As many firms are trying to incorporate the products, pricing, channel, discounts and other data which is available in order to improve the accuracy. This has to lead the forecasting to become more complex. A machine learning forecasting solution is required to increase the forecasting demand complexity and also the combined massive increase in the data volume. The massive data cannot be scaled by the traditional forecasting methods as per the demands of the clients.

Halo boost:

Halo boost is one of the first machine learning systems which is used for demand forecasting. This system has been tested and proven on dozens of databases as it very easy to implement. It is a powerful tool for your planners. The basic Halo architecture is used to align with the ML forecasting solutions. The proven accuracy gains can be viewed by the halo customers when they test with these machine learning tools. Depending on the evidence and value-added basis, the ML forecasting can be adopted.

ML forecasting:

The ML forecasting is very fast and will allow a company to generate many SKU-level forecasts within minutes. The ML forecasting results can be obtained through the report management services and Halo’s dashboard. Those results are brought into action quickly as the halo system has been designed for this type of massive forecasting business scale and enterprise-scale.

Advantages of applying the modern machine tools:

The future sales are projected from the past sales levels with the typical forecasting methods. The cyclical trends and trends are also incorporated. During the forecasting, the factors like product features, discounts, sales channel information, and price are ignored and adjusted later for the accounts. More information can be incorporated into the forecast during the forecasting demand with the machine learning tools. The level of individual SKU is being optimized for the forecast.

The accuracy monitoring in the monthly forecast will ensure that the massive data which is used for the ML forecasting is stable and it is not an introducing bias. The monitoring dashboards developed by the halo will document with the trends accuracy metrics in detail. We can easily spot the tolerance limits which are approached by the variances in the forecast. The business results are impacted by the forecasting variance in order to take the corrective action. The further data can be explored so that the ML tuning may be beneficial.