AI Applications · Retail and distribution
Stock Optimization with a Demand Forecasting Model
For a multi-warehouse distributor we developed a model that produces weekly demand forecasts from sales history and suggests orders.
The problem we faced
Stock decisions were based on experience and Excel spreadsheets. Some products ran out of stock, while excess stock in others tied up cash.
SolutionThe approach we developed
We built a forecasting model that combines historical sales, seasonality, campaign and price data. The model predicts weekly demand by product and warehouse, compares it with current stock in the ERP and generates order suggestions. A management dashboard makes it easy to monitor forecast accuracy and deviations.
Results
Measured gains
Process
How did the project progress?
- 01
Data exploration
Sales and stock data were cleaned and merged.
- 02
Model development
Different methods were compared and the best model selected.
- 03
Integration
Forecasts were connected to ERP and the dashboard.
- 04
Monitoring
Accuracy tracking and periodic retraining were planned.
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Snapshots from the project
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About this project
What problem did this project solve?
Stock decisions were based on experience and Excel spreadsheets. Some products ran out of stock, while excess stock in others tied up cash. For a multi-warehouse distributor we developed a model that produces weekly demand forecasts from sales history and suggests orders.
Which technologies and methods were used in the project?
The project used technologies and methods such as Python, Time series models, ERP integration, Data warehouse, Web dashboard. The technology choice was based on the project's requirements and the existing systems.
How long did the project take and what were the results?
The project took about 3 months. Key results: Number of stockouts: −%27; Value of idle stock: −%18; Forecast accuracy (example): %84. Results are measured by comparing indicators before and after the project.
How much data is needed to start an AI project?
The amount of data needed depends on the problem. In the first meeting we review the available data and clarify together the project's feasibility and which data needs to be collected.
Can the AI solution connect to our existing systems?
Yes. We integrate the models we develop into your ERP, web and mobile applications via API.
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