How Intertec Helped a Leading Manufacturer Reduce Downtime with Azure AI Predictive Maintenance
Outcomes
+40%
Increase in Equipment Uptime
-25%
Reduction in Maintenance Costs
+35%
Faster Anomaly Detection
INDUSTRY
Discrete Manufacturing
PROJECT DURATION
1 year
LOCATION
Germany
CLUTCH REVIEW
5
Client Bio
A high-tech manufacturing firm operating across the DACH region, the client specializes in producing complex industrial equipment used in automotive and energy sectors. With large-scale facilities and mission-critical operations, they required a more proactive approach to equipment maintenance to minimize downtime and optimize resource usage.
Situation
The client faced increasing operational challenges:
- Complex machinery made early failure detection difficult
- Data was spread across various systems with inconsistent formats
- Maintenance was mostly reactive, causing unplanned downtimes
- Existing monitoring tools lacked real-time insights or scalability
To maintain competitiveness and ensure smooth operations, they sought a solution that could harness their data for predictive, rather than reactive, maintenance.
Solution
Solution
Intertec partnered with the client to design and implement a predictive maintenance system using Microsoft Azure’s cloud and AI capabilities.
Key Implementation Highlights:
- Azure IoT Hub connected to industrial machines for real-time sensor data ingestion
- Azure Data Lake for storing large-scale telemetry and maintenance history
- Azure Databricks enabled time-series transformation, cleansing, and feature engineering
- Azure Machine Learning was used to develop, train, and deploy failure-prediction models
- SQL Configuration DB housed system logic and user-defined thresholds
- Azure Data Factory orchestrated automated data flows and model retraining
- Power BI Dashboards visualized anomaly alerts and health status for maintenance teams
Impact
The Challenge
With highly interconnected equipment and scattered data, the manufacturer struggled to identify early signs of failure. The lack of real-time monitoring led to high maintenance costs, productivity losses, and safety concerns.
The Result
By integrating Azure’s AI ecosystem, Intertec enabled the client to predict failures before they occurred. The system now alerts technicians in advance, optimizes repair schedules, and provides decision-makers with live operational insights.
Key Features
- AI-Driven Failure Prediction
Machine learning models analyze sensor patterns to identify issues early and reduce unexpected breakdowns. - Unified Data Infrastructure
Data lakes and pipelines centralize historical and real-time information for scalable analysis. - Actionable Dashboards
Maintenance teams receive clear, visual alerts and recommendations via Power BI for immediate response. - Cloud Flexibility
Azure’s platform supports rapid adaptation as equipment grows in complexity and data volume.
