A US Automotive Manufacturer
Transforming Manufacturing Operations with AI-Powered Predictive Maintenance for Equipment Reliability and Cost Savings
CLIENT
Leading US Automotive Parts Manufacturer
INDUSTRY
Automotive Manufacturing
SERVICES
Power BI Consulting
IoT Application Development
TECHNOLOGIES
Machine Learning, IoT Sensors, Azure Cloud, Power BI
The
Challenge
A leading automotive parts manufacturer based in Springfield, Missouri operates 24/7 production across three facilities with 150+ critical machines. The company supplies major automotive OEMs nationwide and generates $420 million in annual revenue. With a significant market presence in automotive systems manufacturing, the company recognized operational efficiency challenges that were impacting production schedules and customer satisfaction.
The manufacturing firm was trapped in a reactive maintenance cycle where equipment failures dictated production schedules rather than planned operations. This reactive approach created cascading problems across the entire operation.
“It was 2 AM when our main CNC machining center failed—again. We had a $300,000 order due in 36 hours, and we were dead in the water. This was the third time that month.”
— VP of Manufacturing Operations
Critical Operational Issues:
- Unpredictable Equipment Failures: CNC machines (Computer Numerical Control machines that precisely cut and shape metal parts) experienced unexpected breakdowns averaging 12 times per month across all facilities.
- Hidden Equipment Deterioration: Hydraulic systems (pressurized fluid systems that generate force), injection molding presses (machines that melt and shape plastic under high pressure), and industrial robots were degrading silently until catastrophic failure occurred.
- Inefficient Maintenance Practices: The team simultaneously over-maintained some equipment by replacing perfectly functional parts on fixed schedules, while under-maintaining others by missing early warning signs of impending failure.
- Supply Chain Disruptions: Unplanned downtime meant rush orders for spare parts, overnight shipping costs, and frantic calls to suppliers—multiplying costs by 3-5x normal rates.
Business Impact:
- $6.8 million in annual losses from unplanned downtime and emergency repairs
- 18% of production capacity lost to equipment failures and maintenance issues
- Customer confidence eroding with two major contracts at risk due to delivery delays
- Maintenance team burnout from constant firefighting mode with no time for improvement
- Inventory management challenges from either stockpiling expensive parts “just in case” or scrambling when failures occurred
“Our maintenance strategy was basically ‘run it till it breaks.’ We knew there had to be a better way, but we didn’t know where to start. The machines weren’t telling us when they needed help—until it was too late.”
Codeplateau's
Strategic Solution
The management team at the manufacturing firm partnered with Codeplateau to implement a comprehensive predictive maintenance machine learning solution. Our team brought together expertise in IoT application development, Power BI consulting, and industrial AI to create a predictive maintenance system tailored for manufacturing operations.
The solution deployed multiple types of predictive maintenance in manufacturing based on equipment criticality and failure modes, creating a 24/7 support model across Missouri management team and our engineering center in Pune, India.
Predictive Maintenance Technologies Deployed:
By solving the mission-critical machine prectiability puzzle and providing expert Power BI and development services, Codeplateau delivered rapid, measurable value:
| Predictive Maintenance Type Implemented | Applied to | How it works | Real example |
|---|---|---|---|
| Vibration Analysis | CNC machining centers, industrial robots, conveyor systems | IoT sensors measure vibrations in rotating components. Machine learning algorithms learn the normal vibration signature of each machine, then flag unusual patterns indicating bearing wear, misalignment, or imbalance—weeks before human detection is possible. | Detected bearing degradation in Robot Arm #7 fourteen days before predicted failure, allowing scheduled replacement during planned downtime |
| Thermal Monitoring | Electrical panels, motors, hydraulic systems | IoT temperature sensors and thermal camera scans identify hot spots indicating electrical resistance, friction, or fluid leaks. AI learns normal thermal patterns under different load conditions. | Identified a degrading electrical connection in Press #12 running 22°C hotter than baseline—preventing potential fire hazard and $180,000 equipment damage. |
| Oil Analysis | Hydraulic systems, gearboxes, compressor | Regular oil sampling combined with inline sensors track contamination, viscosity changes, and wear particle counts. AI correlates fluid degradation with equipment condition. | Oil analysis showed elevated iron particles in Hydraulic Unit #4, indicating gear wear. Scheduled maintenance replaced components before system failure, saving $85,000 in damage costs. |
| Motor Current Analysis | Electric motors, pumps, fans, compressors | Analyzes electrical current drawn by motors to detect rotor problems, stator issues, or load imbalances without physical sensors on equipment. | Identified developing rotor bar cracks in Conveyor Motor #3B through current signature changes, enabling proactive replacement during scheduled weekend downtime. |
Action
Steps
- IoT Sensors : 850+ sensors across all equipment (vibration, temperature, pressure, acoustic, current)
- Edge Computing : Local processing for real-time alerts—no dependency on cloud connectivity Machine Learning Models Random Forest, LSTM networks, Isolation Forest for anomaly detection
- Cloud Analytics : Azure-based platform for historical analysis, model training, cross-facility insights
- Integration Layer : Connected to existing CMMS, ERP, and SCADA systems—no data silos
- Mobile Accessibility : Maintenance team gets real-time alerts and diagnostics on tablets and phones
Implementation Journey: From Pilot to Full Scale
- Month 1-2: Pilot Phase : Started with 15 critical CNC machines in Springfield. Installed sensors, baseline data collection, initial model training. Quick win: Prevented 2 major failures in first 6 weeks.
- Month 3-4: Model Refinement : Pune engineering team worked overnight shifts (US daytime) to tune ML models, reduce false positives from 28% to 11%, and customize alerts for different equipment types.
- Month 5-6: Facility Expansion : Rolled out to all Springfield equipment, then to two other facilities. Integrated with existing work order system. Maintenance team training completed.
- Month 7-12: Optimization & Scale : Continuous learning enabled—models improved with every maintenance action. Added predictive spares optimization. Achieved full ROI.
The
Result
Operational Excellence Achieved:
73% Reduction in Unplanned Downtime: From 156 hours/month to 42 hours/month across all facilities 89% Prediction Accuracy Rate: 87 of 98 predicted failures materialized as forecasted 62% Reduction in Maintenance Costs: From $7.2M annually to $2.7M through optimized schedulingBeyond the Numbers: Intangible Benefits
Team Morale Transformed: Maintenance team shifted from reactive firefighting to proactive problem-solving. Employee satisfaction scores increased 34 points. Voluntary turnover dropped from 23% to 7%. Customer Relationships Strengthened: On-time delivery improved from 84% to 98.7%. Secured two new multi-year contracts worth $18M based on improved reliability. Production Planning Confidence: Manufacturing can now commit to aggressive schedules, knowing equipment reliability is predictable. Enabled acceptance of 15% more orders without capacity expansion. Knowledge Retention: System captures knowledge from retiring technicians. Machine types and failure patterns are documented and learned by AI models. Cross-Facility Learning: Patterns identified in USA inform maintenance strategies to Pune Power BI Consultants and vice versa—multiplying improvements across the network.
Code Plateau has been a key to our success. They have been a great extension of our team. If you are looking for a dependable company that works hard to meet your IoT Development and Power BI needs, Code Plateau is the company for you.
"We don't have those 2 AM phone calls anymore. Last quarter, we had ZERO emergency shutdowns. Our customers notice the difference—we're delivering ahead of schedule now, not scrambling to catch up. This system didn't just save us money; it gave us back our reputation."
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