Increasing Yield & Reducing Energy Losses in an Ammonia Production Plant
- Fertilizer Production
- Mid-sized manufacturer operating a 20-year-old ammonia plant
- North Africa
Preventing Equipment Failures Through Predictive Analytics in a Fertilizer Plant
- Fertilizer Production
- Fertilizer production facility operating ammonia, urea, and granulation units in North Africa
- Preventing equipment failures through predictive analytics
Obstacles Our Experts Have Transformed into Opportunities
A major fertilizer plant operating ammonia and granulation units faced recurring reliability issues that were quietly draining performance and profit:
Recurring Equipment Failures
12 unplanned shutdowns per quarter, causing production losses of over $3M annually
Inefficient Maintenance Scheduling
Minimal predictive planning and no structured preventive maintenance
Reactive Operator Response
Maintenance execution varied significantly between units and teams
No Predictive Degradation Tracking
Spare parts were overstocked and poorly classified, delaying emergency repairs
High Emergency Costs & Overstock
Maintenance lacked a risk-based framework to prioritize efforts
Data-Blind Reliability Effort
Skilled engineers weren’t aligned under a unified, data-driven maintenance strategy
Teec’s Expert-Led Solution
TEEC delivered its Predictive Analytics Consulting service to transform how the plant viewed and managed asset health:
What Our Experts Did
Installed edge sensors on critical rotating and static equipment to collect vibration, temperature, and pressure data
Built degradation models using historical failure logs and real-time trends
Deployed early warning alerts and failure probability scoring for maintenance prioritization
Integrated dashboards showing asset health status, risk levels, and intervention recommendations
Trained reliability engineers to interpret predictive indicators and adjust preventive plans accordingly
TEEC replaced assumptions with foresight—giving the plant the tools to act before failure.
Execution Approach by Teec’s Experts
- Identified critical rotating and static equipment for predictive monitoring
- Validated sensor availability (vibration, temperature, pressure, acoustic) and installed additional IoT devices where needed
- Integrated historical CMMS and failure logs with real-time asset data into a centralized analytics platform
- Built custom degradation models using statistical analysis and supervised machine learning (random forest, time-series forecasting)
- Trained models on failure modes and lead indicators to generate remaining useful life (RUL) predictions
- Scored assets by failure risk and prioritized interventions based on operational impact
- Developed an intuitive Asset Health Dashboard highlighting red/yellow/green zones by equipment group
- Configured early-warning alerts via email and mobile apps for critical condition changes
- Created maintenance planning tools with predictive maintenance windows and job bundling suggestions
- Integrated alerts and asset insights into existing CMMS workflows
- Trained planners, reliability engineers, and supervisors to interpret dashboards and plan proactive interventions
- Delivered monthly reports and KPI summaries, including MTBF, risk score shifts, and avoided cost estimates
Results & Impact
Reduced unplanned equipment failures by 62% in 4 months
Extended Mean Time Between Failures (MTBF) by 35% on critical rotating equipment
Saved over $1.1M in avoided production losses and emergency repair costs
Reduced maintenance planning workload and improved intervention precision
Integrated Value Through Teec’s Data-Driven Solutions
Asset Reliability & Integrity Solutions
