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

Author

Listed:
  • Nicola Magaletti

    (LUM - Università LUM Giuseppe Degennaro = University Giuseppe Degennaro)

  • Valeria Notarnicola
  • Mauro Di Molfetta
  • Stefano Mariani
  • Angelo Leogrande

    (LUM - Università LUM Giuseppe Degennaro = University Giuseppe Degennaro)

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

  • Nicola Magaletti & Valeria Notarnicola & Mauro Di Molfetta & Stefano Mariani & Angelo Leogrande, 2025. "Integrating IoT, AI, and Data Analytics in Food Machinery Production: A Digital Innovation Model for SMEs," Working Papers hal-05042478, HAL.
    • Handle: RePEc:hal:wpaper:hal-05042478
    Note: View the original document on HAL open archive server: https://hal.science/hal-05042478v1
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    References listed on IDEAS

    as
    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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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Digital Transformation Industry 4.0 Innovation Management IoT in Manufacturing Smart Manufacturing; Digital Transformation; Industry 4.0; Innovation Management; IoT in Manufacturing; Smart Manufacturing;
    All these keywords.

    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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