Pub Date : 2013-07-01DOI: 10.1080/1941658x.2013.863087
{"title":"Acknowledgment of Reviewers’ Services","authors":"","doi":"10.1080/1941658x.2013.863087","DOIUrl":"https://doi.org/10.1080/1941658x.2013.863087","url":null,"abstract":"","PeriodicalId":390877,"journal":{"name":"Journal of Cost Analysis and Parametrics","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114746321","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}
Pub Date : 2013-07-01DOI: 10.1080/1941658X.2013.843418
L. Gregorio, C. A. P. Soares
After bibliographic research on costing methods in civil construction, and a presentation of the mix-based costing method, as well as an application of the activity-based costing method in the costing of civil construction projects based on other authors, a possible application of the mix-based costing was sought. This method allows the distribution of costs and indirect expenses to products without the subjectivities and uncertainties typical of traditional apportionment, by means of analyses of different production scenarios. The main objective of this article is to compare the results obtained from activity-based costing and mix-based costing in the costing of civil construction works.
{"title":"Comparison between the Mix-Based Costing and the Activity-Based Costing Methods in the Costing of Construction Projects","authors":"L. Gregorio, C. A. P. Soares","doi":"10.1080/1941658X.2013.843418","DOIUrl":"https://doi.org/10.1080/1941658X.2013.843418","url":null,"abstract":"After bibliographic research on costing methods in civil construction, and a presentation of the mix-based costing method, as well as an application of the activity-based costing method in the costing of civil construction projects based on other authors, a possible application of the mix-based costing was sought. This method allows the distribution of costs and indirect expenses to products without the subjectivities and uncertainties typical of traditional apportionment, by means of analyses of different production scenarios. The main objective of this article is to compare the results obtained from activity-based costing and mix-based costing in the costing of civil construction works.","PeriodicalId":390877,"journal":{"name":"Journal of Cost Analysis and Parametrics","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131500882","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}
Pub Date : 2013-01-01DOI: 10.1080/1941658x.2013.800371
{"title":"Acknowledgement of Reviewers' Services","authors":"","doi":"10.1080/1941658x.2013.800371","DOIUrl":"https://doi.org/10.1080/1941658x.2013.800371","url":null,"abstract":"","PeriodicalId":390877,"journal":{"name":"Journal of Cost Analysis and Parametrics","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115077821","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}
Pub Date : 2013-01-01DOI: 10.1080/1941658X.2013.766550
Shu-Ping Hu, Alfred Smith
There are two commonly used cost improvement curve theories: unit cost theory and cumulative average cost theory. Ideally, analysts develop the cost improvement curve by analyzing unit cost data. However, it is common that instead of unit costs, analysts must develop the cost improvement curve from lot cost data. An essential step in this process is to estimate the theoretical lot midpoints for each lot, to proceed with the curve-fitting process. Lot midpoints are generally associated with unit cost theory, where the midpoint is always within the lot. The more general lot plot point term is used in the context of both the unit cost and cumulative average cost theories. Many research papers have been published on cost improvement curves, including several that discuss estimating the lot midpoint. A two-term formula has traditionally been used as a useful approximation to derive the lot total cost, as well as the lot midpoint under unit cost theory (see SCEA, 2002–2011; CEBoK, Module 7). There is, however, a more accurate six-term formula to better approximate the lot total cost and lot midpoint. This increase in accuracy may be substantial for high-cost items or an aggregated estimate, consisting of many cost improvement curve-related items. The more accurate formula can also impact cost uncertainty analysis results, especially when thousands of iterations are performed. This article describes how to derive and use lot plot points for both the unit cost and cumulative average cost theories. We describe how the analyst can use lot plot points to construct prediction intervals for cost uncertainty analysis. This approach is more efficient and appropriate than using the unit cost curve directly. In addition, this article will (1) detail an iterative, two-step regression method to implement the six-term formula, (2) describe the advantages of generating the lot plot points for cost improvement curves, (3) recommend an iterative (not direct) approach to fit a cost improvement curve under cumulative average theory, and (4) compare cost improvement curves derived using the two-step regression method with cost improvement curves generated by the simultaneous minimization process. Different error term assumptions and realistic examples are also discussed. In the example section, we show why the goodness-of-fit measures alone should not be used for selecting a best model, especially when either the fit spaces or the dependent variables are different.
