🏭 Case Study: AI-Driven Optimization for Automotive Parts Manufacturer
Client: Mid-Sized Automotive Parts Manufacturer (India)
Industry: Manufacturing & Industrial Automation
Location: Pune, India
Services Deployed: AI/ML Development, Predictive Maintenance, Computer Vision, Forecast Modeling, Digital Twin Simulation
Tech Stack: Python, TensorFlow, OpenCV, AWS, MQTT, PLC Integration, React (Dashboard)
📌 Client Overview
The client is a Tier-2 supplier of precision auto components to OEMs across India and Southeast Asia. With a growing order book and a complex production floor running 20+ CNC and hydraulic machines, the company sought to improve reliability, quality control, and planning through AI.
🔍 Business Challenges
Despite stable business growth, several key operational challenges slowed scalability:
- Frequent unplanned machine breakdowns led to missed delivery deadlines.
- Manual quality checks resulted in inconsistent product quality.
- Overproduction or understocking due to poor demand visibility.
- No safe way to simulate production changes without affecting throughput.
The client required an AI-first strategy that worked with existing infrastructure, didn’t disrupt operations, and delivered ROI within months.
🧠 TechAvtar’s Solution
Our AI consultants and engineering team developed and deployed a fully integrated system consisting of four modules:
🔧 1. Predictive Maintenance Engine
- Integrated real-time sensor data (vibration, temperature, runtime)
- Built an ML model to predict failure signatures before breakdown
- Enabled downtime alerts via dashboard + SMS
Result: 35% reduction in machine downtime in 90 days
🕵️ 2. AI-Powered Quality Inspection (Computer Vision)
- Used OpenCV + CNN models to train defect recognition logic
- Deployed edge cameras across two production lines
- Developed confidence scoring to auto-separate pass/fail units
Result: Defect detection accuracy increased from 88% → 96%
📈 3. Demand Forecasting Model
- Cleaned and processed 3 years of historical order + lead time data
- Applied time-series ML models (Prophet, ARIMA)
- Integrated output with production planning dashboard
Result: Forecasting accuracy improved to 90% (±2% error)
🔁 4. Digital Twin Simulator
- Created a virtual model of the shop floor (machine behavior + production flow)
- Allowed “what-if” scenario testing for workforce allocation, schedule shifts, etc.
- Built with React + backend simulation API in Python
Result: 100% safe testing of scheduling logic and operational changes
📊 Impact Summary
| Metric | Before AI | After TechAvtar Solution |
|---|---|---|
| Unplanned Downtime | 18% monthly | Reduced to 6% |
| Defect Detection Accuracy | 88% | Improved to 96% |
| Forecast Accuracy | ~65% | Boosted to 90% |
| Maintenance Costs | ₹10.5L/Quarter | Saved ₹3.2L/Quarter |
| Operational Visibility | Manual reports | Live dashboard insights |
⚙️ Technical Architecture Snapshot
Sensors (PLC/MQTT) → AWS IoT Core → Python AI Models (TensorFlow + Scikit-Learn)
↓
API Layer → React Dashboard (admin panel + alerts)
↓
Digital Twin Microservice → Simulation Engine (Python)
✅ Client Testimonial
“The team at TechAvtar didn’t just deliver a solution—they delivered transformation. In 4 months, we saw reduced downtime, smarter forecasts, and greater trust from our customers.”
— Operations Director, Automotive Client
🚀 Want Similar Results?
TechAvtar helps manufacturers modernize with practical, production-grade AI systems that integrate into existing processes—with minimal disruption and maximum ROI.
📞 Talk to Our AI Consultant → https://calendly.com/shievamkr/30min
📥 Or email us: shivam@techavtar.com
