Automate the orders you can. Flag the ones you can't.
AI replenishment that validates and executes routine orders automatically, and surfaces recommendations when conditions make automation risky.
Last week's sales data is running your replenishment today
Your replenishment system orders based on what sold last week. The gap between that forecast and actual demand shows up as overstocks, waste, and empty shelves. CINDE Demand Performance closes that gap with three connected capabilities.
AI generates optimized order proposals at the item, store, and day level. Proposals that pass validation — quantity within bounds, supplier confirmed, no conflicting promotions — execute automatically. Anything riskier gets flagged for review.
Promotional stock, seasonal collections, and new items land in the right stores in the right quantities. Each store receives what its demand profile justifies, not a chain average.
Real outcomes, proven in production
−20% shortage, −10% waste, −15% order review effort
Achieved by a major European grocery retailer after deploying AI forecasting and automated replenishment across distribution centers
Order auto-validation up from 20–40% to 70%+
A major European grocery group automated DC replenishment so that orders previously requiring manual review now pass through automatically
5–10 point accuracy gain
A large European grocery group replaced manual tuning across all forecasted categories with AI forecasting
6% fewer shortages, 1.5% better inventory, 0.5% more sales
Mercator Slovenia achieved these results across 370+ supermarkets and 27 hypermarkets after deploying the integrated supply chain suite.
$1.6–$2.8M profit per billion in revenue
End-to-end supply chain optimization—forecasting, replenishment, and planning across store and DC operations—with customers generating value in four to six months
Common questions about retail replenishment and allocation
Traditional replenishment triggers orders when inventory hits a static reorder point. AI replenishment uses demand forecasts that update daily, accounting for promotions, weather, and selling patterns. Order proposals that pass validation criteria execute automatically. When conditions make automation risky, the system generates recommendations for human review. The result is lower inventory, fewer stockouts, and intervention focused on the orders that actually need judgment.
AI allocation distributes merchandise from warehouses to stores based on store-level demand signals, promotional calendars, and available inventory. Each store receives a quantity optimized for its projected demand, rather than an allocation based on averages or historical proportions.
Yes. Automated replenishment layers on top of existing ERP and supply chain systems through standard data feeds. Retailers run AI forecasting and replenishment alongside their current infrastructure without replacing core transactional systems.
Time to value depends on deployment scope. Retailers generate measurable forecast accuracy improvements within the first months of operation. Broader supply chain deployments (forecasting, replenishment, allocation) typically deliver value in 4-6 months.
Start with a better forecast. Everything downstream improves.
