0.5×← Perlahan · Cepat →
💬Tekan Play Demo untuk lihat bagaimana Production Log Agent memantau pengeluaran kilang.
Demo Complete — All Stages Generated
01 WhatsApp Command
02 Requirement Analysis
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03 Python Script Generation
1
🏭
Ambil data productionFetch line output & downtime logs
2
📊
Kira KPICalculate yield, OEE, downtime %
3
⚠️
Kesan anomaliFlag lines below threshold
4
📋
Laporan harianGenerate production log report
import pandas as pd
from datetime import datetime
def calc_oee(run_time, total_time, good_count, total_count):
availability = run_time / total_time
performance = total_count / (run_time * ideal_cycle)
quality = good_count / total_count
return availability * performance * quality
def flag_anomalies(lines, threshold=0.75):
flagged = []
for line in lines:
if line['oee'] < threshold:
flagged.append(line['name'])
return flagged
# Log daily production report
04 Block Diagram
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05 System Schematic
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⚠️ Confirm production data with floor supervisor before publishing daily report.
06 Simulasi Production Monitoring
Mula Produksi
07 Production Report
Terminal Log
[SISTEM] Manufacturing Production Log Agent sedia
System Architecture
🤖 Agent Layer
Output tracker
OEE calculator
Anomaly detector
🏭 Manufacturing Layer
Production DB
Line sensors
Downtime logs
Reports
Safety Notes
- Confirm production data with floor supervisor before publishing daily report.
- Configure role-based access for production KPI data.
- Audit all production log changes for compliance.
- Browser demo uses simulated data only.