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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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).
    7. Rialti, Riccardo & Zollo, Lamberto & Ferraris, Alberto & Alon, Ilan, 2019. "Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    8. Dutta, Pankaj & Choi, Tsan-Ming & Somani, Surabhi & Butala, Richa, 2020. "Blockchain technology in supply chain operations: Applications, challenges and research opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    9. Chih-Hung Hsu & Ming-Ge Li & Ting-Yi Zhang & An-Yuan Chang & Shu-Zhen Shangguan & Wan-Ling Liu, 2022. "Deploying Big Data Enablers to Strengthen Supply Chain Resilience to Mitigate Sustainable Risks Based on Integrated HOQ-MCDM Framework," Mathematics, MDPI, vol. 10(8), pages 1-35, April.
    10. Shao, Xiao-Feng, 2020. "What is the right production strategy for horizontally differentiated product: Standardization or mass customization?," International Journal of Production Economics, Elsevier, vol. 223(C).
    11. Huynh, Minh-Tay & Nippa, Michael & Aichner, Thomas, 2023. "Big data analytics capabilities: Patchwork or progress? A systematic review of the status quo and implications for future research," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    12. Maryam Pervez Khan & Noraini Abu Talib & Tan Owee Kowang, 2018. "Development of Sustainability Framework Based On the Theory of Resource Based View," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 8(7), pages 636-647, July.
    13. Raut, Rakesh D. & Mangla, Sachin Kumar & Narwane, Vaibhav S. & Dora, Manoj & Liu, Mengqi, 2021. "Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    14. Chahal, Hardeep & Gupta, Mahesh & Bhan, Namrita & Cheng, T.C.E., 2020. "Operations management research grounded in the resource-based view: A meta-analysis," International Journal of Production Economics, Elsevier, vol. 230(C).
    15. El-Khalil, Raed & Mezher, Mohamad Ali, 2020. "The mediating impact of sustainability on the relationship between agility and operational performance," Operations Research Perspectives, Elsevier, vol. 7(C).
    16. Chris Turner & John Oyekan, 2023. "Manufacturing in the Age of Human-Centric and Sustainable Industry 5.0: Application to Holonic, Flexible, Reconfigurable and Smart Manufacturing Systems," Sustainability, MDPI, vol. 15(13), pages 1-29, June.
    17. Yongbo Li & Ali Diabat & Chung-Cheng Lu, 2020. "Leagile supplier selection in Chinese textile industries: a DEMATEL approach," Annals of Operations Research, Springer, vol. 287(1), pages 303-322, April.
    18. Omar, Yamila M. & Minoufekr, Meysam & Plapper, Peter, 2019. "Business analytics in manufacturing: Current trends, challenges and pathway to market leadership," Operations Research Perspectives, Elsevier, vol. 6(C).
    19. Benjamin James Ralph & Marcel Sorger & Karin Hartl & Andreas Schwarz-Gsaxner & Florian Messner & Martin Stockinger, 2022. "Transformation of a rolling mill aggregate to a cyber physical production system: from sensor retrofitting to machine learning," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 493-518, February.
    20. Mashalah, Heider Al & Hassini, Elkafi & Gunasekaran, Angappa & Bhatt (Mishra), Deepa, 2022. "The impact of digital transformation on supply chains through e-commerce: Literature review and a conceptual framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(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: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.