Providing a piece of the puzzle: insights into the aircraft availability conundrum

Purpose – Aircraft availability (AA) is a key metric for assessing operational readiness. The declining trend in AA is a documented concern for senior Air Force leaders. This paper aims to investigate the components of non-available time and subsequently focuses on the largest and fastest growing ca...

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Main Authors: Jonathan D. Ritschel, Tamiko L. Ritschel, Nicole B. York
Format: Article
Language:English
Published: Emerald Publishing 2019-02-01
Series:Journal of Defense Analytics and Logistics
Subjects:
Online Access:https://www.emerald.com/insight/content/doi/10.1108/JDAL-09-2018-0015/full/pdf?title=providing-a-piece-of-the-puzzle-insights-into-the-aircraft-availability-conundrum
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author Jonathan D. Ritschel
Tamiko L. Ritschel
Nicole B. York
author_facet Jonathan D. Ritschel
Tamiko L. Ritschel
Nicole B. York
author_sort Jonathan D. Ritschel
collection DOAJ
description Purpose – Aircraft availability (AA) is a key metric for assessing operational readiness. The declining trend in AA is a documented concern for senior Air Force leaders. This paper aims to investigate the components of non-available time and subsequently focuses on the largest and fastest growing category: not mission capable maintenance unscheduled (NMCMU). Then, utilization of aircraft platforms is examined to determine the readiness benefits of increasing available hours. Design/methodology/approach – Stepwise regression is conducted on a data set of 30 aircraft platforms, consisting of 542 observations from 1998 to 2017, to reveal drivers of NMCMU. Next, utilization of aircraft platforms is examined through regression and correlation analysis of aircraft platforms and sorties or hours flown. Findings – Regression analysis reveals drivers of NMCMU include platform type, average age of aircraft, fleet size, breaks and cannibalization. These factors explain 80.6 per cent of the variance in the data set when predicting NMCMU. Additionally, the utilization results show that when more hours are made available, 5 per cent of each new hour is used for flying. Further analysis at the individual platform level finds a strong or moderate correlation between available hours and sorties flown for 93 per cent of the platforms. Originality/value – Implications from the regression analysis demonstrate there are remedies to increase AA, but many of these remedies may be costly. The utilization analysis expresses the potential readiness benefits of increasing available hours.
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spelling doaj.art-6faa290b1a1344d9acc7c3924110101f2022-12-22T04:03:09ZengEmerald PublishingJournal of Defense Analytics and Logistics2399-64392399-64472019-02-0131294010.1108/JDAL-09-2018-0015622346Providing a piece of the puzzle: insights into the aircraft availability conundrumJonathan D. Ritschel0Tamiko L. Ritschel1Nicole B. York2Department of Systems Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, Ohio, USADepartment of Studies and Analysis, Headquarters Air Force Material Command, Wright-Patterson AFB, Ohio, USADepartment of Studies and Analysis, Headquarters Air Force Material Command, Wright-Patterson AFB, Ohio, USAPurpose – Aircraft availability (AA) is a key metric for assessing operational readiness. The declining trend in AA is a documented concern for senior Air Force leaders. This paper aims to investigate the components of non-available time and subsequently focuses on the largest and fastest growing category: not mission capable maintenance unscheduled (NMCMU). Then, utilization of aircraft platforms is examined to determine the readiness benefits of increasing available hours. Design/methodology/approach – Stepwise regression is conducted on a data set of 30 aircraft platforms, consisting of 542 observations from 1998 to 2017, to reveal drivers of NMCMU. Next, utilization of aircraft platforms is examined through regression and correlation analysis of aircraft platforms and sorties or hours flown. Findings – Regression analysis reveals drivers of NMCMU include platform type, average age of aircraft, fleet size, breaks and cannibalization. These factors explain 80.6 per cent of the variance in the data set when predicting NMCMU. Additionally, the utilization results show that when more hours are made available, 5 per cent of each new hour is used for flying. Further analysis at the individual platform level finds a strong or moderate correlation between available hours and sorties flown for 93 per cent of the platforms. Originality/value – Implications from the regression analysis demonstrate there are remedies to increase AA, but many of these remedies may be costly. The utilization analysis expresses the potential readiness benefits of increasing available hours.https://www.emerald.com/insight/content/doi/10.1108/JDAL-09-2018-0015/full/pdf?title=providing-a-piece-of-the-puzzle-insights-into-the-aircraft-availability-conundrumregressionmaintenanceutilizationaircraft availability
spellingShingle Jonathan D. Ritschel
Tamiko L. Ritschel
Nicole B. York
Providing a piece of the puzzle: insights into the aircraft availability conundrum
Journal of Defense Analytics and Logistics
regression
maintenance
utilization
aircraft availability
title Providing a piece of the puzzle: insights into the aircraft availability conundrum
title_full Providing a piece of the puzzle: insights into the aircraft availability conundrum
title_fullStr Providing a piece of the puzzle: insights into the aircraft availability conundrum
title_full_unstemmed Providing a piece of the puzzle: insights into the aircraft availability conundrum
title_short Providing a piece of the puzzle: insights into the aircraft availability conundrum
title_sort providing a piece of the puzzle insights into the aircraft availability conundrum
topic regression
maintenance
utilization
aircraft availability
url https://www.emerald.com/insight/content/doi/10.1108/JDAL-09-2018-0015/full/pdf?title=providing-a-piece-of-the-puzzle-insights-into-the-aircraft-availability-conundrum
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