The need for data-driven inventory and order management
Inventory and ordering at franchise stores often depend on the individual judgment of store owners, leading to overstocking or last-minute, same-day orders. In some cases, stores deviate from standard recipes or independently source ingredients, making it difficult to maintain consistent food quality. Store owners also frequently lack tools to anticipate sales declines, resulting in delayed responses and missed opportunities to adjust operations in time.
Building AI models for inventory optimization and recipe compliance analysis
AI models were developed to analyze sales data by store and product, calculate optimal inventory levels, and improve order accuracy. Using the Runway platform, an MLOps environment was implemented to support data analytics and predictive model operations, and integrated with existing safety stock management systems. Recipe adherence was monitored at the store level to ensure consistent quality, while real-time data drift analysis was used to detect sales fluctuations.
Reducing excess inventory and improving quality consistency
AI-powered order optimization reduced excess inventory and minimized last-minute orders. Recipe adherence analytics helped standardize food quality across stores, strengthening brand trust. Real-time sales monitoring enabled early detection of declining trends, allowing store owners to respond proactively and improve operational efficiency across franchise locations.
