United KingdomRetail and e-commerce2021 – ongoingOne Stop
Machine learning demand forecasting for replenishment
One Stop reported 8.5% higher ultra-fresh availability after adding machine learning forecasts
Problem
One Stop needed forecasts that accounted for weather-sensitive demand and promotions that diverted sales between products. Short shelf lives made excess stock costly.
Approach
One Stop added RELEX machine learning forecasting after introducing its supply chain planning system in 2019. The forecasts covered products with different shelf lives and demand patterns. Results were reported after 4 months of use.
Results
Takeaway
One Stop reported better availability for its shortest-lived products without a corresponding increase in spoilage. The machine learning rollout followed an earlier replenishment implementation.
Categories
- Size
- Large enterprise
- Industry
- Retail and e-commerce
- Business function
- Logistics and supply chain
- Innovation type
- Process innovation
- AI technology
- Predictive analytics and ML
- Adoption
- Vendor solution
- Evidence
- Company's own claim
Sources
Source summaries
Logistics Business reported One Stop's machine learning deployment and quoted its supply chain head. It reported an 8.5% increase in ultra-fresh availability without a corresponding spoilage increase.
Full details are in the original sources.