A Data Mining Approach for Intermittent Demand Forecasting of Aircraft Spare Parts － Focusing on the E-737(AEW&C: Airborne Early Warning & Control) Spare Parts －

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.

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Intermittent Demand Forecasting for Spare Parts: A Critical Review

Omega ◽

10.1016/j.omega.2021.102513 ◽

2021 ◽

pp. 102513

Author(s):

Çerağ Pinçe ◽

Laura Turrini ◽

Joern Meissner

Keyword(s):

Critical Review ◽

Demand Forecasting ◽

Spare Parts ◽

Intermittent Demand

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Stores clustering using a data mining approach for distributing automotive spare-parts to reduce transportation costs

Expert Systems with Applications ◽

10.1016/j.eswa.2011.09.121 ◽

2012 ◽

Vol 39 (5) ◽

pp. 4740-4748 ◽

Cited By ~ 12

Author(s):

Mehrdad Kargari ◽

Mohammad Mehdi Sepehri

Keyword(s):

Data Mining ◽

Spare Parts ◽

Transportation Costs ◽

Data Mining Approach

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Naval Vessel Spare Parts Demand Forecasting Using Data Mining

Journal of Society of Korea Industrial and Systems Engineering ◽

10.11627/jkise.2017.40.4.253 ◽

2017 ◽

Vol 40 (4) ◽

pp. 253-259 ◽

Cited By ~ 1

Author(s):

Hyunmin Yoon ◽

Suhwan Kim

Keyword(s):

Data Mining ◽

Demand Forecasting ◽

Spare Parts ◽

Naval Vessel ◽

Using Data

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Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea

Sustainability ◽

10.3390/su12156045 ◽

2020 ◽

Vol 12 (15) ◽

pp. 6045

Author(s):

Boram Choi ◽

Jong Hwan Suh

Keyword(s):

Data Mining ◽

Time Series ◽

Random Forest ◽

South Korea ◽

Demand Forecasting ◽

Spare Parts ◽

Weapon System ◽

Military Aircraft ◽

Data Mining Techniques ◽

The Military

In a weapon system, the accurate forecasting of the spare parts demand can help avoid the excess inventory, leading to the efficient use of budget. It can also help develop the combat readiness of the weapon system by improving weapon system utilization. Moreover, as performance-based logistics (PBL) projects have recently emerged, the accurate demand forecasting of spare parts has become an important issue for the PBL contractors as well. However, for the demand forecasting of spare parts, the time series methods, typically used in the military sector, have low prediction accuracies and the PBL contractors are mostly based on the judgment of practitioners. Meanwhile, most of the previous studies in the military sector have not considered the managerial characteristics of spare parts (e.g., reparability and the irregularity of maintenance). No previous work has considered any such features, which can indicate the reliability of spare parts (e.g., mean time between failures (MTBF)), although they can affect the spare parts demand. Therefore, to develop a more accurate forecasting of the spare parts demand of military aircraft, we designed and examined a systematic approach that uses data mining techniques. To fill up the research gaps of related works, our approach also considered the managerial characteristics of spare parts and included the new features that represent the reliability of spare parts. Consequently, given the case of South Korea and the full feature set, we found random forest gave better results than the other data mining techniques and the conventional time series methods. Using the best technique Random Forest, we identified the contribution of each managerial feature set to improving the prediction accuracy, and we found the reliability and operation environment are valuable feature sets in a significant way, so they should be collected, managed more carefully, and included for better prediction of spare parts demand of military aircraft.

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Innovative Risk Early Warning Model under Data Mining Approach in Risk Assessment of Internet Credit Finance

Computational Economics ◽

10.1007/s10614-021-10180-z ◽

2021 ◽

Author(s):

Min Lin

Keyword(s):

Data Mining ◽

Risk Assessment ◽

Early Warning ◽

Early Warning Model ◽

Data Mining Approach ◽

Warning Model

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A Data Mining Approach To Improve Military Demand Forecasting

Journal of Artificial Intelligence and Soft Computing Research ◽

10.1515/jaiscr-2015-0009 ◽

2014 ◽

Vol 4 (3) ◽

pp. 205-214 ◽

Cited By ~ 3

Author(s):

Rajesh Thiagarajan ◽

Mustafizur Rahman ◽

Don Gossink ◽

Greg Calbert

Keyword(s):

Data Mining ◽

Planning Process ◽

Prediction Models ◽

Demand Forecasting ◽

Planning System ◽

Prediction Errors ◽

Military Operation ◽

Data Mining Approach ◽

The Military ◽

Usage Rates

Abstract Accurately forecasting the demand of critical stocks is a vital step in the planning of a military operation. Demand prediction techniques, particularly autocorrelated models, have been adopted in the military planning process because a large number of stocks in the military inventory do not have consumption and usage rates per platform (e.g., ship). However, if an impending military operation is (significantly) different from prior campaigns then these prediction models may under or over estimate the demand of critical stocks leading to undesired operational impacts. To address this, we propose an approach to improve the accuracy of demand predictions by combining autocorrelated predictions with cross-correlated demands of items having known per-platform usage rates. We adopt a data mining approach using sequence rule mining to automatically determine cross-correlated demands by assessing frequently co-occurring usage patterns. Our experiments using a military operational planning system indicate a considerable reduction in the prediction errors across several categories of military supplies.

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Using Data Mining Methods for Predicting Sequential Maintenance Activities

Applied Sciences ◽

10.3390/app8112184 ◽

2018 ◽

Vol 8 (11) ◽

pp. 2184 ◽

Cited By ~ 5

Author(s):

Sadok Rezig ◽

Zied Achour ◽

Nidhal Rezg

Keyword(s):

Data Mining ◽

Spare Parts ◽

Threshold Values ◽

Data Mining Approach ◽

History Of ◽

Maintenance Activities ◽

Using Data ◽

Subsequent Failure ◽

Sequential Maintenance

A data mining approach is integrated in this work for predictive sequential maintenance along with information on spare parts based on the history of the maintenance data. For most practical problems, the simple failure of one part of a given piece of equipment induces the subsequent failure of the other parts of said equipment. For example, it is frequently observed in mining industries that, like many other industries, the maintenance of conventional equipment is carried out in sequence. Besides, depending on the state of parts of the equipment, many parts can be consumed and replaced. Consequently, with a group of spare parts consumed sequentially in various maintenance activities, it is possible to discover sequential maintenance activities. From maintenance data with predefined support or threshold values and spare parts information, this work determines the sequential patterns of maintenance activities. The proposed method predicts the occurrence of the next maintenance activity with information on the consumed spare parts. An industrial real case study is presented in this paper and it is well-noticed that our experimental results shed new light on the maintenance prediction using data mining.

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Parameter optimization of intermittent demand forecasting by using spreadsheet

Kybernetes ◽

10.1108/k-03-2015-0062 ◽

2015 ◽

Vol 44 (4) ◽

pp. 576-587 ◽

Cited By ~ 4

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.

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