An Aggregate - Disaggregate Intermittent Demand Approach (ADIDA) to forecasting:

an empirical proposition and analysis

Konstantinos Nikolopoulos, Aris Syntetos, J E Boylan, Fotios Petropoulos, Vassilios Assimakopoulos

Research output: Contribution to journalArticle

51 Citations (Scopus)
118 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)544-554
Number of pages11
JournalJournal of the Operational Research Society
Volume62
Issue number3
DOIs
Publication statusPublished - 2011

Keywords

  • demand forecasting
  • inventory management
  • intermittent demand
  • aggregation
  • empirical investigation

Cite this

An Aggregate - Disaggregate Intermittent Demand Approach (ADIDA) to forecasting: an empirical proposition and analysis. / Nikolopoulos, Konstantinos; Syntetos, Aris; Boylan, J E; Petropoulos, Fotios; Assimakopoulos, Vassilios.

In: Journal of the Operational Research Society, Vol. 62, No. 3, 2011, p. 544-554.

Research output: Contribution to journalArticle

Nikolopoulos, Konstantinos ; Syntetos, Aris ; Boylan, J E ; Petropoulos, Fotios ; Assimakopoulos, Vassilios. / An Aggregate - Disaggregate Intermittent Demand Approach (ADIDA) to forecasting: an empirical proposition and analysis. In: Journal of the Operational Research Society. 2011 ; Vol. 62, No. 3. pp. 544-554.
@article{459328cd711c4e4e931ad5f6df55c32f,
title = "An Aggregate - Disaggregate Intermittent Demand Approach (ADIDA) to forecasting:: an empirical proposition and analysis",
abstract = "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.",
keywords = "demand forecasting, inventory management, intermittent demand, aggregation, empirical investigation",
author = "Konstantinos Nikolopoulos and Aris Syntetos and Boylan, {J E} and Fotios Petropoulos and Vassilios Assimakopoulos",
year = "2011",
doi = "10.1057/jors.2010.32",
language = "English",
volume = "62",
pages = "544--554",
journal = "Journal of the Operational Research Society",
issn = "0160-5682",
publisher = "Palgrave Macmillan",
number = "3",

}

TY - JOUR

T1 - An Aggregate - Disaggregate Intermittent Demand Approach (ADIDA) to forecasting:

T2 - an empirical proposition and analysis

AU - Nikolopoulos, Konstantinos

AU - Syntetos, Aris

AU - Boylan, J E

AU - Petropoulos, Fotios

AU - Assimakopoulos, Vassilios

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - demand forecasting

KW - inventory management

KW - intermittent demand

KW - aggregation

KW - empirical investigation

U2 - 10.1057/jors.2010.32

DO - 10.1057/jors.2010.32

M3 - Article

VL - 62

SP - 544

EP - 554

JO - Journal of the Operational Research Society

JF - Journal of the Operational Research Society

SN - 0160-5682

IS - 3

ER -