Predictive Maintenance eliminates unplanned downtime at a multibillion-dollar semiconductor fab.
When global demand for microchips erupted in response to shifts caused by the pandemic, a multibillion-dollar electronics and semiconductor manufacturer needed to accelerate its zero-waste production programs. They chose Decisyon's Predictive Maintenance application to eliminate unplanned machine downtime at a critical plant that was bleeding production hours to unpredictable equipment failures.
The challenge
A leading on-demand supplier to global tech companies was losing production every time a machine failed — and failures were coming too often, too unpredictably, and with too much cost attached. Missed delivery dates were creating dissatisfied customers and lost revenue. The company needed to predict all failures, distinguish between failure types, identify root causes, and prescribe a prioritized set of corrective actions. The volume and variety of failures made this impossible without an intelligent, automated system.
The solution — Predictive Maintenance on Decisyon LOOP
Decisyon's Predictive Maintenance application goes beyond traditional Asset Performance Management by combining predictive and prescriptive AI to identify maintenance and upgrade needs before failures occur. Built on the Decisyon LOOP platform, it uses an unsupervised machine-learning model based on Complex Network Analysis of equipment sensor signals — exploiting non-linear correlations between groups of signals within sliding windows and representing them as dynamic graphs. The model self-calibrates over time by generating new test and training data, so recommendations get sharper the longer it runs.
How the AI works
The model is trained, tested and calibrated on three data sets sampled at six-second frequency — dense enough to reveal anomaly precursors before they become failures. Live accuracy averages above 90%, and advance warnings arrive in a window of 24 minutes to more than 11 hours before the failure would occur — enough time to schedule the intervention rather than react to a stoppage. Decisyon Adaptors connect the machine sensors, the Decisyon Smart Gateway brokers and exports the time-series data, and the LOOP platform runs the AI algorithms and surfaces executable recommendations in a clean operator UI.
Next Best Action, in the operator's hands
The Predicted Failure Data Control Room lets operators view outlier values across every connected asset and map alerts to the customer's existing classification rules. The Next Best Action Recommender turns those alerts into a ranked, scheduled work order for field technicians — with a live failure countdown, the underlying model dynamics, and the reasoning behind the recommendation exposed in-line. Every accepted or rejected action feeds back into the ML knowledge base, so the system keeps learning from the plant it runs.
The results
The manufacturer has eliminated virtually all unplanned machine downtime at the pilot plant. Maintenance is now performed proactively, the expected lifetime value of the assets has increased, and product quality and on-time delivery have improved in a very short period. Teams collaborate around the same AI-recommended Next Best Action instead of firefighting after the failure — and the same architecture is now scaling across additional plants and use cases.
"Using the native collaboration capabilities and data-driven execution functions of Decisyon's Predictive Maintenance application, we are now able to schedule and implement our maintenance activities with sufficient time for reviewing the data and organizing the recommended actions."
VP, IIoT ManufacturingGlobal electronics & semiconductor manufacturer
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