Supply Chain Vulnerability Identification Using Big Data Techniques
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Authors
MacKinnon, Douglas
Zhao, Ying
Cook, Glenn
Deschler, Peter
Subjects
big data
BD
big data analytics
BDA
business analytics
business intelligence
lexical link analysis
LLA
MV-22
aviation depot-level repairables
AVDLR
data mining
aircraft maintenance
MRO
predictive modeling
big data
BD
big data analytics
BDA
business analytics
business intelligence
lexical link analysis
LLA
MV-20
aviation depot-level repairables
AVDLR
data mining
aircraft maintenance
BD
big data analytics
BDA
business analytics
business intelligence
lexical link analysis
LLA
MV-22
aviation depot-level repairables
AVDLR
data mining
aircraft maintenance
MRO
predictive modeling
big data
BD
big data analytics
BDA
business analytics
business intelligence
lexical link analysis
LLA
MV-20
aviation depot-level repairables
AVDLR
data mining
aircraft maintenance
Advisors
Date of Issue
2019-12
Date
2019
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
Project Summary: Marines operate the MV-22 globally with support from a multi-tiered maintenance and supply system. However, low aircraft readiness levels demand improved optimization of the current MV-22 sustainment system. With diverse datasets being generated across the MV-22 sustainment system, the data environment is ripe for the application of big data analytics (BDA) which has emerged as a discipline for deriving value from large heterogeneous data environments. This research specifically examined maintenance data for MV-22 aviation depot-level repairables (AVDLRs) that demonstrated high levels of reliability-related demand from 2016 to 2018. A cross-industry standard process for data mining (CRISPDM) was employed with the application of quantitative methods of analysis. First, outlier demand behavior for three AVDLRs was identified across aircraft, squadron, and serial number, with the use of Tableau. Second, popular, emerging, and anomalous failure themes contained within maintenance comments assigned the same malfunction code were differentiated using Lexical Link Analysis (LLA). Third, classification models were tested for predicting the intermediate-level (I-level) action taken codes (ATC) and program level failure modes, incorporating LLA comment classification categories as an additional independent variable. The findings of this research illustrate opportunities to derive deeper understanding of AVDLR failure and repair across multiple levels of naval aviation maintenance.
Type
Report
Description
NPS NRP Executive Summary
Series/Report No
Department
Information Science (IS)
Organization
Naval Research Program (NRP)
Identifiers
NPS Report Number
NPS-19-N120-A
Sponsors
Naval Research Program
Topic Sponsor: N41L
Research POC Name: Eric Bach, CAPT, USN
N4 - Fleet Readiness & Logistics
Topic Sponsor: N41L
Research POC Name: Eric Bach, CAPT, USN
N4 - Fleet Readiness & Logistics
Funding
NPS-19-N120-A
This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrp
Chief of Naval Operations (CNO)
This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrp
Chief of Naval Operations (CNO)
Format
5 p.
Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.