ToolsGroup has introduced three new machine learning applications designed to improve demand forecasting and supply chain planning outcomes. They’re available as enhancements to the ToolsGroup SO99+ “Powerfully Simple” supply chain planning platform:
Groover for social sensing, a web and social listening system monitors social channels and gauges consumer sentiment to enhance demand sensing. For example, it can monitor and archive live Tweets on a specific product to sharpen the demand signal. It helps integrate live data streams that hold sentiment information with traditional sources like POS data. Groover can be used as an early indicator to improve forecast accuracy in other machine learning applications, such as new product introduction and trade promotion forecasting.
New Product Introduction (NPI) predicts the potential performance of a new product by analyzing early indicators and understanding product and market characteristics via machine learning. Given the lack of direct quantitative data, accurately forecasting the demand for a new product without a sales history can be a rather difficult problem. ToolsGroup’s NPI solution predicts new product performance and launch profiles by incorporating early data signals from web analytics, product attributes, market characteristics and social media sentiment.
Weather, Macroeconomic and Other External Data help complement the demand forecasting model by identifying the effect of exogenous events. This type of external data can be very difficult to incorporate into a statistical forecast due to the high number of variables. ToolsGroup’s Machine Learning Engine solves this by being able to “crunch” enormous amounts of data, removing the need to make statistical assumptions, while employing advanced algorithms like “Deep Learning.” Instead, open the data feed and let the machine “understand” the impact on the demand baseline.