IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v282y2025ics0925527325000209.html
   My bibliography  Save this article

Production of customized commercial vehicles in assembly line based on modified-to-order demands: A novel method and study case

Author

Listed:
  • Mota, André Luiz Siqueira
  • Lins, Romulo Gonçalves

Abstract

The modernization of production and the consumer market’s demand for customized products in the commercial vehicle segment require new approaches and methodologies for development and manufacturing at competitive prices. Thus, to meet such needs, combined with the fact that companies are only interested in such demand with adequate financial results, it is necessary to adapt the customization process to the existing structure. The main objective of this research is to develop a structured method for designing and manufacturing customized commercial vehicles using serial production lines already installed and available in the automotive industries, mainly in environments where Industry 4.0 (I4.0) technologies are available. To this end, the proposed method is divided into three phases (Scoping phase, Design & Documentation, and Custom Production) in such a way as first to understand the customer’s needs, passing through the execution of the project in an optimized way based on standard products from the company’s portfolio to the manufacture of technically and financially possible modifications in Assembly Line (AL), through the application of Lean tools and I4.0 Technologies. The method is validated with a case study in a large company in the commercial vehicle market installed in Brazil, where the results obtained showed that it is possible to develop customized products with reduced time from existing products and among the modifications that can be manufactured in AL the results show an average reduction of over 80% in production times compared to manufacturing time in dedicated customization workshops, allowing the company to increase its annual production of customized vehicles in ≈35% without any additional investment.

