theMaximoGuys Logo
themaximoguys
ManufacturingCase Study

Manufacturing: AI Work Order Prioritization

Deployed ML models to predict and prioritize work orders based on failure impact and urgency.

Project Details

Client
Global Automotive Manufacturer
Location
Europe
Duration
6 months
Team Size
8 specialists

The Challenge

A global automotive manufacturer was experiencing frequent unplanned downtime due to equipment failures. Manual work order prioritization led to critical assets failing while resources were allocated to less important maintenance tasks, resulting in production delays and quality issues.

Our Solution

We developed an AI-powered work order prioritization system that: • Analyzed historical failure data and maintenance patterns • Integrated real-time sensor data from production equipment • Implemented machine learning models to predict failure probability • Automated work order priority scoring based on business impact • Created dashboard interfaces for maintenance planners

Implementation Timeline

1
Phase 1: Data Collection and Analysis (2 months)
2
Phase 2: AI Model Development and Training (2 months)
3
Phase 3: Integration with Maximo and Testing (1.5 months)
4
Phase 4: User Training and Go-Live (0.5 months)

Results Achieved

30% reduction in unplanned downtime
25% improvement in maintenance efficiency
$2M annual cost savings from optimized maintenance scheduling
95% accuracy in failure prediction models
40% faster response time to critical maintenance issues

Technologies Used

PythonTensorFlowMaximo REST APIsIoT SensorsPower BIAzure ML
"The AI prioritization system has transformed how we approach maintenance. We now prevent failures before they happen instead of just reacting to them."
Maria Rodriguez
Maintenance Manager
Global Automotive Manufacturer

Ready for Similar Results?

Let's discuss how we can help you achieve comparable success with your Maximo environment.