{"title":"Accuracy Matters: Selecting a Lot-Based Cost Improvement Curve","authors":"Shu-Ping Hu, Alfred Smith","doi":"10.1080/1941658X.2013.766550","DOIUrl":"https://doi.org/10.1080/1941658X.2013.766550","url":null,"abstract":"There are two commonly used cost improvement curve theories: unit cost theory and cumulative average cost theory. Ideally, analysts develop the cost improvement curve by analyzing unit cost data. However, it is common that instead of unit costs, analysts must develop the cost improvement curve from lot cost data. An essential step in this process is to estimate the theoretical lot midpoints for each lot, to proceed with the curve-fitting process. Lot midpoints are generally associated with unit cost theory, where the midpoint is always within the lot. The more general lot plot point term is used in the context of both the unit cost and cumulative average cost theories. Many research papers have been published on cost improvement curves, including several that discuss estimating the lot midpoint. A two-term formula has traditionally been used as a useful approximation to derive the lot total cost, as well as the lot midpoint under unit cost theory (see SCEA, 2002–2011; CEBoK, Module 7). There is, however, a more accurate six-term formula to better approximate the lot total cost and lot midpoint. This increase in accuracy may be substantial for high-cost items or an aggregated estimate, consisting of many cost improvement curve-related items. The more accurate formula can also impact cost uncertainty analysis results, especially when thousands of iterations are performed. This article describes how to derive and use lot plot points for both the unit cost and cumulative average cost theories. We describe how the analyst can use lot plot points to construct prediction intervals for cost uncertainty analysis. This approach is more efficient and appropriate than using the unit cost curve directly. In addition, this article will (1) detail an iterative, two-step regression method to implement the six-term formula, (2) describe the advantages of generating the lot plot points for cost improvement curves, (3) recommend an iterative (not direct) approach to fit a cost improvement curve under cumulative average theory, and (4) compare cost improvement curves derived using the two-step regression method with cost improvement curves generated by the simultaneous minimization process. Different error term assumptions and realistic examples are also discussed. In the example section, we show why the goodness-of-fit measures alone should not be used for selecting a best model, especially when either the fit spaces or the dependent variables are different.","PeriodicalId":390877,"journal":{"name":"Journal of Cost Analysis and Parametrics","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121120551","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}
Pub Date : 2013-01-01DOI: 10.1080/1941658X.2013.767073
Erin T. Ryan, Christine M. Schubert Kabban, D. Jacques, J. Ritschel
The authors present a prognostic cost model that is shown to provide significantly more accurate estimates of life cycle costs for Department of Defense programs. Unlike current cost estimation approaches, this model does not rely on the assumption of a fixed program baseline. Instead, the model presented here adopts a stochastic approach to program uncertainty, seeking to identify and incorporate top-level (i.e., “macro”) drivers of estimating error to produce a cost estimate that is likely to be more accurate in the real world of shifting program baselines. The predicted improvement in estimating accuracy provided by this macro-stochastic cost model translates to hundreds of billions of dollars across the Department of Defense portfolio. Furthermore, improved cost estimate accuracy could reduce actual life cycle costs and/or allow defense acquisition officials the ability to make better decisions on the basis of more accurate assessments of value and affordability.
