ETD

• business process management
• business process reengineering
• intelligence process automation
• process intelligence
• process mining
• robotic process automation

L’elaborato analizza l’evoluzione del Business Process Management (BPM) verso paradigmi di automazione avanzata, analizzando come la sinergia tra automazione robotica dei processi e Process Intelligence stia trasformando radicalmente l’efficienza dei processi aziendali.
La ricerca approfondisce inizialmente il ruolo del Process Mining come motore analitico indispensabile per mappare e ottimizzare i flussi reali, ponendo le basi per una transizione efficace verso l’automazione. Viene quindi esaminato il passaggio dalla Robotic Process Automation (RPA) tradizionale, limitata a compiti ripetitivi e rigidi, all’Intelligent Process Automation (IPA). In questo contesto, l'analisi si focalizza sulle architetture di Intelligent Document Processing (IDP) e Optional Character Recognition (OCR), capaci di convertire masse di dati non strutturati in input azionabili, abilitando un'automazione end-to-end fluida e scalabile.
L’analisi teorica condotta trova evidenza nell’analisi del caso studio Poltrona Frau, illustrando come il sistema Kapto consenta di automatizzare complessi flussi documentali e decisionali.

The paper analyses the evolution of Business Process Management (BPM) towards advanced automation paradigms, examining how the synergy between robotic process automation and Process Intelligence is radically transforming the efficiency of business processes.
The research initially explores the role of Process Mining as an indispensable analytical engine for mapping and optimising real flows, laying the foundations for an effective transition to automation. It then examines the shift from traditional Robotic Process Automation (RPA), limited to repetitive and rigid tasks, to Intelligent Process Automation (IPA). In this context, the analysis focuses on Intelligent Document Processing (IDP) and Optional Character Recognition (OCR) architectures, which are capable of converting masses of unstructured data into actionable inputs, enabling smooth and scalable end-to-end automation.
The theoretical analysis conducted is evidenced in the Poltrona Frau case study, illustrating how the Kapto system allows for the automation of complex document and decision-making flows.