INNOVATION • EXECUTION • GROWTH
Case Study
Explore how our innovative solutions have empowered businesses to overcome challenges and achieve remarkable success.
Explore how our innovative solutions have empowered businesses to overcome challenges and achieve remarkable success.
A forecasting system was developed using Facebook Prophet to predict restaurant inventory requirements and sales volumes based on historical data. The restaurant’s existing sales and inventory data was cleaned and structured to identify demand patterns, seasonal trends, and recurring fluctuations. Separate forecasting models were created for sales and inventory consumption, helping the restaurant anticipate future demand and prepare the required stock in advance. The system also accounts for variations such as weekly patterns, peak periods, weekends, and holidays. As more historical data is added, the forecasts can become increasingly accurate, enabling better inventory planning, reducing waste, and supporting more efficient restaurant operations.
A large language model (LLM) was integrated to provide kitchen staff with instant recipe guidance for any dish. By entering a dish name, the system generates the required ingredients, quantities, preparation steps, cooking time, temperature, and plating instructions in a clear, structured format. The system was tailored to the restaurant’s portion sizes, cooking styles, and cuisine standards, ensuring the recipes were practical for daily operations. It supports both existing menu items and new or seasonal dishes, reducing recipe research time and helping maintain consistency across the kitchen.
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The Apriori algorithm was applied to historical transaction data to identify food items that customers frequently purchase together. The system analysed support, confidence, and lift to determine the strength and relevance of different food combinations. The analysis was further segmented by lunch, afternoon, and dinner hours to identify how customer preferences changed throughout the day. Based on these insights, the system generated ranked combo recommendations that helped the restaurant create targeted promotions, improve upselling, and increase average order value.
The three AI modules Prophet forecasting, LLM recipe generation, and Apriori market basket analysis were integrated into a single platform. Kitchen teams could generate recipes instantly, operations teams could access inventory and sales forecasts, and sales teams could identify top food combinations and upselling opportunities. The platform connected with existing restaurant data systems and continuously processed new data, keeping AI insights updated, accurate, and relevant.
