Challenge
A leading retailer had built strong forecasting capabilities using internal sales data to guide demand planning across product lines. To add further value to these existing systems, they looked to enhance their approach by incorporating external data sources that could provide earlier signals of shifting consumer preferences, emerging fashion trends, and macroeconomic changes. This expansion aimed to elevate their forecasting from reactive to more forward-looking, unlocking new opportunities for agility and precision in decision-making.
Solution
*Example product
Our AI consulting team partnered with the retailer to develop a next-generation forecasting system that combined machine learning with intuitive dashboards. We integrated diverse data sources—such as real-time inventory, social media trends, customer sentiment, and economic indicators—into a unified, automated model. This approach enabled more accurate, context-aware predictions and allowed teams to proactively adjust to both demand and supply-side changes. The results were delivered through user-friendly dashboards with built-in scenario testing and actionable recommendations.
Results
The AI-enhanced forecasting system led to significant gains in accuracy, especially for new product lines with limited sales history. With improved demand visibility, the company optimized inventory levels, reduced stockouts and markdowns, and responded faster to emerging trends. These improvements boosted operational efficiency and profit margins. Ultimately, the retailer shifted from reactive forecasting to a proactive, data-driven approach with actionable insights.