IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v30y2019i5d10.1007_s10845-017-1376-5.html
   My bibliography  Save this article

Ontology for cloud manufacturing based Product Lifecycle Management

Author

Listed:
  • Asma Talhi

    (Khtema sas, Lamih, CNRS, Arts et métiers, ParisTech)

  • Virginie Fortineau

    (Khtema sas)

  • Jean-Charles Huet

    (EIGSI)

  • Samir Lamouri

    (Lamih, CNRS, Arts et métiers, ParisTech)

Abstract

The manufacturing environment has become increasingly competitive in the past few years, and product development is getting even more complex. The agility of an information system is a way to manage this complexity, and cloud technologies enable the sharing of tools and information in a new way. In particular, in the field of Product Lifecycle Management, a review of the literature demonstrates that there are technical issues that limit the efficient collaboration between the various stakeholders along the product lifecycle. Cloud manufacturing is a new concept that enables the virtualization of manufacturing resources and capabilities and provides them as a service. Therefore it offers new prospects for usage and collaboration in the extended enterprise, and along the product lifecycle. For instance, it helps to manage variations in production demand, by providing a large set of potential manufacturing resources on demand. However, to share the resources within the cloud manufacturing environment, a unification model of the domain information is required, to which any provider and/or user of cloud manufacturing must conform in order to dialog with the other CM stakeholders. This study provides an ontological model of the cloud manufacturing domain in order to support information exchange between the cloud manufacturing resources. The concepts of the proposed ontology are based on a literature review of models of cloud and models of manufacturing. The detailed ontology is then validated using the OntoClean methodology and within its implementation in an industrial scenario.

Suggested Citation

  • Asma Talhi & Virginie Fortineau & Jean-Charles Huet & Samir Lamouri, 2019. "Ontology for cloud manufacturing based Product Lifecycle Management," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2171-2192, June.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:5:d:10.1007_s10845-017-1376-5
    DOI: 10.1007/s10845-017-1376-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-017-1376-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-017-1376-5?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. Hong Jin & Xifan Yao & Yong Chen, 2017. "Correlation-aware QoS modeling and manufacturing cloud service composition," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1947-1960, December.
    2. Yang-Kuei Lin & Chin Soon Chong, 2017. "Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1189-1201, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shashi Bhushan Jha & Radu F. Babiceanu & Remzi Seker, 2020. "Formal modeling of cyber-physical resource scheduling in IIoT cloud environments," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1149-1164, June.
    2. Juan José Montero Jiménez & Rob Vingerhoeds & Bernard Grabot & Sébastien Schwartz, 2023. "An ontology model for maintenance strategy selection and assessment," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1369-1387, March.
    3. Shiyong Yin & Jinsong Bao & Jie Zhang & Jie Li & Junliang Wang & Xiaodi Huang, 2020. "Real-time task processing for spinning cyber-physical production systems based on edge computing," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2069-2087, December.
    4. Reza Vatankhah Barenji, 2022. "A blockchain technology based trust system for cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1451-1465, June.
    5. Xiaochen Zheng & Xiaodu Hu & Rebeca Arista & Jinzhi Lu & Jyri Sorvari & Joachim Lentes & Fernando Ubis & Dimitris Kiritsis, 2024. "A semantic-driven tradespace framework to accelerate aircraft manufacturing system design," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 175-198, January.
    6. Xiaobao Zhu & Jing Shi & Fengjie Xie & Rouqi Song, 2020. "Pricing strategy and system performance in a cloud-based manufacturing system built on blockchain technology," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1985-2002, December.
    7. Wei Nie & Katharina Vita & Tariq Masood, 2024. "An ontology for defining and characterizing demonstration environments," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3501-3521, October.

    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. Baodong Li & Yu Yang & Jiafu Su & Zhichao Liang & Sheng Wang, 2020. "Two-sided matching decision-making model with hesitant fuzzy preference information for configuring cloud manufacturing tasks and resources," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2033-2047, December.
    2. Yankai Wang & Shilong Wang & Bo Yang & Bo Gao & Sibao Wang, 2022. "An effective adaptive adjustment method for service composition exception handling in cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 735-751, March.
    3. Shiyong Yin & Jinsong Bao & Jie Zhang & Jie Li & Junliang Wang & Xiaodi Huang, 2020. "Real-time task processing for spinning cyber-physical production systems based on edge computing," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2069-2087, December.
    4. Hao Li & Shanghua Mi & Qifeng Li & Xiaoyu Wen & Dongping Qiao & Guofu Luo, 2020. "A scheduling optimization method for maintenance, repair and operations service resources of complex products," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1673-1691, October.
    5. Ali Salmasnia & Zahra Kiapasha & Melika Pashaeenejad, 2024. "Subtasks scheduling of tasks with different structures in cloud manufacturing systems under maintenance policy and focusing on logistics, tardiness, and earliness aspects," Operational Research, Springer, vol. 24(3), pages 1-37, September.
    6. Yu Feng & Biqing Huang, 2020. "Cloud manufacturing service QoS prediction based on neighbourhood enhanced matrix factorization," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1649-1660, October.
    7. Yinan Wu & Gongzhuang Peng & Hongwei Wang & Heming Zhang, 2019. "A Heuristic Algorithm for Optimal Service Composition in Complex Manufacturing Networks," Complexity, Hindawi, vol. 2019, pages 1-20, April.
    8. Haghnegahdar, Lida & Chen, Yu & Wang, Yong, 2022. "Enhancing dynamic energy network management using a multiagent cloud-fog structure," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    9. Wang Shijie & Zhang Yingfeng, 2021. "A credit-based dynamical evaluation method for the smart configuration of manufacturing services under Industrial Internet of Things," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1091-1115, April.
    10. Di Liang & Jieyi Wang & Ran Bhamra & Liezhao Lu & Yuting Li, 2022. "A Multi-Service Composition Model for Tasks in Cloud Manufacturing Based on VS–ABC Algorithm," Mathematics, MDPI, vol. 10(21), pages 1-24, October.
    11. Tianyang Li & Ting He & Zhongjie Wang & Yufeng Zhang, 2020. "SDF-GA: a service domain feature-oriented approach for manufacturing cloud service composition," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 681-702, March.
    12. Shuai Zhang & Yangbing Xu & Wenyu Zhang & Dejian Yu, 2019. "A new fuzzy QoS-aware manufacture service composition method using extended flower pollination algorithm," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2069-2083, June.

    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:spr:joinma:v:30:y:2019:i:5:d:10.1007_s10845-017-1376-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.