IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i21p5766-d439557.html
   My bibliography  Save this article

Smart and Agile Manufacturing Framework, A Case Study for Automotive Industry

Author

Listed:
  • Gullelala Jadoon

    (Department of Information Technology, The University of Haripur, Haripur 22620, Pakistan)

  • Ikram Ud Din

    (Department of Information Technology, The University of Haripur, Haripur 22620, Pakistan)

  • Ahmad Almogren

    (Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia)

  • Hisham Almajed

    (Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia)

Abstract

Smartness and agility are two quality measures that are pragmatic to achieve a flexible, maintainable, and adaptable system in any business. The automotive industry also requires an enhanced performance matrix and refinement in the development strategies for manufacturing. The current development models used in automotive manufacturing are not optimal enough; thus, the overall expenditure is not properly managed. Therefore, it is essential to come up with flexible, agile techniques incorporating traceability methods. It overcomes the traditional manufacturing approaches that are usually inflexible, costly, and lack timely customer feedback. The article focuses on significant Requirements Management (RM) activities, including traceability mechanism, smart manufacturing process, and performance evaluation of the proposed methods in the automotive domain. We propose a manufacturing framework that follows smart agile principles along with proper traceability management. Our proposed approach overcomes the complexities generated by traditional manufacturing processes in automotive industries. It gives an insight into the future manufacturing processes in the automotive industries.

Suggested Citation

  • Gullelala Jadoon & Ikram Ud Din & Ahmad Almogren & Hisham Almajed, 2020. "Smart and Agile Manufacturing Framework, A Case Study for Automotive Industry," Energies, MDPI, vol. 13(21), pages 1-13, November.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5766-:d:439557
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/21/5766/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/21/5766/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Angappa Gunasekaran & Yahaya Y. Yusuf & Ezekiel O. Adeleye & Thanos Papadopoulos & Dharma Kovvuri & Dan’Asabe G. Geyi, 2019. "Agile manufacturing: an evolutionary review of practices," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 5154-5174, August.
    2. Angappa Gunasekaran & Yahaya Y. Yusuf & Ezekiel O. Adeleye & Thanos Papadopoulos, 2018. "Agile manufacturing practices: the role of big data and business analytics with multiple case studies," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 385-397, January.
    3. Elkins, Debra A. & Huang, Ningjian & Alden, Jeffrey M., 2004. "Agile manufacturing systems in the automotive industry," International Journal of Production Economics, Elsevier, vol. 91(3), pages 201-214, October.
    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. Giacosa, Elisa & Culasso, Francesca & Crocco, Edoardo, 2022. "Customer agility in the modern automotive sector: how lead management shapes agile digital companies," Technological Forecasting and Social Change, Elsevier, vol. 175(C).

    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. Ciampi, Francesco & Faraoni, Monica & Ballerini, Jacopo & Meli, Francesco, 2022. "The co-evolutionary relationship between digitalization and organizational agility: Ongoing debates, theoretical developments and future research perspectives," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    2. Ahmad Ibrahim Aljumah & Mohammed T. Nuseir & Md. Mahmudul Alam, 2021. "Traditional marketing analytics, big data analytics and big data system quality and the success of new product development," Post-Print hal-03538161, HAL.
    3. Laurent Lim, Lâm & Alpan, Gülgün & Penz, Bernard, 2014. "Reconciling sales and operations management with distant suppliers in the automotive industry: A simulation approach," International Journal of Production Economics, Elsevier, vol. 151(C), pages 20-36.
    4. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    5. Juan Carlos Barcena-Ruiz & Norma Olaizola, 2007. "Cost-saving production technologies and partial ownership," Economics Bulletin, AccessEcon, vol. 15(6), pages 1-8.
    6. Azevedo, Susana G. & Govindan, Kannan & Carvalho, Helena & Cruz-Machado, V., 2012. "An integrated model to assess the leanness and agility of the automotive industry," Resources, Conservation & Recycling, Elsevier, vol. 66(C), pages 85-94.
    7. Panagiotis Reklitis & Damianos P. Sakas & Panagiotis Trivellas & Giannis T. Tsoulfas, 2021. "Performance Implications of Aligning Supply Chain Practices with Competitive Advantage: Empirical Evidence from the Agri-Food Sector," Sustainability, MDPI, vol. 13(16), pages 1-21, August.
    8. 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).
    9. Jain, Vineet & Raj, Tilak, 2016. "Modeling and analysis of FMS performance variables by ISM, SEM and GTMA approach," International Journal of Production Economics, Elsevier, vol. 171(P1), pages 84-96.
    10. Cotterman, Turner & Fuchs, Erica R.H. & Whitefoot, Kate S. & Combemale, Christophe, 2024. "The transition to electrified vehicles: Evaluating the labor demand of manufacturing conventional versus battery electric vehicle powertrains," Energy Policy, Elsevier, vol. 188(C).
    11. Shan, Siqing & Wang, Li & Xin, Tenglong & Bi, Zhuming, 2013. "Developing a rapid response production system for aircraft manufacturing," International Journal of Production Economics, Elsevier, vol. 146(1), pages 37-47.
    12. Giacosa, Elisa & Culasso, Francesca & Crocco, Edoardo, 2022. "Customer agility in the modern automotive sector: how lead management shapes agile digital companies," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    13. Anna-Theresa Walter, 2021. "Organizational agility: ill-defined and somewhat confusing? A systematic literature review and conceptualization," Management Review Quarterly, Springer, vol. 71(2), pages 343-391, April.
    14. Priyank Srivastava & Dinesh Khanduja & V. P. Agrawal, 2020. "Agile maintenance attribute coding and evaluation based decision making in sugar manufacturing plant," OPSEARCH, Springer;Operational Research Society of India, vol. 57(2), pages 553-583, June.
    15. 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).
    16. Slobodan Acimovic & Nenad Stajic, 2022. "Startups – Business Models For Enhancing Supply Chain 4.0," Business Logistics in Modern Management, Josip Juraj Strossmayer University of Osijek, Faculty of Economics, Croatia, vol. 22, pages 171-190.
    17. Guo, Daqiang & Li, Mingxing & Lyu, Zhongyuan & Kang, Kai & Wu, Wei & Zhong, Ray Y. & Huang, George Q., 2021. "Synchroperation in industry 4.0 manufacturing," International Journal of Production Economics, Elsevier, vol. 238(C).
    18. Feng Xiang & Yefa Hu & Yingrong Yu & Huachun Wu, 2014. "QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(4), pages 663-685, December.
    19. Bárcena-Ruiz, Juan Carlos & Olaizola, Norma, 2008. "Choice of flexible production technologies under strategic delegation," Japan and the World Economy, Elsevier, vol. 20(3), pages 395-414, August.
    20. Ayan Chatterjee & Debmallya Chatterjee, 2024. "A Journey of Business Analytics in Improving Supply Chain Performance: A Systematic Review of Literature," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 49(2), pages 337-361, May.

    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:13:y:2020:i:21:p:5766-:d:439557. 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.