Doctoral Dissertations

Abstract

"Supply chains drive the production world. Virtually all products use a supply chain to produce them and they can determine the success of companies using them. The ability to manage supply chains is currently a mix of relationship management and science, combining personal and company relationships with data analysis.

The increased dependency on supply chains has increased the need to develop and use predictive real-time metrics to manage supply chain risk, measure suppliers and drive performance. Current metrics used are reactive and stagnant in nature. Real-time metrics will allow supply chain professionals to better manage risk. Current management techniques include fire fighting techniques, with limited data analysis or future prediction capabilities. Combining the current metric packages with real-time metrics can create a more transparent supply chain with prediction capabilities and increased risk mitigation opportunities. This dissertation describes why a real-time predictive metric package and model is needed, shows how to create one, provides an analysis on the use of real-time predictive metrics and correlates metrics to performance, and provides future work areas for predictive metrics"--Abstract, leaf iii.

Advisor(s)

Grasman, Scott E. (Scott Erwin)

Committee Member(s)

Cudney, Elizabeth A.
Daughton, William
Long, Suzanna, 1961-
Montgomery, Robert
Trent, Robert

Department(s)

Engineering Management and Systems Engineering

Degree Name

Ph. D. in Engineering Management

Publisher

Missouri University of Science and Technology

Publication Date

2012

Pagination

viii, 68 pages

Note about bibliography

Includes bibliographical references (pages 60-67).

Rights

© 2012 William Scott Hunter, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Library of Congress Subject Headings

Business logistics
Management -- Statistical methods
Management -- Data processing

Thesis Number

T 10641

Print OCLC #

922573018

Electronic OCLC #

922573118

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