IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v226y2020ics0925527320300049.html
   My bibliography  Save this article

Research on horizontal system model for food factories: A case study of process cheese manufacturer

Author

Listed:
  • Matsumoto, Takao
  • Chen, Yijun
  • Nakatsuka, Akihiro
  • Wang, Qunzhi

Abstract

The diary food factories in Japan are facing serious challenges of severe labor shortage and the increased diversity of demand. Food manufacturing companies are forced to improve factories to be more productive and flexible to deal with the expanding market scale in the future and also the product diversity. To improve the productivity and the flexibility, automation technologies have been implemented in manufacturing system with the popularization of Industrial 4.0 and Smart Factory. Based on the actual system construction practice of a dairy factory which is as a case study, this paper proposes a five-level horizontal model with automation technologies, aiming to realize high efficiency, rapid integration and relocation of the manufacturing system. This paper introduces the composition, the specifications and the functions of the horizontal model, and evaluates the function of each level. Finally, through the case study and numerical comparison on cost and labor hours, we verify the superiority of the proposed horizontal hierarchical system model for food factories.

Suggested Citation

  • Matsumoto, Takao & Chen, Yijun & Nakatsuka, Akihiro & Wang, Qunzhi, 2020. "Research on horizontal system model for food factories: A case study of process cheese manufacturer," International Journal of Production Economics, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:proeco:v:226:y:2020:i:c:s0925527320300049
    DOI: 10.1016/j.ijpe.2020.107616
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527320300049
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2020.107616?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hui Yang & Soundar Kumara & Satish T.S. Bukkapatnam & Fugee Tsung, 2019. "The internet of things for smart manufacturing: A review," IISE Transactions, Taylor & Francis Journals, vol. 51(11), pages 1190-1216, November.
    2. Alfred Theorin & Kristofer Bengtsson & Julien Provost & Michael Lieder & Charlotta Johnsson & Thomas Lundholm & Bengt Lennartson, 2017. "An event-driven manufacturing information system architecture for Industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 55(5), pages 1297-1311, March.
    3. SooCheol Yoon & Jumyung Um & Suk-Hwan Suh & Ian Stroud & Joo-Sung Yoon, 2019. "Smart Factory Information Service Bus (SIBUS) for manufacturing application: requirement, architecture and implementation," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 363-382, January.
    4. Andrew Kusiak, 2018. "Smart manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 508-517, 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. Sundarakani, Balan & Ajaykumar, Aneesh & Gunasekaran, Angappa, 2021. "Big data driven supply chain design and applications for blockchain: An action research using case study approach," Omega, Elsevier, vol. 102(C).
    2. Colombari, Ruggero & Geuna, Aldo & Helper, Susan & Martins, Raphael & Paolucci, Emilio & Ricci, Riccardo & Seamans, Robert, 2023. "The interplay between data-driven decision-making and digitalization: A firm-level survey of the Italian and U.S. automotive industries," International Journal of Production Economics, Elsevier, vol. 255(C).
    3. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    4. Dahlbeck, Mirko & Fischer, Anja & Fischer, Frank & Hungerländer, Philipp & Maier, Kerstin, 2023. "Exact approaches for the combined cell layout problem," European Journal of Operational Research, Elsevier, vol. 305(2), pages 530-546.
    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. Masoud Zafarzadeh & Magnus Wiktorsson & Jannicke Baalsrud Hauge, 2021. "A Systematic Review on Technologies for Data-Driven Production Logistics: Their Role from a Holistic and Value Creation Perspective," Logistics, MDPI, vol. 5(2), pages 1-32, April.
    7. Emanuele Gabriel Margherita & Alessio Maria Braccini, 2021. "Exploring Sustainable Value Creation of Industry 4.0 Technologies Within the Socio-technical Perspective: A Meta-review," Post-Print hal-03410741, HAL.
    8. Bianco, Débora & Bueno, Adauto & Godinho Filho, Moacir & Latan, Hengky & Miller Devós Ganga, Gilberto & Frank, Alejandro G. & Chiappetta Jabbour, Charbel Jose, 2023. "The role of Industry 4.0 in developing resilience for manufacturing companies during COVID-19," International Journal of Production Economics, Elsevier, vol. 256(C).
    9. Bustinza, Oscar F. & Opazo-Basaez, Marco & Tarba, Shlomo, 2022. "Exploring the interplay between Smart Manufacturing and KIBS firms in configuring product-service innovation performance," Technovation, Elsevier, vol. 118(C).
    10. Tao, Zhibin & Chao, Jiaxiao, 2024. "Unlocking new opportunities in the industry 4.0 era, exploring the critical impact of digital technology on sustainable performance and the mediating role of GSCM practices," Innovation and Green Development, Elsevier, vol. 3(3).
    11. Juliana Basulo-Ribeiro & Carina Pimentel & Leonor Teixeira, 2024. "Digital Transformation in Maritime Ports: Defining Smart Gates through Process Improvement in a Portuguese Container Terminal," Future Internet, MDPI, vol. 16(10), pages 1-27, September.
    12. Lu, Shixiang & Xu, Qifa & Jiang, Cuixia & Liu, Yezheng & Kusiak, Andrew, 2022. "Probabilistic load forecasting with a non-crossing sparse-group Lasso-quantile regression deep neural network," Energy, Elsevier, vol. 242(C).
    13. Kyu Tae Park & Jinho Yang & Sang Do Noh, 2021. "VREDI: virtual representation for a digital twin application in a work-center-level asset administration shell," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 501-544, February.
    14. Cheng-Wen Lee & Budi Hasyim & Jan-Yan Lin, 2024. "Digital Technology for Supply Chain Management- marketing Integration," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 14(1), pages 1-4.
    15. Ronny Seiger & Marco Franceschetti & Barbara Weber, 2023. "An Interactive Method for Detection of Process Activity Executions from IoT Data," Future Internet, MDPI, vol. 15(2), pages 1-31, February.
    16. 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.
    17. Weckenborg, Christian & Schumacher, Patrick & Thies, Christian & Spengler, Thomas S., 2024. "Flexibility in manufacturing system design: A review of recent approaches from Operations Research," European Journal of Operational Research, Elsevier, vol. 315(2), pages 413-441.
    18. Gillani, Fatima & Chatha, Kamran Ali & Sadiq Jajja, Muhammad Shakeel & Farooq, Sami, 2020. "Implementation of digital manufacturing technologies: Antecedents and consequences," International Journal of Production Economics, Elsevier, vol. 229(C).
    19. D.-Y. Kim & J.-W. Park & S. Baek & K.-B. Park & H.-R. Kim & J.-I. Park & H.-S. Kim & B.-B. Kim & H.-Y. Oh & K. Namgung & W. Baek, 2020. "A modular factory testbed for the rapid reconfiguration of manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 661-680, March.
    20. Asadi, Shahla & Nilashi, Mehrbakhsh & Iranmanesh, Mohammad & Hyun, Sunghyup Sean & Rezvani, Azadeh, 2022. "Effect of internet of things on manufacturing performance: A hybrid multi-criteria decision-making and neuro-fuzzy approach," Technovation, Elsevier, vol. 118(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:eee:proeco:v:226:y:2020:i:c:s0925527320300049. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.