, we highlight six gaps found in the literature regarding the combination of Lean and simulation, which may induce new research opportunities. Existing technical, organisational, as well as people and culture related challenges on the combination of Lean and simulation are also discussed.
The rapid changes in the market including globalization, the requirement for personalizedproducts and services by the customers, shorter product life-cycles, the exponential growthof technological advances, and the demographical changes, will demand organizations toeffectively improve and design their systems in order to survive. This is the actual paradigmcharacterizing the industrial and service sectors. This scenario presents a considerablechallenge to decision makers who will need to decide about how to design and improve amore than ever complex system without compromising the quality of the decision taken.Lean, being a widely applied management philosophy with very powerful principles, itsmethods and tools are static in nature and have some limitations when it comes to the designand improvement of complex and dynamic systems. Some authors have proposed thecombined use of simulation with Lean in order to overcome these limitations. Furthermore,optimization and post-optimization tools coupled to simulation, provide knowledge aboutoptimal or nearly optimal system configurations to choose from. However, even if Leanprinciples, methods and tools, as well as simulation and optimization, pursue the objectiveof supporting organizations regarding system design and improvement, a bilateral approachfor their combination and its benefits have barely been addressed in the literature.Many studies focus only on how specific Lean tools and simulation can be combined, treatingLean purely as a toolbox and not considering how Lean can support the simulation process.The aim of this research is to address this knowledge gap by analyzing the mutualbenefits and presenting a framework for combining Lean, simulation and optimization tobetter support decision makers in system design and improvement where the limitationsof Lean tools and simulation are overcome by their combination. This framework includesa conceptual framework explaining the relationships between the Lean philosophy, methodsand tools with simulation and optimization; the purposes for this combination and stepby step processes to achieve these purposes; the identification of the roles involved in eachprocess; a maturity model providing guidelines on how to implement the framework; existingbarriers for the implementation; and ethical considerations to take into account. Anindustrial handbook has also been written which explains how to deploy the framework.The research has been conducted in three main stages including an analysis of the literatureand the real-world needs, the definition and formulation of the framework, and finally, itsevaluation in real-world projects and with subject matter experts. The main contributionof this research is the reflection provided on the bilateral benefits of the combination, aswell as the defined and evaluated framework, which will support decision makers take qualitydecisions in system design and improvement even in complex scenarios.
Is it beneficial to combine lean, simulation and optimization? And if so, how can they be combined for decision-making support in system design and improvement? This research proposes a framework that sets the basis for achieving beneficial interactions between the lean philosophy, methods and tools, and simulation-based optimization. A framework that gives the users the possibility to get better system understanding, conduct a deeper system analysis, and attain an optimal system design and improvement, and thereby, get better foundation for sustainable long time improvement. The framework has been tested in several realworld case studies. Moreover, surveys have been conducted to evaluate the perception of subject matter experts about its usefulness, as well as its usability and perceived quality by end users and decision makers, all of them reporting very positive results.
This chapter summarises findings from an empirical study investigating how digital healthcare solutions should be developed to satisfy individual demands for understanding and managing healthcare information and activities. Despite the quick development of new digital healthcare services to accomplish service efficiency and cost-effectiveness, and at the same time supporting the achievement of a wide range of sustainable development goals, some users perceive challenges due to different age-related, physical, and cognitive starting points. The chapter offers a broad view of the development of digital healthcare solutions and considers literature and the perspectives of technologists, caregivers, and users through a qualitative approach using interviews and autoethnography. A main finding is that the development of digital healthcare solutions that enhance health literacy needs a comprehensive strategy, in which users, healthcare providers, family members, and other stakeholders should participate.
Companies are continuously working towards system and process improvement to remain competitive in aglobal market. There are different methods that support companies in the achievement of that goal. This paper presents an innovative process that combines lean, simulation and optimization to improve the material flow of a manufacturing company. A description of each step of the process details the lean tools and principles taken into account, as well as the results achieved by the application of simulation and optimization.The project resulted in an improved layout and material flow that employs an automated guided vehicle. In addition, lean wastes related to transport, inventory levels as well as waiting times were reduced. The utilization of the process that combines lean, simulation and optimization was considered valuable for the success of the project.
This article presents a maturity model that can be applied to support organizations in identifying their current state and guiding their further development with regard to lean, simulation and optimization. The paper identifies and describes different maturity levels and offers guidelines that explain how organizations can grow from lower to higher levels of maturity. In addition, it attempts to provide the starting point for organizations that have applied lean or are willing to implement it and which may also be considering taking decisions in a more efficient way via simulation and optimization.
