Demand Categorisation, Forecasting, and Inventory Control for Intermittent Demand Items

Abstract

This paper considers the bi-criteria scheduling problem of simultaneously minimising the total completion time and the number of tardy jobs with release dates on a single machine. Since the problem had been classified as NP-Hard, two heuristics (HR9 and HR10) were proposed for solving this problem. Performance evaluations of the proposed heuristics and selected solution methods (HR7 and BB) from the literature were carried out on 1,100 randomly generated problems ranging from 3 to 500 jobs. Experiment results show that HR7 outperformed HR10 when the number of jobs (n) is less than 30, while HR10 outperformed HR7 for n≥ 30. OPSOMMINGIn hierdie artikel word die bi-kriteria-skeduleringsprobleem bestudeer waar die totale voltooiingstyd en die aantal take wat laat is op 'n enkele masjien geminimiseer moet word. Verskeie heuristieke word voorgestel en getoets om sodoende die beste benadering te identifiseer.

Forecasting spare part demand with installed base information: A review

“…Our work contributes to the growing body of literature reviews on spare part demand forecasting along the following lines. Babiloni et al (2010) provide a review on intermittent demand forecasting, whereas Boylan & Syntetos (2010), Rego & Mesquita (2011), and Bacchetti & Saccani (2012) review the literature on spare parts demand forecasting specifically. The focus mainly lies on techniques based on historical demand data, and the authors hardly consider other sources of information.…”

Forecasting of Sporadic Products: Practical and Theoretical Considerations

“…Its core idea is to calculate the number of periods with 0 demand in the analyzed timeframe and build in an additional smoothing equation. The mathematical-statistical description (Babiloni et al, 2010;Boylan et al, 2008) can be found in the work of Croston published in 1972. Syntetos and Boylan have pointed out, however, that the Croston method results in biased estimation.…”

“…According to Boylan et al (2008) and Babiloni et al (2010) different product categories require different forecasting methods to be used. Where demand volume variation (that is CV DE ) is over 0.7 and/or the sporadicity of a product are high (value of p is higher than 1.32), the recommended forecasting method is the Syntetos-Boylan method.…”

“…Investigating the phenomena of sporadic demand is relevant from a managerial perspective because it can increase operational effectiveness and efficiency. New forecasting methods considering the sporadic nature of demand have been developed and proposed which promise a higher forecast accuracy and thus an improved level of service with lower inventory investment (Babiloni et al, 2010;Chitturi et al, 2010;Kourentzes, 2014). The objective of our paper is to empirically test these newly developed methods and their efficacy.…”

Previsión de demanda intermitente con métodos de series de tiempo y redes neuronales artificiales: Estudio de caso

“…Para este fin son calculados dos indicadores: un Coeficiente de Variación Cuadrática (CV2), y el Promedio del Intervalo entre Demandas (ADI). Este último valor puede ser clasificado en otros cuatro grupos: i) errática, cuando el tamaño de la demanda presenta elevada variabilidad; ii) intermitente, cuando la serie presenta varios valores nulos de demanda; iii) granulada ó irregular (en la literatura es conocida como lumpy) cuando la variabilidad del tamaño de la demanda y los períodos entre dos demandas no nulas son altos; y iv) atenuada, cuando la variabilidad del tamaño de la demanda y el período entre dos demandas no nulas son bajas [3][4][5]. La …”