From smart firms to smart consumers - Complex Systems and Machine Learning for Industry 4.0

Aug 4, 2026

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The thesis investigates how Industry 4.0 technologies reshape firm design, production, and distribution by intertwining Industrial Internet of Things, cloud connectivity, and machine learning into an integrated manufacturing process. Firms are modeled as complex systems, where the evolution of each productive element depends on tightly coupled connections across the whole organization. The study develops graph-theoretic and machine-learning methods to extract actionable insights, analyze startup ecosystems, and interpret consumers’ reviews. It applies community detection, graph metrics, and text-to-number techniques to support innovation, forecast startup success, and support future perspectives.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages130

Reading Time5 h 28 min

AccessFree to download

Industry 4.0, Complex Systems and Machine Learning

Complex Systems in Industry 4.0

Taming Complex Systems: graph theory

Exploiting Complex Systems: machine learning

Thesis organization

How to extract insights for Industry 4.0: graph theory and Machine Learning

Fundamentals of graph theory

Machine learning algorithms

Startups and consumers’ reviews: use-cases and why they are important

The startup ecosystem: importance and modelling

Why studying tourists’ tastes in Industry 4.0

How to boost innovation and customers’ satisfaction: using graph theory and Machine Learning

StartupBlink: equity oriented rethinking through community detection

Crunchbase: graph model and forecasting success

TripAdvisor: extracting insights from tourists’ reviews

Insights and future perspectives for startups and reviews’ analysis in Industry 4.0

Best practices via community detection

Graph metrics and startups’ success

Unveiling tourists’ tastes and needs

Future perspectives

Appendices

Repository Istituzionale dei Prodotti della Ricerca del Politecnico di Bari From smart firms to smart consumers: Complex Systems and Machine Learning for Industry 4.0 This is a PhD Thesis Original Citation: From smart firms to smart consumers: Complex Systems and Machine Learning for Industry 4.0 / De Nicolò, Francesco. -ELETTRONICO. - (2024) . [10 .60576/poliba/iris/de-nicol-francesco_phd2024] Availability: This version is available at [http://hdl.handle.net/11589/266240 since: 2024-03-10](http://hdl.handle.net/11589/266240 since: 2024-03-10) Published version DOI:10.60576/poliba/iris/de-nicol-francesco_phd2024 Publisher: Politecnico di Bari Terms of use: (Article begins on next page) DE NICOLO' FRANCESCO PUTIGNANO (BA) 25/04/1989 PUTIGNANO (BA) FRATELLI BANDIERA, 6 [francesco.denicolo@poliba.it](francesco.denicolo@poliba.it) INDUSTRIA 4.0 36 FROM SMART FIRMS TO SMART CONSUMERS: COMPLEX SYSTEMS AND MACHINE LEARNING FOR INDUSTRY 4.0 INDUSTRIA 4.0 36 BARI, 27/12/2023 BARI, 27/12/2023 Contents 1 Industry 4.0, Complex Systems and Machine Learning 3 1.1 Complex Systems in Industry 4.0.................. 3 1.2 Taming Complex Systemgsr:aph theory .............. 5 1.3 Exploiting Complex Systema: chine [Learning mode.ls.... 6](Learning mode.ls.... 6) [1.4 Thesis organization.......................... 7](1.4 Thesis organization.......................... 7) [2 How to extract insights for Industry 4.0: graph theory and](2 How to extract insights for Industry 4.0: graph theory and)[ ](2 How to extract insights for Industry 4.0: graph theory and)Machine Learning 9 2.1 Fundamentals of graph theor.y................... 9 2.1.1 Complex Systems and grap.h.s .............. 9 2.1.2 Graphs’ value in modelling Complex Syst.em..s .... 11 2.1.3 Multi-graph, simple graph and weighted gr.ap. h.... 12 2.1.4 Degree and Adjacency matrix................ 13 2.1.5 Geodetic paths........................ 15 2.1.6 Closeness. . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.7 Betweenness . . . . . . . . . . . . . . . . . . . . . . . 18 2.2 Machine Learning algorithm. s................... 19 2.2.1 Unsupervised Machine Learnim: munity detection in graphs ............................. 19 2.2.2 Supervised Machine Learninl:gorithms and Explainability ............................. 24 3 Startups and consumersr’eviews:use-cases and why they are important 33 3.1 The startup ecosystemits: importance and modellin.g..... 34 3.1.1 The importance of the startup ecosystem for Industry34.0 3.1.2 Countries’ innovation ecosystes:StartupBlink ranking 35 3.1.3 Interplay among startups and investCorusn: chbase... 38 3.2 Why studying tourists’ tastes in Industry 4...0......... 39 3.2.1 Tourists’ experiences in Apulia from TripAdvreisvoire:wsand rating ........................... 40 4 How to boost innovation and customers’ satisfacieopnl:oying graph theory and Machine Learning 43 4.1 StartupBlink:equity oriented rethinking through community detection ................................. 43 4.1.1 StartupBlink country network............... 44 4.1.2 Community detection algorithms and Resolution R.ati4. 4.1.3 WDI country communities and StartupBlink [rethink.ing50](rethink.ing50) 4.2 Crunchbaseg: raph model and forecasting succe. s.s....... 55 4.2.1 Modelling the economic interpla.y............. 55 [4.2.2 Defining and measuring succe.ss](4.2.2 Defining and measuring succe.ss)............. 57 4.2.3 Strategic elements in the startup ecosyst.em....... 58 4.2.4 Forecasting success..................... 61 4.3 TripAdvisor:extracting insights from tourists’ revie. w. s.... 66 4.3.1 From text to number:F-IDF matrix . . . . . . . . . . . 66 4.3.2 Reviews’ classification . . . . . . . . . . . . . . . . . 67 4.3.3 Strengths and weaknesses of the Apulian tourism. o. ffr8 5 Insights and future perspectives for startups and reviews’ analysis in Industry 4.0 71 5.1 Highlighting

FAQs

How does the thesis connect Industry 4.0 with complex systems modeling?

It frames a firm as a complex system where every productive element is linked to others, so its evolution cannot be studied in isolation but within a holistic framework driven by these connections.

Which analytical approach is used to extract insights for Industry 4.0?

The work combines graph theory with machine learning, using graph fundamentals and machine-learning algorithms to analyze relationships and derive interpretable insights.

How are startup ecosystems and consumer/tourist reviews addressed in the research?

Startup ecosystems are modeled and analyzed using community detection and graph metrics, while consumer insights are extracted from tourists’ reviews using techniques such as transforming text into numeric representations and review classification.

Aug 4, 2026

Upload by Riley