IDEAS home Printed from https://ideas.repec.org/a/gam/jlogis/v9y2025i3p82-d1686649.html
   My bibliography  Save this article

A Novel Approach Based on IoT and Log-Normal Distribution for Supplier Lead Time Optimization in Smart Engineer-to-Order Supply Chains

Author

Listed:
  • Aicha Alaoua

    (LIMAS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdallah University, Fez 30000, Morocco)

  • Mohammed Karim

    (LIMAS Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdallah University, Fez 30000, Morocco)

Abstract

Background : In Engineer-to-Order (EtO) supply chains, managing supplier lead times is particularly challenging due to high customization and intensive customer involvement. This study addresses the critical need for more accurate and dynamic lead time prediction to enhance supply chain resilience and efficiency in EtO environments. Methods : We propose a novel approach that integrates Internet of Things (IoT) technologies with statistical modeling using the log-normal distribution to model and optimize supplier lead times, especially for customized raw materials. The model incorporates real-time data from IoT-enabled suppliers and considers long-term contractual relationships to reduce variability. Monte Carlo simulation is employed to validate the model’s predictive capabilities. Results : The results demonstrate significant improvements in predicting supplier performance and reducing uncertainty. Simulation outputs reveal reductions in lead times and enhanced reliability. Statistical metrics such as the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) confirm the robustness and accuracy of the predictions. Conclusions : The proposed methodology supports better decision-making in supplier selection and procurement planning by enabling effective risk management. It contributes to improved performance and greater resilience in Engineer-to-Order supply chains.

