scholarly journals Intermittent Demand Forecasting for Spare Parts: A Critical Review

Omega ◽  
2021 ◽  
pp. 102513
Author(s):  
Çerağ Pinçe ◽  
Laura Turrini ◽  
Joern Meissner
Keyword(s):  
Critical Review ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Intermittent Demand

scholarly journals Improving intermittent demand forecasting based on data structure

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
S. Fatemeh Faghidian ◽  
◽  
Mehdi Khashei ◽  
Mohammad Khalilzadeh ◽  
◽  
...  
Keyword(s):  
Hybrid Model ◽  
Prediction Models ◽  
Moving Average ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Percentage Error ◽  
Complex Nature ◽  
Intermittent Demand ◽  
Classical Models ◽  
The Cost

Forecasting spare parts requirements is a challenging problem, because the normally intermittent demand has a complex nature in patterns and associated uncertainties, and classical forecasting approaches are incapable of modeling these complexities. The present study introduces a hybrid model that can impressively overcome the limitations of classical models while simultaneously using their unique advantages in dealing with the complexities in intermittent demand. The strategy of the proposed hybrid model is to use the three individual autoregressive moving average (ARMA), single exponential smoothing (SES), and multilayer perceptron (MLP) models simultaneously. Each of them has the potential of modeling a different structure and patterns of behavior among the data. The accuracy in forecasting ability is also increased by the suitable examination of these in the intermittent data. Croston’s method is the backbone of the suggested model. The proposed hybrid model is based on CV2 and ADI criteria, which improve its efficacy in examining inappropriate structures by reducing the cost of inappropriate modeling while increasing the prediction model accuracy. Using these results prevents the hybrid model from being confused or weakened in the modeling of all groups and reduces the risk of choosing the disproportionate model. The accuracy of prediction models was evaluated and compared using mean absolute percentage error (MAPE) by implementing an example, and promising results were achieved.

Parameter optimization of intermittent demand forecasting by using spreadsheet

Kybernetes ◽  
2015 ◽  
Vol 44 (4) ◽  
pp. 576-587 ◽  
Author(s):  
Gamze Ogcu Kaya ◽  
Omer Fahrettin Demirel
Keyword(s):  
Parameter Optimization ◽  
Demand Forecasting ◽  
Real Data ◽  
Spare Parts ◽  
Aviation Industry ◽  
Intermittent Demand ◽  
Demand Characteristics ◽  
Content Type ◽  
Forecasting Methods ◽  
Smoothing Parameters

Purpose – Accurate forecasting of intermittent demand is very important since parts with intermittent demand characteristics are very common. The purpose of this paper is to bring an easier way of handling the hard work of intermittent demand forecasting by using commonly used Excel spreadsheet and also performing parameter optimization. Design/methodology/approach – Smoothing parameters of the forecasting methods are optimized dynamically by Excel Solver in order to achieve the best performance. Application is done on real data of Turkish Airlines’ spare parts comprising 262 weekly periods from January 2009 to December 2013. The data set are composed of 500 stock-keeping units, so there are 131,000 data points in total. Findings – From the results of implementation, it is shown that using the optimum parameter values yields better performance for each of the methods. Research limitations/implications – Although it is an intensive study, this research has some limitations. Since only real data are considered, this research is limited to the aviation industry. Practical implications – This study guides market players by explaining the features of intermittent demand. With the help of the study, decision makers dealing with intermittent demand are capable of applying specialized intermittent demand forecasting methods. Originality/value – The study brings simplicity to intermittent demand forecasting work by using commonly used spreadsheet software. The study is valuable for giving insights to market players dealing with items having intermittent demand characteristics, and it is one of the first study which is optimizing the smoothing parameters of the forecasting methods by using spreadsheet in the area of intermittent demand forecasting.

scholarly journals Intermittent demand forecasting for aircraft inventories: a study of Brazilian’s Boeing 737NG aircraft´s spare parts management

