Food & Beverage
·Singapore·7 monthsAI-Powered Operations for a Multi-Outlet F&B Chain
Deployed AI-driven operations automation for a multi-outlet F&B chain covering staff scheduling, inventory management, and demand forecasting. The system optimises labour deployment based on predicted foot traffic, automates ingredient ordering based on menu-level demand forecasts, and reduces food waste through expiry tracking and dynamic menu recommendations.
22%
Reduction in food waste
15%
Improvement in labour cost efficiency
Outlet-level
Hourly demand forecasting
Challenge
F&B operations compound complexity across every outlet: staff scheduling must account for peak hours that vary by location, day of week, and season; inventory must balance freshness requirements against stockout risk for perishable ingredients; and demand is influenced by weather, events, holidays, and neighbourhood dynamics that differ across outlets. This chain operated multiple outlets across Singapore, each with a slightly different menu mix and customer profile, managed by outlet managers who made scheduling and ordering decisions based on personal experience. Labour was the single largest controllable cost. Overstaffing during quiet periods eroded margins; understaffing during peak periods degraded service quality and drove negative reviews. The chain's scheduling approach was reactive — managers adjusted next week's roster based on this week's experience, which meant they were always solving last week's problem. Staff dissatisfaction with unpredictable schedules contributed to high turnover, which compounded the scheduling problem because new staff were less efficient. Food waste was both a financial and reputational concern. The chain committed to sustainability goals but lacked the operational tools to deliver on them. Ingredient ordering was based on par levels set months ago, not actual consumption patterns. Prep quantities were based on manager estimates. The gap between what was ordered, what was prepped, and what was sold represented both waste and missed revenue opportunity.
Approach
Reluvate deployed a three-module operations platform covering scheduling, inventory, and demand forecasting. The demand forecasting engine uses historical transaction data, weather forecasts, local event calendars, and holiday schedules to predict customer volume and menu-item demand at the outlet-day-hour level. These forecasts drive both the scheduling and inventory modules. The scheduling module generates optimised staff rosters based on predicted demand, staff availability preferences, skill requirements (kitchen vs. front-of-house), labour regulations (maximum hours, mandatory breaks, overtime thresholds), and cost targets. Managers can adjust AI-generated schedules but the system flags when adjustments deviate significantly from demand predictions, providing a data-driven check on intuition-based decisions. The inventory module translates menu-item demand forecasts into ingredient-level requirements using the chain's standardised recipe database. It generates daily prep recommendations and weekly purchase orders, accounting for ingredient shelf life, supplier lead times, and minimum order quantities. The system tracks actual consumption against forecast and adjusts future predictions based on observed accuracy, continuously improving its models. An expiry tracking module monitors ingredient freshness dates and recommends menu specials or promotions to utilise ingredients approaching expiry, reducing waste while driving sales.
Design Notes
The forecasting model was designed to be outlet-specific rather than chain-wide. Each outlet's demand patterns are influenced by hyperlocal factors — a CBD outlet peaks at lunch on weekdays while a suburban outlet peaks at dinner on weekends. Reluvate trained individual models for each outlet, with shared learning across the chain for macro-level trends (weather effects, public holidays) while preserving outlet-specific patterns. New outlets with limited history bootstrap from the most similar existing outlet's model. Change management was critical because outlet managers had strong opinions about their business, often backed by years of experience. Reluvate ran the AI recommendations in parallel with manager decisions for six weeks, tracking outcomes side by side. In cases where the AI's predictions outperformed manager intuition — and in cases where they didn't — the data was shared transparently. This built trust and also helped refine the models, as managers contributed domain knowledge that the data alone didn't capture. Exception handling accounts for the unpredictability of F&B operations. Equipment failures, unexpected staff absences, ingredient delivery delays, and viral social media events can all disrupt normal demand patterns. The system monitors for real-time deviations from forecast and triggers alerts when actual demand exceeds or falls significantly below predictions. Managers can activate contingency modes that adjust downstream recommendations — reducing prep quantities, calling in standby staff, or triggering emergency supplier orders.
Result
Labour costs decreased as AI-optimised scheduling reduced overstaffing during off-peak periods while ensuring adequate coverage during peak times. Food waste dropped through more accurate demand-driven ordering and prep recommendations. Staff satisfaction with scheduling improved due to more predictable, preference-aware rosters, contributing to reduced turnover. Outlet managers reported spending less time on administrative scheduling and ordering tasks, freeing them to focus on customer experience and team development.
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