The Challenge

• Retailers faced high levels of product waste due to inaccurate demand forecasting.
• Inventory stockouts and overstocks led to lost sales and reduced customer satisfaction.
• Legacy systems lacked the intelligence needed for real-time inventory optimization.

Our Approach

• Data Discovery: Assessed historical sales data, seasonal trends, and supply chain metrics.
• Model Development: Designed and trained machine learning models for demand forecasting.
• Integration: Connected the models to inventory management systems for automated insights.
• Scenario Planning: Simulated inventory scenarios to support decision-making.
• Change Enablement: Provided training to operations and merchandising teams on using AI insights.

The Solution: OJ StratTech developed a custom AI-powered forecasting solution that dynamically adjusted reorder levels, identified potential overstock risks, and optimized safety stock based on demand signals. The models were trained on sales history and external variables such as promotions and weather data.

The Results

• Waste Reduction: Reduced inventory waste by 35% through better forecasting accuracy.
• Service Levels: Stock availability improved by 20%, reducing lost sales.
• Agility: Enabled real-time adjustments to purchasing based on demand shifts.

Client Testimonial: "OJ StratTech's AI solution transformed how we manage inventory. Our shelves are stocked with what our customers need—no more, no less. It's been a game changer for our bottom line."