Problem Statement

The client struggled with inaccurate demand forecasting and inefficient inventory distribution across its omni-channel sales network. These inefficiencies led to frequent stockouts in high-demand locations and excess inventory in others, negatively impacting both profitability and customer satisfaction.

Key Challenges

• Outdated forecasting methods

  resulted in unreliable demand predictions.

• Inefficient inventory allocation

  caused stock imbalances across stores and online channels.

• Lack of seasonal and external factor considerations

  led to missed sales opportunities.

• Limited demand insights

  restricted the ability to optimize inventory decisions.

Solution Implemented

• Machine learning-powered forecasting models : analyzed historical sales, seasonal patterns, and external factors to improve accuracy.

• Dynamic demand prediction algorithms: adapted to real-time market changes.

• Optimized inventory distribution: across physical stores and digital platforms based on AI-driven insights.

• Actionable intelligence dashboards: empowered data-backed decision-making for replenishment and stock positioning.

• Seamless integration with the client’s ERP system : streamlined forecasting and inventory workflows.

Business Impact

• 31% improvement in demand forecast accuracy,

  reducing stockouts and overstock situations.

• 24% increase in inventory turnover,

  optimizing working capital and minimizing holding costs.

• 22% boost in omni-channel customer satisfaction,

  ensuring product availability across all sales channels.