How Kotsovolos Rewired Demand Planning with AI to Improve Retail Replenishment
Kotsovolos — AI-Driven Demand Planning and Retail Replenishment, in partnership with ToolsGroup.
About This Video
In this session, Kotsovolos — one of Greece's leading electronics and appliance retailers — demonstrates how AI-driven demand planning and intelligent automation have transformed their retail replenishment operations. The session covers how ToolsGroup's probabilistic planning approach helped Kotsovolos reduce manual planning effort, improve on-shelf product availability, and enable their planning team to make smarter decisions across a large and complex SKU catalog.
Retail replenishment at the scale of a major electronics retailer involves thousands of SKUs, strongly seasonal demand, and multi-location inventory decisions that cannot be managed manually at acceptable accuracy. This session shows how replacing spreadsheet-based processes with AI-powered demand forecasting and automated replenishment delivers measurable improvements in both service levels and working capital efficiency.
Key Takeaways
- AI automation reduces manual replenishment effort at catalog scale. When thousands of SKUs require daily replenishment decisions, automation is the only way to maintain consistency and quality across the full assortment.
- Probabilistic forecasting handles seasonal and promotional volatility better than traditional methods. Electronics retail demand spikes around promotions and product launches — probabilistic models capture this variability where single-point forecasts fail.
- Empowered planners spend less time on data and more time on decisions. Removing repetitive execution tasks from the planning team allows them to focus on exceptions, strategic trade-offs, and customer outcomes.
- Service level improvement and inventory reduction are achieved simultaneously. ToolsGroup's approach optimizes both dimensions at once — higher availability does not require higher inventory when the underlying forecast quality improves.
- Connected forecasting and replenishment close the gap between prediction and action. The platform translates demand probability distributions directly into replenishment orders — eliminating the manual handoff that loses precision between planning and execution.
About Kotsovolos
Kotsovolos is one of Greece's largest consumer electronics and home appliance retailers, operating a network of stores nationwide. The company manages a large and diverse product catalog with significant seasonal demand variation driven by promotions, new product launches, and holiday peaks. Kotsovolos partnered with ToolsGroup to modernize its supply chain planning, moving from manual, spreadsheet-based replenishment processes to AI-powered demand forecasting and automated inventory optimization.
Frequently Asked Questions
What challenge did Kotsovolos face before using ToolsGroup?
Kotsovolos faced the challenge of managing retail replenishment at scale across a large electronics and appliance catalog with highly seasonal and promotional demand patterns. Manual planning processes could not handle the volume and complexity of SKU-location decisions required, resulting in inefficiencies in both service levels and inventory investment.
How does AI-driven demand planning improve retail replenishment?
AI-driven demand planning improves retail replenishment by generating probabilistic forecasts at the SKU-location level that account for seasonality, promotions, and demand variability. These forecasts are automatically translated into replenishment orders that maintain target service levels while minimizing excess inventory — replacing the manual, error-prone processes that characterize spreadsheet-based planning.
What results can retailers expect from ToolsGroup's planning platform?
Retailers using ToolsGroup's AI-powered supply chain planning platform typically achieve higher service levels, reduced inventory investment, and significant reduction in planner workload. The platform's probabilistic approach allows retailers to optimize both service and working capital simultaneously — rather than trading one off against the other as traditional planning methods require.
