Implementing an Automated Ordering System for a Producer’s Own Retail Chain
Client: Agricultural group with a chain of retail stores.
Project Goal: To enhance service levels and optimize inventory through more accurate sales forecasting.
Solution: An auto-ordering system was developed utilizing machine learning algorithms on a SaaS model, enabling daily sales forecasts for each store and SKU with a one-week horizon. The forecasting process takes into account promotions, days of the week, holidays, seasonality, and product categories.
Project Goal: To enhance service levels and optimize inventory through more accurate sales forecasting.
Solution: An auto-ordering system was developed utilizing machine learning algorithms on a SaaS model, enabling daily sales forecasts for each store and SKU with a one-week horizon. The forecasting process takes into account promotions, days of the week, holidays, seasonality, and product categories.
Solution advantages:
- Forecast Accuracy: The system accounts for various factors, such as promotions and seasonality, allowing for precise demand forecasting.
- Automation: The algorithm automatically translates forecasts into orders for suppliers, considering delivery schedules and calculating safety stock.
- Integration: Results from the service are seamlessly integrated into the client’s ERP system, ensuring smooth operation and ease of use.
- Cloud Computing: Leveraging cloud technology for distributed computing accelerates service deployment and reduces the burden on the client’s server capacities.
Results and Benefits
The project achieved the following:
Improved accuracy:
Fully automated order calculation, which allowed to reduce the share of surplus orders by 20%.
Automation:
Capability to process large volumes of data in real-time.
Adaption:
Algorithm adaptation to new input data, enhancing forecast accuracy and minimizing the risks of stock-outs and overstock.
Scalability:
System implemented across 161 stores in the retail chain.
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