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.
    6. Miroshnychenko, Ivan & Strobl, Andreas & Matzler, Kurt & De Massis, Alfredo, 2021. "Absorptive capacity, strategic flexibility, and business model innovation: Empirical evidence from Italian SMEs," Journal of Business Research, Elsevier, vol. 130(C), pages 670-682.
    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. 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.
    3. Cannavacciuolo, Lorella & Capaldo, Guido & Ponsiglione, Cristina, 2023. "Digital innovation and organizational changes in the healthcare sector: Multiple case studies of telemedicine project implementation," Technovation, Elsevier, vol. 120(C).
    4. Broccardo, Laura & Tenucci, Andrea & Agarwal, Reeti & Alshibani, Safiya Mukhtar, 2024. "Steering digitalization and management control maturity in small and medium enterprises (SMEs)," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    5. 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).
    6. 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).
    7. Castro-Lopez, Adrian & Iglesias, Victor & Santos-Vijande, María Leticia, 2023. "Organizational capabilities and institutional pressures in the adoption of circular economy," Journal of Business Research, Elsevier, vol. 161(C).
    8. Özköse, Hakan & Güney, Gül, 2023. "The effects of industry 4.0 on productivity: A scientific mapping study," Technology in Society, Elsevier, vol. 75(C).
    9. Shuting Chen & Dengke Yu, 2024. "What Drives Business Model Innovation? Exploring the Role of Knowledge Management Capability in Chinese Top-Ranking Innovative Enterprises," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(2), pages 6390-6424, June.
    10. 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.
    11. Hamed Ali Al Azzani & Normal Mat Jusoh & Aamir Abbas, 2024. "Market and Supply Chain Orientation; Dynamic Capabilities Leading to Innovation and Operational Capabilities," SAGE Open, , vol. 14(2), pages 21582440241, June.
    12. 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.
    13. 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.
    14. Navarro-García, Antonio & Ledesma-Chaves, Pablo & Gil-Cordero, Eloy & De-Juan-Vigaray, María Dolores, 2024. "Intangible resources, static and dynamic capabilities and perceived competitive advantage in exporting firms. A PLS-SEM/fsQCA approach," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    15. 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).
    16. 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).
    17. 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).
    18. Cowden, Birton & Tang, Jintong, 2022. "Institutional entrepreneurial orientation: Beyond setting the rules of the game for blockchain technology," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    19. Hua Zhang & Shaofeng Yuan, 2023. "How and When Does Big Data Analytics Capability Boost Innovation Performance?," Sustainability, MDPI, vol. 15(5), pages 1-19, February.
    20. Siltori, Patricia F.S. & Anholon, Rosley & Rampasso, Izabela Simon & Quelhas, Osvaldo L.G. & Santa-Eulalia, Luis A. & Leal Filho, Walter, 2021. "Industry 4.0 and corporate sustainability: An exploratory analysis of possible impacts in the Brazilian context," Technological Forecasting and Social Change, Elsevier, vol. 167(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.