Excel Demand & Inventory Forecaster 📦

Turn sales history into the two numbers every stock-keeper actually needs: safety stock and the reorder point — per SKU, for your lead time and target service level.

Built for the operational question templates never answer: "how low do I let this SKU get before I reorder — without stocking out or over-ordering?"

📖 New here? Read the full step-by-step guide: Reorder Point & Safety Stock in Excel on ExcelGuru.io.

The formulas (industry-standard)

safety stock  = z × demand_std × √(lead_time)
reorder point = avg_demand × lead_time + safety stock

z is the service-level factor from the normal distribution (95% → 1.645), computed here with a proper inverse-normal function so any service level works — not just the handful of hard-coded values most templates offer.

Installation

git clone https://github.com/Synth88Labs/excel-inventory-forecast.git
cd excel-inventory-forecast
pip install -r requirements.txt

Requires Python 3.9+.

Usage

python inventory.py <history.csv> --lead-time DAYS [--service-level 0.95] [-o reorder.csv]

Input is demand history — one row per period per SKU:

sku,day,units
A,1,20
A,2,18
B,1,5

Quick start (try it on the included sample — 2 SKUs, 14 days each)

python inventory.py sample_data/demand_history.csv --lead-time 7 --service-level 0.95

Example output:

Demand & Inventory Forecast
  SKUs: 2   Lead time: 7   Service level: 95% (z=1.645)
    A: avg 20.x/period  ->  safety stock ~14, reorder at ~15x
    B: avg 5.x/period   ->  safety stock ~7,  reorder at ~4x

Options

Option	Description
--lead-time DAYS	Required. Lead time, in the same period unit as your rows
--service-level	Target service level 0–1. Default: 0.95
--sku-col / --units-col	Column names (defaults: sku, units)
-o, --output	Output path (.csv or .xlsx)

Test results

See TEST_RESULTS.md, or run them yourself:

pip install pytest
python -m pytest

📚 Learn More — Free Excel Tutorials

Practical Excel, inventory & forecasting guides at ExcelGuru.io.

License

MIT — see LICENSE.