Forecasting the intermittent demand for slow-moving inventories: A modelling approach

Snyder, Ralph D.; Ord, J. Keith; Beaumont, Adrian

doi:10.1016/j.ijforecast.2011.03.009

Cited by 100 publications

(84 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In particular insurers and policy makers are like the owner of the pond casino: they have to set premiums or make policies on the basis of imperfect model outcomes. Examples can be drawn from domains as different as load forecasting in power systems (Fan and Hyndman 2012), inventory demand management (Snyder et al 2012) But how can nonlinear models be so widely used if their predictive power is as limited as we say it is? Are we overstating the case, or is science is embroiled in confusion?…”

Section: Imperfect Models In Actionmentioning

confidence: 99%

Laplace’s Demon and the Adventures of His Apprentices

Frigg¹,

Bradley²,

Du³

et al. 2014

Philos. of Sci.

View full text Add to dashboard Cite

The sensitive dependence on initial conditions (SDIC) associated with nonlinear models imposes limitations on the models' predictive power. We draw attention to an additional limitation than has been under-appreciated, namely structural model error (SME). A model has SME if the model-dynamics differ from the dynamics in the target system. If a nonlinear model has only the slightest SME, then its ability to generate decision-relevant predictions is compromised. Given a perfect model, we can take the effects of SDIC into account by substituting probabilistic predictions for point predictions. This route is foreclosed in the case of SME, which puts us in a worse epistemic situation than SDIC.

show abstract

Section: Imperfect Models In Actionmentioning

confidence: 99%

Laplace’s Demon and the Adventures of His Apprentices

Frigg¹,

Bradley²,

Du³

et al. 2014

Philos. of Sci.

View full text Add to dashboard Cite

show abstract

“…The proposed model was compared with the forecasting procedure using demands of car parts. A static model is preferred for the stock control of products having demand one or two per year [28].…”

Section: B Computerized Modelmentioning

confidence: 99%

Effects of Slow Moving Inventory in Industries: Insights of Other Researchers

K.Balaji¹,

Kumar²

2013

IJTEF

View full text Add to dashboard Cite

Abstract-This paper discusses the various aspects of several researchers over slow moving inventory. Slow moving item constitute a large volume of firm items. The decision over the liquidation of some quantity of an on-hand stock slow moving items is an unpredictable one. Due to over stock situation, managing the slow moving or obsolescent items is the main problem for several industries. The various strategies involving in the slow moving inventory like optimal level, forecasting and obsolescence are discussed. Further research on the slow moving inventory areas are suggested in this paper.

show abstract

“…Winkelmann, 2008) and intermittent demand forecasting (see e.g. Snyder et al, 2012), with a traditional ARIMA-GARCH model which has been specialized to an ARIMA(1,1,0)-IGARCH(1,1) model to make statistical estimation feasible.…”

Section: Introduction: Problem Dataset and Main Ideasmentioning

confidence: 99%

“…This model has been applied in the intermittent demand forecasting literature, together with similar zero-inflated models and the related hurdle models (see e.g. Snyder et al, 2012, andKüsters &Speckenbach, 2012). Related models include Markov chains, which can be used to model conditional transition probabilities.…”

Section: Introduction: Problem Dataset and Main Ideasmentioning

confidence: 99%