Predicted Defect Risk
Probability this heat results in any defect class
0%
Low risk
Most likely defect
—
prediction loading…
Predicted disposition
OK
Yield: —% ·
Severity: —
Business impact (expected per casting)
How each number is computed
- Scrap cost:
(P(Scrap) + 0.4 × P(Major_Rework)) × ₹6,500
i.e. probability this casting ends up scrapped × scrap-cost-per-casting. The 0.4× factor accounts for ~40% of Major Rework cases that get scrapped instead of reworked when the cost-benefit doesn't justify it.
- Rework cost:
(P(Minor_Rework) + 0.6 × P(Major_Rework)) × ₹1,800
Minor + the remaining 60% of Major Rework that goes through the rework loop. Cost includes inspection, machining time, requalification.
- Delay cost:
expected_delay_min × ₹450/min
Expected delay = 8 min × P(Minor) + 18 min × P(Major) + 35 min × P(Scrap). Captures downstream line stoppage at machining/assembly.
- OEM complaint:
P(complaint_escape) × ₹85,000
P(complaint_escape) ≈ 5% × P(any defect) + 5% × (warranty_risk/10). Cost includes logistics, investigation, customer concession per incident.
- Warranty reserve:
(warranty_risk / 10) × 0.04 × ₹4,50,000
4% of complaints escalate to warranty claims. Each claim costs ₹4.5 L (part replacement, field service, brand exposure).
- Total expected loss: sum of all five rows above.
All assumptions live in data/cost_assumptions.json — editable per plant. See full breakdown at /assumptions.
(P(Scrap) + 0.4 × P(Major_Rework)) × ₹6,500i.e. probability this casting ends up scrapped × scrap-cost-per-casting. The 0.4× factor accounts for ~40% of Major Rework cases that get scrapped instead of reworked when the cost-benefit doesn't justify it.
(P(Minor_Rework) + 0.6 × P(Major_Rework)) × ₹1,800Minor + the remaining 60% of Major Rework that goes through the rework loop. Cost includes inspection, machining time, requalification.
expected_delay_min × ₹450/minExpected delay = 8 min × P(Minor) + 18 min × P(Major) + 35 min × P(Scrap). Captures downstream line stoppage at machining/assembly.
P(complaint_escape) × ₹85,000P(complaint_escape) ≈ 5% × P(any defect) + 5% × (warranty_risk/10). Cost includes logistics, investigation, customer concession per incident.
(warranty_risk / 10) × 0.04 × ₹4,50,0004% of complaints escalate to warranty claims. Each claim costs ₹4.5 L (part replacement, field service, brand exposure).
Scrap cost₹0
Rework cost₹0
Delay cost₹0
OEM complaint₹0
Warranty reserve₹0
Total expected loss₹0
Root cause (SHAP attribution)
For predicted defect: —
Adjust a slider to see attributions.
Recommended corrective actions
Predictions will surface targeted actions here.
Warranty risk
What this score predicts
The probability that this specific casting, once shipped, will trigger a warranty
claim in the field within 24 months — i.e., a customer-side failure that costs
₹4.5 L per claim in part replacement, field service, and brand exposure.
What drives the score
- Chemistry: Cr > 0.10% and P > 0.06% promote brittleness & hot cracking that show up months later. Mn/S violations leave free FeS at grain boundaries.
- Dimensional capability: bore diameter drift from the 89.500 mm target — even within spec — accelerates ring-pack wear and head-gasket failures.
- Cooling rate & superheat: off-optimum thermal history → residual stresses → field cold-cracks.
- Predicted severity: heats classified as Major_Rework or Scrap carry baseline warranty risk even if reworked.
How to read the scale
0–2 safe · 3–5 monitor (inspect twice) · 6–8 high — hold for QC review · 9–10 field failure likely — scrap rather than ship.
Model: LightGBM regressor trained on 5,000 heats with R² 0.73 against synthetic warranty outcomes.
0–10 scale · probability this casting becomes a field warranty claim
0 · safe5 · monitor10 · field-failure likely
— / 10
If your plant ran 14,000 castings at these conditions →
This setpoint extrapolated
—
per-casting × 14,000 / yr
AI-optimal extrapolated
≈ ₹2.5 Cr
~₹1,800 × 14,000 / yr
Gap closed by AI
—
improvement if all heats matched AI-optimal
Note: these numbers are worst-case extrapolations — they assume every heat for the year looked like this one. Your real annual loss is a mix of good and bad heats. The point is the per-casting delta: every heat you steer toward AI-optimal recovers ~₹(this − 1,800) of expected loss.