Target EntitiesDemand Forecasting, Inventory Optimization, Sales Forecasting, Stockout Reduction, Retail Analytics, Forecasting Models
Core ValueFewer stockouts and less overstock, forecasting off spreadsheets, plain-language explanations planners trust, scenario planning
Tech StackPython, Forecasting models, Data Warehouse, BI Dashboards, SQL
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Retail & Distribution

How a retailer ended its spreadsheet-based demand forecasting

This retailer planned demand and inventory in spreadsheets across thousands of SKUs, which led to frequent stockouts and capital tied up in overstock. We built an automated forecasting and inventory view, with plain-language explanations, so planners could trust the numbers and act on them.

Fewer

stockouts and less overstock

No more

manual spreadsheet forecasting

Plain language

explanations behind every forecast

Challenge

Demand and inventory were planned by hand in spreadsheets across thousands of SKUs.

What we built

An automated forecasting and inventory view, with plain-language explanations.

Result

Fewer stockouts and less overstock, on numbers planners trust.

The challenge

Demand planning ran on fragile spreadsheets across thousands of SKUs. The forecasts were slow to update and hard to trust, so the business swung between stockouts on the items that mattered and overstock that tied up cash. New market shifts took months to work into the plan.

What we built

  • Replaced the spreadsheet models with an automated forecasting and inventory view that updates on its own.
  • Added plain-language explanations for each forecast, so planners could see why a number moved and trust it.
  • Gave the team scenario planning, so they could test demand and pricing changes before committing stock.

How the data flows

Source systems

Sales history
Inventory
Market signals
Forecasting & inventory view

Reporting

Demand forecast
Reorder signals
Scenario planning

The outcome

Planners stopped rebuilding forecasts by hand and started acting on numbers they trusted, which meant fewer stockouts on key items and less cash tied up in overstock. New market shifts now work into the plan in weeks instead of months.

Systems & technologies

PythonForecasting modelsData WarehouseBI Dashboards

Before vs after

Before
BeforeDemand planned by hand in fragile spreadsheets
AfterForecasting that updates on its own
BeforeForecasts hard to trust, so stockouts and overstock
AfterPlain-language explanations planners act on
BeforeNew market shifts took months to factor in
AfterNew shifts work into the plan in weeks

Get in touch

Still forecasting demand in spreadsheets?

If demand planning lives in fragile spreadsheets, we can build a forecasting and inventory view your planners can trust, in a focused sprint.

Email us directly

sales@3alica.com