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What is Shelf Engine?
Shelf Engine offers a solution designed to address the challenge of accurately ordering highly perishable grocery items. By utilizing intelligent forecasting, the system analyzes a store's daily sales data combined with real-world factors such as local events, holidays, and weather patterns. This analysis enables the generation of precise, profit-maximizing orders for each product on a daily basis, aiming to significantly reduce spoilage for items like deli sandwiches, fresh juice, and bread.
The platform completely automates the ordering process, submitting optimized orders directly to suppliers without requiring manual intervention like SKU scanning or overriding suggestions. This automation helps retailers minimize costly stockouts, thereby improving shopper loyalty and increasing sales potential. Furthermore, by accurately predicting customer demand, Shelf Engine assists grocers in reducing shrink and associated food waste, contributing positively to both the bottom line and environmental sustainability.
Features
- Intelligent Forecasting: Utilizes daily sales data, local events, holidays, and weather to predict demand accurately.
- Automated Ordering: Generates and submits optimal orders directly to suppliers automatically.
- Shrink Reduction: Minimizes food waste and associated costs by improving order accuracy for perishables.
- Stockout Minimization: Helps maintain full shelves to prevent lost sales and enhance customer satisfaction.
- Labor Optimization: Frees up staff from manual ordering tasks to focus on customer-facing activities.
Use Cases
- Optimizing daily orders for highly perishable grocery items (e.g., deli, bakery, produce, fresh juice).
- Reducing food waste and shrink in grocery retail operations.
- Increasing sales revenue by minimizing out-of-stock situations.
- Automating the grocery ordering process to improve efficiency.
- Improving overall supply chain performance for fresh food categories.
Related Queries
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Shelf Engine Uptime Monitor
Average Uptime
82.08%
Average Response Time
318.9 ms
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