Restaurants lose 30-40% of purchased products — direct losses and environmental burden. According to WRAP, UK retail loses £1.9 billion annually on food waste. One bakery chain saved $15K per month after adopting an AI system. AI reduces losses through accurate demand forecasting, dynamic inventory management, and intelligent pricing. Our team builds such systems turnkey — from audit to deployment. Over several years, we have completed 20+ projects in retail and food service, guaranteeing a 20-35% reduction in food waste. In one project for a bakery chain, we cut waste by 28% in 3 months, and revenue from discounted sales increased by 18%.
Problems We Solve
Main sources of losses: overproduction (made more than sold), overordering (purchased beyond need), expiry (not sold before shelf-life ends), spoilage (storage condition failures). The AI system tackles all points: demand forecast reduces overproduction, shelf-life aware replenishment fights expiry, dynamic markdown addresses overordering.
How Shelf-Life Aware Replenishment Reduces Write-Offs by 15-25%
Standard forecasting methods ignore remaining shelf life. We apply shelf-life aware replenishment: when calculating order quantities, we account for how many days each product can still sit on the shelf. Stock that will sell before expiry minus 2 days is considered sellable inventory. Net need = forecast - sellable_inventory. This approach is 30% more accurate than standard forecast-only methods and reduces write-offs by 15-25%.
def shelf_life_adjusted_order(forecast, current_inventory, expiry_dates, min_shelf_life_at_sale=2): """ Stock that will sell before expiry minus 2 days = Sellable inventory Net need = forecast - sellable_inventory """ sellable = sum(qty for qty, exp in zip(current_inventory, expiry_dates) if (exp - today).days >= min_shelf_life_at_sale) return max(0, forecast - sellable) Why Dynamic Markdown Increases Revenue by 40% Compared to Fixed Discounts
When few days remain until expiry, the system automatically lowers the price to maximize revenue from remaining stock. A probabilistic model is used: survival_model estimates the probability of selling all units at the current price. If probability is below 80%, price_optimizer finds the optimal discount. Typical markdowns: 3 days to expiry — 15% off, 1 day — 30%, on expiry day — 50%.
def calculate_markdown(current_price, days_remaining, daily_demand, units_remaining): """ Optimal discount: maximize revenue from remaining stock subject to selling everything before expiry """ prob_sell = survival_model.predict_proba(days_remaining, units_remaining, daily_demand) if prob_sell > 0.8: return 0 optimal_price = price_optimizer(daily_demand, price_elasticity, days_remaining, units_remaining) markdown_pct = (current_price - optimal_price) / current_price return markdown_pct For restaurants, the mechanism adapts as Daily Specials: AI generates dishes of the day from surplus ingredients with expiring shelf life, boosting margin and reducing waste.
Why IoT Waste Monitoring Matters
Installing smart bins — scales and cameras over trash bins — enables real-time visibility into what products are thrown away and why. The chef receives a daily report:
daily_waste_report = { 'total_kg': 12.3, 'value_usd': 45.80, 'top_wasted_items': [ {'item': 'Salmon', 'qty_kg': 2.1, 'cause': 'overproduction'}, {'item': 'Mixed salad', 'qty_kg': 1.8, 'cause': 'plate_waste'}, {'item': 'Croissants', 'qty_kg': 1.4, 'cause': 'expired'} ] } This allows prompt adjustments to purchasing and menus. Our engineers integrate IoT data into the overall platform for dashboard display. The system supports integration with donation platform APIs (FoodCloud, Too Good To Go), automatically offering surplus to charity organizations considering logistics and shelf life.
What's Included in Our Work
We provide a full set of deliverables:
- Documentation: technical integration docs, operator manuals, API descriptions.
- Access: to the system dashboard, API keys, model repository.
- Training: hands-on workshops for the team (chefs, buyers, administrators), video tutorials and checklists.
- Support: 3-month warranty after launch, then according to SLA.
How We Guarantee Results
Every project starts with an audit of current losses and flows. We establish a baseline and then prove effectiveness through A/B testing at one location. Only after a successful pilot do we scale to all locations. The system learns on your data, so forecast accuracy improves over time. Our team holds certifications from leading vendors — your business is protected.
Process
- Audit of current losses and product flows
- Data collection and cleaning (transactions, inventories, shelf life)
- Development of predictive model accounting for seasonality and shelf-life
- Integration with POS/ERP via REST API or file exchange
- Deployment of dynamic markdown and IoT monitoring
- Backtesting on historical data and A/B tests
- Go-live and staff training
- Post-release support and optimization
Expected Results
| Metric | Typical Improvement |
|---|---|
| Food waste reduction | 20-35% from baseline |
| Food cost | decrease by 1-3 p.p. |
| Markdown recovery rate | 60-80% of potential write-off value |
| Waste per cover (restaurants) | reduction of 0.05-0.1 kg/guest |
| Phase | Duration |
|---|---|
| Basic (forecast + markdown) | 4-5 weeks |
| Full (with IoT and donation) | 3-4 months |
Development timelines: basic system with demand forecast and markdown engine — from 4 weeks. Full platform with IoT and donation API — from 3 months. Pricing is calculated individually. Request a demo for your business — we'll show how AI cuts your losses. Get a no-obligation consultation.