Let's discuss your automation needs
Real scenarios showing how smart manufacturers doubled quality, cut costs 40%, and paid back in 18 months
Energy, Food, Textiles, Electronics, Chemicals
ROI, timelines, and measurable results
Step-by-step solutions and lessons learned
These scenarios show the depth we bring to every assessment. Get a taste of what's possible.
Click any scenario to explore detailed analysis based on real manufacturing patterns, solution architectures, and quantified results from industry research
Deploy sensors and edge analytics to monitor tool wear in real time, alerting plant teams before performance drops or failures occur
In many Indian factories, tools (cutters, dies, molds, etc.) are monitored manually, leading to:
Goal: Deploy sensors and edge analytics to monitor tool wear in real time, alerting plant teams before performance drops or failures occur.
| Layer | Description |
|---|---|
| Sensors | Vibration, acoustic, current draw, strain gauge sensors (non-invasive) |
| IoT Gateway | Edge device processes real-time signals from machines |
| Wear Prediction Model | Machine-learning trained on historical tool failure patterns |
| Dashboard | Tool condition score, predicted failure window, maintenance alerts |
| Mobile Alerts | WhatsApp/SMS notifications for critical tool degradation |
| Component | Details |
|---|---|
| Factory Type | Automotive parts machining plant |
| Machines Monitored | 2 CNC milling machines + 1 injection mold press |
| Tools Tracked | 6 milling cutters, 2 die molds |
| Pilot Duration | 4 weeks |
| Data Collected | Tool vibration, motor current, cut depth, cycle time |
Cutter starts showing irregular vibration spectrum after 800 cycles
AI model predicts remaining useful life = ~70 more cycles → scheduled replacement avoids line stoppage.
Strain sensor detects increase in mold flexing at 18,000 cycles → triggered preventive maintenance.
CNC motor current exceeds normal range for 4+ seconds → flags as potential tool jam → operator notified immediately.
| Metric | Before IoT Monitoring | After System Deployment |
|---|---|---|
| Unplanned Tool Failures / Month | 5–6 | <1 |
| Tool Replacement Cost Variability | ±18% | ±4% |
| Reject Rate Due to Tool Wear | 7.2% | <2.5% |
| Maintenance Planning Accuracy | Manual estimates | >90% predictive precision |
Tools are replaced just in time — not too late, not too early
Extend tool life with predictive insights, not guesswork
Maintain spec tolerance through optimal tooling
Every replacement event is logged with timestamp and cause
| Phase | Activity | Duration |
|---|---|---|
| Phase 1 | Tool inventory mapping + sensor selection | Week 1 |
| Phase 2 | Sensor installation + data integration | Week 2–3 |
| Phase 3 | AI model calibration using past failure data | Week 4 |
| Phase 4 | Real-time alerts + reporting dashboard activation | Week 5 |
Implement an IoT system that monitors inventory levels in real time, predicts demand based on usage trends, and automatically triggers restocking orders through integrated procurement workflows
Implement an IoT system that monitors inventory levels in real time, predicts demand based on usage trends, and automatically triggers restocking orders through integrated procurement workflows.
| Component | Description |
|---|---|
| IoT Sensors | Load cells, smart bins, flow sensors, RFID level tags |
| Gateway Device | Edge controller collects stock data and sends to cloud |
| AI Forecast Engine | Predicts consumption rate and restock lead time |
| ERP/Procurement API | Auto-initiates purchase orders when threshold is hit |
| User Dashboard | Real-time stock view, alerts, supplier performance logs |
| Component | Details |
|---|---|
| Factory Type | Mid-scale herbal products manufacturer |
| Materials Tracked | 14 raw herbs, 3 packaging SKUs, 6 consumables |
| Sensors Used | 23 smart bin load cells, 8 RFID-tagged drums |
| Pilot Duration | 5 weeks |
| ERP Integration | Zoho Inventory + custom vendor portal |
Weight drops below 25 kg → AI forecasts 3 days of usage left
Vendor lead time = 4 days → system triggers early restock request with PO number
Shrink film usage rate spikes due to festival demand
AI detects deviation → dynamic reorder threshold adjusted from 5 rolls to 8 → prevents halt
50L of aloe concentrate tagged as slow-moving → system halts reordering and alerts planner for alternate use
| Metric | Before Automation | After IoT-Based System |
|---|---|---|
| Stockout-Triggered Halts (monthly) | 4–6 | <1 |
| Emergency Procurement Orders | 7–8 | 1–2 |
| Overstock Expiry Loss (quarterly) | ₹72,000 | ₹14,000 |
| Manual Stock Check Time / Week | ~11 hours | <1.5 hours |
No more last-minute raw material panic
Based on real usage patterns, not guesswork
Cuts manual PO processing and email trails
Avoid over-purchasing and expiry of unused stock
| Phase | Activity | Duration |
|---|---|---|
| Phase 1 | Material categorization & sensor mapping | Week 1 |
| Phase 2 | Smart bin setup + gateway configuration | Week 2 |
| Phase 3 | AI engine tuning + restock logic setup | Week 3 |
| Phase 4 | ERP/API integration + live testing | Week 4–5 |
Factory-wide IoT system tracks energy usage at machine, line, and plant level in real time, enabling cost-saving decisions and predictive optimizations, reducing electricity bills by 21%
Indian factories often face high electricity bills, untracked power surges, and inefficient equipment use due to lack of visibility into real-time energy consumption. Common issues include:
Goal: Install a factory-wide IoT system that tracks energy usage at the machine, line, and plant level in real time — enabling cost-saving decisions and predictive optimizations.
