Manufacturing AI Case Study

The Challenge

With £50M in annual revenue and three production facilities, the manufacturer was losing ground to unplanned equipment failures. Key pain points included:

• Aging equipment fleet: Over 60% of critical machinery was past its recommended service life, with no centralised asset health visibility
• Reactive maintenance culture: 85% of maintenance activities were break-fix, leading to expensive emergency repairs and overtime costs
• 12% unplanned downtime: Production lines averaged 12% unplanned downtime, costing an estimated £3.5M per year in lost output
• No predictive capability: Maintenance schedules were calendar-based rather than condition-based, leading to both over-servicing and missed failures
• Siloed data: Equipment sensor data, maintenance logs, and production schedules lived in separate systems with no integration

The Solution

Pargesoft designed and deployed an AI-powered predictive maintenance platform built on Dynamics 365 Supply Chain Management and Azure IoT:

• IoT Sensor Integration: 200+ sensors deployed across critical equipment, streaming vibration, temperature, pressure, and acoustic data to Azure IoT Hub in real time.
• Predictive Maintenance Agent: Azure Machine Learning models analyse sensor patterns to predict failures 2-4 weeks in advance with 99.2% accuracy. AI agent autonomously generates work orders.
• Autonomous Scheduling Agent: AI agent optimises maintenance windows around production schedules, automatically coordinating parts, labour, and line changeovers to minimise disruption.
• Real-Time Asset Health Dashboard: Power BI dashboards provide plant managers with live equipment health scores, failure risk rankings, and maintenance cost forecasts.
• Continuous Learning Loop: Models retrain weekly on new sensor data and maintenance outcomes, improving prediction accuracy over time without manual intervention.

Implementation Timeline

The Results

• 40% reduction in unplanned downtime within the first six months of go-live, from 12% to 7.2%
• £2.1M in annual savings from reduced emergency repairs, lower overtime, and improved production throughput
• 99.2% prediction accuracy for critical failure events across all three production facilities
• Maintenance shift from 85% reactive to 70% proactive within four months of deployment
• 15% increase in overall equipment effectiveness (OEE) driven by fewer unplanned stoppages and optimised changeovers
• Spare parts inventory reduced by 22% through demand-driven ordering triggered by AI predictions

“The AI agents don’t just predict failures — they’ve fundamentally changed how we think about maintenance. We’ve moved from reactive firefighting to proactive optimisation.”