Statistical forecasting for inventory control

By R. Brown. 1960

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Citations in the corpus

Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale (2017, unknown): “Maximum likelihood learning for exponential smoothing models [6] is developed in [11].”
25 Years of IIF Time Series Forecasting: A Selective Review (2005, methodology): “These methods originated in the 1950s and 1960s with the work of Brown (1959, 1963), Holt (1957, 2004) and Winters (1960).”
FORECASTING USING SIMPLE EXPONENTIAL SMOOTHING METHOD (2012, methodology): “To determine the optimal α value in the paper was used a traditional optimalization method based on the lowest mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE).”
Demand Forecasting in the Fashion Industry: A Review (2013, unknown): “Traditional forecasting methods, such as exponential smoothing [34], are designed for smooth, high‐volume demand and don’t work well with intermittent, erratic or lumpy demand.”
Forecasting seasonals and trends by exponentially weighted moving averages (2004, unknown): “Little [2].”
EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II (2006, unknown): “This presentation formed the basis of Brown’s first book, Statistical Forecasting for Inventory Control (Brown, 1959).”
A history-based test prioritization technique for regression testing in resource constrained environments (2002, methodology): “performance to used in the curr taken from st moving avera smoothing)[2].”
Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping ☆ (2015, background): “Since the early work of Brown (1959), the problem of forecasting for fast moving inventory items has attracted an enormous body of academic research.”
Exponential Smoothing Model Selection for Forecasting (2006, methodology): “It underpins the simple exponential smoothing method (Brown, 1959).”