# AI predictive maintenance across multi-plant manufacturing

Unplanned breakdowns on bottleneck CNC machines, presses and robotic lines kept stopping production and putting OEM delivery schedules at risk. A hybrid edge-cloud platform that scores asset health, predicts failures days ahead with explainable causes, and raises work orders in SAP PM.

- **Sector**: Automotive components
- **Region**: India
- **Disciplines**: Machine learning · IoT · Mobile
- **Practices**: [AI & machine learning](https://www.winapps.co.in/services/ai-ml/), [Data & analytics](https://www.winapps.co.in/services/data-analytics/)
- **Client**: Under NDA. References available on a call.

## The challenge

A Tier-1 automotive components supplier with several plants in India was losing production to unplanned breakdowns, most painfully on bottleneck machines such as 5-axis machining centres and robotic welders. Fixed-interval preventive maintenance didn't fit machines that wear differently under different loads and materials, and sensor data sat in separate PLCs, SCADA systems and vibration analysers.

The client wanted failure warnings with enough lead time to plan around, a clear reason for every alert, and an interface technicians would actually use on the floor.

## Our approach

We built a hybrid edge-cloud platform. Edge gateways read machine data over OPC UA, Modbus and MQTT, extract features and flag critical faults locally, so alerts still work when the connection drops. In the cloud, an ensemble of time-series and gradient-boosting models trained on historical failures produces a health score, a failure-probability timeline and the parameters driving each prediction.

Technicians get a mobile app with prioritised alerts and one-tap work orders that flow into SAP PM. Leadership sees asset health in Power BI. Verified repairs feed back into weekly retraining, with drift monitoring on every model.

## Architecture

- **Shop floor**: CNC machines, Presses, Robotic lines, Sensors (OPC UA, Modbus, MQTT)
- **Edge**: Edge gateways, Feature extraction, First-pass anomaly detection
- **Cloud ML**: Ensemble failure models, Explainable root cause (SHAP), Weekly retraining
- **People and systems**: Technician mobile app, SAP PM work orders, Power BI dashboards

Illustrative architecture, simplified to protect client confidentiality.

## Results

Outcome details for this engagement are shared under NDA. We're happy to walk through results and put you in touch with a reference on a call.

## Stack

- XGBoost
- LSTM
- React Native
- Power BI
- SAP PM
- MQTT

Next case study: [AI supply-chain optimisation for global electronics manufacturing](https://www.winapps.co.in/work/ai-supply-chain-optimisation/) ([Markdown](https://www.winapps.co.in/work/ai-supply-chain-optimisation.md))

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