Intermittent demand: Linking forecasting to inventory obsolescence

Teunter, Ruud H.; Syntetos, Aris A.; Babaï, Mohamed Zied

doi:10.1016/j.ejor.2011.05.018

Cited by 163 publications

(111 citation statements)

References 28 publications

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“…Según [14] una de las propuestas más interesantes considera el aspecto de la obsolescencia en la previsión de la demanda intermitente. Denominado modelo TSB [11] propone la previsión para el período como la multiplicación del resultado de la atenuación exponencial de las demandas positivas por la probabilidad de no tener demanda nula en ese período. De esta manera la estimación de la probabilidad de una demanda es actualizada para cada período.…”

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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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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“…Some other Croston variants have also been discussed recently in the literature (e.g. Teunter et al 2011) but SBA is the only adaptation to Croston's method with empirical evidence in its support (e.g., Fildes et al 2008, Syntetos et al 2009b). The method has been assessed on 18,750 SKUs from the Royal Air Force and found to outperform Croston's estimator (Eaves and Kingsman 2004).…”

Section: Research Background: the Bernoulli Casementioning

confidence: 99%

Forecasting of compound Erlang demand

Syntetos

Babaï

Luo

2015

Journal of the Operational Research Society

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Intermittent demand items dominate service and repair inventories in many industries and they are known to be the source of dramatic inefficiencies in the defense sector. However, research in forecasting such items has been limited. Previous work in this area has been developed upon the assumption of a Bernoulli or a Poisson demand arrival process. Nevertheless, intermittent demand patterns may often deviate from the memory-less assumption. In this work we extend analytically previous important results to model intermittent demand based on a compound Erlang process, and we provide a comprehensive categorisation scheme to be used for forecasting purposes. In a numerical investigation we assess the benefit of departing from the memory-less assumption and we provide insights into how the degree of determinism inherent in the process affects forecast accuracy. Operationalised suggestions are offered to managers and software manufacturers dealing with intermittent demand items.

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“…Then they then suggested an adjustment for this in a following paper [5]. Finally, Teunter et al [6] [7] proposed an improvement on Croston's method that suggests to update the demand probability instead of the demand interval.…”

Section: Relevant Literaturementioning

confidence: 99%

Forecasting Spare Parts Demand Using Statistical Analysis

Hemeimat¹,

Al-Qatawneh²,

Arafeh³

et al. 2016

AJOR

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Spare parts are very essential in most industrial companies. They are characterized by their large number and their high impact on the companies' operations whenever needed. Therefore companies tend to analyze their spare parts demand and try to estimate their future consumption. Nevertheless, they face difficulties in figuring out an optimal forecasting method that deals with the lumpy and intermittent demand of spare parts. In this paper, we performed a comparison between five forecasting methods based on three statistical tools; Mean squared error (MSE), mean absolute deviation (MAD) and mean error (ME), where the results showed close performance for all the methods associated with their optimal parameters and the frequency of the spare part demand. Therefore, we proposed to compare all the methods based on the tracking signal with the objective of minimizing the average number of out of controls. This approach was tested in a comparative study at a local paper mill company. Our findings showed that the application of the tracking signal approach helps companies to better select the optimal forecasting method and reduce forecast errors.

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Intermittent demand: Linking forecasting to inventory obsolescence

Cited by 163 publications

References 28 publications

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

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

Forecasting of compound Erlang demand

Forecasting Spare Parts Demand Using Statistical Analysis

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