⌘K
ScopePeriod

Water-tube Boiler BL-01

Asset 360 · Boiler · Mill A
BL-01RUNNINGCriticality ABoiler
Manufacturer
Boilerindo
Model
WTB-45
Location
Mill A · Utilities
Commissioned
2012-09-28
Age
14 years
Operating hours
86,201 h
Utilization
81%
Availability (90 d)
96.9%
MTBF / MTTR
3,195 h / 8.7 h
Replacement cost
Rp 19.36 bn
Sensors
pressure, temperature, vibration
Region
Sumatra
78/100
Health score

Predictive maintenance

Asset Failure Prediction v3.2 · gradient-boosted survival model on IoT, SCADA and CMMS history · refreshed 06:52

Confidence81%
Health score
78/100
Failure probability
13%7 days
Remaining useful life
184days
Anomaly score
0.230–1
Likely cause
No abnormal failure mode detected
Recommendation
Continue condition monitoring; next PM as scheduled
What the model sees
Confidence81%
All monitored signals are within normal bands. Failure probability 13% over 7 days; continue condition monitoring and scheduled PM.
Why this prediction — top drivers (pp of failure probability)
Baseline failure rate for class ≈ 12% · bars push probability up or down
Pressure variation
+3.8 pp
Age / operating hours
+3.5 pp
Temperature deviation
+2.3 pp
Vibration RMS trend
+1.5 pp
Recent preventive maintenance
−2.0 pp
◀ lowers predictionraises ▶

Sensor trends & failure forecast

60-day history (daily) · dashed = model forecast · anomaly began 14 Sep

Pressure · bar
Warning 3.2 · Alarm 3.4 bar
No degradation trend — forecast shown only when the model detects a trajectory toward the failure threshold (forecast available for vibration & temperature).

Maintenance Decision Intelligence

Weighs failure probability, criticality, downtime impact, repair vs replacement cost, spare availability and production schedule

Decision inputs
Failure probability (7 d)13%
CriticalityClass A · production-critical
Downtime impactRp 165 m / h
Planned repair costRp 581 m
Replacement costRp 19.36 bn
Spare availabilityTUBE-BL-51: in local store
Production schedulePeak crop · next shutdown Sun
Option suitability
Monitor77
Failure risk low; condition monitoring sufficient
Schedule maintenance49
Fits next planned shutdown; balances risk and production
Replace asset30
Health 78/100 and age 14 yrs do not justify capex
Replace component12
No single failing component identified
Repair now2
Stops the line during peak crop — restan and emergency premium
Scenario comparison
OPTION A
Repair immediately
Stop line now, replace component using emergency transfer
Failure risk2%
Expected costRp 997 m
Planned downtime4 h

Lowest risk, but interrupts peak-intake processing and creates restan.

OPTION B
Repair in low-production window
Replace at next planned shutdown
Failure risk5%
Expected costRp 660 m
Planned downtime4 h

Balances failure risk against production loss at the next planned shutdown.

Recommended
OPTION C
Continue monitoring
Run to next scheduled shutdown (14 days)
Failure risk13%
Expected costRp 405 m
Planned downtime0 h

Recommended — risk is low; keep condition monitoring and standard PM.

Recommendation: Monitor
Confidence80%
Continue condition monitoring; next PM as scheduled. Option C expected cost Rp 405 m vs Rp 405 m if left to run; residual risk 13%.
Estimated impact based on synthetic scenario assumptions. Recommendation requires validation by the Maintenance Manager.

Spare parts availability

Key parts for BL-01 across the warehouse network · home store WH-01

Cross-module: Inventory AI
TUBE-BL-51 · Boiler tube 51 mm (per length)
Lead time 35 d · Rp 4.1 m/length · 30-d demand 4.3
12 in home store
WH-01 · Mill A (home)
12 length · SS 3
WH-02 · Mill B
3 length · SS 4
FAN-ID-75 · FAN-ID-75
Lead time 14 d · Rp 0/pcs
0 in home store
Not stocked — procured on demand.
Open Spare Parts Intelligence

Maintenance records

CMMS history, open work orders, failure events and spares

No open work orders — asset on standard PM schedule.
Synthetic asset data and simulated model output. Maintenance recommendations are decision support requiring validation by qualified personnel.
V-TEKI — IT & Business ConsultingDeveloped by V-TEKI · IT & Business ConsultingPrime Agri AI Intelligence Platform — prototype. All operational records are synthetic; model outputs are simulated; financial impacts are scenario estimates; recommendations require human validation. No actual company confidential data is used.