Interactive Dashboard with Predictive system for Buffers Management Optimization

The advent of Deep Neural Networks has shown how powerful these kinds of models can be and how well they can perform in many different fields. Incredible results have been obtained in Image Classification and Natural Language Processing, but lately their usage was extended also to Time Series Forecasting. Deep Models are able to overcome some of the main limitations of the classical Statistical models and allow to discover hidden relations among different features. Even though those models are widely used nowadays, they are rarely adopted in Time Series Forecasting. In this thesis we will see a comparison between the most widely used models in order to predict the optimal stock quantities for the company products, as well as a descriptive Dashboard that aims to display all the most relevant information to the sales people at a glance. The success of this project would bring relevant benefits to the company, which will be able to drastically cut the stock management expenses.