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An aggregate-disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis

Nikolopoulos, Konstantinos, Syntetos, Argyrios ORCID:, Boylan, J. E., Petropoulos, Fotios and Assimakopoulos, V. 2010. An aggregate-disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis. Journal of the Operational Research Society 62 (3) , pp. 544-554. 10.1057/jors.2010.32

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Intermittent demand patterns are characterised by infrequent demand arrivals coupled with variable demand sizes. Such patterns prevail in many industrial applications, including IT, automotive, aerospace and military. An intuitively appealing strategy to deal with such patterns from a forecasting perspective is to aggregate demand in lower-frequency ‘time buckets’ thereby reducing the presence of zero observations. However, such aggregation may result in losing useful information, as the frequency of observations is reduced. In this paper, we explore the effects of aggregation by investigating 5000 stock keeping units from the Royal Air Force (UK). We are also concerned with the empirical determination of an optimum aggregation level as well as the effects of aggregating demand in time buckets that equal the lead-time length (plus review period). This part of the analysis is of direct relevance to a (periodic) inventory management setting where such cumulative lead-time demand estimates are required. Our study allows insights to be gained into the value of aggregation in an intermittent demand context. The paper concludes with an agenda for further research.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Subjects: H Social Sciences > HD Industries. Land use. Labor
Uncontrolled Keywords: demand forecasting; inventory management; intermittent demand; aggregation; empirical investigation
Additional Information: Pdf uploaded in accordance with publisher's policy at (accessed 3.10.14).
Publisher: Palgrave Macmillan
ISSN: 0160-5682
Last Modified: 02 May 2023 14:05

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