Deep Analytics for Readiness Impacts of Underfunding Spares Backlogs

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Authors
Zhao, Ying
Subjects
financially restricted work queue
FRWQ
business intelligence
machine learning
artificial intelligence
non-mission capable supply
NMCS
casualty report
item mission essentiality code
intermittency
coefficient of variance
causal learning
Advisors
Date of Issue
2021
Date
2021
Publisher
Monterey, California: Naval Postgraduate School
Monterey, California. Naval Postgraduate School.
Language
en_US
Abstract
It is imperative to adopt more advanced business intelligence (BI) methodologies and tools to conduct comprehensive statistical and deep analyses to understand the entire spectrum of the Navy logistics enterprise including maintenance, supply, transportation, health services, general engineering, and finance. The ultimate goal is to enhance total force readiness and project combat power across the whole range of military operations and spectrum of conflict at any time. The objective of this proposal is to apply advanced business intelligence methodologies and tools to conduct comprehensive statistical and deep analyses of the readiness impacts resulting from not funding spares requirements. The research questions are listed as follows: Conduct a comparison of Fleet Demands against requirements in the FRWQ. When not funded, spares requirements accumulate in a Financially Restricted Work Que (FRWQ) awaiting resourcing. In the meantime, the systems these parts support are still fielded and the Fleet still generates requirements to replace these parts. Conduct an assessment of items in the FRWQ against high priority demands (CASREPs, NMCS, Crossdecks, etc.) Deliver the designs of the tool to score and prioritize the items in the FRWQ against maritime requirements data CASREP (impact to the weapon system, WSEC code, if critical) and aviation readiness data NMCS. The tool needs to take an input of FRWQ and match and score against CASREPs, NMCS, and CrossDecks, output a priority list. We propose to use business intelligence including tools such as Tableau or Microsoft power BI, and data mining tools such as Orange and lexical link analysis (LLA) to perform comprehensive statistics and deep analysis to address the research questions. If successful, the resulted research will help improve and determine the most efficient and effective method of stocking, forward staging, or contracting for the materials that have the highest likelihood of demand, balanced with the potential impact of failure, spare, and improve total readiness. We propose the three tasks including working with the sponsor to understand the business processes, extracting data samples from relevant databases, and applying the proposed BI tools to address the research questions. The project deliverables include a detailed report and briefings, a demonstration, and conference/journal paper to validate the methodologies approved by the sponsor. (Source: The proposal)
Type
Poster
Description
NPS NRP Project Poster
Department
Information Sciences
Organization
Naval Research Program (NRP)
Identifiers
NPS Report Number
Sponsors
N4 - Fleet Readiness & Logistics
Funder
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
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.
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