Halo Develops Machine Learning Solution that Radically Enhances Demand

Halo announced today the worldwide release of HaloBoost, Halo's proprietary demand forecasting engine that leverages proven Machine Learning algorithms. HaloBoost combines Machine Learning methods to improve forecast accuracy over time, a high-speed modeling workflow to improve analyst productivity and knowledge discovery, and a simple, scalable method to introduce external factors like pricing, promotion, social media, and weather predictors. "Manufacturers, Distributors, and Retailers have been seeking tools that can provide simplification in the forecasting process to improve accuracy and throughput, and we've responded by introducing our most powerful modeling engine, HaloBoost . Traditional approaches are limited in their ability to maximize forecast accuracy without significant analyst effort across broad and sparse data dimensions such as regions, points-of-sale, and SKU-level granular forecasts. Our proprietary modeling workflow effectively uses the computer to simulate a large team of forecast experts, working in real-time, to find the best result across a broad range of forecast scenarios," said Bill Panak, Ph.D. Vice President of Data Sciences, Halo.