Forecasting delays in a supply chain with machine learning algorithms

Supply Chain exists since commerce has been created, and nowadays it is one of the main keys of the success of any business. Therefore, its e ciency and good functioning is a must and a priority for every company. The main goal of this project is to create a Supply Chain Management tool, with the objective of predicting when an material will arrive late to the plant or the warehouse and anticipate its end of stock. The tool will have the shape of a Web application tracking every material order and giving insight on its probability of delay, its stock current availability, and the past history of the previous order of the same material. This master thesis report will focus on the prediction of the delays. This project is developed within Accenture with the purpose of winning an RFP (Request for Proposal) of an Aerospace and Defense company. The prototype of the tool will be presented in Le Salon International de l'Aeronautique et de l'Espace in Paris from the 17th to the 23rd of June