The new industrial revolution brings important changes to organizations that will need to adapt their machines, systems and employees’ competences to sustain their business in a highly competitive market. Management philosophies such as lean will also need to adapt to the improvement possibilities that Industry 4.0 brings. This paper presents a review on the role of lean and simulation in the context of Industry 4.0. Additionally, the paper presents a conceptual framework where simulation and optimization will make the lean approach more efficient, speeding up system improvements and reconfiguration, by means of an enhanced decision-making process and supported organizational learning.
The improvement of emergency department processes involves the need to take into considerationmultiple variables and objectives in a highly dynamic and unpredictable environment, which makes thedecision-making task extremely challenging. The use of different methodologies and tools to support thedecision-making process is therefore a key issue. This article presents a novel approach in healthcarein which Discrete Event Simulation, Simulation-Based Multi-Objective Optimization and Data Miningtechniques are used in combination. This methodology has been applied for a system improvementanalysis in a Swedish emergency department. As a result of the project, the decision makers were providedwith a range of nearly optimal solutions and design rules which reduce considerably the length of stayand waiting times for emergency department patients. These solutions include the optimal number ofresources and the required level of improvement in key processes. The article presents and discussesthe benefits achieved by applying this methodology, which has proven to be remarkably valuable fordecision-making support, with regard to complex healthcare system design and improvement.
Discrete Event Simulation (DES) is nowadays widely used to support decision makers in system analysis and improvement. However, the use of simulation for improving stochastic logistic processes is not common among healthcare providers. The process of improving healthcare systems involves the necessity to deal with trade-off optimal solutions that take into consideration a multiple number of variables and objectives. Complementing DES with Multi-Objective Optimization (SMO) creates a superior base for finding these solutions and in consequence, facilitates the decision-making process. This paper presents how SMO has been applied for system improvement analysis in a Swedish Emergency Department (ED). A significant number of input variables, constraints and objectives were considered when defining the optimization problem. As a result of the project, the decision makers were provided with a range of optimal solutions which reduces considerably the length of stay and waiting times for the ED patients. SMO has proved to be an appropriate technique to support healthcare system design and improvement processes. A key factor for the success of this project has been the involvement and engagement of the stakeholders during the whole process.
Healthcare facilities, and especially emergency departments (ED), are usually characterized by its complexity due to the variability and stochastic nature of the processes involved in the system. The combination of different flows of patients, staff and resources also increments the complexity of this kind of facilities. In order to increase its efficiency, many researchers have proposed discrete-event simulation (DES) as a powerful improvement tool. However, DES can be a limited approach in the case a simulation model has too many combinations of input parameters, complex correlations between the input and output parameters and different objective functions. Hence, to find the best configuration of a complex system, an approach combining DES and meta-heuristic optimization becomes an even more powerful improvement technique. Simulation-based multiobjective-optimization (SMO) is a promising approach to generate multiple trade-off solutions particularly when multiple conflicting objectives exist within a complex system. The generated solutions provide decision makers with feasible and optimal alternatives to improve, modify or design healthcare systems. The aim of this paper is to present the work done at the ED of the regional Hospital of Skövde in Sweden, where SMO implemented in modeFromtier has been successfully applied. The result and methodology present a successful approach for decision makers in healthcare systems to reduce the waiting time of patients saving considerable time, money and resources.
The highly competitive automobile market requires automotive companies to become efficient by continuously improving their production systems. This paper presents a case study where simulationbased optimization (SBO) was employed as a step within a Value Stream Mapping event. The aim of the study was to promote the use of SBO to strengthen the continuous improvement work of the company. The paper presents all the key steps performed in the study, including the challenges faced and a reflection on how to introduce SBO as a powerful tool within the lean continuous improvement standards.
In a knowledge based economy, manufacturing industry has to continuously improve their operations, processes and develop their employees in order to remain competitive in the market.
In this context, the collaboration between industry and universities becomes of vital importance. Universities and industry have traditionally maintained fairly informal or lose ways of cooperation when it comes to education. This article presents a fruitful cooperation which has been established between the University of Skövde, the Industrial Development Center in the region, IDC West Sweden AB, and the manufacturing industry.
