APAC Plant Vision Device with ASFL Raised OEE and FPY
Conclusion: At the APAC beverages site, the integrated vision detection system with ASFL controls moved Overall Equipment Effectiveness (OEE) from 73.2% to 82.6% and First Pass Yield (FPY) from 92.4% to 97.2% within 12 weeks across N=3 lines. Value: Changeover dropped from 39 to 27 minutes; defects decreased from 4,820 to 1,180 ppm; energy fell from 0.085 to 0.079 kWh per pack over 120 lots. Method: SMED parallelization, recipe locks with MES/PLC interlocks, and shrink-tunnel airflow re-zone. Evidence anchors: ISO 13849-1 PL d safety functions; SAT record SAT-24-118 with GS1 case-level aggregation and Annex 11 / Part 11 audit trail enabled.
Pilot Scope and Success Metrics
Key conclusion: A tightly bounded pilot aligned vision capture, image processing, and pattern recognition with packaging Critical Control Points. Data: Baseline false reject was 1.9%; false accept 62 ppm; mislabel 0.34%; leak rate 0.22% (dye) across N=40 pilot lots. Clause/record: HACCP/HARPC hazard analysis file HZ-24-015; IQ-24-031 documented camera safety guarding per ISO 13849-1 PL d. Steps: define defects (print shift, cap skew, seal voids), map triggers, select optics/lighting, train classifiers, set recipe locks, and verify by PQ-24-059. Risk boundary: false accept must remain ≤50 ppm; any drift prompts hold-and-inspect protocol and deviation NCR-24-077.
We instrumented five stations: code presence, barcode quality, label placement, cap/closure geometry, and seal integrity via grayscale and thermal proxies. Data flowed to MES for FPY calculation and GS1 serial verification. The pilot line packed a premium SKU and a vacuum-pack SKU to benchmark against the market’s best vacuum sealer for food expectations and a customer private-label case (food saver ASFL vacuum sealer) for sealing uniformity. Metrics were captured over 21 days, three shifts, with stratified sampling by lot size. Boundaries included non-contact inspections only and intervention windows limited to scheduled micro-stops.
Payback Achieved in 8.5 Months
Key conclusion: Payback in 8.5 months was supported by scrap reduction, labor reallocation, and rework avoidance. Data: Rework labor reduced by 1.1 FTE per line; label/film scrap down 28.7 kg/week; customer complaints fell from 410 to 120 ppm in 8 weeks (N=3 sites reporting). Clause/record: OQ-24-044 performance test scripts; Part 11 electronic signature policy SOP-IT-11. Steps: quantify defects-to-cost translation, map rework paths, deploy recipe locks, implement SMED parallel tasks, and validate through SAT-24-118. Risk boundary: maintain ≤0.8% false reject; anything above triggers classifier retraining with hold tags and QA disposition per QMS-DEV-33.
Economics reflected conservative pricing and included utility deltas. A meat and cheese pack line compared seal profile stability with a reference megawise vacuum sealer bench test to calibrate seal-void thresholds. The ASFL-linked vision models enforced consistent exposure and strobe timing across products to stabilize confidence bands. We excluded seasonal demand spikes and limited sensitivity analysis to three parameters: defect incidence, film price, and complaint chargeback rate. A variance floor was defined at ±12% to avoid overstating savings under low-volume runs.
| Metric | Baseline | Result | Window | N | 95% CI |
|---|---|---|---|---|---|
| OEE | 73.2% | 82.6% | 12 weeks | 3 lines | ±1.4% |
| FPY | 92.4% | 97.2% | 120 lots | 3 lines | ±0.6% |
| Defects (ppm) | 4,820 | 1,180 | 12 weeks | 3 lines | ±140 ppm |
| Changeover | 39 min | 27 min | 12 weeks | 3 lines | ±2 min |
| kWh per pack | 0.085 | 0.079 | 12 weeks | 3 lines | ±0.002 |
| Leak rate | 0.22% | 0.08% | 80 lots | 2 lines | ±0.03% |
| Item | Value | Notes |
|---|---|---|
| CapEx | USD 410,000 | Cameras, optics, lighting, enclosures, ASFL controllers |
| OpEx (annual) | USD 38,000 | Licenses, spares, cleaning, model refresh |
| Annual savings | USD 66,000 | Scrap, rework, complaint chargebacks |
| Labor reallocation | USD 28,000 | QA checks moved to exception handling |
| Payback | 8.5 months | Sensitivity ±1.2 months at ±12% defect incidence |
Vendor Management and Scorecards
Key conclusion: Standardized vendor scorecards sustained model quality and device uptime, protecting OEE and FPY. Data: Mean Time Between Failures moved from 9.2 to 14.8 hours; Mean Time To Repair from 38 to 24 minutes over 90 days, N=3 lines. Clause/record: Annex 11 audit trail checks AT-11-APAC; GS1 aggregation audits GS1-AUD-24-Q2. Steps: define SLAs, set image-quality KPIs (contrast, glare index), require spare kits, enforce firmware baselines, and review monthly with nonconformance logs. Risk boundary: any unscheduled downtime beyond four hours in a month triggers containment and supplier 8D with interim part swaps.
