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

Abstract

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 loyalty and the enterprise image. Satisfied customers are one of the enterprise's greatest assets. In doing so, we are mainly interested in detection of fraudulent transactions and late delivery of orders, as well as tracking commodities and related supply chain costs over different countries. Two datasetes are used for model training and validation: DataCo Supply 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 proposed models.