Improve planning and boost your bottom line
If you’re relying solely on historical data to forecast demand, you may not be seeing the whole picture. Identifying patterns from previous years can be helpful, but it doesn’t account for outside factors like changing consumer preferences or the market entry of new competitors.
Without accounting for those externalities, you run the risk of underproducing (harming customer relationships), overproducing (incurring excess costs), or simply being operationally inefficient. Here’s how RapidMiner helps you fill in the gaps.
Overcoming the computational demands of time series analysis
Ryan Frederick of Dominos explains how his data science team improved their supply chain by providing highly accurate and scalable demand forecasts through extensible time series forecasting and scaled R-based models using RapidMiner. Watch the full presentation.
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