Process Engineering and AI Sales Prediction: The Case Study of an Italian Small Textile Company

Abstract

The paper describes the case study of a production process engineering applied to a company working in the textile sector and upgraded by digital technologies. The process engineering is performed by means the Business Process Modelling Notation (BPMN) approach. The new engineered processes are enabled by adopting a software platform able to extract data from work documents using a Robotic Process Automation (RPA) technology based on digital document features recognition. The implemented platform also integrates a Decision Support System (DSS) based on the estimation of priority rules and of Key Performance Indicators (KPIs) supporting subcontractor’s management and related activities. Furthermore, the DSS integrates sales forecasting Artificial Intelligence (AI) algorithms. A comparative analysis about regression-based algorithms and Artificial Neural Network (ANN) Multilayer Perceptron (MLP), is performed to check the best algorithm performance about the product quantity prediction in function of the price, finding ANN-MLP as a good candidate for the estimation. The ANN-MLP model is optimized to provide sales forecasting results with a low Mean Absolute Error (MAE) of 0, 00113. All the analysed algorithms are applied to an experimental dataset. The results have been developed within the framework of a Ministerial Italian project named Smart District 4.0 (SD 4.0).