TRANSPORTES ◽  
2019 ◽  
Vol 27 (2) ◽  
pp. 102-116
Author(s):  
Jersone Tasso Moreira Silva ◽  
Luiz Henrique Santos ◽  
Alexandre Teixeira Dias ◽  
Hugo Ferreira Braga Tadeu
Keyword(s):  
Moving Average ◽  
Demand Forecasting ◽  
Exponential Smoothing ◽  
Spare Parts ◽  
Next Generation ◽  
Intermittent Demand ◽  
Spare Parts Management ◽  
Single Exponential ◽  
Airline Company

Este estudo tem como objetivo avaliar cinco métodos de previsão para demanda intermitente usando uma série histórica de consumo de peças sobressalentes da aeronave 737 Next Generation, fabricado pela Boeing, da maior frota aérea brasileira gerenciada pela VRG Airline Company S/A. Os métodos de Winter, Croston, Single Exponential Smoothing, Weight Moving Average e Método de Distribuição de Poisson foram testados em um histórico de 53 peças sobressalentes e cada uma delas possui um histórico de demanda de trinta e seis meses (janeiro de 2013 a dezembro de 2015). Os resultados mostraram que os métodos Weight Moving Average, Distribuição de Poisson e Croston apresentaram os melhores ajustes. Além disso, observou-se que a maior parte das demandas por peças sobressalentes apresentaram um padrão smooth ao contrário do resultado obtido pelo estudo de Ghobbar and Friend (2003) que apresentou um padrão lumpy. Por outro lado, tem-se que o Método de Winter apresentou-se como o de pior ajuste em ambos os estudos. Conclui-se que os métodos de Weight Moving Average e Distribuição de Poisson são os mais adequados para avaliar a demanda intermitente para o caso da VRG Airline Company S/A.

scholarly journals Performance tradeoffs for spare parts supply chains with additive manufacturing capability servicing intermittent demand

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kyle C. McDermott ◽  
Ryan D. Winz ◽  
Thom J. Hodgson ◽  
Michael G. Kay ◽  
Russell E. King ◽  
...  
Keyword(s):  
Supply Chain ◽  
Additive Manufacturing ◽  
Production Capacity ◽  
Spare Part ◽  
Spare Parts ◽  
Mixed Integer ◽  
Network Configuration ◽  
Intermittent Demand ◽  
Content Type ◽  
Demand Patterns

PurposeThe study aims to investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns.Design/methodology/approachThis work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network.FindingsThis research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance.Research limitations/implicationsThis research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity and post-processing requirements.Originality/valueThis research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.

scholarly journals Electrical Spare Parts Demand Forecasting

2014 ◽  
Vol 20 (10) ◽  
Author(s):  
V. Vaitkus ◽  
G. Zylius ◽  
R. Maskeliunas
Keyword(s):  
Demand Forecasting ◽  
Spare Parts

scholarly journals Intermittent demand forecasting for medical consumables with short life cycle using a dynamic neural network during the COVID-19 epidemic

2020 ◽  
Vol 26 (4) ◽  
pp. 3106-3122
Author(s):  
Peipei Liu
Keyword(s):  
Neural Network ◽  
Life Cycle ◽  
Model Selection ◽  
Demand Forecasting ◽  
Seasonal Adjustment ◽  
Intermittent Demand ◽  
Short Life ◽  
Dynamic Neural Network ◽  
Demand Models ◽  
The Impact

Accurate demand forecasting is always critical to supply chain management. However, many uncertain factors in the market make this issue a huge challenge. Especially during the current COVID-19 outbreak, the shortage of certain types of medical consumables has become a global problem. The intermittent demand forecast of medical consumables with a short life cycle brings some new challenges, such as the demand occurring randomly in many time periods with zero demand. In this research, a seasonal adjustment method is introduced to deal with seasonal influences, and a dynamic neural network model with optimized model selection procedure and an appropriate model selection criterion are introduced as the main forecasting models. In addition, in order to reduce the impact of zero demand, it adds some input nodes to the neural network by preprocessing the original input data. Lastly, a modified error measurement method is proposed for performance evaluation. Experimental results show that the proposed forecasting framework is superior to other intermittent demand models.

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