Manufacturing Firm Adopts SLM-Based Predictive Maintenance
Transforming Modern Manufacturing with AI-driven Automation
The Challenge
Specializing in automotive components, TurboForge faced concerns related to:
Unexpected Equipment Failures
Heavy reliance on complex machinery, caused frequent equipment breakdowns forcing the company to halt operations and deal with machinery malfunctioning.
Production Delay due to Downtime
Unplanned machine failures caused delay in the delivery of finished products to customers, hampering client satisfaction.
Increased Maintenance Costs
Emergency repairs and unplanned maintenance led to higher-than-expected maintenance costs.
Approach
Installing IoT Sensors
Set sensors on critical machinery to measure essential operational parameters.
Integrating Sensor Data
Incorporated the data with maintenance logs to build a dataset for training the SLM and developed custom SLM to detect early signs of equipment failures.
Deploying the SLM Model
Seamlessly integrated the custom SLM model into the company’s maintenance management system.
Enabling Real-time Monitoring
Analyzed the data to ensure timely tracking of machine performance, anomaly detection and predictive alerts.
Solution
We implemented a tailored AI-enabled predictive maintenance system:
Data Acquisition and Integration
IoT were installed to collect real-time machinery related data and maintenance logs were integrated with sensor data to train the SLM.
Custom SLM Development
A lightweight SLM was developed to detect patterns of equipment failures.
Implementation and Rollout
The SLM was deployed across the production facilities and alerts were integrated in the existing management system to flag potential issues.
Continuous Monitoring and Predictive Analysis
The customized SLM predicted machine failures by analyzing incoming data and identifying possible anomalies.
Results & Impact
Conclusion
By implementing our SLMs powered predictive maintenance, TurboForge successfully minimised equipment failures, optimised their maintenance schedules and enhanced overall productivity resulting in the company achieving full ROI within 8 months. The significant reduction in downtime and recurring maintenance costs lead to faster ROI and scalable benefits across the enterprise. This case study highlights the transformative impact of AI-powered automation in modern manufacturing.
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