Applied AI Use‑Case Catalog: Forecasting, Scheduling, Waste Reduction

1) Demand Forecasting Pilot (dayparts + item-level)

Hunger: Reduce stockouts, overproduction, and waste by forecasting sales at the item and daypart level.

• 3–10% reduction in food waste from overproduction within first 3 months (pilot dependent).
• Fewer stockouts and improved guest satisfaction.
• More accurate ordering and lower emergency purchases.

• Historical POS transactions (timestamp, item id, qty, price) — 6–12 weeks minimum; 12+ months preferred.
• Calendar (holidays, promotions).
• Weather (optional but helpful for daypart demand patterns).
• Booking/reservation data (if used).
• Operational constraints (hours, item availability, lead times).

1. Choose 10 high-volume SKUs or top recipe categories.
2. Build a simple time-series model (e.g., SARIMA or gradient-boosted tree with lag features) to predict next 7–14 days by daypart.
3. Surface predictions in a simple dashboard and export weekly order suggestions for manager review.
4. Run pilot for 4 weeks with manager adjustment allowed; collect actuals vs forecast.

• Forecast MAPE reduced by X% vs baseline (target 10–25% improvement depending on variability).
• Reduction in weekly overproduction waste (kg or $) by target percent.
• Reduction in emergency orders or stockouts.

6–12 weeks of POS data to run a conservative pilot; pilot duration 4–8 weeks.

• Predictions presented as suggestions; managers retain final ordering authority.
• Limit automated adjustments to low-risk items initially.
• Track and log every suggested vs executed change for audit.

2) Labor-Scheduling Assistant

Hunger: Match staffing to demand to control labor costs without harming service.

• 5–12% improvement in labor cost as a percentage of sales by reducing overstaffing and underutilization.
• Improved shift coverage for peak periods with fewer last-minute call-ins.

• Historical sales by hour/day, footfall (if available), transaction counts.
• Existing schedules and shift templates.
• Employee availability constraints and labor rules (breaks, max hours, roles).
• Local rules (labor laws, scheduling notice requirements).

1. Create a simple rule-based assistant that combines recent forecasted demand (from the forecasting pilot or simple moving averages) with standard prep/service times to recommend shift coverage by role.
2. Deliver recommendations to shift leaders 48–72 hours before shift for review and manual publish.
3. Track accepted vs recommended schedules and service KPIs (ticket times, average check, complaints).

• Labor % of sales improved vs baseline without adverse effects on checkout times or complaint rates.
• Reduction in last-minute overtime or call-ins.

4–8 weeks of recent scheduling and sales data; pilot 4–6 weeks.

• Managers always review and approve schedules (no unilateral automation at first).
• Enforce legal and contractual constraints in the recommendation engine.
• Measure service quality; if service KPIs degrade, pause automation.

3) Spoilage Prediction from Temperature Telemetry

Hunger: Prevent costly spoilage by predicting when perishable inventory is at elevated risk due to temperature excursions.

• Reduce spoilage-related loss through timely corrective action (expected 10–30% reduction depending on current practices).
• Fewer health-safety incidents and better compliance with temperature logs.

• Continuous temperature telemetry from sensors (timestamped).
• Inventory location mapping (which items are stored where and their shelf life).
• Historical spoilage/loss records if available.

1. Set simple threshold alerts for critical sensors and add a short predictive model that scores stored inventory by spoilage risk (based on recent temperature deviations, time since breach, and item vulnerability).
2. Send risk alerts to shift manager with recommended actions (e.g., move to different equipment, use first, discard) and log manager response.
3. Pilot 2–6 weeks focusing on a single storage area (walk-in, freezer) and a few high-value items.

• Reduction in spoilage incidents and associated $ losses.
• Faster response time to temperature excursions.

• All discard recommendations require manager confirmation and an entry in a spoilage log with justification.
• Model conservative: err on the side of safety, not economy.
• Maintain audit trails for compliance and inspection.

4) Menu Personalization for Loyalty

Hunger: Increase repeat visits and check size by recommending items to loyalty members based on past behavior and context.

• Higher average check and visit frequency among loyalty users.
• Better-targeted promotions with improved ROI.

• Loyalty transaction history (member id, items, timestamp).
• Basic customer attributes (opt-in preferences, dietary restrictions if available).
• Context signals: time of day, device/ordering channel, current promotions.

1. Start with simple rules: top complementary items or substitutes based on past purchases (e.g., customers who order X often add Y).
2. Expose recommendations in the loyalty app or POS-driven offers; A/B test with a control group.
3. Pilot for 4–8 weeks focusing on a single customer segment (e.g., weekday lunch regulars).

• Uplift in attach-rate for recommended items (target 5–15%).
• Increase in average check among exposed users vs control.

• Respect privacy and opt-in rules; do not personalize for customers who have opted out.
• Avoid recommendations that could cause allergens to be added unintentionally; surface allergen info prominently.
• Monitor negative feedback; if recommendations produce complaints, pause the test.