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.

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. There are many good reasons why demand forecasting is important.