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Integrating IoT, AI, and Data Analytics in Food Machinery Production: A Digital Innovation Model for SMEs

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  • Magaletti, Nicola
  • Leogrande, Angelo
  • Nortarnicola, Valeria
  • Mariani, Stefano
  • Di Molfetta, Mauro

Abstract

This case study features Tecnomulipast, an SME from Southern Italy that specializes in machinery production for the food processing industry. The study is in fact centered on the company's digital transformation process, facilitated by investments in advanced production systems and innovation-driven managerial practices both facilitated by regional co-financing initiatives, including from Regione Puglia. At the center of it all is the integration between a new Industry 4.0-compliant laser welding system in the company's ERP system. Through Internet of Things (IoT) technologies, the system is inherently equipped to collect and transmit batch-level as well as real-time data, instantiating a cyber-physical system for advanced manufacturing. Easy to connect by standard interface (i.e. OPC-UA), the system is tied to an analytics data framework capable of working on structured data (e.g., KPIs, sensors' metrics) as well as on unstructured data (e.g., images), allowing for real-time monitoring, early anomaly signaling, and optimization of processes. Designed for scalability, the related technology architecture is future-proof to include artificial intelligence (AI) integration for augmenting decision-making with predictive and prescriptive analytics. Beyond the technological enhancement, however, the transformation was facilitated by an excellence managerial model that focuses on flexibility, data-driven governance, as well as on constant learning. Tecnomulipast's case offers an replicable template for SMEs—especially in low digital maturity areas—showing that targeted investment, innovation-driven management, and system-level integration might finally eliminate the gap between tech potential and operational performance in Industry 4.0 transitions.

Suggested Citation

  • Magaletti, Nicola & Leogrande, Angelo & Nortarnicola, Valeria & Mariani, Stefano & Di Molfetta, Mauro, 2025. "Integrating IoT, AI, and Data Analytics in Food Machinery Production: A Digital Innovation Model for SMEs," OSF Preprints g7qy2_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:g7qy2_v1
    DOI: 10.31219/osf.io/g7qy2_v1
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    References listed on IDEAS

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    1. Ricci, Riccardo & Battaglia, Daniele & Neirotti, Paolo, 2021. "External knowledge search, opportunity recognition and industry 4.0 adoption in SMEs," International Journal of Production Economics, Elsevier, vol. 240(C).
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    5. Battistoni, Elisa & Gitto, Simone & Murgia, Gianluca & Campisi, Domenico, 2023. "Adoption paths of digital transformation in manufacturing SME," International Journal of Production Economics, Elsevier, vol. 255(C).
    6. Giorgio Scarton & Marco Formentini & Pietro Romano, 2025. "Automating quality control through an expert system," Electronic Markets, Springer;IIM University of St. Gallen, vol. 35(1), pages 1-19, December.
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    More about this item

    JEL classification:

    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • D23 - Microeconomics - - Production and Organizations - - - Organizational Behavior; Transaction Costs; Property Rights
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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