{"title":"A Macro-Stochastic Model for Improving the Accuracy of Department of Defense Life Cycle Cost Estimates","authors":"Erin T. Ryan, Christine M. Schubert Kabban, D. Jacques, J. Ritschel","doi":"10.1080/1941658X.2013.767073","DOIUrl":"https://doi.org/10.1080/1941658X.2013.767073","url":null,"abstract":"The authors present a prognostic cost model that is shown to provide significantly more accurate estimates of life cycle costs for Department of Defense programs. Unlike current cost estimation approaches, this model does not rely on the assumption of a fixed program baseline. Instead, the model presented here adopts a stochastic approach to program uncertainty, seeking to identify and incorporate top-level (i.e., “macro”) drivers of estimating error to produce a cost estimate that is likely to be more accurate in the real world of shifting program baselines. The predicted improvement in estimating accuracy provided by this macro-stochastic cost model translates to hundreds of billions of dollars across the Department of Defense portfolio. Furthermore, improved cost estimate accuracy could reduce actual life cycle costs and/or allow defense acquisition officials the ability to make better decisions on the basis of more accurate assessments of value and affordability.","PeriodicalId":390877,"journal":{"name":"Journal of Cost Analysis and Parametrics","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129080987","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}
Pub Date : 2013-01-01DOI: 10.1080/1941658X.2013.766547
Andrew A. Yeung, Keenan D. Yoho, J. Arkes
Arms exports have increasingly become an attractive option for reducing escalating unit costs of new weapon systems to the United States Department of Defense. However, while there is no lack of conjecture, there is little data that show weapon system costs to the United States actually decrease when the same weapon is sold to a foreign buyer. The authors use the sale of the F-16 multi-role fighter aircraft to foreign nations as a case study to quantify the financial gains realized through learning and economies of scale attributed to export production. Using a rate-adjustment cost improvement analysis, the authors' case study shows the unit costs the United States Department of Defense would have incurred without the concurrent export production of F-16s. Estimates suggest that the production savings resulting from export production were in excess of the research, development, test, and evaluation costs of the F-16 for the period 1975 to 1991. The potential benefits associated with keeping the F-16 production line “warm” through export production and the limits of applying the findings to other weapon systems are discussed.
{"title":"Estimates of Unit Cost Reductions of the F-16 Fighter as a Result of U.S. Arms Export Production","authors":"Andrew A. Yeung, Keenan D. Yoho, J. Arkes","doi":"10.1080/1941658X.2013.766547","DOIUrl":"https://doi.org/10.1080/1941658X.2013.766547","url":null,"abstract":"Arms exports have increasingly become an attractive option for reducing escalating unit costs of new weapon systems to the United States Department of Defense. However, while there is no lack of conjecture, there is little data that show weapon system costs to the United States actually decrease when the same weapon is sold to a foreign buyer. The authors use the sale of the F-16 multi-role fighter aircraft to foreign nations as a case study to quantify the financial gains realized through learning and economies of scale attributed to export production. Using a rate-adjustment cost improvement analysis, the authors' case study shows the unit costs the United States Department of Defense would have incurred without the concurrent export production of F-16s. Estimates suggest that the production savings resulting from export production were in excess of the research, development, test, and evaluation costs of the F-16 for the period 1975 to 1991. The potential benefits associated with keeping the F-16 production line “warm” through export production and the limits of applying the findings to other weapon systems are discussed.","PeriodicalId":390877,"journal":{"name":"Journal of Cost Analysis and Parametrics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125089470","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}
Pub Date : 2012-07-01DOI: 10.1080/1941658X.2012.734754
A. Salam, F. Defersha, N. Bhuiyan, M. Chen
Cost estimation of new products has always been difficult as only few attributes will be known. In these situations, parametric methods are commonly used using a priori determined cost function where parameters are evaluated from historical data. Neural networks, in contrast, are non-parametric, i.e., they attempt to fit curves without being provided a predetermined function. In this article, this property of neural networks is used to investigate their applicability for cost estimation of certain major aircraft subassemblies. The study is conducted in collaboration with an aerospace company located in Montreal, Canada. Two neural network models, one trained by the gradient descent algorithm and the other by genetic algorithm, are considered and compared with one another. The study, using historical data, shows an example for which the neural network model trained by genetic algorithm is robust and fits well both the training and validation data sets.
