The client needed to improve demand forecasting across a rapidly growing restaurant network to better balance customer demand, inventory availability, and food waste reduction. The challenge was complicated by changing consumer behavior, seasonal demand fluctuations, fragmented historical data, and the operational complexity of processing live transaction data at scale. The primary challenges were:
Disconnected operational data spread across seven systems of record, including POS, inventory, and recipe systems.
Need for accurate, scalable demand forecasting capable of adapting to changing customer demand patterns.
Operational complexity of real-time analytics, including streaming live POS data into a centralized cloud data environment.
The business selected Data Futures because it needed a partner capable of translating complex operational data into actionable, real-time decision support across thousands of restaurants.
We Start with Outcomes, Not Tools — Data Futures kept the work anchored to measurable operational outcomes: reducing food waste, minimizing shortages, and improving restaurant-level decision-making.
Modern Expertise Without the Hype — The team combined large-scale data integration, cloud data engineering, and machine learning forecasting into a practical operational solution deployable across thousands of locations.
We Solve Through Collective Wisdom — The forecasting challenge required bringing together multiple operational perspectives and expertise, including POS activity, inventory, recipes, seasonality, and live restaurant behavior, and resolving them into a unified model that operators could trust and use in real time.
Data Futures designed and implemented a real-time demand forecasting platform integrated into the company’s operational workflows. Key features included:
An enterprise data lake that integrated and standardized 12 years of historical data.
An ARIMA-based demand forecasting model, a well-established time-series machine learning method adept at predicting future demand based on historical trends, seasonality, and recent transaction patterns.
A real-time, POS-integrated dashboard with a built-in nudge engine that prompted operators in the restaurant to proactively replenish inventory and reduce shortages or waste as forecasted demand data was updated throughout the day.
Deployed chain-wide across 2,000+ restaurants, supporting operational decision-making at scale.
Improved supply chain efficiency and product availability by reducing food waste by approximately 12–15% and shortages by approximately 15–18% through more accurate forecasting and inventory management.
Achieved high operator satisfaction and forecast confidence, driving adoption of the dashboard and forecasting capabilities.
