Focus on Second Open Call winners: Generative and Enhanced AI-driven Intelligent Mining of Manufacturing processes to foster transition to Industry 5.0 – GEDAIM

WHAT ABOUT THE EXPERIMENT?

Currently maintenance service processes in manufacturing continue to struggle, facing value chain execution challenges in maintaining shorter fulfilment requirements, decreased cost and on-time deliveries. Maintenance service process accounts for 20–30% of total process costs. Maintenance service ensures that the necessary resources are available at the right place and time, while also encompassing planning and design-for-maintenance. While Industry 4.0 introduced frameworks that revolutionized maintenance service through digitalization and automation, the emerging paradigm of Industry 5.0 demands a shift towards people-centered, sustainable, and resilient systems. Current practices, however, fall short in addressing these novel requirements. This project proposes a ground breaking approach to maintenance service management tailored for Industry 5.0. Its objective is to design a data-driven decision support system that optimizes the entire maintenance service value chain through Generative and Enhanced AI-driven Intelligent Mining. Intelligent mining of business processes (also known as Process Mining) leverages event logs from information systems to map, model, and visualize actual business process flows, uncovering inefficiencies, deviations, and bottlenecks. The proposed framework enhances traditional process mining by integrating AI-driven and generative AI capabilities to transform maintenance operations. In particular, the GEDAIM experiment aims to support the maintenance team/operators and managers in the following steps:

· Discovery: Identify and map existing maintenance service process paths and behaviours, generating an as-is process model.

· Conformance and Performance Analysis: Evaluate the as-is process against best-practice standards to detect deviations, non-conformities, and inefficiencies.

· Enhancement: Evolve the as-is model into an optimized to-be model by recommending corrective actions (WHAT, WHEN, HOW).

The enhancement phase represents a major innovation. An AI-driven module proactively identifies potential issues (WHAT), such as deviations or cost overruns, predicting their occurrence (WHEN) based on historical data. Generative AI and large language models (LLMs) offer domain-specific insights to empower operators with actionable solutions (HOW) before issues rise.

This novel framework ensures efficient and sustainable maintenance services management and demonstrates Industry 5.0’s vision of harmonizing human expertise with advanced AI for transformative industrial practices.

WHAT IS THE EXPECTED IMPACT?

GEDAIM solution represents a completely new process management approach, which will determine an important business growth, bringing great innovation in maintenance service value chain frameworks, bringing the work to new levels of decision making ability, and spreading innovation to exponential levels in organizations and beyond

The expected impact of GEDAIM experiment is the following:

· 20% Reduction in Time Spent by Human Operators: through deep analysis of current process (discovery and conformance checking) and consequent implantation of AI-powered process enhancements, GEDAIM reduces the time human operators need to spend on maintenance service activities. This increases efficiency and allows teams to focus on more critical or value-added tasks.

· 15% Reduction in Maintenance Service Costs: with less time and fewer resources needed for each maintenance task, the overall cost associated with providing maintenance services is reduced. This leads to significant savings for the organization.

· 10% Increase in Eco-Efficiency: achieved by reducing resources usage within the process through minimizing the number of deviations along the process value chai

· Higher Quality and Process Compliance; GEDAIM solution enhances the quality of maintenance processes by minimizing errors and non-conformities. It ensures that over 90% of operations align with best practice standards, leading to more reliable service delivery and increased customer satisfaction