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Built and launched a centralized AI-powered sales analytics platform that unified demand data across all locations and gave management actionable insights into menu performance, ingredient procurement, and revenue trends.
More case studiesIndustry: HoReCa / Food & Beverage Core business: Full-service restaurant chain operating across multiple city locations Locations: 7 restaurants Geography: Middle East
The restaurant group had no centralized view of sales performance across locations, making it impossible to make data-driven decisions on menu, procurement, or staffing — each restaurant operated in isolation with no shared intelligence.
Managers relied on experience rather than actual sales data to decide which dishes to promote, price, or remove — missing revenue opportunities and carrying underperforming items on the menu.
Without demand forecasting, ingredient orders were estimated manually per location, leading to systematic over-purchasing and significant spoilage costs week after week.
Leadership had no shared reporting layer across restaurants, making it impossible to identify what was working, which locations were underperforming, and where to intervene.
We built a centralized AI analytics platform that unified POS data across all locations and delivered demand forecasts, menu performance insights, and procurement recommendations directly to management.
Integrated point-of-sale data from all locations into a single analytics layer, enabling cross-restaurant comparison and real-time trend analysis from one dashboard.
Predictive models analyzed historical sales, day-of-week patterns, seasonality, and local demand signals to forecast expected demand per dish and per location on a weekly basis.
Identified top-revenue dishes, low-margin underperformers, and cross-sell opportunities — giving management a data-driven foundation for menu engineering decisions across all locations.
Translated demand forecasts into weekly ingredient order recommendations per location, replacing manual estimation and aligning purchasing directly with expected demand.
The platform gave leadership a unified, data-driven foundation for menu, procurement, and operational decisions — directly improving profitability and reducing waste across the entire chain.
Demand-aligned ingredient ordering eliminated systematic over-purchasing and significantly reduced spoilage costs across all locations.
Menu optimization based on actual sales performance enabled higher-margin dish promotion and improved the overall revenue mix chain-wide.
Weekly AI-driven order recommendations replaced manual estimation, reducing both ingredient shortages and excess inventory simultaneously.
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