Representative End User Clients
Representative Automation Clients
Representative Software Clients
KEYWORDS: Industrial AI, Industrial Data Fabrics, Data Platforms, Digital Transformation, Data Integration, Interoperability, Real-Time Analytics, Cybersecurity, Cloud Computing, Edge Computing, Data Governance
Overview
After this year’s ARC European Industry Forum, ARC Europe’s analysts initiated follow-up meetings with clients and attendees of the event, asking them questions about the applications, and strategies, as well as their opinions and ideas regarding Industrial AI solutions. This Insight highlights the replies from end users and suppliers of AI solutions.
Use Cases for Industrial AI
Asked about current usage or offerings of Industrial AI, end users focused on predictive maintenance, quality control or visual inspections as well as process and energy optimization.
One end user reported using an AI-driven predictive maintenance platform across their main production lines. This system monitors vibration, temperature, and acoustic signals of critical rotating equipment and uses its anomaly detection model to flag potential failures up to several weeks in advance. This system has been upgraded by adding new sensor types (including ultrasonic sensors on the bearings) and the model was retrained using six months of newly collected data. As a result, the system’s false positive rate has dropped by roughly 40 percent, and detection lead time (time between alert and actual maintenance need) has increased from around 3 days to 10–14 days.
In terms of quality control, the end user applies a computer-driven AI vision solution for the final inspection of cast components. It scans each piece using high-resolution cameras and automatically classifies surface defects (cracks, porosity, deformations), a process that previously required manual inspection.
For process optimization in the chemical industry, one company employs an AI tool that adjusts multi-variable process parameters (temperature, pressure, feed rate, catalyst dosage) to maximize yield while minimizing energy consumption and waste. In the future they plan to introduce seasonal ambient temperature variation. While this requires a retraining of the model, the end user hopes to boost yield and reduce energy consumption even further.
Regarding supplier offerings, we were told about a new AI-powered warehouse-optimization platform. It forecasts demand for different stock keeping units (SKUs), optimizes storage layout and suggests retrieval and packing sequences to minimize picking times.
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