When one of the largest retailers in the MENA region approached GrozAI, they faced a common but costly problem: inventory inefficiency was eating into margins. Overstocking tied up capital in slow-moving products, while stockouts drove customers to competitors. Manual replenishment processes couldn't keep pace with the complexity of managing 50,000+ SKUs across 200+ locations.
The Challenge
Traditional inventory management relied on static reorder points and safety stock levels calculated from historical averages. This approach failed to account for the dynamic nature of modern retail: seasonal variations, promotional effects, competitor actions, and rapidly changing consumer preferences.
- Average stockout rate of 8% resulting in $12M annual lost sales
- Overstock levels consuming 25% more working capital than benchmarks
- Manual replenishment decisions requiring 40+ hours of analyst time weekly
- Reactive approach to demand changes, often lagging market by 2-3 weeks
The Solution
We deployed our AI Replenishment Engine, a system that combines advanced demand forecasting with intelligent inventory optimization. The engine ingests data from multiple sources—point-of-sale transactions, inventory levels, supplier lead times, promotional calendars, weather data, and market intelligence—to generate daily replenishment recommendations at the SKU-location level.
Machine learning models identify demand patterns that human analysts would miss: the impact of school holidays on snack categories, weather-driven shifts in beverage preferences, and cannibalization effects between similar products. These insights feed into optimization algorithms that balance service levels against inventory costs.
Within 90 days of deployment, we saw stockout rates drop from 8% to under 3%, while simultaneously reducing inventory holding costs by 18%.
Implementation Approach
Rollout followed a phased approach. We began with a pilot covering 50 stores and 5,000 high-velocity SKUs. This allowed us to validate forecast accuracy, tune optimization parameters, and build confidence with the merchandising team. Key success factors included close collaboration with domain experts and transparent model explanations that helped users trust AI recommendations.
Training focused not on how the AI worked, but on how to interpret its outputs and when to override recommendations. This human-in-the-loop approach was critical for adoption. Users felt empowered rather than replaced.
Results
After full deployment across all categories and locations, the results exceeded expectations:
- 40% reduction in analyst time spent on replenishment decisions
- 62% reduction in stockout incidents
- 18% decrease in inventory carrying costs
- $28M annual benefit from improved availability and reduced waste
- ROI achieved within 4 months of full deployment
Lessons Learned
This project reinforced several key principles for successful AI implementation. First, data quality matters more than model sophistication—we spent 40% of project time on data cleansing and integration. Second, change management is as important as technology—executive sponsorship and frontline engagement were essential. Third, continuous improvement is non-negotiable—the system gets smarter over time as it learns from each replenishment cycle.
The retailer has since expanded AI capabilities to pricing optimization and assortment planning, building on the data infrastructure and organizational capabilities developed during this initiative. AI has become a core competency, not just a point solution.