Journal of Parametrics最新文献

{"title":"Estimation of Software Size and Effort Distributions Using Paired Ratio Comparison Matrices","authors":"K. Lum, J. Hihn","doi":"10.1080/10157891.2007.10462278","DOIUrl":"https://doi.org/10.1080/10157891.2007.10462278","url":null,"abstract":"This paper describes the approach and algorithms used to generalize the paired ratio comparison matrix technique to use information inherent in multiple estimates, multiple reference projects, and estimator range information to generate estimated effort and size distributions.","PeriodicalId":311790,"journal":{"name":"Journal of Parametrics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130460478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What are Quality Cost Estimates? or the 260 Hz Cost Estimate","authors":"J. Hamaker","doi":"10.1080/10157891.2007.10462275","DOIUrl":"https://doi.org/10.1080/10157891.2007.10462275","url":null,"abstract":"In the last issue of JoP, the Summer 2006 issue, Rich Hartley, Director of the Air Force Cost Analysis Agency and Chair of the Air Force Cost Analysis Improvement Group, wrote a very insightful article on “What Are Quality Cost Estimates?”. He wrote from his perspective as lead for Air Force cost estimating. Inspired by Rich’s article (and dogged by the JoP editor) I offer in this issue a companion essay from the NASA perspective.","PeriodicalId":311790,"journal":{"name":"Journal of Parametrics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127844375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Profit as an Independent Variable: The Case of Business Aircraft","authors":"D. Howarth","doi":"10.1080/10157891.2007.10462277","DOIUrl":"https://doi.org/10.1080/10157891.2007.10462277","url":null,"abstract":"Methodology This investigative method uses multiple log-linear regression, boundary condition analysis and analytic geometry to define market limits and financially optimized entry points into a marketplace. While portions of this approach have been applied successfully to a variety of products and industries (jet engines, automobiles and aircraft radios, to name a few), in order to create a point of detailed analytical departure, the business aircraft market was chosen for study here. Several data sources exist for this marketplace. Most aircraft manufacturers have no-fee websites in which they list the specifications of the various vehicles they offer for sale. They post their pricing and order books there as well. As a group, these sites formed the primary source of data used in this paper. A small number of companies provide a wide range of information about business aircraft. Included among these are Forecast International, Jane’s Information Group and The Teal Group. The finished dataset incorporated information from each of these services, which by and large agreed with one another. In those instances in which there were discrepancies, the figures from competing data sources were compared. In cases in which a source agreed with a number from the manufacturer, that figure went into the analysis. In instances in which the manufacturer did not provide a figure, but all three of the aforementioned services did, either a figure upon which the majority agreed was used, or failing such a majority, an average of the figures was used. The resulting database consisted of 46 aircraft models from 15 manufacturers, and considered 20 variables over the decade running from the beginning of 2002 to the end of 2011.","PeriodicalId":311790,"journal":{"name":"Journal of Parametrics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132827779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editor's Note","authors":"Stephen A. Book","doi":"10.1080/10157891.2007.10462274","DOIUrl":"https://doi.org/10.1080/10157891.2007.10462274","url":null,"abstract":"Welcome to the first and only issue of The Journal of Parametrics for 2007 and the second for which I have served as editor. From this point on, the respective boards of ISPA and SCEA (Society of Cost Estimating and Analysis) have agreed to merge The Journal of Parametrics with SCEA’s technical publication, The Journal of Cost Analysis & Management. Persons who have submitted articles to The Journal of Parametrics that are still in the reviewing pipeline will see their articles, if accepted for publication, appear in the new joint journal. The name of the new journal has not yet been determined.","PeriodicalId":311790,"journal":{"name":"Journal of Parametrics","volume":"84 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120836383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Process-Based Cost Modeling","authors":"C. Smart, G. Reese, L. Adams, A. Batchelor, A. Redrick","doi":"10.1080/10157891.2007.10462279","DOIUrl":"https://doi.org/10.1080/10157891.2007.10462279","url":null,"abstract":"Weight-based parametric estimating has been used for predicting cost since the 1950s. The state of the art has advanced significantly during the last 50 years. Most of these advances – such as estimating by analogy, multiple independent variables, and nonlinear techniques – have focused on improving cost estimating relationships that are based on regression analysis. While highly useful, regression-based estimates have limitations. Thus new tools may be useful in supplementing existing approaches. Process-based cost modeling is a technique that has the potential to improve the fidelity of traditional parametric estimates. Process-based modeling estimates cost by relating cost drivers directly to the processes involved in designing, developing, testing, and producing a program. A research project to develop a process-based model to estimate acquisition costs for launch vehicles for Marshall Space Flight Center’s Engineering Cost Office was in development for 18 months. The methodology, data collection, analysis, and algorithm development are described in detail. Introduction Process-based modeling is a relatively new approach to modeling cost. Traditional parametric analysis has focused on the “what” of cost – the weight and other technical parameters. Process-based modeling focuses on the “how” of cost by relating cost drivers to the individual processes involved in the design, development, test, and production of a program. A research project to develop a process-based acquisition cost model for the Engineering Cost Office (ECO) of Marshall Space Flight Center (MSFC) was recently conducted. The objective was to improve state-of-the-art cost modeling techniques by using process-based estimation. Process-based models