Intermittent Demand of Spare parts based on Modem Technology

2021 ◽  
Author(s):  
Xi Ren ◽  
Xiao-fei Zhang
Keyword(s):  
Spare Parts ◽  
Intermittent Demand

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.

Exploiting elapsed time for managing intermittent demand for spare parts

2017 ◽  
Vol 258 (3) ◽  
pp. 958-969 ◽  
Author(s):  
Clint L.P. Pennings ◽  
Jan van Dalen ◽  
Erwin A. van der Laan
Keyword(s):  
Spare Parts ◽  
Intermittent Demand ◽  
Elapsed Time

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.

Innovation in Scientific Knowledge Based on Forecasting Assessment

2019 ◽  
pp. 247-263
Author(s):  
Ignacio Aranís Mahuzier ◽  
Pablo A. Viveros Gunckel ◽  
Rodrigo Mena Bustos ◽  
Christopher Nikulin Chandía ◽  
Vicente González-Prida Díaz
Keyword(s):  
Scientific Knowledge ◽  
Exponential Smoothing ◽  
Spare Parts ◽  
Moving Averages ◽  
Intermittent Demand ◽  
Automotive Market ◽  
Knowledge Based ◽  
Automotive Company ◽  
Forecasting Methods ◽  
Historical Series

This chapter presents a study of forecasting methods applicable to the spare parts demand faced by an automotive company that maintains a share of nearly 25% of the automotive market and sells approximately 13,000 parts per year. These parts are characterized by having intermittent demand and, in some cases, low demand, which makes it difficult for such companies to perform well and to obtain accurate forecasts. Therefore, this chapter includes a study of methods such as the Croston, Syntetos and Boylan, and Teunter methods, which are known to resolve these issues. Furthermore, the rolling Grey method is included, which is usually used in environments with short historical series and great uncertainty. In this study, traditional methods of prognosis, such as moving averages, exponential smoothing, and exponential smoothing with tendency and seasonality, are not neglected.

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

On the empirical performance of some new neural network methods for forecasting intermittent demand

2020 ◽  
Vol 31 (3) ◽  
pp. 281-305
Author(s):  
M Z Babai ◽  
A Tsadiras ◽  
C Papadopoulos
Keyword(s):  
Neural Network ◽  
Empirical Investigation ◽  
Forecast Accuracy ◽  
Spare Parts ◽  
Intermittent Demand ◽  
Network Methods ◽  
Empirical Performance ◽  
Single Exponential ◽  
Better Than ◽  
Airline Company

Abstract In this paper, new neural network (NN) methods are proposed to forecast intermittent demand and we empirically study their performance as compared to parametric and non-parametric forecasting methods proposed in the literature. The empirical investigation uses demand data for 5,135 spare parts for the fleet of aircrafts of an airline company. Three parametric benchmark methods are examined: single exponential smoothing (SES), Croston’s method and Syntetos–Boylan approximation, along with two bootstrapping methods: Willemain’s method and Zhou and Viswanathan’s method. The benchmark NN method considered in this paper is that proposed by Gutierrez et al. (2008) The paper shows the outperformance of SES and the NN methods for (a) their forecast accuracy and (b) their inventory efficiency (trade-off between holding volumes and backordering volumes) when compared to the other methods. Moreover, among the NN methods, a new proposed method is shown to be better than that proposed by Gutierrez et al. in terms of forecast accuracy and inventory efficiency.

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