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  • 1.
    Barrera Diaz, Carlos Alberto
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Oscarsson, Jan
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Lidberg, Simon
    Manufacturing Engineering, Research and Simulation, Volvo Car Corporation, Skövde, Sweden.
    Sellgren, Tommy
    Manufacturing Engineering, Research and Simulation, Volvo Car Corporation, Skövde, Sweden.
    Discrete Event Simulation Output Data-Handling System in an Automotive Manufacturing Plant2018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 25, p. 8p. 23-30Article in journal (Refereed)
    Abstract [en]

    Discrete Event Simulation is a comprehensive tool for the analysis and design of manufacturing systems. Over the years, considerable efforts to improve simulation processes have been made. One step in these efforts is the standardisation of the output data through the development of an appropriate system which presents the results in a standardised way. This paper presents the results of a survey based on simulation projects undertaken in the automotive industry. In addition, it presents the implementation of an automated output data-handling system which aims to simplify the project’s documentation task for the simulation engineers and make the results more accessible for other stakeholders.

  • 2.
    Goienetxea Uriarte, Ainhoa
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Urenda Moris, Matías
    Division of Industrial Engineering and Management, Department of Engineering Science, Uppsala University, Uppsala, Sweden.
    Supporting the lean journey with simulation and optimization in the context of Industry 4.02018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 25, p. 586-593Article in journal (Refereed)
    Abstract [en]

    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.

  • 3.
    Lidberg, Simon
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Volvo Car Corporation, Skövde, Sweden.
    Pehrsson, Leif
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Frantzén, Marcus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Applying Aggregated Line Modeling Techniques to Optimize Real World Manufacturing Systems2018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 25, p. 89-96Article in journal (Refereed)
    Abstract [en]

    The application of discrete event simulation methodology in the analysis of higher level manufacturing systems has been limited due to model complexity and the lack of aggregation techniques for manufacturing lines. Recent research has introduced new aggregation methods preparing for new approaches in the analysis of higher level manufacturing systems or networks. In this paper one of the new aggregated line modeling techniques is successfully applied on a real world manufacturing system, solving a real-world problem. The results demonstrate that the aggregation technique is adequate to be applied in plant wide models. Furthermore, in this particular case, there is a potential to reduce storage levels by over 25 %, through leveling the production flow, without compromising deliveries to customers.

  • 4.
    Morshedzadeh, Iman
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Oscarsson, Jan
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Aslam, Tehseen
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Frantzén, Marcus
    Volvo Car Corporation, Skövde, Sweden.
    Multi-level management of discrete event simulation models in a product lifecycle management framework2018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 25, p. 74-81Article in journal (Refereed)
    Abstract [en]

    Discrete event simulation (DES) models imitates the behavior of a production system. Models can be developed to reflect different levels of the production system, e.g supply chain level or manufacturing line level. Product Lifecycle Management (PLM) systems have been developed in order to manage product and manufacturing related data. DES models is one kind of product lifecycle’s data which can be managed by a PLM system. This paper presents a method and its implementation for management of interacting multi-level models utilizing a PLM system.

  • 5.
    Ore, Fredrik
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Eskilstuna, Sweden / Scania CV AB, Global Industrial Development, Södertälje, Sweden.
    Hansson, Lars
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Scania CV AB, Global Industrial Development, Södertälje, Sweden / Chalmers University of Technology, Department of Product and Production Development, Gothenburg, Sweden.
    Wiktorsson, Magnus
    Mälardalen University, School of Innovation, Design and Engineering, Eskilstuna, Sweden.
    Method for design of human-industrial robot collaboration workstations2017In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 11, p. 4-12Article in journal (Refereed)
    Abstract [en]

    In order to fully utilise a 3D simulation software capable of evaluating hand-guided human-industrial robot collaborative (HIRC) work tasks, there is a need of a HIRC design process for early production development stages. This paper proposes a HIRC design method that uses the possibilities of the demonstrator software in the HIRC workstation design process. The method is based on Pahl and Beitz's engineering design method; it interprets all their phases and activities into HIRC design-specific ones.

