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EXECUTIVE SUMMARY | Large retailers and consumer packaged goods (CPG) companies are using machine learning combined with predictive analytics to help them enhance consumer engagement, and create more accurate demand forecasts as they expand into new sales channels like the omnichannel. Now with cloud computing using supercomputers'neural network, algorithms, along with ARIMAX, dynamic regression, and unobserved components models (UCM), are becoming the catalyst for "machine learning-based forecasting." Compared to traditional demand forecasting methods, machine learning-based forecasting helps companies understand and forecast consumer demand that, in many cases, would otherwise be impossible. Companies that have implemented machine learning have found it easy to use, and its ability to learn from existing data takes relatively less time to implement, deliver benefits, and produce high ROI (return on investment).
Machine learning is taking a significant role in many big data initiatives. Large retailers and consumer packaged goods (CPG) companies are using machine learning combined with predictive analytics to help them enhance consumer engagement, and create more accurate demand forecasts as they expand into new sales channels like the omnichannel. With machine learning, supercomputers learn from mining masses of big data without human intervention to provide unprecedented consumer demand insights.
Predictive analytics and advanced algorithms, such as neural networks, have emerged as the hottest (and sometimes controversial) topic among senior management teams. Neural network algorithms are self-correcting and powerful, but are difficult to replicate and explain using traditional predictive analytics methods. For years, neural network models have been discarded due to the lack of storage and processing capabilities required to implement them. Now with cloud computing using supercomputers' neural network algorithms, along with ARIMAX, dynamic regression, and unobserved components models (UCM), are becoming the catalyst for "machine learning-based forecasting."
According to an article in Consumer Goods Technology magazine, through pattern recognition there will be a shift from active engagement to automated engagement. As part of this shift, technology (machine learning) takes over tasks from information gathering to actual execution. Compared to traditional demand forecasting methods, machine learning-based forecasting helps companies understand and forecast consumer demand that, in many cases, would otherwise be impossible. Here are several reasons why.
Traditional demand forecasts are based on time series forecasting methods (Exponential Smoothing, ARIMA, and others) that can only use...