We implement machine learning algorithms into delivery management, warehouse load planning and supply chain optimization. Measurable result — lower operational costs and faster throughput.
Specific use cases with proven ROI — from global leaders to emerging markets.
How it works: The system receives real-time data on traffic, weather, and courier workload. The ML model recalculates optimal routes every 3-5 minutes, dynamically redistributing orders between couriers.
JD.com case: After implementing dynamic routing in 2019, the company reduced average delivery time by 30% and cost per order (CPO) by 15%. The system processes over 150M parcels daily.
How it works: The predictive model analyzes historical sales, seasonality, marketing campaigns and external factors. The system automatically moves goods between fulfillment centers before shortages occur.
Amazon case: Anticipatory Shipping — the system predicts orders and begins moving goods to the nearest warehouse before purchase. 35% reduction in out-of-stock, 8% increase in conversion.
How it works: The model forecasts incoming parcel flow 24-72 hours ahead with 95% accuracy. Based on the forecast, shift schedules are automatically generated, equipment is allocated and additional capacity is reserved.
Cainiao (Alibaba) case: During Singles Day 2023, the system processed over 1 billion parcels in 24 hours without disruption. Equipment utilization increased by 25%, unit processing cost decreased by 18%.
How it works: Instead of fixed time windows, the ML model calculates realistic delivery times considering current load, routes, weather and historical delay data for specific areas.
Instacart case: On-time accuracy increased from 65% to 91%. NPS grew by 12 points. Support tickets about late deliveries dropped by 40%.
How it works: The model estimates return probability before dispatch — based on product category, buyer history, size and order type. For high-probability returns, reverse logistics is pre-planned.
Zalando case: With an average return rate of ~50% (fashion), the predictive model reduced return processing costs by 20% and accelerated re-stocking by 35%.
We work at the intersection of e-commerce operations and applied AI. Our experience is not academic — we managed real supply chains.
Our approach is not implementation for its own sake — it's measurable ROI at every step. Pilot → validate → scale.
Leave a request — we'll explain what can realistically be implemented and what results to expect.