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

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


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).

Data di pubblicazione

status editing


Smart District 4.0 


  • Prof. Ing. Alessandro Massaro
  • Ing. Nicola Magaletti
  • Ing. Gabriele Cosoli
  • Dr. Angelo Leogrande


In this section is described the general scenario of the technologies and approaches adopted in the pilot case of study. Automating repetitive tasks can produce human errors that can be decreased by the adoption of the Robotic Process Automation (RPA) technology. RPA is useful to eliminate repetitive activities, to reduce the labour consumption, to assign employees to new production areas, and in general to minimize human errors. Different tools can be applied for advanced data processing. Artificial Intelligence (AI) surely plays an important role in the context of business processes, and can be combined with RPA for data processing, especially when in the production processes are identified routinely tasks. The use of AI can improve the efficiency of RPA in Industry 4.0 scenarios, by providing good application performances in data recognition, classification, and forecasting. The association AI-RPA is suitable in different marketing sectors, such as finance and banking, thus suggesting the application also in other sectors as for the analysed case study. RPA is strictly connected to AI, and is suitable to construct workflows, to select processes], to digitize transactions, and in different cases requiring document management automation. Document recognition by AI, can support data flow automation. Automation process of practices is an industrial research topic for companies working in services and implementing association rules to improve contract classification. Moreover, data warehouse and software integration such as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM), play an important role for the company Business Intelligence (BI). In this technological scenario, innovative platforms oriented on company performance integrates software tools and Key Performance Indicators (KPIs, improving production activities. Production processes can be modelled and simulated by Business Process Modelling Notation (BPMN) approaches, suitable to map “AS IS” and “TO BE” processes. The analysed state of the art, highlights that different tools can be implemented to realize an Information Technology (IT) collaborative framework, controlling and simulating company core-processes. Following the technological scenario, has been developed the Smart District 4.0 (SD 4.0) project, initiative funded with the contribution of the Italian Ministry of the Economic Development, sustaining the digitization process of the Italian Small Medium Enterprises (SMEs), by focusing the experimentation on a pilot company (GEMITEX srl), leading in the national and international market for the production and marketing of tablecloths, cushions, chair covers, ironing board covers, carpets, bathroom and kitchen furniture, doormats, sheets, quilts, curtains and other items for home. The pilot company manages different subcontractors. For this purpose, the main process to optimize is the subcontractor’s management by means of Decision Support System (DSS) based on priority rules and KPIs. This is possible by digitizing information by RPA. A further important requirement of the pilot industry is to have graphical dashboards based on AI predicting sales. Following the company’s need, the IT platform is designed for the case of study to trace production and marketing activities, and to optimize subcontractor’s management. 

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Pubblicazione Details

Corresponding author: Prof. Ing. Alessandro Massaro can be contacted at: