Uncertainty propagation and sensitivity analysis in composite manufacturing cost estimation: ALPHA-framework and cost tool development

被引:3
作者
Hueber, Christian [1 ]
Schwingshandl, Nikolaus [1 ]
Schledjewski, Ralf [1 ,2 ]
机构
[1] Univ Leoben, Christian Doppler Lab High Efficient Composite Pr, Leoben, Austria
[2] Univ Leoben, Dept Polymer Engn, Proc Composites Grp, Leoben, Austria
关键词
Cost estimation; sensitivity analysis; Monte Carlo simulation; composite processing; aerospace manufacturing; MODEL; METHODOLOGY; DESIGN; RISK;
D O I
10.1080/20550340.2019.1599536
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The presented ALPHA cost tool is a novel highly flexible bottom-up parametric hybrid cost estimation framework. It combines the benefits of both methods with the aim of providing cost information during all product development phases. The software offers full transparency to the user and advanced two-level uncertainty management to not only understand any project's cost structure but also aid to identify its cost driving parameters. The implementation of sensitivity analysis makes the intrinsic uncertainty inevitable embedded in cost estimation become graspable. Gaussian error propagation offers direct feedback without extra calculation time while classic Monte Carlo Simulation gives detailed insight through post estimation analysis. From the vast number of commercially available or self-developed cost tools many probably already incorporate uncertainty measures similar to those proposed here. But this article shows both the potential of the additionally obtainable information from uncertainty propagation and demonstrates a way of integrating these risk considerations into a self-developed cost tool. [GRAPHICS] .
引用
收藏
页码:69 / 84
页数:16
相关论文
共 50 条
[1]  
[Anonymous], 2007, SENSITIVITY ANAL PRA
[2]   Bias and error in mine project capital cost estimation [J].
Resource Capital Funds, Denver, CO, United States ;
不详 ;
不详 .
Engineering Economist, 2008, 53 (02) :118-139
[3]  
Cheung JMW, 2009, P 1 CIRP IND PROD SE
[4]   Assessing new product development project risk by Bayesian network with a systematic probability generation methodology [J].
Chin, Kwai-Sang ;
Tang, Da-Wei ;
Yang, Jian-Bo ;
Wong, Shui Yee ;
Wang, Hongwei .
EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (06) :9879-9890
[5]   Probabilistic simulation for developing likelihood distribution of engineering project cost [J].
Chou, Jui-Sheng ;
Yang, I-Tung ;
Chong, Wai Kiong .
AUTOMATION IN CONSTRUCTION, 2009, 18 (05) :570-577
[6]  
Curran MW, 1990, AACE INT T
[7]   Review of aerospace engineering cost modelling: The genetic causal approach [J].
Curran, R ;
Raghunathan, S ;
Price, M .
PROGRESS IN AEROSPACE SCIENCES, 2004, 40 (08) :487-534
[8]   Cost estimation during design step: Parametric method versus case based reasoning method [J].
Duverlie, P ;
Castelain, JM .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 1999, 15 (12) :895-906
[9]   Cost estimates to guide pre-selection of processes [J].
Esawi, AMK ;
Ashby, MF .
MATERIALS & DESIGN, 2003, 24 (08) :605-616
[10]   Model for cost estimation in a finite-capacity environment [J].
Feldman, P ;
Shtub, A .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2006, 44 (02) :305-327