Case Study
Predictive Demand Forecasting Platform for Retail Operations
AI Systems & Applied Intelligence
Challenge
Spreadsheet-based planning led to frequent stockouts in high-demand locations, excess inventory in low-velocity locations, and limited confidence in weekly planning cycles.
System built
Algorys built an ML forecasting system that combines historical sales, seasonality, promotion signals, and regional behavior, then publishes forecasts directly into inventory planning workflows.
ResultInventory planning gained a repeatable forecasting workflow.
Overview
Industry: Retail
A regional retailer needed better demand forecasting across products, locations, promotions, and seasonal patterns.
Algorys designed a forecasting workflow that turns operational sales and inventory data into planning inputs.
Case Study Section
The Challenge
Inventory planning relied on spreadsheets, historical averages, and manual adjustments.
Key friction points included
manual forecast updates
limited visibility into demand variation
hard-to-explain planning assumptions
reactive inventory decisions
The Solution
Algorys built a forecasting layer that combines historical sales, product context, seasonality, and operational planning inputs.
Forecasts are delivered through dashboards and can be reviewed before teams commit to inventory decisions.
Implementation
The work included data integration, feature preparation, model setup, forecast review flows, and handover documentation.
Measured Results
Results
Inventory teams gained a repeatable forecasting workflow with clearer assumptions and a stronger basis for planning conversations.