A Sampling Methodology for INSURV Material Inspections

Abstract

This technical report summarizes research into sampling methods that the U.S. Navy Board of Inspection and Survey (INSURV) could employ during Material Inspections (MIs) of ships and submarines. The goal is to improve the Board s efficiency in conducting MIs without compromising either Board effectiveness or inspection rigor. The idea of sampling is that, rather than inspecting every item of a specific type for example, portable CO2 bottles onboard a ship or submarine, INSURV will only inspect a sample of those items. From the sample, INSURV would then characterize the ship s or submarine s entire complement of that item. This report outlines a sampling methodology that is statistically rigorous and therefore quantitatively defensible, and it is implementable. It is based on well-known sampling methods, such as those described in Cochran (1977) and Lohr (1999). The method described herein allows INSURV to specify the desired margin of error of the results on each item. It is expected that this decision will be based on the mission essentiality and/or safety criticality of each item, where items that are mission essential or safety critical will be given very small margins of error. Similarly, items that are not mission essential or safety critical will be given appropriately larger margins of error.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2012
Accession Number
ADA561716

Entities

People

  • Ronald D. Fricker Jr.

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Beta Testing
  • California
  • Data Analysis
  • Efficiency
  • Information Science
  • Inspection
  • Instructions
  • Materials
  • Naval Operations
  • Navy
  • Operations Research
  • Sampling
  • Ships
  • Submarines
  • Surveys
  • United States
  • Watertight Doors

Readers

  • Facility/Structural Engineering.
  • Logistics and Supply Chain Management.
  • Statistical inference.