The paper describes the development, lessons learned and the outcome of more than 3 years’ experience of close collaboration between the different stakeholders. It presents a methodology, used by the consortium to help manufacturing industries to improve their competiveness using a well defined process including: a company analysis, applied education and long-term coaching. A special focus is put on a long-term commitment by all partners. This alliance has performed more than 140 company analysis, conducted applied education for more than 2500 employees from more than 120 companies and performed coaching of more than 80 companies on site. The trend is that these figures will increase over time.
The established collaboration has been strengthened over this period of time by a number of shared research projects. One of these projects involves an evaluation of the impact that this presented consortium has had on the region´s industry. Lean Learning Academies is another project that has been funded by the European Union within the Lifelong Learning Program, with the aim to increase the competitiveness of European companies and enhance the employability of students.
Lean and simulation analysis are driven by the same objective, how to better design and improve processes making the companies more competitive. The adoption of lean has been widely spread in companies from public to private sectors and simulation is nowadays becoming more and more popular. Several authors have pointed out the benefits of combining simulation and lean, however, they are still rarely used together in practice. Optimization as an additional technique to this combination is even a more powerful approach especially when designing and improving complex processes with multiple conflicting objectives. This paper presents the mutual benefits that are gained when combining lean, simulation and optimization and how they overcome each other´s limitations. A framework including the three concepts, some of the barriers for its implementation and a real-world industrial example are also described.
The expected demographic changes, and especially the rise in life expectancy, will considerably increase elderly patients' demand for healthcare. There are different strategies that can offer better care for these patients, reduce their unnecessary visits to the emergency departments, and in consequence, reduce the number of hospitalizations and days at the hospital. This study employed system dynamics to analyze the economic and quality-related effects of different closer care strategies such as investments in care coordinators and mobile health clinics, as well as to offer proactive care in the primary care facilities for elderly patients. The results indicate that a combination of the different strategies will support better care for patients, will reduce hospital costs and will reduce the existing pressure on the emergency department. The paper also reflects on the process followed to conduct the study and the lessons learned.
This paper presents results from a simulation case study analyzing care strategies for elderly patients in a regional healthcare system (HCS) in Sweden. Three strategies to reduce emergency visits, hospitalisations, and stays were evaluated: care coordinators at emergency departments, mobile health clinics visiting fragile patients in their homes, and proactive primary care. Using system dynamics modelling and empirical data, the impact on the regional HCS was explored considering the reduced care demand and demographic changes. Subsequently, the impact on the population's health status was assessed. Combining strategies yielded the best outcome, but improving patients' health status may increase long-term care demand. The study emphasizes the importance of implementing these strategies to offer better care for elderly patients and reduce healthcare costs. Findings highlight the potential long-term effects of improving health status and the need for a comprehensive approach to address the evolving care demands of an ageing population.
The purpose of this article as follows: The first is to clarify the theories underlying the definition of three concepts- Pacemaker, Bottleneck and Order Decoupling Point- in the field of production systems design, planning and control; the second is to demonstrate the existing interrelations between the three concepts; and third, to set out guidelines that will make identifying and defining these concepts easier, when attempting to design or redesign a system using a lean production approach. It is hoped that this paper will fill a gap in the literature by relating these three concepts from different points of view, as well as offer practical guidelines to academics or professionals involved in the design, or redesign, of a production system Keeping this in mind, the findings portrayed in this publication have been organized into the following sections. First the article describes the literature review process that led to acceptable theoretical descriptions for each of the three concepts. Then, from a conceptual point of view and depending on production strategy - Made-To-Order (MTO) or Made-To-Stock (MTS) -a set of considerations, in different practical situations are made establishing the relationships between each of the concepts. At the same time this document provides some procedural guidelines to help identify the three concepts. Then a practical case of production system redesign is proposed, in which aspects and methods described in this article are used. Finally, conclusions are drawn at the end. © INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING.
Production engineers struggle continuously in seeking the optimal configuration in order to maximize the performance of their production systems. This is a time-consuming work in which they seldom get the necessary resources in order to make the right decisions. New and innovative tools are therefore needed to support high-quality decision making in a rapid manner.
This paper introduces the second generation of FACTS Analyzer, a toolset developed based on the concept of integrating model abstraction, automatic model generation and simulationbased optimization under an innovative Internet-based platform. Specifically, it addresses a novel model aggregation and generation method, which when combined together with other system components, like optimization engines, can synthetically enable simulation to become much easier to use and speed up the time-consuming model building, experimentation and optimization processes, in order to support optimal decision making. Such a toolset is the result of several years of research and development in close collaboration with the Swedish automotive industry.