Aerospace & Defense Technology - May 2024 - 19

Artificial Intelligence
Predicting
failures
Backorders
3.
Availability
4.
Marginal analysis
Unit RSP
kit
How aircraft sustainability models calculate readiness spare packages. (Image: RAND Corporation)
might be unrealistic to conduct this analysis
on multiple platforms by attempting to
make these LIMS-EV data pulls manually.
Automation might be necessary for these
data pulls before AI can be applied.
AI cannot alleviate the scarcity of wartime
data. Additional assumptions and
policy considerations will be needed to
account for this scarcity. As mentioned,
one of the main limitations for the application
of AI in this use case is its inability
to estimate truly rare events. Certain
events in war could be considered rare
because of their unpredictable nature. As a
result, different approaches to modeling
AI might be required to deal with these
changing circumstances. However, a regular
retraining and updating policy, which
is possible with an AI model, can ensure
adaptability during war.
Recommendations for Air Force
Materiel Command
Work with USAF Logistics to build a
data operations pipeline to conduct retrospective
analysis of aircraft maintenance
and RSP efficiency. Aircraft maintenance
programs and databases function effectively
for the purposes for which they were
designed, but they were not designed for
retrospective analysis or to train AI models.
Unless the data can be properly conditioned
and pulled for this analysis, none of
the following recommendations can be
implemented.
Experiment with AI to improve failure
analysis for RSPs. Extend the proof-ofconcept
models to all aircraft. This extenAerospace
& Defense Technology, May 2024
mobilityengineeringtech.com
19
sion will likely have to be done on a partby-part,
platform-by-platform basis.
Automated or partially automated data
extraction will likely be necessary if AI is
used to conduct these analyses. For RSP
This research was performed by Li Ang
Zhang, Yusuf Ashpari, Anthony Jacques
for RAND Corporation (Santa Monica,
CA). The article features a summary of
their results. For more information,
visit www.rand.org.
1.
2.
Uses a Poisson distribution to predict
part failures within 30 days
Given on-shelf inventory, calculates
expected backorders in a month
Calculates aircraft availability due to
backorders
Generates a prioritized " shopping list " of
parts that yields the largest increases to
availability
parts with hard-to-predict rare failures,
we can modify the AI model's cost function
to prefer overpredictions or rely on
overpredictions via a Poisson distribution
or the problem can be modeled as survival
analysis (predict time to failure).
Limit AI to failure analysis within the
RSP process. The ASM software tackles a
large and complex operations research
problem of selecting which parts to send
from which depot to which base. Current
AI capabilities are data hungry and better
suited to solving narrowly scoped problems.
Splitting parts failures across multiple
depots and bases will fragment the
data too much for algorithms to learn
anything useful.

http://www.rand.org http://info.hotims.com/86255-750 http://mobilityengineeringtech.com

Aerospace & Defense Technology - May 2024

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