A fast-growing retail brand struggled with frequent stockouts, overstocking, and missed sales due to inaccurate demand forecasting. Their legacy process relied heavily on manual planning and basic spreadsheets, making it impossible to account for key variables like seasonal shifts, real-time sales trends, promotional impacts, or regional demand fluctuations.
As a result, inventory decisions were reactive. Some stores sat on excess inventory while others ran out of best-sellers, frustrating customers and creating operational inefficiencies.
We deployed an AI-based forecasting and replenishment system tailored to the client’s retail network. The platform combined historical sales data, promotional calendars, and external signals like weather patterns and social media trends to predict demand with far greater precision.
The system provided a real-time view of inventory across all channels. It triggered smart reorder alerts, adjusted safety stock dynamically based on demand variability and supplier lead times, and continuously refined forecasts using machine learning.
By combining real-time data with AI-driven forecasting, we helped the retailer take control of their inventory, minimize losses, and improve customer experience. With more accurate predictions, their team could move from firefighting to strategic decision-making.
Want to forecast demand with greater confidence and reduce stockouts in your business?
Contact us today to learn how we can help.
We were skeptical at first — AI felt like a buzzword. But seeing the case studies on QanaFlow’s site changed that. Their work with companies facing challenges just like ours gave us the confidence to move forward. The results they delivered for others weren’t just impressive — they felt achievable. That transparency made all the difference.
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