Step 1
Select Files
0 / 5 selected
Step 2
Upload to S3
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Step 3
Parse & Validate
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Step 4
Monte Carlo
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Step 5
Display Results
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📦
parts.csv
PART_NO, PROD_RATE, PALLET_FACTOR, TRIG_LEVEL, MAX_STOCK, DERIV, PART_DESC
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📊
Stock.csv
PART_NO, LOCATION, LOT_BATCH, QTY_ONHAND
Waiting
🕐
OxfordShift.csv
Day, Shift, Prod Time, Shift Start Time, Shift End Time
Waiting
🏗
SwindonShift.csv
Day, Shift, Prod Time, Shift Start Time, Shift End Time
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🔀
RK1.csv
ORDRNUM.SBAUREIHENR — F65 / F66 / F67
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Files selected: 0 / 5
Step 3 — Parsed & Validated by Lambda (pandas)
Parts
—
valid parts
Stock Rows
—
total rows
Parts With Stock
—
matched
Oxford Shifts/Wk
—
active shifts
Oxford Hrs/Shift
—
avg prod hrs
Swindon Shifts/Wk
—
active shifts
Swindon Hrs/Shift
—
avg prod hrs
Sequence Orders
—
F65/F66/F67
F65 Mix
—
%
F66 Mix
—
%
F67 Mix
—
%
JIT Lead Time
—
calculated hrs
🏭 Oxford Plant
Prod hrs / shift—
Shifts / week—
Weekly prod hrs—
Rest allowance10.5%
🏗 Swindon Plant
Prod hrs / shift—
Shifts / week—
Weekly prod hrs—
Rest allowance5.95%
Effective JIT Lead Time
—
Oxford–Swindon shift overlap + 45-min transit.
Passed into every Monte Carlo simulation run.
Passed into every Monte Carlo simulation run.
Upload all 5 files to enable analysis
Total Saving / Shift
—
all parts combined
Avg Portfolio Risk
—
avg breach % per shift
Weekly Saving
—
all parts combined
Annual Saving
—
all parts combined
Low Risk Parts
—
< 5% breach probability
Medium Risk Parts
—
5–20% breach probability
High Risk Parts
—
> 20% breach probability
Parts Risk Summary — Per Shift
Click Detail for full breakdown
| Part No | Description | Deriv | Stock | Risk | Breach % / Shift | Zero Stock % | % Time < Safety | Saving / Shift | |
|---|---|---|---|---|---|---|---|---|---|
| Select all 5 files and click Upload & Parse Files | |||||||||
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