Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 485 - 496 |
| Number of pages | 12 |
| Journal | International Journal of Forecasting |
| Volume | 28 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2012 |
Cite this
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Forecasting the intermittent demand for slow-moving inventories: A modelling approach. / Snyder, Ralph David; Ord, J Keith; Beaumont, Adrian Nicholas.
In: International Journal of Forecasting, Vol. 28, No. 2, 2012, p. 485 - 496.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Forecasting the intermittent demand for slow-moving inventories: A modelling approach
AU - Snyder, Ralph David
AU - Ord, J Keith
AU - Beaumont, Adrian Nicholas
PY - 2012
Y1 - 2012
N2 - Organizations with large-scale inventory systems typically have a large proportion of items for which demand is intermittent and low volume. We examine various different approaches to demand forecasting for such products, paying particular attention to the need for inventory planning over a multi-period lead-time when the underlying process may be non-stationary. This emphasis leads to the consideration of prediction distributions for processes with time-dependent parameters. A wide range of possible distributions could be considered, but we focus upon the Poisson (as a widely used benchmark), the negative binomial (as a popular extension of the Poisson), and a hurdle shifted Poisson (which retains Croston s notion of a Bernoulli process for the occurrence of active demand periods). We also develop performance measures which are related to the entire prediction distribution, rather than focusing exclusively upon point predictions. The three models are compared using data on the monthly demand for 1046 automobile parts, provided by a US automobile manufacturer. We conclude that inventory planning should be based upon dynamic models using distributions that are more flexible than the traditional Poisson scheme.
AB - Organizations with large-scale inventory systems typically have a large proportion of items for which demand is intermittent and low volume. We examine various different approaches to demand forecasting for such products, paying particular attention to the need for inventory planning over a multi-period lead-time when the underlying process may be non-stationary. This emphasis leads to the consideration of prediction distributions for processes with time-dependent parameters. A wide range of possible distributions could be considered, but we focus upon the Poisson (as a widely used benchmark), the negative binomial (as a popular extension of the Poisson), and a hurdle shifted Poisson (which retains Croston s notion of a Bernoulli process for the occurrence of active demand periods). We also develop performance measures which are related to the entire prediction distribution, rather than focusing exclusively upon point predictions. The three models are compared using data on the monthly demand for 1046 automobile parts, provided by a US automobile manufacturer. We conclude that inventory planning should be based upon dynamic models using distributions that are more flexible than the traditional Poisson scheme.
U2 - 10.1016/j.ijforecast.2011.03.009
DO - 10.1016/j.ijforecast.2011.03.009
M3 - Article
VL - 28
SP - 485
EP - 496
JO - International Journal of Forecasting
JF - International Journal of Forecasting
SN - 0169-2070
IS - 2
ER -