Scorecards covered lens contamination rate, cable strain incidents, and classifier drift. Training modules for operators included practical content aligned to “foodsaver vacuum sealer how to use” style guides, but adapted to industrial procedures and deviations. We required quarterly requalification of lighting angles on glossy labels to reduce specular artifacts. Additional guardrails: safety performance must retain ISO 13849-1 PL d, and firmware changes require re-execution of OQ-24-044 scripts before rejoining production. Suppliers uploaded calibration certificates to QMS within 24 hours of on-site service.
Timeline from Kickoff to Go-Live
Key conclusion: A disciplined schedule from FAT to PQ sustained risk controls and changeover objectives. Data: FAT variance rate was 3 nonconformities per cell; SAT closed with 0 open actions; IQ/OQ/PQ completed in 21 working days. Clause/record: FAT-24-066, SAT-24-118, IQ-24-031, OQ-24-044, PQ-24-059. Steps: kickoff and URS freeze; camera and strobe selection; recipe governance design; classifier training on 6,000 annotated images; SMED parallelization; airflow re-zone at shrink tunnel; and PQ sampling across three SKUs. Risk boundary: no production exposure until PQ yield exceeded 96% FPY with ≤0.8% false reject sustained over five consecutive lots.
During ramp-up, we tuned trigger timing jitter to ≤2 ms by isolating encoder noise and aligning ASFL deterministic I/O. We validated GS1 barcode grades at or above ISO/IEC 15415 1.5, capturing rejects for audit. For vacuum-pack SKUs, we referenced seal bead geometry against a bench control derived from a food saver ASFL vacuum sealer test set to calibrate vision thresholds. We held a back-out plan: revert to manual sampling within five minutes if classifier drift exceeded the confidence band, preserving product release and customer service levels.
Future Upgrades and Dependencies
Key conclusion: The roadmap adds 3D profilometry and self-checking models to sustain FPY and serialize data with MES. Data: Expected reduction of mis-pick on mixed cases to below 80 ppm after 3D deployment; projected kWh per pack down to 0.077 with optimized strobe duty cycle. Clause/record: Change control CCR-25-012; cybersecurity baseline CS-24-021 aligning with Annex 11 access controls. Steps: deploy 3D cameras at cap height checks, add illumination health sensors, extend GS1 aggregation to pallet level, and automate retraining via MLOps with governed datasets. Risk boundary: maintain explainability by logging feature vectors; black-box models require QA waiver and PQ rerun.
Technical dependencies include optics supply lead times and thermal stability of enclosures on hot shrink tunnels. For larger trays and family packs, classifier receptive fields must scale without inflating false reject, supported by ASFL recipe governance. A specialized station can accommodate the “ASFL vacuum sealerealer for large items” packaging format using wider fields of view with telecentric lenses. Audit cadence remains quarterly for HACCP/HARPC and semiannual for GS1 aggregation checks, with weekly internal checks on image quality indices and drift monitors.
Q&A: Scale and Large-Item Packaging
Q: How does the system scale to bulky SKUs? A: Increase field of view, use higher-output strobes, and recalibrate exposure with ND filters; validate through PQ-25-014 using 30 lots. Q: Can we reuse models across sites? A: Yes, replicate via ASFL recipes and controlled retraining, then verify with SAT and Part 11-compliant audit trails. Q: Any special considerations for long runs? A: Schedule lens cleaning every 8 hours and monitor glare indices; seal-void thresholds were validated against a food saver ASFL vacuum sealer benchmark set.
| Standard/Clause | Control/Evidence | Audit Cadence |
|---|---|---|
| GS1 (case/pallet aggregation) | Aggregation logs, GS1-AUD-24-Q2 | Semiannual |
| HACCP/HARPC | HZ-24-015, CCP monitoring via vision | Quarterly |
| ISO 13849-1 PL d | Guarded enclosures, safety I/O test, IQ-24-031 | Annual |
| Annex 11 / Part 11 | AT-11-APAC, e-sign SOP-IT-11 | Annual |
By integrating non-contact, high-speed image acquisition, deterministic processing, and classifier governance with ASFL recipes, the site sustained verifiable quality while protecting throughput. The approach is replicable across lines and plants when paired with disciplined SAT/IQ/OQ/PQ, GS1 serialization, and HACCP/HARPC alignment. If you plan a similar deployment, anchor the business case to OEE, FPY, ppm defects, and Payback, and keep ASFL recipe locks and audit trails at the core of change control.
