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Learning mechanism of Machine Learning Tool

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The process of conducting forecasting is becoming difficult day by day as many firms are trying to incorporate channel, product, pricing, discount and other data available aiming for improved accuracy. As a result of increasing difficult in demand forecasting and due to increase in the data volume made it essential for the creation of new learning tools and the purpose is served by Machine learning tool.

The methods followed by the traditional forecast will not be in a state to meet the client demands. In order to meet the demand Halo introduced the first machine learning tool, being tested on number of data bases to use and a powerful took for planners. The customers are allowed to test the tool to prove accuracy. There is a huge learning mechanism of the machine learning tool. The tool allows generating thousands of forecasts in a minute.

Benefits of Machine Learning Tools – A knowhow

The Benefits: By applying the modern tools to the demand planning the firm can gain so many advantages.

Get Data with more accuracy: projection of future sales with past sales is only done in typical forecasting. The information related to product features, price, channel, discounts and sales are unnoticed during forecast and are adjusted during accounting. The tool used for forecast demands more information in the forecast to avoid ignorance.  The forecasting is done at an individual level incorporating the knowledge of discounts and pricing history and other aspects which involves management control.  Raw materials price, economic data of third party, packaging and product ingredients are included in the forecast.

Faster forecast: Forecasting has been made faster by developing ML computer algorithms which capitalizes the capabilities on the modern computer. The demonstration has proved the capacity of building more than a million forecast without compromising on accuracy.

Deep view into sales and demand forecasting: Even minute issues are considered for forecasting. The out put of the sales demand forecast of important documents, data sources and helps in improving interpretation to attain feedback. On data which adds value and retain it for future use.

Working process: product mix plays a major role as in many cases 20% of the product demands 90% sales. Through the process of segmenting on price, frequency of sales and volume a massive forecasting can be broken down into segments and this process is implemented in the work flow.

Important pointers of the Machine Tools

The tool used two levels of forecasting and in the stage 1 25% of accuracy is proved in most cases and in the stage 2 accuracy is attained.

Validation of accuracy is done as per client requests using custom accuracy metrics and industry standard accuracy metrics.

Monitoring also helps to conduct monthly forecast accuracy monitoring to make sure that the bulk data used in the ML forecasting are stable and are conducted without bias.

Variance that appears in the forecasting are taken into consideration at any early stage in order to take corrective action to avoid the impact of varianceto avoid issues in business results.