IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i24p8071-d1300475.html
   My bibliography  Save this article

Modelling the Reliability of Logistics Flows in a Complex Production System

Author

Listed:
  • Bożena Zwolińska

    (Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30-059 Krakow, Poland)

  • Jakub Wiercioch

    (Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30-059 Krakow, Poland)

Abstract

This paper analyses the disruptions occurring in a production system determining the operating states of a single machine. A system with a convergent production character, in which both single flows (streams) and multi-stream flows occur, was considered. In this paper, a two-level formalisation of the production system (PS) was made according to complex systems theory. The continuity analysis was performed at the operational level (manufacturing machine level). The definition of the k th survival value and the quasi-coherence property defined on chains of synchronous relations were used to determine the impact of interruption of the processed material flow on uninterrupted machine operation. The developed methodology is presented in terms of shaping the energy efficiency of technical objects with the highest power demand (the furnace of an automatic paint shop and the furnace of a glass tempering line were taken into consideration). The proposed methodology is used to optimise energy consumption in complex production structures. The model presented is utilitarian in nature—it can be applied to any technical system where there is randomness of task execution times and randomness of unplanned events. This paper considers the case in which two mutually independent random variables determining the duration of correct operation T P and the duration of breakdown T B are determined by a given distribution: Gaussian and Gamma family distributions (including combinations of exponential and Erlang distributions). A formalised methodology is also developed to determine the stability of system operation, as well as to assess the potential risk for arbitrary system evaluation parameters.

Suggested Citation

  • Bożena Zwolińska & Jakub Wiercioch, 2023. "Modelling the Reliability of Logistics Flows in a Complex Production System," Energies, MDPI, vol. 16(24), pages 1-22, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:8071-:d:1300475
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/24/8071/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/24/8071/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Diana Filipe & Carina Pimentel, 2023. "Production and Internal Logistics Flow Improvements through the Application of Total Flow Management," Logistics, MDPI, vol. 7(2), pages 1-22, June.
    2. Büchi, Giacomo & Cugno, Monica & Castagnoli, Rebecca, 2020. "Smart factory performance and Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 150(C).
    3. Sven-Vegard Buer & Marco Semini & Jan Ola Strandhagen & Fabio Sgarbossa, 2021. "The complementary effect of lean manufacturing and digitalisation on operational performance," International Journal of Production Research, Taylor & Francis Journals, vol. 59(7), pages 1976-1992, April.
    4. Kristina Höse & Afonso Amaral & Uwe Götze & Paulo Peças, 2023. "Manufacturing Flexibility through Industry 4.0 Technological Concepts—Impact and Assessment," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(2), pages 271-289, June.
    5. Vandana & Shiv Raj Singh & Mitali Sarkar & Biswajit Sarkar, 2023. "Effect of Learning and Forgetting on Inventory Model under Carbon Emission and Agile Manufacturing," Mathematics, MDPI, vol. 11(2), pages 1-20, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gilberto Santos & Jose Carlos Sá & Maria João Félix & Luís Barreto & Filipe Carvalho & Manuel Doiro & Kristína Zgodavová & Miladin Stefanović, 2021. "New Needed Quality Management Skills for Quality Managers 4.0," Sustainability, MDPI, vol. 13(11), pages 1-22, May.
    2. Federica Murmura & Laura Bravi & Gilberto Santos, 2021. "Sustainable Process and Product Innovation in the Eyewear Sector: The Role of Industry 4.0 Enabling Technologies," Sustainability, MDPI, vol. 13(1), pages 1-16, January.
    3. Zander, Bennet & Lange, Kerstin & Haasis, Hans-Dietrich, 2020. "Impacts of a smart factory on procurement logistics," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Lo, volume 29, pages 459-485, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    4. Catherine Maware & David M. Parsley, 2022. "The Challenges of Lean Transformation and Implementation in the Manufacturing Sector," Sustainability, MDPI, vol. 14(10), pages 1-24, May.
    5. Lee, Jeongwon & Hwang, Junseok & Kim, Hana, 2022. "Different government support effects on emerging and mature ICT sectors," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    6. Ghadimi, Pezhman & Donnelly, Oisin & Sar, Kubra & Wang, Chao & Azadnia, Amir Hossein, 2022. "The successful implementation of industry 4.0 in manufacturing: An analysis and prioritization of risks in Irish industry," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    7. Erhan Baran & Tulay Korkusuz Polat, 2022. "Classification of Industry 4.0 for Total Quality Management: A Review," Sustainability, MDPI, vol. 14(6), pages 1-20, March.
    8. Arpan Kumar Kar & P. S. Varsha & Shivakami Rajan, 2023. "Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 659-689, December.
    9. Xi, Mengjie & Liu, Yang & Fang, Wei & Feng, Taiwen, 2024. "Intelligent manufacturing for strengthening operational resilience during the COVID-19 pandemic: A dynamic capability theory perspective," International Journal of Production Economics, Elsevier, vol. 267(C).
    10. Kajikawa, Yuya & Mejia, Cristian & Wu, Mengjia & Zhang, Yi, 2022. "Academic landscape of Technological Forecasting and Social Change through citation network and topic analyses," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    11. Özköse, Hakan & Güney, Gül, 2023. "The effects of industry 4.0 on productivity: A scientific mapping study," Technology in Society, Elsevier, vol. 75(C).
    12. Peerally, Jahan Ara & Santiago, Fernando & De Fuentes, Claudia & Moghavvemi, Sedigheh, 2022. "Towards a firm-level technological capability framework to endorse and actualize the Fourth Industrial Revolution in developing countries," Research Policy, Elsevier, vol. 51(10).
    13. Eleonora Di Maria & Marco Bettiol & Mauro Capestro, 2023. "How Italian Fashion Brands Beat COVID-19: Manufacturing, Sustainability, and Digitalization," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    14. Marco Bettiol & Mauro Capestro & Eleonora Maria & Stefano Micelli, 2021. "Reacting to the COVID-19 pandemic through digital connectivity with customers: the Italian experience," Italian Journal of Marketing, Springer, vol. 2021(4), pages 305-330, December.
    15. Maria C Tavares & Graça Azevedo & Rui P. Marques, 2022. "The Challenges and Opportunities of Era 5.0 for a More Humanistic and Sustainable Society—A Literature Review," Societies, MDPI, vol. 12(6), pages 1-21, October.
    16. Jun Dong & A-Ru-Han Bao & Yao Liu & Xi-Hao Dou & Dong-Ran Liu & Gui-Yuan Xue, 2022. "Dynamic Differential Game Strategy of the Energy Big Data Ecosystem Considering Technological Innovation," Sustainability, MDPI, vol. 14(12), pages 1-24, June.
    17. Majdouline, Ilias & Baz, Jamal El & Jebli, Fedwa, 2022. "Revisiting technological entrepreneurship research: An updated bibliometric analysis of the state of art," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    18. Calış Duman, Meral & Akdemir, Bunyamin, 2021. "A study to determine the effects of industry 4.0 technology components on organizational performance," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    19. Barbara Aquilani & Michela Piccarozzi & Tindara Abbate & Anna Codini, 2020. "The Role of Open Innovation and Value Co-creation in the Challenging Transition from Industry 4.0 to Society 5.0: Toward a Theoretical Framework," Sustainability, MDPI, vol. 12(21), pages 1-21, October.
    20. Shet, Sateesh V. & Pereira, Vijay, 2021. "Proposed managerial competencies for Industry 4.0 – Implications for social sustainability," Technological Forecasting and Social Change, Elsevier, vol. 173(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:8071-:d:1300475. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.