Machine Tool Usage Data for Informed Decisions
General Information
Challenge, Value & Description
Performance, Access & Contact
1 – General Information
Partners
Sectors addressed
Application categories covered
Lifecycle level covered
Digital Engineering
Planning & Commissioning
Smart Production & Operations
Smart Logistics
Smart Maintenance
Customer Service
Circularity
Corso Lombardia, 11, 10099 San Mauro Torinese TO, Italia
Geographical Scope
- Europe
- Southern Europe (Italy, Spain, Portugal, Greece)
2 – Challenge, Value & Description
Challenge
Fidia currently uses MachineMonitor to collect real-time data on system performance. However, its potential is limited by siloed data, reactive and scheduled maintenance, and weak supply chain collaboration. Part manufacturers lack visibility into component lifespan and usage; Fidia cannot optimize equipment design or offer tailored support; and customers face frequent downtime and inefficiencies. There’s no mechanism to assess equipment productivity, hindering informed decisions. Technological gaps, organizational barriers, and poor data sharing reduce efficiency and value creation across the ecosystem. Additionally, the inability to innovate and offer advanced services such as predictive maintenance or servitization reduces business success and innovation.
Value
With the implementation of the Data Space, Fidia’s supply chain becomes collaborative and data-driven. Real-time machine data is securely shared among stakeholders, enabling predictive maintenance, optimized equipment usage, and AI-driven insights. Part manufacturers improve product design and durability using actual usage data; Fidia reduces inefficiencies and offer tailored services; customers enhance productivity, reduce machines downtime and receive timely, data-informed support. This integrated ecosystem lowers operational costs, increases equipment reliability, and allow business servitization. The Data Space fosters transparency, continuous improvement, and innovation, transforming the supply chain into a resilient and efficient network that delivers measurable value to all participants.
Description
In the SM4RTENANCE Data Space, every participant is both a Data Consumer and a Data provider.
The data value chain is described below.
- Machine User (Fidia’s Customer)
Provides: data from machines sensors and CNC systems
Consumes: components lifetime information, usage and maintenance prescriptions
- Machine Manufacturer (Fidia)
Provides: machine usage data, components lifetime information and prescriptions on machines performance
Consumes: data from machines sensors and CNC systems
- Part Maker (Fidia’s Supplier)
Provides: lifetime information for components wear and parts usage guidelines
Consumes: parts life information in real usage conditions
Data Value Chain Description
Infraestructure Elements
- IoT gateways / hub
- Field Devices
Platforms & Tools used
Acquisition: Fidia MachineMonitor, Digital Lake (DCF Device Connector Framework).
Analysis: DGS Digital Lake
Preparation: DGS Digital Lake
Storage: DGS Digital Lake
Usage: DGS Digital Lake
Sharing: Data Space
3 – Performance, Access & Contact Info
Performance
Before the SM4RTENANCE pilot, Fidia’s maintenance processes relied on heuristic estimates and manual inspections to assess components wear, often requiring production stops and technician interventions. Data was fragmented and inaccessible to key stakeholders, limiting collaboration and innovation. Component life was predicted without real usage data, and machine users had little visibility into equipment conditions.
The pilot introduces a secure, interoperable Data Space where real-time data from sensors and CNC systems is collected, consolidated, and shared across part makers, Fidia and its customers. This enables AI-driven life prediction, targeted maintenance and improved component design.
In particular, the solutions devised in this pilot will let
- Fidia (Machine Manufacturer) to make easier diagnosis of machine problems, offer troubleshooting assistance and provide real-time support to customers. Collected data can be used to assess the quality of components with their manufacturers. By tracking a machine’s history, manufacturers can identify opportunities for remanufacturing and refurbishment. Sharing machine information can facilitate circular business models, such as leasing and subscription services, where products are continuously reused and recycled, and generate revenue from selling spare parts, refurbished machines, and recycled materials.
- Part manufacturers to improve life evaluation heuristic models or to train AI models with real usage data. It can help businesses comply with environmental regulations and product traceability requirements. By tracking the data chain, businesses can identify and mitigate potential risks, such as product recalls and counterfeit goods.
- Machine users will gain better insights on real machine usage and plan correct maintenance, reducing downtimes due to machine failure. Data collected can be used for Digital Machine Passport tracking machine lifecycle: shared data can record a machine’s entire journey, from manufacturing to end-of-life, including repairs, refurbishments, and recycling.
Compared to the previous scenario, the pilot fosters transparency, traceability and collaboration across the supply chain while enabling innovation. The result is a more efficient, resilient, and data-driven supply chain.
The expected outcomes are expressed in KPIs showing measurable impact for Fidia as the main end user:
- 20% Reduction of machine parts checks
- 20% Reduction of time to assess component wear state
- 15% Reduction of in-site service interventions to check machine conditions
And in general we expect a non-directly measurable benefits:
- A reduction of in-site service interventions to check machine conditions
- A reduction of costs related to machine guarantees
- Income increases on selling of spare parts