Geometric quantification of cost uncertainty propagation: A case study

Oliver Schwabe, Essam Shehab, John Erkoyuncu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


This paper presents a novel technique for quantifying cost uncertainty propagation through the metrics of spatial geometry. Case study data was drawn from U.S. Department of Defense Selected Acquisition Report Summary Tables covering total base year cost variance of acquisition programs for between 1986 and 2013 for various whole product life cycle phases (i.e. concept, development, manufacturing, utilisation, support and retirement). The cost variances for quantity, schedule, engineering, estimating, other and support are treated as manifested uncertainty and considered as vertices of simplex geometries propagating over time thus creating an uncertainty "cloud". Length, size, volume, density, mass and symmetry are investigated for identifying, describing and predicting cloud propagation. The hypothesis is raised that the symmetry metric is suitable for determining confidence in the propagation behavior of cloud uncertainty across the whole product life cycle. Further work is suggested to develop standards for the evaluation of topological symmetries in cost estimation.

Original languageEnglish
Title of host publicationCIRPe 2015 - Understanding the Life Cycle Implications of Manufacturing
EditorsJohn Erkoyuncu
PublisherElsevier B.V.
Number of pages6
ISBN (Electronic)9781510815216
Publication statusPublished - Jan 1 2015
Externally publishedYes
Event4th CIRP Global Web Conference, CIRPe 2015 - Cranefield, United Kingdom
Duration: Sep 29 2015Oct 1 2015

Publication series

NameProcedia CIRP
ISSN (Print)2212-8271


Conference4th CIRP Global Web Conference, CIRPe 2015
CountryUnited Kingdom


  • Cloud Uncertainty
  • Cost Variance
  • Spatial Geometry
  • Topological Data Analysis
  • Uncertainty Propagation
  • Whole Product Life Cycle

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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