Halo's strength in and focus on Machine Learning (ML) is the foundation of our Data Science initiative, where we develop thin layers of R statistical programming that integrate with Halo Data Warehousing, visualization, and reporting technology. Stakeholders who care about forecasting in demand planning care about accuracy, and usually will not accept a new forecasting method unless it is rigorously validated against known forecasting benchmarks with proven accuracy. Even when accuracy to the second decimal place is not critical, accuracy is the benchmark because it is an objective measure, and demand planning executives know the economic impact of inaccuracy. Machine Learning forecasting is highly accurate; this is proven over and over again in Kaggle competitions and modeling benchmarking studies. For the more curious data scientist, Machine Learning forecasting also has stable accuracy / bias trade-offs that can be adjusted on an'efficient frontier' of data science workflow, so that an accurate Machine Learning forecasting solution can be implemented quickly, and then studied over time to further improve the forecast.
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.
Machine Learning Forecasting is attracting an essential role in several significant data initiatives today. Year ago, I have mentioned machine learning as top 7 future trends in supply chain. Big retailers, Supply chain, and logistics experts are using Machine Learning Forecasting to aid improve customer engagement and produce more precise demand forecasts better than traditional forecasting techniques. They can also use Machine Learning Forecasting to expand into new sales channels, improve customer service, reduce inventory and improve productivity.
Amazon uses forecasting to make sure that the right product is in the right place at the right time by predicting demand for hundreds of millions of products every day. Amazon Forecast uses this same technology to build precise forecasts for virtually any business condition, including product demand and sales, infrastructure requirements, energy needs, and staffing levels – with predictions that are up to 50% more accurate than traditional methods. Amazon Forecast is easy to use and requires no machine learning experience. The service automatically provisions the necessary infrastructure, processes data, and builds custom, private machine learning models that are hosted on AWS and ready to make predictions. To get started with Amazon Forecast, visit https://aws.amazon.com/forecast/.
Listening to current retail technology discussions, it's safe to say that artificial intelligence is the early favorite for buzzword of the year, with countless taglines promising unprecedented productivity improvements based on AI. Advanced forecasting is often cited as one of the top areas where AI holds great promise – but how do you separate the hype from the reality?