  • 6.
    Rösiö, Carin
    et al.
    Jönköping University School of Engineering, Department of Industrial Engineering and Management, Jönköping, Sweden.
    Aslam, Tehseen
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Srikanth, Karthik Banavara
    Jönköping University School of Engineering, Department of Industrial Engineering and Management, Jönköping, Sweden.
    Shetty, Savin
    Jönköping University School of Engineering, Department of Industrial Engineering and Management, Jönköping, Sweden.
    Towards an assessment criterion of reconfigurable manufacturing systems within the automotive industry2019In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 28, p. 76-82Article in journal (Refereed)
    Abstract [en]

    To increase changeability and reconfigurability of manufacturing systems, while maintaining cost-efficiency and environmental sustainability they need to be designed in accordance to the need for change. Since companies often need to convert existing manufacturing systems to handle variation, implementation of reconfigurable manufacturing systems calls for an analysis of the current system to understand to what extent they fulfil reconfigurability characteristics. This requires an assessment of the needs for reconfigurability as well as assessment of the existing ability to reconfigure the manufacturing system. Although a lot of reconfigurable manufacturing system assessment models are proposed in theory there is an evident knowledge gap pertaining to what extent the existing systems in the industry are in achieving reconfigurability. The purpose with this paper is to propose an assessment criterion for existing manufacturing systems to measure reconfigurability and their readiness to change with respect to products and volume variations. Based on a literature review of existing reconfigurability assessment models and a case study within the automotive industry, a criterion is developed and tested to analyze how reconfigurable a system is and to decide which parameters that need more attention to achieve higher degree of reconfigurability. © 2019 The Authors.

  • 7.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden.
    Predictive Maintenance of Machine Tool Linear Axes: A Case from Manufacturing Industry2018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 17, p. 118-125Article in journal (Refereed)
    Abstract [en]

    In sustainable manufacturing, the proper maintenance is crucial to minimise the negative environmental impact. In the context of Cloud Manufacturing, Internet of Things and Big Data, amount of available information is not an issue, the problem is to obtain the relevant information and process them in a useful way. In this paper a maintenance decision support system is presented that utilises information from multiple sources and of a different kind. The key elements of the proposed approach are processing and machine learning method evaluation and selection, as well as estimation of long-term key performance indicators (KPIs) such as a ratio of unplanned breakdowns or a cost of maintenance approach. Presented framework is applied to machine tool linear axes. Statistical models of failures and Condition Based Maintenance (CBM) are built based on data from a population of 29 similar machines from the period of over 4 years and with use of proposed processing approach. Those models are used in simulation to estimate the long-term effect on selected KPIs for different strategies. Simple CBM approach allows, in the considered case, a cost reduction of 40% with the number of breakdowns reduced 6 times in respect to an optimal time-based approach.

  • 8.
    Wiktorsson, Magnus
    et al.
    KTH Royal Insitute of Technology, Dept of Sustainable Production Development, Södertälje, Sweden.
    Noh, Sang Do
    Sungkyunkwan University, Dept of Industrial Engineering, Suwon, South Korea.
    Bellgran, Monica
    KTH Royal Insitute of Technology, Dept of Sustainable Production Development, Södertälje, Sweden.
    Hanson, Lars
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Scania CV, Global Industrial Development, Södertälje, Sweden.
    Smart Factories: South Korean and Swedish examples on manufacturing settings2018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 25, p. 471-478Article in journal (Refereed)
    Abstract [en]

    What constitutes a company's capabilities to develop a Smart Factory? South Korean and Swedish perspectives are here illustrated by company examples of smart factory solutions and related strategic aspects of their digitalization. It is concluded that the "smart-factory-capability" of a manufacturing company is integrated with its corporate production systems and includes perspectives on application areas, value adding processes as well as enabling technologies. It is furthermore challenged by the transformational inabilities of legacy systems. By its concrete examples is use and financial benefits, the paper contributes to the definition of the smart factory and its corresponding development scheme. 

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