supplement traditional parametric tools, and are not intended to replace parametric estimates. Traditional regression analysis provides an early estimate of a project’s cost. These traditional weight-based statistical estimates can be significantly refined using a process-based model. The beta version of the process-based model consists of an integrated, Excelbased interface that includes all the work breakdown structure (WBS) elements relevant to building a new manned launch vehicle. This integrated model has been calibrated to Space Shuttle Orbiter (SSO), Apollo Command Service","PeriodicalId":311790,"journal":{"name":"Journal of Parametrics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129800860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"System Cost Growth Associated with Technology-Readiness Level","authors":"Roy E. Smoker, Sean Smith","doi":"10.1080/10157891.2007.10462276","DOIUrl":"https://doi.org/10.1080/10157891.2007.10462276","url":null,"abstract":"Introduction Today most cost-estimating tools being used by the Air Force and other DOD organizations fail to take into account the technical maturity of various technologies required for successful completion of the program. In this paper, we develop a methodology to account for anticipated cost growth as technologies mature across the scale of 1-9 Technology-Readiness Levels (TRLs) defined by NASA (See Appendix A). We recognize that numerous programs in the Air Force, DOD, and NASA have their initial cost estimate prepared early in the cycle of technological maturity. At those early TRLs (3-5) much knowledge of how the technology will mature has yet to be revealed to the program engineers and hence to the cost estimators. As investments are made in these programs, the technologies mature, and updated cost estimates show a growth over initial estimates. This cost growth tends to require increased budgets and occasionally results in program managers getting fired or, at best, blaming cost estimators for not providing good initial estimates. This study offers a method for planning more accurately for cost growth associated with the time to mature technologies associated with each specific system under development, thereby reducing the risk of incurring unanticipated overruns to the plan.","PeriodicalId":311790,"journal":{"name":"Journal of Parametrics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126015602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Trouble with R2","authors":"Stephen A. Book, P. H. Young","doi":"10.1080/10157891.2006.10462273","DOIUrl":"https://doi.org/10.1080/10157891.2006.10462273","url":null,"abstract":"Abstract In the theory of cost-estimating-relationship (CER) development using the method of ordinary least-squares (OLS) linear regression, the dependent variable is y (e.g., cost) and the independent variable is x (e.g., weight, power, thrust, etc.). The square of the correlation coefficient between x and y is called the “coefficient of (linear) determination.” Usually denoted by the symbol R2 , the coefficient represents the proportion of variation in y that can be explained by passing variations in x up through the linear relationship. As such, it is often interpreted as providing a measure of the quality of the CER as a predictor of cost. Unfortunately, due to a quirk of mathematical theory, the interpretation of R2 as the “proportion of variation” is valid only in the case of OLS linear regression.","PeriodicalId":311790,"journal":{"name":"Journal of Parametrics","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123094182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editor's Notes","authors":"Stephen A. Book","doi":"10.1080/10157891.2006.10462268","DOIUrl":"https://doi.org/10.1080/10157891.2006.10462268","url":null,"abstract":"","PeriodicalId":311790,"journal":{"name":"Journal of Parametrics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127506893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"R&D Budget Profiles and Metrics","authors":"E. Burgess","doi":"10.1080/10157891.2006.10462270","DOIUrl":"https://doi.org/10.1080/10157891.2006.10462270","url":null,"abstract":"Abstract Previously published work on R&D time-phasing methods primarily addresses the suitability of various functional forms, such as Rayleigh and Weibull curves, to fit individual historical program profiles. Little guidance exists on how to select values of the Rayleigh or Weibull parameters for a program cost estimate or on how to measure accuracy of the resulting profile. In this study we present four quality metrics that model developers can use to evaluate budget-phasing methods. With metrics in hand, we demonstrate two ways to improve model accuracy. First, independent variables such as percent nonrecurring and number of development units cause a phasing profile to be more or less front-loaded and should be taken into account when developing an R&D budget. Second, to further improve predictive accuracy, we demonstrate some advantages of replacing a previously published approach of curve fitting large numbers of individual program profiles by an approach based on single-stage multivariate regression. Finally, a case study on military and intelligence satellite acquisition programs leads to new parametric schedule-estimating and time-phasing models.","PeriodicalId":311790,"journal":{"name":"Journal of Parametrics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133709370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of Quality Cost into a Total Cost Model for a Distribution Network","authors":"C. Considine, P. Kauffmann, D. Dryer","doi":"10.1080/10157891.2006.10462272","DOIUrl":"https://doi.org/10.1080/10157891.2006.10462272","url":null,"abstract":"Abstract Effective supply chain management requires integration of the interconnected operational steps from raw material supplier through production, distribution, and final product delivery. These links must operate effectively and efficiently for the supply chain to achieve its primary objective: increased customer value at each step. Distribution systems are particularly critical components in supply chain networks since they are often the last direct interface with the customer. As a result, distribution error in the supply chain cannot be corrected and will impact customer satisfaction, ultimately leading to possible lost sales or customers.","PeriodicalId":311790,"journal":{"name":"Journal of Parametrics","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126004860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}