A MODIFIED BOOTSTRAP METHOD FOR INTERMITTENT DEMAND FORECASTING FOR RARE SPARE PARTS.

Jung, G; Park, J; Kim, Y; Kim, YB

A MODIFIED BOOTSTRAP METHOD FOR INTERMITTENT DEMAND FORECASTING FOR RARE SPARE PARTS.

Jung, G Park, J Kim, Y

Kim, YB

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Publisher:: Publishing Horizons
Publication Type:: Journal Article
Citation:: International Journal of Industrial Engineering, 2017, 24, (2), pp. 245-254
Issue Date:: 2017

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Field	Value	Language
dc.contributor.author	Jung, G
dc.contributor.author	Park, J
dc.contributor.author	Kim, Y https://orcid.org/0000-0002-2829-1212
dc.contributor.author	Kim, YB
dc.date.accessioned	2022-08-11T23:18:10Z
dc.date.available	2022-08-11T23:18:10Z
dc.date.issued	2017
dc.identifier.citation	International Journal of Industrial Engineering, 2017, 24, (2), pp. 245-254
dc.identifier.issn	1072-4761
dc.identifier.issn	1943-670X
dc.identifier.uri	http://hdl.handle.net/10453/159986
dc.description.abstract	An effective inventory management requires timely and accurate forecasting of demand for parts or items. In many realworld scenarios, however, the demand for rare, high-cost spare parts are scarce and erratic, making it highly challenging to perform a reliable forecast for intermittent demand. Studies in the past offered two approaches to such intermittent demand forecasting: a traditional approach to estimating demand parametrically, and a non-parametric approach that estimates the distribution of demands. The bootstrap method is considered to be one of the key non-parametric methods available. Despite its usefulness, however, application of conventional bootstrap methods in intermittent demand forecasting does not take into account any existing dependent structures in lead time demand, leading to inaccurate forecasting. In this paper, we suggest a new bootstrap method that takes into consideration the unique characteristics of intermittent demand to improve forecasting performance. We conclude by demonstrating the applicability of suggested new method through the results of a simple case experiment
dc.language	English
dc.publisher	Publishing Horizons
dc.relation.ispartof	International Journal of Industrial Engineering
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0913 Mechanical Engineering
dc.subject.classification	Operations Research
dc.title	A MODIFIED BOOTSTRAP METHOD FOR INTERMITTENT DEMAND FORECASTING FOR RARE SPARE PARTS.
dc.type	Journal Article
utslib.citation.volume	24
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Institute For Sustainable Futures
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-11T23:18:09Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	24
utslib.citation.issue	2

Abstract:

An effective inventory management requires timely and accurate forecasting of demand for parts or items. In many realworld scenarios, however, the demand for rare, high-cost spare parts are scarce and erratic, making it highly challenging to perform a reliable forecast for intermittent demand. Studies in the past offered two approaches to such intermittent demand forecasting: a traditional approach to estimating demand parametrically, and a non-parametric approach that estimates the distribution of demands. The bootstrap method is considered to be one of the key non-parametric methods available. Despite its usefulness, however, application of conventional bootstrap methods in intermittent demand forecasting does not take into account any existing dependent structures in lead time demand, leading to inaccurate forecasting. In this paper, we suggest a new bootstrap method that takes into consideration the unique characteristics of intermittent demand to improve forecasting performance. We conclude by demonstrating the applicability of suggested new method through the results of a simple case experiment

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http://hdl.handle.net/10453/159986