Suggested Citation

  • Aicha Alaoua & Mohammed Karim, 2025. "A Novel Approach Based on IoT and Log-Normal Distribution for Supplier Lead Time Optimization in Smart Engineer-to-Order Supply Chains," Logistics, MDPI, vol. 9(3), pages 1-22, June.
    • Handle: RePEc:gam:jlogis:v:9:y:2025:i:3:p:82-:d:1686649
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2305-6290/9/3/82/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2305-6290/9/3/82/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. John E. Tyworth, 2025. "The Gamma Distribution and Inventory Control: Disruptive Lead Times Under Conventional and Nonclassical Conditions," Logistics, MDPI, vol. 9(2), pages 1-23, May.
    2. Ahmed Zainul Abideen & Veera Pandiyan Kaliani Sundram & Jaafar Pyeman & Abdul Kadir Othman & Shahryar Sorooshian, 2021. "Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics," Logistics, MDPI, vol. 5(4), pages 1-22, November.
    3. Tadikamalla, Pandu R, 1979. "The lognormal approximation to the lead time demand in inventory control," Omega, Elsevier, vol. 7(6), pages 553-556.
    4. Tadikamalla, Pandu R, 1984. "A comparison of several approximations to the lead time demand distribution," Omega, Elsevier, vol. 12(6), pages 575-581.
    5. Strijbosch, L.W.G. & Moors, J.J.A., 2006. "Modified normal demand distributions in (R, S)-inventory control," European Journal of Operational Research, Elsevier, vol. 172(1), pages 201-212, July.
    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. Vernimmen, Bert & Dullaert, Wout & Willemé, Peter & Witlox, Frank, 2008. "Using the inventory-theoretic framework to determine cost-minimizing supply strategies in a stochastic setting," International Journal of Production Economics, Elsevier, vol. 115(1), pages 248-259, September.
    2. Daniela Favaretto & Alessandro Marin & Marco Tolotti, 2023. "A theoretical validation of the DDMRP reorder policy," Computational Management Science, Springer, vol. 20(1), pages 1-28, December.
    3. Janssen, E., 2010. "Inventory control in case of unknown demand and control parameters," Other publications TiSEM 9ba3c7c9-213b-47aa-9449-e, Tilburg University, School of Economics and Management.
    4. John E. Tyworth, 2025. "The Gamma Distribution and Inventory Control: Disruptive Lead Times Under Conventional and Nonclassical Conditions," Logistics, MDPI, vol. 9(2), pages 1-23, May.
    5. Jiamuyan Xie, 2022. "Information Sharing in a Supply Chain with Asymmetric Competing Retailers," Sustainability, MDPI, vol. 14(19), pages 1-21, October.
    6. Ernesto Armando Pacheco-Velázquez & Manuel Robles-Cárdenas & Saúl Juárez Ordóñez & Abelardo Ernesto Damy Solís & Leopoldo Eduardo Cárdenas-Barrón, 2023. "A Heuristic Model for Spare Parts Stocking Based on Markov Chains," Mathematics, MDPI, vol. 11(16), pages 1-21, August.
    7. Janssen, E. & Strijbosch, L.W.G. & Brekelmans, R.C.M., 2006. "Assessing the Effects of using Demand Parameters Estimates in Inventory Control," Other publications TiSEM e61834bf-8202-4a25-9311-f, Tilburg University, School of Economics and Management.
    8. Ashish Dwivedi & Anirban Ganguly & Sanjoy Kumar Paul, 2024. "Critical success factors for linking digital technologies and circular supply chains," Business Strategy and the Environment, Wiley Blackwell, vol. 33(8), pages 8332-8360, December.
    9. E A Silver & H Naseraldin & D P Bischak, 2009. "Determining the reorder point and order-up-to-level in a periodic review system so as to achieve a desired fill rate and a desired average time between replenishments," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(9), pages 1244-1253, September.
    10. Weng Siew Lam & Weng Hoe Lam & Pei Fun Lee, 2023. "A Bibliometric Analysis of Digital Twin in the Supply Chain," Mathematics, MDPI, vol. 11(15), pages 1-24, July.
    11. A K Mishra & P R Tadikamalla, 2006. "Order splitting in single sourcing with scheduled-release orders," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(2), pages 177-189, February.
    12. Hon‐Shiang Lau & Amy Hing‐Ling Lau, 2003. "Nonrobustness of the normal approximation of lead‐time demand in a (Q, R) system," Naval Research Logistics (NRL), John Wiley & Sons, vol. 50(2), pages 149-166, March.
    13. Tsega Y. Melesse & Chiara Franciosi & Valentina Di Pasquale & Stefano Riemma, 2023. "Analyzing the Implementation of Digital Twins in the Agri-Food Supply Chain," Logistics, MDPI, vol. 7(2), pages 1-17, June.
    14. Prak, Dennis & Teunter, Ruud & Syntetos, Aris, 2017. "On the calculation of safety stocks when demand is forecasted," European Journal of Operational Research, Elsevier, vol. 256(2), pages 454-461.
    15. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E. & Fildes, Robert & Goodwin, Paul, 2009. "The effects of integrating management judgement into intermittent demand forecasts," International Journal of Production Economics, Elsevier, vol. 118(1), pages 72-81, March.
    16. Strijbosch, Leo W.G. & Syntetos, Aris A. & Boylan, John E. & Janssen, Elleke, 2011. "On the interaction between forecasting and stock control: The case of non-stationary demand," International Journal of Production Economics, Elsevier, vol. 133(1), pages 470-480, September.
    17. Malena Zielske & Tobias Held & Athanasios Kourouklis, 2022. "A Framework on the Use of Agile Methods in Logistics Startups," Logistics, MDPI, vol. 6(1), pages 1-21, February.
    18. Babai, M.Z. & Jemai, Z. & Dallery, Y., 2011. "Analysis of order-up-to-level inventory systems with compound Poisson demand," European Journal of Operational Research, Elsevier, vol. 210(3), pages 552-558, May.
    19. Kabadurmus, Ozgur & Kayikci, Yaşanur & Demir, Sercan & Koc, Basar, 2023. "A data-driven decision support system with smart packaging in grocery store supply chains during outbreaks," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    20. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E., 2010. "Judging the judges through accuracy-implication metrics: The case of inventory forecasting," International Journal of Forecasting, Elsevier, vol. 26(1), pages 134-143, January.

    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:gam:jlogis:v:9:y:2025:i:3:p:82-:d:1686649. 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.