AI & Automation in Pharmaceutical Packaging —Reducing Downtime and Rejection at a Hyderabad-Based Pharma Plant

Company Name: (Confidential – referred to as “MediPack Formulations”)
Industry: Pharmaceuticals – Generic Drugs & OTC Products
Location: Hyderabad, Telangana
Plant Area: 70,000 sq. ft.
Employees: 300+
Product Line: Tablets, capsules, syrups, ointments
Markets: India (Tier-1 hospitals), South-East Asia, Africa
Daily Output : ~2 million units packaged

MediPack Formulations had built a strong manufacturing setup with WHO-GMP compliance. Their bottleneck wasn’t in production, but packaging. With 24×7
operations across 5 high-speed lines, they faced frequent breakdowns, erratic stoppages, and post-packaging rejection due to human and mechanical inconsistencies. To overcome this, they adopted a phased AI + automation upgrade specifically focused on packaging lines.

Challenges Before Automation

  1. Frequent Line Stoppages
    Packaging lines stopped 3–4 times per shift, each costing 20–40
    minutes of idle time.
  2. Human Error in Labeling
     Wrong batch codes or smudged print led to regulatory
    non-compliance and rework.
  3. High Rejection Rates
     Finished packs were rejected due to misalignment, seal leakage, or
    underfilled blisters.
  4. Manual QA Checks
    Visual checks were slow and error-prone under long shifts.
  5. Lack of Root Cause Visibility
     No data to understand why specific errors occurred on certain lines or shifts.
    Packaging accounted for 35–40% of all product rejections, increasing costs and hurting delivery schedule

Packaging accounted for 35–40% of all product rejections, increasing costs and hurting delivery schedule

The Objective

Build a smart, AI-enabled packaging system to:
● Automate defect detection in real time
● Reduce stoppages and track line efficiency
● Identify bottlenecks using data
● Predict component (e.g. printer) failures
● Create an audit trail for every pack

Solution Stack

  1. Computer Vision for Real-Time Defect Detection
    ● 8 high-speed cameras installed across 5 lines (at blister filling, sealing, labeling, and cartoning stages).
    ● AI models trained to detect:
    Blister fill gaps
    Label misalignment
    Seal integrity issues
    Faded batch codes
    ● Alerts sent via light + sound, and defective pack auto-rejected.
  2. IIoT Sensors + Predictive Maintenance
    ● Vibration, temperature, and motor current sensors installed on sealers,
    printers, and cartoners.
    ● The AI model identified anomalies in behavior patterns and predicted part
    fatigue or miscalibration.
    ● Maintenance alerts are issued 24–48 hours before probable failure.
  3. Centralized Packaging Analytics Dashboard
    ● Real-time KPIs on:
    OEE (Overall Equipment Effectiveness)
    Mean Time Between Failures (MTBF)
    Defect type heatmaps
    Operator shift-wise performance
    ● Line supervisors received tablet-based dashboards.
  4. Auto-Sync with QA & Compliance
    ● Every batch is linked with a digital log of packaging quality data.
    ● Audit-ready traceability for each unit’s print, seal, and pack record.
    ● Auto-export PDFs for US FDA, CDSCO inspections.

Implementation Timeline

Phase Duration Focus
Phase 1: Pilot Line 1.5 months Vision camera + rejection arm on Line 2
Phase 2: Data Collection 1 month Collected image and sensor data (50,000+ samples)
Phase 3: AI Training 1 month CV model trained on YOLOv5, LSTM for sensors
Phase 4: Plant-Wide Rollout 2 months Deployed on all lines
Phase 5: Optimization ongoing Weekly model retraining and improvement

Impact After 6 Months

Metric Before After Improvement
Avg Rejection Rate 3.4% 0.9% ↓ 73.5%
Line Downtime 6.5 hrs/day 1.2 hrs/day ↓ 81%
Labeling errors 18/month 1–2/month ↓ ~90%
QA Manual Checks 100% 25% ( Rest automated ) ↓ 75% load
FDA/WHO Observations 3 (last audit) 0 100% Clean
ROI Break-even – 9 months Based on rejection savings

How AI Helped

  1. Pattern Recognition at Speed:
    Vision models scanned and processed each pack in under 0.07 seconds with 98.3% accuracy.
  2. Root Cause Analysis:
    Shift-based data exposed that Line 4’s cartoner had roller wear issues between 2–4 AM (solved via timely replacement).
  3. Dynamic Maintenance Scheduling:
    AI forecasted seal bar burnout on Line 3, allowing pre-emptive part replacement and avoiding ₹8 lakh in losses.
  4. Audit-Ready Traceability:
    Real-time dashboards helped satisfy WHO-GMP and US FDA inspections with digital proof of packaging integrity

Workforce Transition

● QA officers received training on interpreting heatmaps and defect analytics.
● 7 operators trained as camera system technicians.
● No layoffs staff was reallocated to upstream activities and secondary packing QA

Lessons Learned

● Start Small, Scale Fast: Pilot 1 line, then replicate after proving ROI.
● Training Is Crucial: First month saw high false positives until operators were trained on lighting and camera calibration.
● Data Ownership: A data governance plan was put in place to ensure traceability and privacy compliance.
● AI Needs Feedback: The defect model improved after weekly retraining using live production data

Upcoming Enhancements

● Barcode scan automation with serial number validation
● Deep learning for OCR on faded/angled batch codes
● Expansion to secondary packaging QA
● Integration with MES and SAP for batch-level traceability

Conclusion

MediPack Formulations turned its packaging floor into a smart, AI-enabled production asset—without disrupting existing workflows or needing massive CapEx. By focusing AI and automation on one of the most error-prone, compliance-critical areas, they unlocked significant gains in speed, quality, and cost efficiency. With India’s pharmaceutical exports rising and regulatory scrutiny intensifying, such automation-driven transformation is not just smart it’s essential