Bestseller goods out of stock in store, unsold goods overstocks in store.
Different experience of merchandise operators, poor effect of commodity circulation
Lack of overall view and long-term strategy, replenishment goods frequently
Newcomers need months to inherit after merchandise operator leaves
Build a dynamic model of target inventory predictive, achieve short-term and high-frequency forecast
According to more than 20 collaborative dimensions, such as speed of operation, sales speed, replenishment cycle, freight length, stage of sales cycle, bestsellers elements, specialty of stores and AI machine learning to achieve high-frequency and short-term prediction.
Help headquarters staff to understand the product health index, overall inventory distribution, single-store SKU inventory and inventory evolution trend map. Help to identify problems in the operation of goods and support decision-making, timely detection of out-of-stock and multi-stock trends.
With the use of AI machine learning, fine commodity operations, the use of big data algorithms and the use of big data algorithms to provide enterprises with smart inventory allocation and replenishment solutions that can be connected to WMS directly drive the flow of goods to avoid human decision-making errors caused by the shortage of overstock.
Find the best spot of sales for merchandise, reduce the loss of protentional sales opportunities;
Avoid fluctuations and confusion in commodity operations due to personnel changes;
Intelligent replenishment model and AI algorithm to make brand merchandise operations more accurate and efficient；
Precise merchandise operation to one store, one color, one style and one size.