Case Study Demand Forecasting
Know how much you are going to sell before you produce, buy or store
Demand forecasting with AI avoids over-manufacturing, running out of stock or making business decisions too late.
Industry
Retail and distribution company
98%
accuracy in demand forecast
+35%
Inventory optimization
+30%
Demand forecast accuracy
↓ stock-outs
Less sales lost due to product shortage
↑ business planning
Faster production and purchasing decisions
BEFORE
Estimates were made using spreadsheets and manual estimates
Overstocking of some products and breakage of others
Purchasing and production reacted too late to demand
NOW
Future demand is predicted by product, zone and period with an effectiveness of 98%.
Inventory and resources are adjusted before demand peaks
Production and commercial planning with actual and updated forecasts
THE PROBLEM
Purchased, produced and sold without knowledge of demand
- Unreliable manual forecasts that are difficult to update.
- Overstocking of some products and unavailability of others.
- Purchasing and production reacted late to changes in demand.
- Business decisions based on historical, not forecasted demand.
THE SOLUTION
Development of a predictive algorithm with a probability of error of 2%.
- Predictive model trained with sales, stock, orders, campaigns and seasonality.
- Forecast by product, zone, channel and period for detailed planning.
- Alerts on demand peaks, risk of breakage and excess inventory.
- Operational dashboard to decide what to buy, produce, move or promote.
THE IMPACT
Less tied-up stock, less breakage and better business decisions
The company stopped reacting late to changes in demand and started planning well in advance. Teams can decide what to buy, produce and sell much more quickly and accurately.
Apply predictive maintenance
in your organization.
Detect incidents before they affect production.
From estimating sales to anticipating decisions
The demand was already leaving signals in the data: sales, orders, campaigns, seasonality and inventory. The difference was turning those signals into actionable forecasts.
More accuracy, less tied-up stock and much more efficient business planning.
Artificial Intelligence
How does demand forecasting help retail and distribution companies?
Demand forecasting helps companies anticipate how much they are likely to sell by product, area, channel and period. This allows teams to plan purchasing, production, promotions and stock movements more accurately, instead of relying only on historical data or manual estimates.
What data is used for demand forecasting in retail?
Retail demand forecasting can use sales, inventory, orders, marketing campaigns, seasonality, geographic areas, sales channels and product history. The key is to turn these data signals into actionable forecasts for purchasing, production and commercial teams.
How does demand forecasting reduce stockouts?
Demand forecasting helps identify peaks in demand and stockout risks before they happen. By anticipating which products may require more stock, companies can adjust purchasing, production and distribution in advance, reducing lost sales caused by lack of availability.
What are the inventory benefits of predictive demand forecasting?
Predictive demand forecasting helps align inventory with expected demand. This reduces both excess stock and product shortages, improves resource planning and prevents companies from holding inventory that does not match real commercial needs.
Why do manual forecasts limit commercial planning?
Manual forecasts are often slower, harder to update and less accurate when demand changes. In retail and distribution, this can lead to late decisions about purchasing, production or promotions. Automated demand forecasting gives teams more up-to-date information and helps them plan ahead with greater confidence.