Suggested Citation

  • Mota, André Luiz Siqueira & Lins, Romulo Gonçalves, 2025. "Production of customized commercial vehicles in assembly line based on modified-to-order demands: A novel method and study case," International Journal of Production Economics, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:proeco:v:282:y:2025:i:c:s0925527325000209
    DOI: 10.1016/j.ijpe.2025.109535
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527325000209
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2025.109535?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Zheng, Ting & Ardolino, Marco & Bacchetti, Andrea & Perona, Marco, 2021. "The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 129469, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Lars Hvam & Niels Henrik Mortensen & Jesper Riis, 2008. "Product Customization," Springer Books, Springer, number 978-3-540-71449-1, January.
    3. Cuihong Li & Brian Tomlin, 2022. "After-Sales Service Contracting: Condition Monitoring and Data Ownership," Manufacturing & Service Operations Management, INFORMS, vol. 24(3), pages 1494-1510, May.
    4. Bhamra, Ran & Hicks, Christian & Small, Adrian & García-Villarreal, Enrique, 2022. "Value, product delivery strategies and operational performance in the medical technology industry," International Journal of Production Economics, Elsevier, vol. 245(C).
    5. L. Zhang & Élise Vareilles & Michel Aldanondo, 2013. "Generic bill of functions, materials and operations for SAP2 configuration," Post-Print hal-00846451, HAL.
    6. Nitin S. Solke & Pritesh Shah & Ravi Sekhar & T. P. Singh, 2022. "Machine Learning-Based Predictive Modeling and Control of Lean Manufacturing in Automotive Parts Manufacturing Industry," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(1), pages 89-112, March.
    7. Lage Junior, Muris & Godinho Filho, Moacir, 2010. "Variations of the kanban system: Literature review and classification," International Journal of Production Economics, Elsevier, vol. 125(1), pages 13-21, May.
    8. Cannas, Violetta G. & Gosling, Jonathan & Pero, Margherita & Rossi, Tommaso, 2019. "Engineering and production decoupling configurations: An empirical study in the machinery industry," International Journal of Production Economics, Elsevier, vol. 216(C), pages 173-189.
    9. Ting Zheng & Marco Ardolino & Andrea Bacchetti & Marco Perona, 2021. "The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 59(6), pages 1922-1954, March.
    10. Cannas, Violetta G. & Gosling, Jonathan & Pero, Margherita & Rossi, Tommaso, 2020. "Determinants for order-fulfilment strategies in engineer-to-order companies: Insights from the machinery industry," International Journal of Production Economics, Elsevier, vol. 228(C).
    11. Fernanda Saidelles Bataglin & Daniela Dietz Viana & Carlos Torres Formoso, 2022. "Design Principles and Prescriptions for Planning and Controlling Engineer-to-Order Industrialized Building Systems," Sustainability, MDPI, vol. 14(24), pages 1-22, December.
    12. Thomas J. Goldsby & Thu Trang Hoang & Theodore P. Stank & John E. Bell, 2023. "A Modernized Framework for Transportation Decision‐Making in a Hyper‐Integrated Global Supply Chain Environment," Transportation Journal, John Wiley & Sons, vol. 62(1), pages 16-42, January.
    13. Camba, Jorge D. & Contero, Manuel & Company, Pedro & Pérez, David, 2017. "On the integration of model-based feature information in Product Lifecycle Management systems," International Journal of Information Management, Elsevier, vol. 37(6), pages 611-621.
    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. Govindan, Kannan & Kannan, Devika & Jørgensen, Thomas Ballegård & Nielsen, Tim Straarup, 2022. "Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    2. Juhás Martin & Juhásová Bohuslava & Nemlaha Eduard & Charvát Dominik, 2021. "Increasing the Efficiency of a Robotic Cell Using Simulation," Research Papers Faculty of Materials Science and Technology Slovak University of Technology, Sciendo, vol. 29(49), pages 24-35, September.
    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. Jože M. Rožanec & Luka Bizjak & Elena Trajkova & Patrik Zajec & Jelle Keizer & Blaž Fortuna & Dunja Mladenić, 2024. "Active learning and novel model calibration measurements for automated visual inspection in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(5), pages 1963-1984, June.
    5. Mahdi Mokhtarzadeh & Jorge Rodríguez-Echeverría & Ivana Semanjski & Sidharta Gautama, 2025. "Hybrid intelligence failure analysis for industry 4.0: a literature review and future prospective," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2309-2334, April.
    6. Moazzeni, Sahar & Sgarbossa, Fabio, 2025. "Collaborative logistics and digital technologies in rural contexts: a systematic review and a decision aid model for logistics decision-makers," Discussion Papers 2025/12, Norwegian School of Economics, Department of Business and Management Science.
    7. Pfaff, Yuko Melanie & Birkel, Hendrik & Hartmann, Evi, 2023. "Supply chain governance in the context of industry 4.0: Investigating implications of real-life implementations from a multi-tier perspective," International Journal of Production Economics, Elsevier, vol. 260(C).
    8. Ranaboldo, M. & Aragüés-Peñalba, M. & Arica, E. & Bade, A. & Bullich-Massagué, E. & Burgio, A. & Caccamo, C. & Caprara, A. & Cimmino, D. & Domenech, B. & Donoso, I. & Fragapane, G. & González-Font-de-, 2024. "A comprehensive overview of industrial demand response status in Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
    9. Luo, Shiyue & Yu, Mengyao & Dong, Yilan & Hao, Yu & Li, Changping & Wu, Haitao, 2024. "Toward urban high-quality development: Evidence from more intelligent Chinese cities," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    10. Saraswat, Jeetendra Kumar & Choudhari, Sanjay, 2025. "Integrating big data and cloud computing into the existing system and performance impact: A case study in manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 210(C).
    11. Biman Darshana Hettiarachchi & Stefan Seuring & Marcus Brandenburg, 2022. "Industry 4.0-driven operations and supply chains for the circular economy: a bibliometric analysis," Operations Management Research, Springer, vol. 15(3), pages 858-878, December.
    12. Haodong Chen & Niloofar Zendehdel & Ming C. Leu & Zhaozheng Yin, 2024. "Fine-grained activity classification in assembly based on multi-visual modalities," Journal of Intelligent Manufacturing, Springer, vol. 35(5), pages 2215-2233, June.
    13. Yang, Li & Zou, Haobo & Shang, Chao & Ye, Xiaoming & Rani, Pratibha, 2023. "Adoption of information and digital technologies for sustainable smart manufacturing systems for industry 4.0 in small, medium, and micro enterprises (SMMEs)," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    14. Mengze Zheng & Te Li & Jing Ye, 2025. "The Confluence of AI and Big Data Analytics in Industry 4.0: Fostering Sustainable Strategic Development," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 5479-5515, March.
    15. Bettiol, Marco & Capestro, Mauro & Di Maria, Eleonora & Ganau, Roberto, 2024. "Is this time different?: how Industry 4.0 affects firms' labor productivity," LSE Research Online Documents on Economics 124545, London School of Economics and Political Science, LSE Library.
    16. Qi, Quansong & Xu, Zhiyong & Rani, Pratibha, 2023. "Big data analytics challenges to implementing the intelligent Industrial Internet of Things (IIoT) systems in sustainable manufacturing operations," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    17. Liu, Yanping & Farooque, Muhammad & Lee, Chang-Hun & Gong, Yu & Zhang, Abraham, 2023. "Antecedents of circular manufacturing and its effect on environmental and financial performance: A practice-based view," International Journal of Production Economics, Elsevier, vol. 260(C).
    18. Ganesh Narkhede & Vishwas Dohale & Yash Mahajan, 2024. "Darker side of industry 4.0 and its impact on triple‐bottom‐line sustainability," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(6), pages 5999-6016, December.
    19. Piccarozzi, Michela & Silvestri, Luca & Silvestri, Cecilia & Ruggieri, Alessandro, 2024. "Roadmap to Industry 5.0: Enabling technologies, challenges, and opportunities towards a holistic definition in management studies," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    20. Cagno, Enrico & Accordini, Davide & Thollander, Patrik & Andrei, Mariana & Hasan, A S M Monjurul & Pessina, Sonia & Trianni, Andrea, 2025. "Energy management and industry 4.0: Analysis of the enabling effects of digitalization on the implementation of energy management practices," Applied Energy, Elsevier, vol. 390(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:282:y:2025:i:c:s0925527325000209. 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.