Goods and Activities Tracking Through Supply Chain Network Using Machine Learning Models

Lahcen Tamym (1) , Ahmed Nait Sidi Moh (2) , Lyes Benyoucef (3) , Moulay Driss El Ouadghiri (1)

1 UMI - جامعة مولاي إسماعيل = Université Moulay Ismaïl
2 LTI - Laboratoire des technologies innovantes - UR UPJV 3899
3 LIS - Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon)

End-consumers satisfaction with the higher efficiency and reliability of the products and services provided by the enterprises is a highly important factor in their competitiveness. However, providing efficient tracking and tracing of shipped products enhance customer loy-alty and the enterprise image. Satisfied customers are one of the enter-prise’s greatest assets. In doing so, we are mainly interested in detection of fraudulent transactions and late delivery of orders, as well as track-ing commodities and related supply chain costs over different countries. Two datasetes are used for model training and validation: DataCo Sup-ply Chain Dataset and SCMS Delivery History Dataset. A case study is worked out, and the finding results are compared to some related works in the literature. The obtained results show the added value of our pro-posed models.

Mots clés: Artificial Intelligence; Goods Tracking; Machine Learning; Supply Chain Network

Domaines: Recherche opérationnelle [math.OC]

Lahcen Tamym, Ahmed Nait Sidi Moh, Lyes Benyoucef, Moulay Driss El Ouadghiri. Goods and Activities Tracking Through Supply Chain Network Using Machine Learning Models. IFIP International Conference on Advances in Production Management Systems, Sep 2021, Nantes, France. pp.3-12, ⟨10.1007/978-3-030-85874-2_1⟩. ⟨hal-03526656⟩