LYNA (Lina) Package Quantity Forecast

basic information

General machine learning requires vast amounts of training data, which poses challenges in rapidly changing distribution environments, such as product modifications. "LYNA Packaging Quantity Prediction" utilizes a unique learning algorithm specialized for logistics data, developed based on the latest machine learning algorithms, to achieve high-accuracy predictions even from about a week’s worth of training data. Since the learning process is fully automated, no product master data is needed at all. For new products without shipping records, the AI automatically estimates packaging and quantity based on category information. Furthermore, once actual shipments begin, it immediately learns and adjusts from a few days of actual data to enhance prediction accuracy. This frees busy site managers from the cumbersome and time-consuming task of managing size and packaging method masters for each product, eliminating unnecessary burdens and waste for pickers and truck counts with high-accuracy predictions.

Price range

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Applications/Examples of results

- Based on the order placement, the AI automatically predicts the actual load (number of baskets, number of weights, etc.). - Since the AI calculates automatically, data organization skills (master management) are not required.