Real-time OEE, downtime, and changeover monitoring — fed by shop-floor sensors, not manual spreadsheets. See how a Loss Pareto finding becomes a running DMAIC project in one click.
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Facility:Apex Automotive Stamping LLCIATF 16949
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PlantPilot AI · Enterprise
Plant Overview
Apex Automotive Stamping LLC · 9 machines across 3 lines · fed by real sensor hardware + Tier 2 bridge, not manual entry
Plant OEE
79%
↓ vs 85% world class
Machines Running
7 / 9
2 stopped
Downtime Today
6.8h
unplanned
Good Parts
4,812
today
Reject Parts
149
3.0% scrap
Changeovers
6
avg 31 min
Line 2 · Progressive Die — Live Status
Press 4
PDIE-04
Running
Press 5
PDIE-05
Down — Failure
Press 6
PDIE-06
Running
Weld Cell 1
WC-01
Running
PlantPilot AI Insights
🚨
Press 5 down 41 min — die clearance, station 4
Sensor-reported stop, same failure mode as the prior Press 4 incident. Loss Pareto shows Machine Failure leading Line 2's losses this week.
⚠️
Line 2 OEE at 74% against an 82% target
Machine Failure and Changeover account for 64% of this week's unplanned downtime, cumulative.
💡
Weld Cell 1 changeover trending down: 31 → 24 min
SMED pilot from the Kaizen event is holding. On pace to hit the 20-minute target by month end.
Floor View
Live status grid — every machine, updated in real time via Supabase Realtime, no page refresh
Realtime channel connected — machine_events
Line 1 · Stamping
Press 1
Running
86%
Line 1 · Stamping
Press 2
Running
81%
Line 1 · Stamping
Press 3
Changeover
—
Line 2 · Progressive Die
Press 4
Running
78%
Line 2 · Progressive Die
Press 5
Running
80%
Line 2 · Progressive Die
Press 6
Running
83%
Line 3 · Weld/Assembly
Weld Cell 1
Running
88%
Line 3 · Weld/Assembly
Assembly A
Material Wait
62%
Loss Pareto — Line 2
Unplanned downtime by category, last 7 days · cumulative % ranks where to focus first
Machine Failurecum. 41% · 41% · 9.8h
Changeovercum. 64% · 23% · 5.5h
Materialcum. 78% · 14% · 3.4h
Qualitycum. 89% · 11% · 2.6h
Operatorcum. 96% · 7% · 1.7h
Plannedcum. 100% · 4% · 1.0h
Machine Failure is 41% of Line 2's unplanned downtime
Press 4 and Press 5 — same root cause pattern: die clearance drift at station 4.
DMAIC Project Manager
Started directly from the Loss Pareto finding — one click, not a blank form
DMAIC BRIDGE · AUTO-PREFILLED FROM LOSS PARETO
Created a new project and prefilled it from what Track already knows: problem statement from the Pareto's leading category, department and machine from the source data, and a business case estimate from the downtime-dollar figures Track has been tracking all week. Nothing re-typed.
Reduce Line 2 Progressive Die Machine Failure Downtime
Auto-generated at shift end — plant-local time, not a fixed UTC clock — and queued straight to Meeting Intelligence
AI RECOMMENDATION · MORNING SHIFT · TODAY
Plant OEE 79%, 1,612 good / 48 reject parts, 2.1h unplanned downtime. Top loss: Machine Failure on Press 5 — 41 minutes, die clearance at station 4. Same failure signature as the open DMAIC project; recommend expediting the die insert replacement already scheduled for this project rather than opening a separate maintenance ticket.
Morning
79%
1,612 good · 48 reject · 2.1h downtime
Complete
Afternoon
83%
1,704 good · 39 reject · 1.4h downtime
Complete
Night
—
In progress · generates automatically at 06:00 plant-local
Pending
Plant-Wide OEE, LiveAvailability × Performance × Quality, computed continuously from real machine events — not a manual end-of-shift entry.
AI Insights, Floor-LevelThe same CI Agent that reads DMAIC and 5S data also reads Track's live machine events — it connects a sensor-reported stop to the DMAIC project already open for it.
Realtime, Not PollingA Supabase Realtime subscription pushes state changes to this screen the instant a machine event is written — watch Press 5.
Cumulative ParetoRanks downtime causes by dollar/time impact, not alphabetically — Machine Failure alone is 41% of this line's losses.
DMAIC BridgeOne click from a Pareto finding to a structured DMAIC project — problem statement, department, and machine prefilled from the data, not a blank form.
Plant-Local Shift TimingShift boundaries follow the plant's own configured timezone, so this generates at the plant's actual shift change — not a fixed UTC time that happens to work for some facilities and not others.