Deep Analytics for Readiness Impacts of Underfunding Spares Backlogs

NPS NRP Executive Summary ; 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, ...