IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v244y2022ics092552732100325x.html
   My bibliography  Save this article

Supply network design for mass personalization in Industry 4.0 era

Author

Listed:
  • Katoozian, Hoora
  • Zanjani, Masoumeh Kazemi

Abstract

The manufacturing industry is confronted with the growing demand of personalized products of small batch sizes. In other words, the producers are faced with the satisfaction of heterogeneous customer needs through individualization and the realization of scale effects along the value chain. This study is among the first that proposes a mixed-integer programming model to obtain the optimal configuration of a supply network comprising of a pool of suppliers to satisfy the demand of highly-customized and modular-structured products. The product individualization is incorporated into the model by considering different design complexity levels for the components/sub-assemblies in the bill-of-material. Furthermore, the impact of batch size is modeled by considering piece-wise production cost functions in different echelons of the network. Our numerical results inspired by the case of tunable lasers indicate that the configuration of supply network varies as a function of the demand at different design complexity levels. Whereas, the profitability of supply network is closely tied to the market condition as well as the production capacity, flexibility of processes, and cost structure of manufacturers.

Suggested Citation

  • Katoozian, Hoora & Zanjani, Masoumeh Kazemi, 2022. "Supply network design for mass personalization in Industry 4.0 era," International Journal of Production Economics, Elsevier, vol. 244(C).
  • Handle: RePEc:eee:proeco:v:244:y:2022:i:c:s092552732100325x
    DOI: 10.1016/j.ijpe.2021.108349
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S092552732100325X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijpe.2021.108349?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. Alexandre Dolgui & Dmitry Ivanov & Boris Sokolov, 2020. "Reconfigurable supply chain: the X-network," International Journal of Production Research, Taylor & Francis Journals, vol. 58(13), pages 4138-4163, July.
    2. Chien, Chen-Fu & Chen, Yun-Ju & Peng, Jin-Tang, 2010. "Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle," International Journal of Production Economics, Elsevier, vol. 128(2), pages 496-509, December.
    3. Lamothe, Jacques & Hadj-Hamou, Khaled & Aldanondo, Michel, 2006. "An optimization model for selecting a product family and designing its supply chain," European Journal of Operational Research, Elsevier, vol. 169(3), pages 1030-1047, March.
    4. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov, 2019. "The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics," International Journal of Production Research, Taylor & Francis Journals, vol. 57(3), pages 829-846, February.
    5. Sun, Jing & Yamamoto, Hisashi & Matsui, Masayuki, 2020. "Horizontal integration management: An optimal switching model for parallel production system with multiple periods in smart supply chain environment," International Journal of Production Economics, Elsevier, vol. 221(C).
    6. Satya S. Malladi & Alan L. Erera & Chelsea C. White, 2020. "A dynamic mobile production capacity and inventory control problem," IISE Transactions, Taylor & Francis Journals, vol. 52(8), pages 926-943, August.
    7. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov & Frank Werner & Marina Ivanova, 2016. "A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 54(2), pages 386-402, January.
    8. Baud-Lavigne, Bertrand & Agard, Bruno & Penz, Bernard, 2012. "Mutual impacts of product standardization and supply chain design," International Journal of Production Economics, Elsevier, vol. 135(1), pages 50-60.
    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. Mohammed, Ahmed & Lopes de Sousa Jabbour, Ana Beatriz & Koh, Lenny & Hubbard, Nicolas & Chiappetta Jabbour, Charbel Jose & Al Ahmed, Teejan, 2022. "The sourcing decision-making process in the era of digitalization: A new quantitative methodology," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    2. Amirhosein Gholami & Nasim Nezamoddini & Mohammad T. Khasawneh, 2023. "Customized orders management in connected make-to-order supply chains," Operations Management Research, Springer, vol. 16(3), pages 1428-1443, September.

