A large retail company sought to optimize its inventory management system to reduce stockouts and excess inventory across multiple locations.

Challenges:

• The company struggled with maintaining appropriate stock levels in different regions, leading to missed sales opportunities and excess inventory costs.
• They needed a system that could predict demand more accurately, factoring in real-time sales data, seasonal trends, and external influences like weather or market conditions.

Solution:

Quanois designed and implemented a real-time data analytics platform that integrated sales, inventory data, and external factors to optimize inventory levels and improve demand forecasting.

• Data Engineering:

  We developed a data integration system that collected data from point-of-sale systems, weather APIs, and social media sentiment to predict customer demand.

• Machine Learning:

  We applied predictive modeling techniques to forecast product demand and prevent stockouts, allowing the company to keep inventory levels balanced.

• Results:

  • Reduced stockouts by 20% and excess inventory by 25%.
  • Increased sales by 15% through better inventory availability.
  • Improved supply chain efficiency, saving 10% in operational costs.