A Predictive Approach to Define the Best Forecasting Method for Spare Parts: A Case Study in Business Aircrafts’ Industry

Babajanivalashedi, R.; Baboli, Armand; Shahzad, M.K.; Tonadre, R.

doi:10.1109/ieem.2018.8607461

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…Boylan and Syntetos 77 summarized the methods and techniques of spare parts demand prediction in recent years, most of which focus on SVM and ANN. Babajanivalashed et al 78 proposed a methodology to select the best prediction method based on binary classifier machine learning, the results indicated that neural network is the best method for 98% of demand compared with the performance of random forest. The above methods have self-learning and fast optimization capabilities in prediction, but there is still room for improvement in error control.…”

Section: Machine Learning Of Spare Parts Predictionmentioning

confidence: 99%

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Civil Aircraft Spare Parts Prediction and Configuration Management Techniques: Review and Prospect

Feng

Chen

et al. 2021

Advances in Mechanical Engineering

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Spare parts are treated as the basis to guarantee the safe and economic operation of civil aircraft, its scientific prediction and reasonable configuration play an important role in perfecting integrated logistics support (ILS) and achieving win-win situation of stakeholders (e.g. manufacturers, operators, maintenance providers). This paper studies the existing spare parts prediction and configuration methods of civil aircraft, and discusses future development trend of spare parts from the perspective of prediction and configuration, respectively. The current development status of civil aircraft spare parts prediction techniques are firstly introduced, according to demand characteristics of spare parts; the present research status of civil aircraft spare parts configuration methods are then elaborated, based on the traditional methods and machine learning methods; the development trend of civil aircraft spare parts prediction and configuration methods is finally analyzed, combined with the advantages and disadvantages of the existing methods of civil aircraft spare parts management methods. The efforts of this work provide a reference for the comprehensive management of civil aircraft spare parts and improve the perfection degree for the ILS of civil aircraft.

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Section: Machine Learning Of Spare Parts Predictionmentioning

confidence: 99%

“…Artificial neural network [76][77][78] Back-propagation neural network [81][82][83][84][85] demand patterns. It should be noted that the above method is limited by the assumptions of METRIC theory.…”

Section: Nonlinear Systemmentioning

confidence: 99%

Civil Aircraft Spare Parts Prediction and Configuration Management Techniques: Review and Prospect

Feng

Chen

et al. 2021

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

A Predictive Approach to Define the Best Forecasting Method for Spare Parts: A Case Study in Business Aircrafts’ Industry

Cited by 1 publication

References 13 publications

Civil Aircraft Spare Parts Prediction and Configuration Management Techniques: Review and Prospect

Civil Aircraft Spare Parts Prediction and Configuration Management Techniques: Review and Prospect

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