The thesis deals with the topic of Digital Twin in the industrial sector, an increasingly relevant paradigm for the digital transformation process of companies. Despite the growing diffusion, there is still a lack of a standardized models capable of describing and comparing existing projects, which are heterogeneous in terms of objectives and technological configurations adopted. To address this gap, a literature study was conducted aimed at outlining the state of the art of the paradigm and understanding its definitions, models, applications in the industrial domain and main adoption challenges. Simultaneously, Industrial IoT and Digital Twin cases were collected and analysed, integrating case studies with the Internet of Things Observatory database of the Polytechnic University of Milan, to build a solid and comprehensive knowledge base. On these premises, a classification and interpretation framework composed of different elements has been developed. First, a mapping matrix that classify all Industrial IoT projects analysed according to two different axes: technological level and context of use. Subsequently, an evaluation radar was applied to projects with a focus on Digital Twin, built to measure the degree of development of five advanced smart manufacturing technologies: Industrial Analytics, Advanced HMI, Additive Manufacturing, Advanced Automation and Cloud Manufacturing. Finally, through the application of a clustering algorithm, the radar graphs of the Digital Twin projects were aggregated, allowing three distinct profiles to be identified, each characterized by specific technological configurations. In this way, the framework offers a useful tool for the comparative analysis of projects belonging to different application areas and for supporting enterprises in innovation decisions.
La tesi tratta il tema dei Digital Twin in ambito industriale, un paradigma sempre più rilevante per il processo di trasformazione digitale delle imprese. Nonostante la crescente diffusione, manca ancora un modello standardizzato capace di descrivere e confrontare i progetti esistenti, che risultano eterogenei per obiettivi e configurazioni tecnologiche adottate. Per colmare questa lacuna, è stato condotto uno studio della letteratura volto a delineare lo stato dell’arte del paradigma e a comprenderne definizioni, modelli, applicazioni in ambito industriale e principali sfide di adozione. Simultaneamente, sono stati raccolti e analizzati casi di Industrial IoT e Digital Twin, integrando casi studio con il database dell’Osservatorio Internet of Things del Politecnico di Milano, al fine di costruire una base di conoscenza solida e completa. Su queste premesse è stato sviluppato un framework di classificazione e interpretazione composto da diversi elementi. In primo luogo, una matrice di mappatura che classifica tutti i progetti IoT industriali analizzati secondo due assi diversi: livello tecnologico e contesto di utilizzo. Successivamente, ai progetti con focus sul Digital Twin è stato applicato un radar di valutazione, costruito per misurare il grado di sviluppo di cinque tecnologie avanzate di smart manufacturing: Industrial Analytics, Advanced HMI, Additive Manufacturing, Advanced Automation e Cloud Manufacturing. Infine, attraverso l’applicazione di un algoritmo di clustering, i grafici radar dei progetti Digital Twin sono stati aggregati, consentendo di individuare tre profili distinti, ciascuno caratterizzato da specifiche configurazioni tecnologiche. In questo modo il framework offre uno strumento utile per l’analisi comparativa di progetti appartenenti a diversi ambiti applicativi e per supportare le imprese nelle decisioni di innovazione.
Digital Twin in the industrial sector: a profiling framework for adoption assessment
Tacchi, Rebecca Maria
2024/2025
Abstract
The thesis deals with the topic of Digital Twin in the industrial sector, an increasingly relevant paradigm for the digital transformation process of companies. Despite the growing diffusion, there is still a lack of a standardized models capable of describing and comparing existing projects, which are heterogeneous in terms of objectives and technological configurations adopted. To address this gap, a literature study was conducted aimed at outlining the state of the art of the paradigm and understanding its definitions, models, applications in the industrial domain and main adoption challenges. Simultaneously, Industrial IoT and Digital Twin cases were collected and analysed, integrating case studies with the Internet of Things Observatory database of the Polytechnic University of Milan, to build a solid and comprehensive knowledge base. On these premises, a classification and interpretation framework composed of different elements has been developed. First, a mapping matrix that classify all Industrial IoT projects analysed according to two different axes: technological level and context of use. Subsequently, an evaluation radar was applied to projects with a focus on Digital Twin, built to measure the degree of development of five advanced smart manufacturing technologies: Industrial Analytics, Advanced HMI, Additive Manufacturing, Advanced Automation and Cloud Manufacturing. Finally, through the application of a clustering algorithm, the radar graphs of the Digital Twin projects were aggregated, allowing three distinct profiles to be identified, each characterized by specific technological configurations. In this way, the framework offers a useful tool for the comparative analysis of projects belonging to different application areas and for supporting enterprises in innovation decisions.| File | Dimensione | Formato | |
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2025_10_Tacchi.pdf
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https://hdl.handle.net/10589/243056