Accurate forecasting is crucial for making informed decisions about the future. Time series forecasting is a powerful tool that can help you predict future trends and plan accordingly. Today we are introducing Zeaware Forecasting which is a powerful tool to help organisations predict future measures such as product sales, bookings or other forms of demand.
Many organisations have been experimenting with various forms of forecasting including both traditional methods (time series) and more advance machine learning based models. Typically results can vary, especially where large numbers of items are being forecast. This is often caused by varying demand patterns for each item, requiring planning and tuning of forecast models and parameters for each item. This has been difficult and unwieldy when large numbers of items are being forecast as many organisations may have hundreds to thousands of items.
Additionally, it has been hard to understand the impact of forecast settings overtime, measuring the accuracy, impact of changes to configuration as well as adjustments made by demand planning teams. Zeaware is introducing our Forecasting product to help improve this situation, providing users a simple to use interface for managing the forecasting process. Zeaware Forecasting is current in BETA which means we will work with a small number of customers to help optimise the solution before a full product release later in 2023.
Zeaware Forecasting runs in Microsoft Azure and leverages standard Azure Services such as Azure Databricks and Azure ML where forecasting modern forecasting models (R/Python libraries) are used to execute forecasts. This includes both traditional models (such as ARIMA) and more modern approaches (such as Machine Learning). Zeaware Forecaster allows organisations to leverage the vast array of forecasting libraries available, but provides the framework to manage, optimise and understand forecasting outcomes.
If you are interested in participating in the BETA program for Zeaware Forecasting please contact us using the links in the product page.