1. Scenario opening - the problem in a sentence
You're a procurement manager chasing shorter delivery windows and consistent supply. I remember standing on the shop floor at 18:00 watching crates wait for night staff - we needed predictable throughput without the variability of shift changeovers. We built a 24-hour unmanned line to remove that bottleneck.
2. Measured case study
Pilot setup: mid-volume metal components (consumer electronics brackets), single line retrofitted with automation cells, AGVs, inline vision, PLC + MES integration.
Baseline (before):
Average lead time: 10 days
Average daily throughput: 2,000 units/day
OEE: 68%
Defect rate: 1.8%
After 24-hour unmanned line (measured over 90 days):
Average lead time: 7 days (-30%).
Average daily throughput: 2,600 units/day (+30%).
OEE: 86%
Defect rate: 0.9%
Direct labour cost on cell: -22%
Inventory days reduced: 18%
Pilot capex payback: ~14 months (projected, including energy & maintenance)
Notes: figures above are from our factory pilot (90-day production window). Your mileage will vary by product complexity, cycle time and floor layout.
3. Why delivery time fell
Continuous production - 24/7 operation eliminates waiting for day shifts and reduces queueing.
Predictable cycle times - robots + fixed tooling cut variability.
Reduced manual handoffs - fewer errors and faster handling.
Integrated scheduling (MES) - dynamic order routing and prioritized runs cut lead time on urgent SKUs.
Faster replenishment - automated kanban with AGV delivered parts just in time.
4. Step-by-step implementation
Phase 0 - Assess
Map current lead time and takt time.
Identify top 3 bottleneck stations and 3 SKUs with highest delivery impact.
Phase 1 - Design
Define cell boundaries, AGV paths, safety zones.
Specify control architecture: PLC + OPC-UA to MES.
Phase 2 - Pilot build
Retrofit 1 cell: robot + fixture + vision.
Add inline measurement (non-contact) and reject bin.
Implement remote monitoring dashboard.
Phase 3 - Integration
MES: real-time scheduling, traceability (lot & serial).
Data lake: capture cycle times, downtime, reject causes.
Phase 4 - Scale
Harden SOPs, preventive maintenance schedules, and spare parts kit.
Train an on-call technician team for night support (not on-floor staff).
Checklist (minimum viable):
Robot + tooling (1 cell)
AGV or conveyor handoff automated
Inline vision for first-article inspection
MES with alerts for exceptions
Remote ops dashboard + alarm routing
5. Technical architecture
Hardware: Collaborative robot / articulated robot, vision camera, PLC, inline gages, AGV.
Software: MES (order orchestration), SCADA, PLC ladder, OPC-UA bridge, cloud analytics for trend detection.
Networking: Industrial Ethernet, VLAN for OT, VPN for remote access.
Quality: Vision + statistical process control (SPC) with automatic hold on out-of-spec parts.