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

Aicha Alaoua () and Mohammed Karim
Additional contact information
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

Logistics, 2025, vol. 9, issue 3, 1-22

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.

Keywords: supplier lead time; IoT; log-normal distribution; Monte Carlo simulation; Engineer-to-Order; modeling; optimization; smart supply chain (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2305-6290/9/3/82/pdf (application/pdf)
https://www.mdpi.com/2305-6290/9/3/82/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlogis:v:9:y:2025:i:3:p:82-:d:1686649

Access Statistics for this article

Logistics is currently edited by Ms. Mavis Li

More articles in Logistics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().