Recovered this month
$0
Investment payback
— days
to full payback
30-day value accumulation
OEE Gap Loss
$0
—
Downtime Cost
$0
—
Action Plans ROI
$0
—
Net Financial Impact
$0
—
AI Recommendations
Top 3 actions
Key Performance Indicators
May 17, 2026 · Morning Shift
⚙️
OEE — Line A
0%
⚙️
OEE — Line B
0%
🏭
Throughput — A
0u/hr
🏭
Throughput — B
0u/hr
⏱️
Downtime — A
0%
✅
First Pass Yield
0%
♻️
Scrap Rate — B
0%
🚨
Worst OEE — A
0%
🏭 Throughput Comparison
units/hr⏱️ Downtime by Shift
%📉 OEE Loss Waterfall — Line A May 16
Root Cause Analysis🔴 Availability Loss: 24% — Downtime 14%
🟡 Performance Loss: 15% — Speed reduction
🔵 Quality Loss: 3% — Scrap 2.5%
📋 Production Lines — Full Comparison
May 15–17, 2026| KPI | Line A · May 17 | Line A · May 16 | Line A · May 15 | Line B · May 16 | Target |
|---|---|---|---|---|---|
| ⚙️ OEE % | 88.35% ✅ | 62.7% ❌ | 73.15% ⚠️ | 86.11% ✅ | ≥ 85% |
| 🏭 Throughput | 105 u/hr ✅ | 85 ❌ | 95 ⚠️ | 110 ✅ | ≥ 100 |
| ⏱️ Downtime | 3% ✅ | 14% ❌ | 8% ❌ | 2% ✅ | ≤ 5% |
| ✅ FPY | 97% ✅ | — | 92% ⚠️ | 92% ⚠️ | ≥ 95% |
| ♻️ Scrap Rate | — | 2.5% ❌ | — | 1.1% ✅ | ≤ 2% |
| 🔹 Availability | 93% ✅ | 76% ❌ | 85% ✅ | 92% ✅ | ≥ 90% |
| 🔹 Performance | 96% ✅ | 85% ✅ | 90% ✅ | 95% ✅ | ≥ 85% |
| 🔹 Quality | 99% ✅ | 97% ✅ | 95.6% ✅ | 98.5% ✅ | ≥ 95% |
🔮 What-If Simulator
Adjust OEE to see financial impact
⚙️ OEE
0%→0%
🏭 Throughput
0→0
✅ FPY
0%→0%
⏱️ Downtime
0%→0%
📈 Revenue Gain
$0
💰 Net Impact
$0
Analytics — Deep Dive
📊 OEE Radar
🍩 KPI Categories Distribution
📈 Full OEE History — All Shifts
May 15–17🎯 Target Achievement Rings
📏 KPI Measurements — Live Data
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KPI Master — 25 Indicators
AI-Powered Alerts
1 Critical · 1 Warning · 2 InsightsCRITICAL — Line A OEE Collapsed (May 16 Morning)
OEE = 62.7% vs target ≥85% (gap: -22.3%). Downtime = 14% (3× limit). Throughput = 85 u/hr. Immediate investigation required.
IMMEDIATE ACTION
WARNING — Line A Morning Instability (May 15)
OEE = 73.15%, Downtime = 8%, FPY = 92%. Morning shift shows recurring quality gap.
MONITOR
EXCELLENCE — Line A Peak Performance (May 17 Morning)
OEE 88.35% · Throughput 105 u/hr · Downtime 3% · FPY 97%. Document all practices as SOPs.
BENCHMARK
INSIGHT — Line B Consistently Outperforms Line A
OEE 86.11% · Throughput 110 u/hr (week best) · Downtime 2% · Scrap 1.1%. Replicate Line B protocols to Line A.
REPLICATE
Action Plans
AI Suggestion: Create 3 More Action Plans
(1) May 16 downtime root cause · (2) Line A morning FPY improvement · (3) Line A Scrap Rate reduction
AI SUGGESTED
Weekly Report — May 15–17, 2026
⚙️
Avg OEE — Line A
74.7%
⚙️
Avg OEE — Line B
86.1%
⏱️
Avg Downtime — A
8.3%
🏭
Best Throughput
110u/hr
📊 Weekly OEE — Line A vs Line B
OEE Heatmap — May 2026
🗓️ Line A — Daily OEE Performance
Low
High
📌 Data available for May 15–17 only. Remaining days shown as no-data (grey).
Trends — KPI Over Time
Aggregate Delta Improvement
—
from Progress Tracker
Realized ROI
$0
from completed projects
Success Velocity
0
projects reached Verified
📊 Impact Step-Up — Baseline vs Post-Intervention
per project🔧 CI Pipeline — Project Phase Gates
0 projects📈 OEE % — Shift by Shift
Target ≥85%
🏭 Throughput — Units / Hour
Target ≥100 u/hr
✅ First Pass Yield %
Target ≥95%
⏱️ Downtime % — Line A vs Line B
Limit ≤5%
Daily Operations Report
📅 May 17, 2026
Showing all shifts for selected date
🕐 Shift-by-Shift Performance
| Shift | Line | OEE % | Throughput | Downtime | FPY % | Avail. | Perf. | Quality | Status |
|---|
⚙️ OEE by Shift
May 17📊 KPIs Snapshot
🤖
Daily AI Analysis
May 17, 2026
Select a date to see AI analysis.
Monthly Analysis — May 2026
⚙️
Best OEE (Month)
88.35%
⚙️
Worst OEE (Month)
62.7%
📊
Avg OEE (All Lines)
79.4%
⏱️
Avg Downtime
5.4%
📈 OEE Trend — Full Month
May 2026
🏭 Throughput Trend
units/hr
📋 Complete Monthly Log — All Shifts
| Date | Line | Shift | OEE % | Throughput | Downtime | FPY % | Availability | Performance | Quality |
|---|
📊 Monthly KPI Summary — Line A vs Line B
🤖
Monthly AI Executive Summary
May 2026 · Partial data (May 15–17)
Performance Overview: May 2026 shows mixed results. Line B is the star performer with a stable OEE of 86.11% and the highest throughput of 110 u/hr recorded this month. Line A has high variance — ranging from a critical 62.7% to an excellent 88.35%, indicating process instability rather than capability limitation.
Key Risk: Average Downtime for Line A = 8.3% (target ≤5%). This single metric is responsible for most OEE losses. Root cause investigation is the highest priority action this month.
Recommendation: Implement the May 17 morning shift protocol as the standard SOP for all Line A shifts. Expected improvement: +10–15% OEE.
Key Risk: Average Downtime for Line A = 8.3% (target ≤5%). This single metric is responsible for most OEE losses. Root cause investigation is the highest priority action this month.
Recommendation: Implement the May 17 morning shift protocol as the standard SOP for all Line A shifts. Expected improvement: +10–15% OEE.
📈 Monthly OEE Target
79.4% vs 85% target
🔴 Biggest Gap
Downtime Line A avg 8.3%
🏆 Best Performance
Line B Stable ✅
💡 Action Required
3 Plans Pending creation
📈 Progress Tracker — Before / After Intervention
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🤖 Operational Prompts Library
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