    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. 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).
    2. Núñez-Merino, Miguel & Maqueira-Marín, Juan Manuel & Moyano-Fuentes, José & Castaño-Moraga, Carlos Alberto, 2022. "Industry 4.0 and supply chain. A Systematic Science Mapping analysis," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    3. 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.
    4. 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).
    5. Burgos, Diana & Ivanov, Dmitry, 2021. "Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    6. Giuseppe Fragapane & Dmitry Ivanov & Mirco Peron & Fabio Sgarbossa & Jan Ola Strandhagen, 2022. "Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics," Annals of Operations Research, Springer, vol. 308(1), pages 125-143, January.
    7. Baud-Lavigne, Bertrand & Agard, Bruno & Penz, Bernard, 2016. "Simultaneous product family and supply chain design: An optimization approach," International Journal of Production Economics, Elsevier, vol. 174(C), pages 111-118.
    8. Guoqing Zhang & Yiqin Yang & Guoqing Yang, 2023. "Smart supply chain management in Industry 4.0: the review, research agenda and strategies in North America," Annals of Operations Research, Springer, vol. 322(2), pages 1075-1117, March.
    9. Dmitry Ivanov & Boris Sokolov, 2019. "Simultaneous structural–operational control of supply chain dynamics and resilience," Annals of Operations Research, Springer, vol. 283(1), pages 1191-1210, December.
    10. Xiong, Yixuan & Du, Gang & Jiao, Roger J., 2018. "Modular product platforming with supply chain postponement decisions by leader-follower interactive optimization," International Journal of Production Economics, Elsevier, vol. 205(C), pages 272-286.
    11. Bertrand Baud-Lavigne & Samuel Bassetto & Bruno Agard, 2016. "A method for a robust optimization of joint product and supply chain design," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 741-749, August.
    12. Weili Yin & Wenxue Ran, 2021. "Theoretical Exploration of Supply Chain Viability Utilizing Blockchain Technology," Sustainability, MDPI, vol. 13(15), pages 1-25, July.
    13. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Bryde, David J. & Giannakis, Mihalis & Foropon, Cyril & Roubaud, David & Hazen, Benjamin T., 2020. "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations," International Journal of Production Economics, Elsevier, vol. 226(C).
    14. Lohmer, Jacob & Bugert, Niels & Lasch, Rainer, 2020. "Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study," International Journal of Production Economics, Elsevier, vol. 228(C).
    15. Hosseini, Seyedmohsen & Ivanov, Dmitry & Dolgui, Alexandre, 2019. "Review of quantitative methods for supply chain resilience analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 285-307.
    16. De Giovanni, Pietro, 2020. "Blockchain and smart contracts in supply chain management: A game theoretic model," International Journal of Production Economics, Elsevier, vol. 228(C).
    17. Imane Ballouki & Mohammed Douimi & Latifa Ouzizi, 2018. "Decision Support Tool Selection for Eco-Design Integration into the Simultaneous Design of Product and its Supply Chain," Journal of Environmental Assessment Policy and Management (JEAPM), World Scientific Publishing Co. Pte. Ltd., vol. 20(02), pages 1-32, June.
    18. Rezapour, Shabnam & Hassani, Ashkan & Farahani, Reza Zanjirani, 2015. "Concurrent design of product family and supply chain network considering quality and price," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 81(C), pages 18-35.
    19. Kaur, Harpreet & Prakash Singh, Surya, 2021. "Multi-stage hybrid model for supplier selection and order allocation considering disruption risks and disruptive technologies," International Journal of Production Economics, Elsevier, vol. 231(C).
    20. Li, Yan-Lai & Tang, Jia-Fu & Chin, Kwai-Sang & Jiang, Yu-Shi & Han, Yi & Pu, Yun, 2011. "Estimating the final priority ratings of engineering characteristics in mature-period product improvement by MDBA and AHP," International Journal of Production Economics, Elsevier, vol. 131(2), pages 575-586, 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:eee:proeco:v:244:y:2022:i:c:s092552732100325x. 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.