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195

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Library Hi Tech News, vol. 20 no. 10
Type: Research Article
ISSN: 0741-9058

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Article
Publication date: 20 November 2009

511

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Library Hi Tech, vol. 27 no. 4
Type: Research Article
ISSN: 0737-8831

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Book part
Publication date: 4 March 2024

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Managing Destinations
Type: Book
ISBN: 978-1-83797-176-3

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Book part
Publication date: 14 September 2018

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Authenticity & Tourism
Type: Book
ISBN: 978-1-78754-817-6

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Book part
Publication date: 9 November 2017

Sizwe Timothy Phakathi

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Production, Safety and Teamwork in a Deep-Level Mining Workplace
Type: Book
ISBN: 978-1-78714-564-1

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Book part
Publication date: 7 July 2017

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Inclusive Principles and Practices in Literacy Education
Type: Book
ISBN: 978-1-78714-590-0

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Book part
Publication date: 27 November 2020

Arthur Seakhoa-King, Marcjanna M Augustyn and Peter Mason

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Tourism Destination Quality
Type: Book
ISBN: 978-1-83909-558-0

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Book part
Publication date: 19 December 2017

Karin Klenke

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Women in Leadership 2nd Edition
Type: Book
ISBN: 978-1-78743-064-8

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Article
Publication date: 6 December 2021

Thomas R. O'Neal, John M. Dickens, Lance E. Champagne, Aaron V. Glassburner, Jason R. Anderson and Timothy W. Breitbach

Forecasting techniques improve supply chain resilience by ensuring that the correct parts are available when required. In addition, accurate forecasts conserve precious resources…

Abstract

Purpose

Forecasting techniques improve supply chain resilience by ensuring that the correct parts are available when required. In addition, accurate forecasts conserve precious resources and money by avoiding new start contracts to produce unforeseen part requests, reducing labor intensive cannibalization actions and ensuring consistent transportation modality streams where changes incur cost. This study explores the effectiveness of the United States Air Force’s current flying hour-based demand forecast by comparing it with a sortie-based demand forecast to predict future spare part needs.

Design/methodology/approach

This study employs a correlation analysis to show that demand for reparable parts on certain aircraft has a stronger correlation to the number of sorties flown than the number of flying hours. The effect of using the number of sorties flown instead of flying hours is analyzed by employing sorties in the United States Air Force (USAF)’s current reparable parts forecasting model. A comparative analysis on D200 forecasting error is conducted across F-16 and B-52 fleets.

Findings

This study finds that the USAF could improve its reparable parts forecast, and subsequently part availability, by employing a sortie-based demand rate for particular aircraft such as the F-16. Additionally, our findings indicate that forecasts for reparable parts on aircraft with low sortie count flying profiles, such as the B-52 fleet, perform better modeling demand as a function of flying hours. Thus, evidence is provided that the Air Force should employ multiple forecasting techniques across its possessed, organically supported aircraft fleets. The improvement of the forecast and subsequent decrease in forecast error will be presented in the Results and Discussion section.

Research limitations/implications

This study is limited by the data-collection environment, which is only reported on an annual basis and is limited to 14 years of historical data. Furthermore, some observations were not included because significant data entry errors resulted in unusable observations.

Originality/value

There are few studies addressing the time measure of USAF reparable component failures. To the best of the authors’ knowledge, there are no studies that analyze spare component demand as a function of sortie numbers and compare the results of forecasts made on a sortie-based demand signal to the current flying hour-based approach to spare parts forecasting. The sortie-based forecast is a novel methodology and is shown to outperform the current flying hour-based method for some aircraft fleets.

Details

Journal of Defense Analytics and Logistics, vol. 5 no. 2
Type: Research Article
ISSN: 2399-6439

Keywords

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Book part
Publication date: 28 June 2023

Xinru Liu and Honggen Xiao

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Poverty and Prosperity
Type: Book
ISBN: 978-1-80117-987-4

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