FIGARO project

Challenge

Manufacturing is under intense pressure to become more efficient, data-driven, and resilient, yet many plants still struggle with fragmented data pipelines, inconsistent data quality, opaque AI models, and cybersecurity vulnerabilities across OT/IT boundaries.

As factories adopt advanced analytics and automation, the interplay between data governance, predictive services, human–machine interaction, and business impact becomes critical: without trustworthy data and explainable decision flows, predictive maintenance and KPI monitoring remain isolated pilot projects rather than scalable solutions.

FIGARO addresses this gap by defining an enabling framework that integrates Data Quality, Predictive Services, Decision Support Systems (DSS), and Cybersecurity & Data Management, complemented by visualisation and user-centric Human–Machine Interfaces (HMI) that keep operators in the loop. The 36-month initiative targets tangible improvements in process reliability, asset performance, and operational decision-making across industrial contexts.

Applied expertise

FIGARO delivers an integrated Methodological and Technological Platform that facilitates the implementation of AI governance in the day-to-day operations of manufacturing companies. On the methodological side, it provides models for (i) maturity assessment and enterprise profiling in AI, (ii) business-impact and cybersecurity awareness analysis, and (iii) performance monitoring of assets and processes, forming a qualitative framework to evaluate organisational and business improvements resulting from digital transformation. On the technological side, FIGARO implements a Data Quality Assessment Framework and an Asset & Process Governance Framework that combine AI-based predictive maintenance, an Intelligent Decision Support System (DSS), and KPI definition and management, alongside Data Management and Security modules and data visualisation with bi-directional human–machine interaction. All components are integrated into an interoperable platform, validated through an industrial use case to enable continuous improvement cycles.

Within FIGARO, RINA leads both the design and the prototyping and validation of the Asset Management & Process Governance Framework, aimed at defining and implementing a technological architecture based on Artificial Intelligence and Machine Learning to enable advanced Asset Management and Process Governance strategies, with a particular focus on Predictive Maintenance. The framework collects and analyses data from interconnected industrial to support real-time monitoring and data-driven decision-making. The design phase includes definition of the framework architecture and the Decision Support System (DSS), integration of predictive maintenance models, and development of innovative Human–Machine Interfaces (HMI) for visualising key performance indicators (KPIs) and process parameters, enabling users to monitor performance and receive early alerts or recommendations based on predictive models.

On the technological side, RINA develops and integrates the software components of the Process Governance Framework, including acquisition nodes for collecting and pre-processing sensor data, messaging and data-broker services for managing information flows, data collectors and time-series databases for secure storage and analysis, data processing modules for streaming and batch analytics with alert generation and ML-based anomaly detection, as well as authentication, middleware and integration services to ensure interoperability and cybersecurity. The integrated Decision Support System optimises maintenance through condition-based and predictive logic, offering real-time monitoring dashboards. All components are progressively combined into a unified, interoperable platform tested and validated in an industrial use case, demonstrating its scalability and real-world applicability. Through this work, RINA contributes to building a robust and intelligent framework that transforms raw industrial data into actionable insights, enabling predictive, secure, and efficient management of manufacturing processes.

Impact

FIGARO aims to equip manufacturers and technology providers with a framework to operationalise AI governance across the full data lifecycle, improving data reliability, enabling explainable predictive maintenance services, and strengthening cybersecurity while maintaining human oversight through intuitive Human–Machine Interfaces (HMI) and advanced visual analytics. By coupling robust assessment methods with an interoperable technological platform, the project supports evidence-based decisions on maintenance, process optimisation, and KPI management, accelerating the transition from pilot implementations to full-scale deployment at plant and enterprise levels. Validation within an industrial pilot environment demonstrates FIGARO’s transferability and impact, positioning it as a practical pathway to trustworthy, human-centred digital transformation in manufacturing.

Project Consortium

1. Eka S.r.l., 2. Università del Salento 3. Dhitech S.c.a r.l., 4. Pidiemme Consulting S.r.l., 5. Rina Consulting S.p.A.