Real-time alert when dyeing boiler exceeded 90% capacity for 30+ min → operator intervention avoided short circuit.
System auto-flags finishing motor drawing 4kWh during off-shift → scheduled shutdown routine updated.
Dashboard identifies 11:30–1:00 pm as highest energy cost window → production sequencing adjusted to reduce compressor load then.
| Metric | Before IoT | After IoT Monitoring |
|---|---|---|
| Monthly Energy Cost | ₹3.1 L | ₹2.45 L |
| Power Factor Penalties | ₹7,800 | ₹0 |
| Undetected Idle Load Events | 9/week | <2/week |
| Visibility to Machine-Level Usage | None | Full (per line/machine) |
Average 15–20% reduction in electricity bills through optimized usage patterns.
Shift planning based on energy load patterns for maximum efficiency.
Power factor control avoids utility penalties and regulatory issues.
CO₂ reduction data supports ESG reporting and green certification.
Predict next day/hour load patterns for better planning
Enable auto shutdown triggers based on consumption
For overuse during production shifts
Compare performance with industry standards
Vision AI + IoT sensors + real-time analytics continuously monitor production lines and optimize performance based on predictive insights, increasing OEE from 61% to 77%
Vibration, temperature, and current sensors with ML models predict machine failures 5-10 days in advance, reducing breakdowns from 3-5 per month to under 1.2 hours downtime monthly
Food processing facilities face significant losses due to unexpected equipment failures. Unplanned downtime can cost ₹2-5 lakhs per incident, spoil perishable inventory, and disrupt production schedules. Common challenges include:
Goal: Deploy IoT sensors and AI models to predict equipment failures 5-10 days in advance, enabling proactive maintenance scheduling and minimizing unplanned downtime.
System detected increasing vibration amplitude 8 days before bearing failure → scheduled maintenance prevented ₹3.5L production loss.
Temperature sensors flagged cooling system blockage → preventive cleaning avoided motor burnout and ₹2.1L replacement cost.
Current signature analysis identified impeller wear → timely replacement improved efficiency by 12% and saved ₹45K monthly.
| Metric | Before AI | After AI Implementation |
|---|---|---|
| Unplanned Downtime Events | 4-5 per month | 1-2 per month |
| Average Downtime Duration | 8-12 hours | 2-3 hours |
| Maintenance Cost per Month | ₹4.2 L | ₹2.8 L |
| Production Loss Prevention | ₹0 | ₹52L annually |
Shift from reactive to predictive maintenance reduces emergency repairs by 65%.
Maintained delivery schedules prevent customer penalty costs and reputation damage.
Optimal maintenance timing extends equipment life by 20-30%.
Early fault detection prevents catastrophic failures and workplace accidents.
Integration with CMMS for automatic maintenance scheduling
Real-time alerts and maintenance guidance on mobile devices
AI-driven inventory management for maintenance parts
Deep learning models for complex failure pattern recognition
Advanced optical inspection catches micro-defects, preventing warranty costs of ₹1.2 crore annually
Advanced process control and heat recovery systems cut energy costs by ₹2.4 crore annually
Every assessment draws from real manufacturing wins. Your roadmap is built on proven patterns, not theory.