Forecasting for intermittent demand: the estimation of an unbiased average

Shale, Estelle A.; Boylan, John E.; Johnston, Freya

doi:10.1057/palgrave.jors.2602031

Cited by 53 publications

(22 citation statements)

References 13 publications

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“…However, the method has been proved biased. Two articles present to use correlation factor to overcome the bias (Shale & Johnston, 2006;A. A. Syntetos & Croston, 2005) and other two articles prove the veracity with calculation examples(Aris A.…”

Section: Research Backgroundmentioning

confidence: 93%

The Effects of the Correlation of Electric Materials on Forecasting and Stock Control

Jiang

Gao

Chi

et al. 2016

ESJ

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Forecasting and stock control play an important role in the electric companies because outstanding forecasting and stock control increase service level obviously and decrease stock cost effectively. However, the majority of the electric materials are intermittent demand, resulting in poor forecasting and stock control performance. Therefore, exploring the reasons that affect forecasting performance and stock control is necessary. This paper explores the effects of the correlation of intermittent electric materials on forecasting and stock control. First, we divide the correlation into three categories: autocorrelation in demand sizes, autocorrelation in intervals and cross-correlation between demand size and interval. Forecasting by SBA approach and using periodic dynamic inventory strategy (T, S) to control stock, exploring the effects of these three correlations on forecast accuracy, stock cost and service level. The data shows that correlations of electric materials affect their forecasting and stock control, which will help company find more accurate forecast approach and lower the cost of stock in the future.

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Section: Research Backgroundmentioning

confidence: 93%

The Effects of the Correlation of Electric Materials on Forecasting and Stock Control

Jiang

Gao

Chi

et al. 2016

ESJ

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“…The empirical validity and utility of this estimator have been independently established in work conducted by Eaves and Kingsman (2004) and Gutierrez et al (2008). Correction factors to overcome the bias associated with Croston's approach have also been discussed by Boylan and Syntetos (2003) and Shale et al (2006). Levén and Segerstedt (2004) proposed a modification to Croston's method that nevertheless was found by Boylan and Syntetos (2006) to be more biased than the original estimator.…”

Section: Intermittent Demand Forecasting and Stock Controlmentioning

confidence: 97%

Periodic control of intermittent demand items: theory and empirical analysis

Syntetos

Babaï

Dallery

et al. 2009

Journal of the Operational Research Society

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In this paper we propose a modification to the standard forecasting, periodic order-up-to-level inventory control approach to dealing with intermittent demand items, when the lead-time length is shorter than the average interdemand interval. In particular, we develop an approach that relies upon the employment of separate estimates of the inter-demand intervals and demand sizes, when demand occurs, directly for stock control purposes rather than first estimating mean demand and then feeding the results in the stock control procedure. The empirical performance of our approach is assessed by means of analysis on a large demand data set from the Royal Air Force (RAF, UK). Our work allows insights to be gained on the interactions between forecasting and stock control as well as on demand categorization-related issues for forecasting and inventory management purposes.

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“…En [13] fue propuesta otra modificación, denominada corrección SBJ, cuando la llegada de los pedidos sigue comportamiento de tipo Poisson, introduciendo el siguiente factor de corrección:…”

Section: Métodos Clásicos De Previsiónunclassified

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

Cruz

Corrêa

2017

DYNA

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This article aims to study the intermittent demand forecasting for a specific type of spare part of a Brazilian refrigeration industry that commercialize its products in the Latin American market. Demand characterization is performed in terms of their intermittency and variability. Results are obtained with classical intermittent forecasting methods outside the sample: Croston, Syntetos-Boylan Approximation (SBA), Shale-Boylan-Johnston Correction (SBJ), Multiple Aggregation Prediction Algorithm (MAPA) and with Artificial Neural Networks (ANN) based models. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are used for comparison and selection of forecast model. The comparative analysis results shows that the predictions based on a simple three-layer ANN model trained with the Resilient Backpropagation algorithm present better performance. The calculations were performed using R software with RStudio, "forecast", "tsintermittent" and "neuralnet" libraries.Keywords: demand forecasting; intermittent demand; artificial neural networks.Previsión de demanda intermitente con métodos de series de tiempo y redes neuronales artificiales: Estudio de caso ResumenEste artículo tiene como objetivo estudiar la previsión de la demanda intermitente de un tipo específico de pieza de reposición en una industria brasilera de sistemas de refrigeración que comercializa sus productos en el mercado latinoamericano. La demanda es caracterizada en términos de intermitencia y variabilidad. Los resultados son calculados usando métodos clásicos de previsión intermitente fuera de la muestra: Croston, Aproximación Syntetos-Boylan (SBA), Corrección Shale-Boylan-Johnston (SBJ), Algoritmo de Previsión de Agregación Múltiple (MAPA) y modelos basados en Redes Neuronales Artificiales (RNA). El Error Cuadrático Medio (RMSE) y Desvío Medio Absoluto (MAE) son utilizados para efectos de comparación y selección del modelo de previsión. El análisis comparativo de los resultados muestra que las previsiones basadas en modelos RNA simple de tres capas y entrenadas con el algoritmo Resilient Backpropagation presentan mejor desempeño. Los cálculos fueron realizados con el software R, RStudio, bibliotecas "forecast", "tsintermittent" y "neuralnet".Palabras clave: previsión de la demanda; demanda intermitente; redes neuronales artificiales.

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Forecasting for intermittent demand: the estimation of an unbiased average

Cited by 53 publications

References 13 publications

The Effects of the Correlation of Electric Materials on Forecasting and Stock Control

The Effects of the Correlation of Electric Materials on Forecasting and Stock Control

Periodic control of intermittent demand items: theory and empirical analysis

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

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