On the stock control performance of intermittent demand estimators
By A. Syntetos, John E. Boylan. 2006
166 citations recorded by the source corpus.
The purpose of this paper is to assess the empirical stock control performance of intermittent demand estimation procedures. The forecasting methods considered are the simple moving average, single exponential smoothing, Croston's method and a new method recently developed by the authors of this paper. We first discuss the nature of the empirical demand data set (3000 stock keeping units) and we specify the stock control model to be used for experimentation purposes. Performance measures are then selected to report customer service level and stock volume differences. The out-of-sample empirical comparison results demonstrate the superior stock control performance of the new intermittent demand forecasting method and enable insights to be gained into the empirical utility of the other estimators.
Subfields: Management Information Systems, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/5775710fd236e114c55ba01f37312750e75155c0
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In Management Information Systems
- Nikolaos Kourentzes
- Xun Wang
- Stephen M. Disney
- Konstantinos Nikolopoulos
- Robert Fildes
- Fotios Petropoulos
- Sven F. Crone
- A. Syntetos
In Management Science and Operations Research
- Fotios Petropoulos
- Rob J Hyndman
- Konstantinos Nikolopoulos
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Spyros Makridakis
- Christoph Bergmeir
- Evangelos Spiliotis
Citations in the corpus
- Forecasting: theory and practice (2020, background): “A more direct approach is to smooth the mean square errors over the lead time (Syntetos and Boylan, 2006).”
- Forecasting and operational research: a review (2008, methodology): “Croston’s method and its variants (in conjunction with an appropriate distribution) have been reported to offer tangible benefits to stockists facing intermittent demand (Eaves and Kingsman, 2004; Syntetos and Boylan, 2006).”
- The inventory performance of forecasting methods: Evidence from the M3 competition data (2019, background): “Syntetos and Boylan (2006) performed a similar analysis considering both the stock volume and the realised customer service level.”
- Intermittent demand forecasts with neural networks (2013, methodology): “Inventory metrics were evaluated following the suggestions of the literature(for e.g. see Levén and Segerstedt, 2004; Eaves and Kingsman, 2004; Syntetos and Boylan, 2006).”