Intermittent demand—when a product or SKU experiences several periods of zero demand—is highly variable. Intermittent demand is very common in industries such as aviation, automotive, defense, manufacturing, and retail. It also typically occurs with products nearing the end of their lifecycle.
However, due to the many zero values in intermittent demand time series, the usual methods of forecasting, such as exponential smoothing and ARIMA, do not give an accurate forecast. In these cases, approaches such as Croston may provide a better accuracy over traditional methods. Prateek Nagaria compares traditional and Croston methods in R on intermittent demand time series.
Prateek Nagaria is a data scientist for the Data Team. Prateek is an advanced analytics expert with more than five years of experience. He specializes in business analytics, big data technologies, and statistical modeling as well as programming languages like R, Python, Java, C, C++. Prateek holds a master’s degree in enterprise business analytics from the National University of Singapore and a bachelor’s degree in computer science and engineering.
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