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Data quality problems in Army logistics : classification, examples, and solutions

Author: Lionel A Galway; Christopher H Hanks; United States. Army.; Arroyo Center.; Rand Corporation.
Publisher: Santa Monica, CA : Rand, 1996.
Edition/Format:   Print book : Document   Computer File : EnglishView all editions and formats
Summary:
Many new Army initiatives such as Velocity Management and Force XXI are based on the assumption that information will be a key asset for U.S. armed forces of the future. Many Army logistics data, however, are widely perceived to be of poor quality. In this report, the authors review the current literature on data quality, develop a three-way scheme for classifying data quality problems, and apply the classification  Read more...
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Details

Material Type: Document, Internet resource
Document Type: Book, Computer File, Internet Resource
All Authors / Contributors: Lionel A Galway; Christopher H Hanks; United States. Army.; Arroyo Center.; Rand Corporation.
ISBN: 0833024175 9780833024176
OCLC Number: 34933358
Notes: "Prepared for the United States Army."
"Arroyo Center."
"Rand."
"MR-721-A"--Page 4 of cover.
Description: xix, 70 pages : illustrations ; 23 cm
Responsibility: Lionel A. Galway, Christopher H. Hanks.

Abstract:

Many new Army initiatives such as Velocity Management and Force XXI are based on the assumption that information will be a key asset for U.S. armed forces of the future. Many Army logistics data, however, are widely perceived to be of poor quality. In this report, the authors review the current literature on data quality, develop a three-way scheme for classifying data quality problems, and apply the classification to the analysis of an important logistics data element, the End Item Code (EIC). The authors argue that the EIC has quality problems of all three types, and review the evidence and efforts of the Army to address each. The most fundamental problem is due to the deep gap between the retail organizations that create EIC data and the wholesale organizations that use it. The authors propose several strategies to bridge the gap in order to improve the quality of the EIC data. An appendix applies the data classification scheme to a number of other important logistics data elements exhibiting data-quality problems and reaches similar conclusions about their causes.

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