A framework for considering uncertainty in quantitative life cycle cost estimation

Life Cycle Cost (LCC) is important information that is useful for decision making affecting complex engineering systems with extended life. Uncertainty in the estimation of LCC, especially in the early concept and definition stage, has great influence on the robustness of such decisions. Conventionally, Verification and Validation (V&V) of cost estimates is not performed, either due to economic or practical constraints. This paper presents a framework for considering uncertainties in quantitative life cycle cost estimation, focusing on the aspects that are important for understanding the discrepancies between the estimated and actual costs. Built on experience in verification and validation in engineering, the framework will be used to guide further research in this topic, where emphasis on suitable theories and models of different types of uncertainties in the estimation as well as strategies to deal with them effectively to improve decision making involving LCC will be discussed.

[1]  Mohammed Kishk,et al.  Combining various facets of uncertainty in whole‐life cost modelling , 2004 .

[2]  Hans-Jürgen Zimmermann,et al.  An application-oriented view of modeling uncertainty , 2000, Eur. J. Oper. Res..

[3]  W. T. Chan,et al.  Feature-based cost estimation for packaging products using neural networks , 1996 .

[4]  Didier Dubois,et al.  Probability-Possibility Transformations, Triangular Fuzzy Sets, and Probabilistic Inequalities , 2004, Reliab. Comput..

[5]  Timothy G. Trucano,et al.  Statistical Validation of Engineering and Scientific Models: Background , 1999 .

[6]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[7]  S. Ferson,et al.  Different methods are needed to propagate ignorance and variability , 1996 .

[8]  Daniel DeLaurentis.  A probabilistic approach to aircraft design emphasizing stability and control uncertainties , 1998 .

[9]  Jerry M. Sollinger,et al.  Impossible Certainty: Cost Risk Analysis for Air Force Systems , 2006 .

[10]  農林水産奨励会農林水産政策情報センター,et al.  The green book : appraisal and evaluation in central government , 2003 .

[11]  Yee Mey Goh,et al.  Development and characterisation of error functions in design , 2007 .

[12]  J. M. Castelain,et al.  Use of Parametric Models in an Economic Evaluation Step During the Design Phase , 2001 .

[13]  Antony R Mileham,et al.  On-screen real-time cost estimating , 2007 .

[14]  Terje Aven,et al.  Models and model uncertainty in the context of risk analysis , 2003, Reliab. Eng. Syst. Saf..

[15]  Timothy G. Trucano,et al.  Verification and validation benchmarks , 2008 .

[16]  Jian S. Dai,et al.  Product Cost Estimation: Technique Classification and Methodology Review , 2006 .

[17]  Peter Sandborn.  Cost Model for Assessing the Transition to Lead-Free Electronics , 2007 .

[18]  Singiresu S. Rao,et al.  Analysis of uncertain structural systems using interval analysis , 1997 .

[19]  Daniel Brissaud,et al.  A Product Lifecycle Costing System with Imprecise End-of-Life Data , 2007 .

[20]  P. Walley.  Statistical Reasoning with Imprecise Probabilities , 1990 .

[21]  Kathryn B. Laskey.  Model uncertainty: theory and practical implications , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[22]  George J. Klir,et al.  On Measuring Uncertainty and Uncertainty-Based Information: Recent Developments , 2001, Annals of Mathematics and Artificial Intelligence.

[23]  Bilal M. Ayyub,et al.  From dissecting ignorance to solving algebraic problems , 2004, Reliab. Eng. Syst. Saf..

[24]  Anthony O'Hagan,et al.  Eliciting expert beliefs in substantial practical applications , 1998 .

[25]  Jeremy E. Oakley,et al.  Probability is perfect, but we can't elicit it perfectly , 2004, Reliab. Eng. Syst. Saf..

[26]  J. E. Gordon,et al.  Structures: Or Why Things Don't Fall Down , 1978 .

[27]  Lionel A. Galway,et al.  Subjective Probability Distribution Elicitation in Cost Risk Analysis: A Review , 2007 .

[28]  James Scanlan,et al.  DATUM Project: Cost Estimating Environment for Support of Aerospace Design Decision Making , 2006 .

[29]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[30]  Enrico Zio,et al.  Two methods for the structured assessment of model uncertainty by experts in performance assessments of radioactive waste repositories , 1996 .

[31]  David Lewis,et al.  Life-cycle cost in a highly uncertain economic environment: The case of managing the U.S. Department of Energy's nuclear waste legacy , 2004 .

[32]  Timothy G. Trucano,et al.  Verification and Validation in Computational Fluid Dynamics , 2002 .

[33]  Li Qian,et al.  Activity-based cost management for design and development stage , 2003 .

[34]  C. Tomkins,et al.  Can Target Costing and Whole Life Costing be Applied in the Construction Industry?: Evidence from Two Case Studies , 2000 .

[35]  C. Paredis,et al.  The Value of Using Imprecise Probabilities in Engineering Design , 2006 .