Forecasting and Stock Control for Intermittent Demands

By J. D. Croston. 1972

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Citations in the corpus

Effective Bayesian Modeling of Groups of Related Count Time Series (2014, methodology): “We compare the forecasting performance of the proposed H-NBSS model against the following benchmarks: (i) Croston’s (1972) method, (ii) simple exponential smoothing (E-S) with additive errors and an automaticallyadjusted smoothing constant, and (iii) the damped dynamic model with negative binomial o”
Automatic Time Series Forecasting: The forecast Package for R (2008, methodology): “croston() implements Croston’s (1972) method for intermittent demand forecasting.”
A new approach to forecasting intermittent demand for service parts inventories (2004, methodology): “…1987; Dunsmuir & Snyder, 1989); simple statistical smoothing methods (Bier, 1984; Sani & Kingsman, 1997); Croston’s variant of exponential smoothing (Croston, 1972; Rao, 1973; Segerstedt, 1994; Willemain, Smart, Shockor, & Desautels, 1994; Johnston & Boylan, 1996); bootstrap methods (Bookbinder &…”
Mean-based error measures for intermittent demand forecasting (2013, methodology): “We also tried geometrically distributed demand sizes as in Teunter, Syntetos, and Babai (2011), and regular intermittent demand as in Croston (1972), with similar results. However, in further experiments MSE was also unreliable. Willemain et al. (1994) point out that demand in industrial data is oft”
EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II (2006, unknown): “The standard method of forecasting intermittent series was developed by Croston (1972) and works as follows.”
Retail forecasting: Research and practice (2019, methodology): “Nevertheless, Shenstone & Hyndman note that Croston’s point forecasts and prediction intervals may still be useful. As mentioned in the stock-out discussion, POS sales are not the same as the latent demand. The observed zero sales may either be due to the product’s temporary unavailability (e.g., st”
Stochastic models underlying Croston ’ s method for intermittent demand forecasting 2 February 2005 (2005, methodology): “Stochastic models underlying Croston’s method for intermittent demand forecasting 1. Introduction Inventories with intermittent demands are quite widespread in practice. Data for such items consist of time series of non-negative integer values where some values are zero. We shall denote the historic”
The inventory performance of forecasting methods: Evidence from the M3 competition data (2019, background): “In the slow-moving demand context, Sani and Kingsman (1997) were the ﬁrst to assess the performance of various estimators, namely simple moving averages (SMA), simple exponential smoothing (SES) and Croston’s method (Croston, 1972), under diﬀerent inventory control systems.”
The accuracy of intermittent demand estimates (2005, methodology): “When all points in time are considered, SES is shown to perform more accurately than Croston’s method does. This finding does not agree with the empirical results presented in Willemain et al. (1994), where Croston’s estimator was found to outperform SES in a reorder interval context.”
On the demand distributions of spare parts (2012, unknown): “One other compound binomial distribution that appeared in the literature is that involving normally distributed demand sizes (Croston 1972, 1974).”
Statistical models underlying functions of 'smooth' package for R (2017, methodology): “There are several ways of modelling this probability (for example, Croston (1972), Teunter et al.”
Forecasting and operational research: a review (2008, methodology): “A particular operational problem of method selection first laid out in ORQ by Croston (1972) that faces both the retailer and the spare parts supplier is one of intermittent demand—that is, when demand is spasmodic with many periods experiencing zero demand (Section 2.1.2).”
Forecasting model selection through out-of-sample rolling horizon weighted errors (2011, background): “…2000) • Exponential Smoothing Models: Simple smoothing; Holt (Holt et al., 1960); HoltWinter (additive or multiplicative) (Winters, 1960); Croston (Croston, 1972); Syntetos-Boylan (Syntetos & Boylan, 2005) • Moving Average Models: Simple moving average; Weighted simple moving average • ARIMA…”
On the categorization of demand patterns (2005, methodology): “It is this approach that is discussed in this paper and its application is demonstrated by considering EWMA, Croston’s method and an alternative to Croston’s estimator developed by the first two authors of this paper. Comparison results are based on a theoretical analysis of the mean square error du”