{"title":"A Case Study on Target Cost Estimation Using Back-Propagation and Genetic Algorithm Trained Neural Networks","authors":"A. Salam, F. Defersha, N. Bhuiyan, M. Chen","doi":"10.1080/1941658X.2012.734754","DOIUrl":"https://doi.org/10.1080/1941658X.2012.734754","url":null,"abstract":"Cost estimation of new products has always been difficult as only few attributes will be known. In these situations, parametric methods are commonly used using a priori determined cost function where parameters are evaluated from historical data. Neural networks, in contrast, are non-parametric, i.e., they attempt to fit curves without being provided a predetermined function. In this article, this property of neural networks is used to investigate their applicability for cost estimation of certain major aircraft subassemblies. The study is conducted in collaboration with an aerospace company located in Montreal, Canada. Two neural network models, one trained by the gradient descent algorithm and the other by genetic algorithm, are considered and compared with one another. The study, using historical data, shows an example for which the neural network model trained by genetic algorithm is robust and fits well both the training and validation data sets.","PeriodicalId":390877,"journal":{"name":"Journal of Cost Analysis and Parametrics","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115128685","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}
Pub Date : 2012-07-01DOI: 10.1080/1941658X.2012.734752
C. Smart
The “portfolio effect” is a common designation of a supposed reduction of cost risk achieved by funding multiple projects (the “portfolio”) that are not perfectly correlated with one another. It is often relied upon in setting confidence-level policy for program or organization budgets that are intended to fund multiple projects. The idea of a portfolio effect has its roots in modern finance, as pioneered by 1990 Nobel Memorial Prize in Economic Sciences recipient Harry Markowitz (1959). On the other hand, in presentations to four recent ISPA-SCEA conferences, 2007–2010, the present author argued that, when applied to Government budgeting, the portfolio effect is more myth than fact. However, current National Aeronautics and Space Administration and Department of Defense policy guidance relies heavily upon this apparently chimerical effect. The objective of the present article is to propose a superior alternative budgeting decision process based on a concept called “conditional tail expectation” that better measures project risk exposure in terms of the project's expected shortfall in funding. Also called “tail value at risk,” use of this risk-assessment technique is growing in popularity in a variety of financial contexts, including insurance.
{"title":"Here, There Be Dragons: Considering the Right Tail in Risk Management","authors":"C. Smart","doi":"10.1080/1941658X.2012.734752","DOIUrl":"https://doi.org/10.1080/1941658X.2012.734752","url":null,"abstract":"The “portfolio effect” is a common designation of a supposed reduction of cost risk achieved by funding multiple projects (the “portfolio”) that are not perfectly correlated with one another. It is often relied upon in setting confidence-level policy for program or organization budgets that are intended to fund multiple projects. The idea of a portfolio effect has its roots in modern finance, as pioneered by 1990 Nobel Memorial Prize in Economic Sciences recipient Harry Markowitz (1959). On the other hand, in presentations to four recent ISPA-SCEA conferences, 2007–2010, the present author argued that, when applied to Government budgeting, the portfolio effect is more myth than fact. However, current National Aeronautics and Space Administration and Department of Defense policy guidance relies heavily upon this apparently chimerical effect. The objective of the present article is to propose a superior alternative budgeting decision process based on a concept called “conditional tail expectation” that better measures project risk exposure in terms of the project's expected shortfall in funding. Also called “tail value at risk,” use of this risk-assessment technique is growing in popularity in a variety of financial contexts, including insurance.","PeriodicalId":390877,"journal":{"name":"Journal of Cost Analysis and Parametrics","volume":"11 suppl_1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116572878","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}
Pub Date : 2012-07-01DOI: 10.1080/1941658x.2012.752699
{"title":"List of Reviewers","authors":"","doi":"10.1080/1941658x.2012.752699","DOIUrl":"https://doi.org/10.1080/1941658x.2012.752699","url":null,"abstract":"","PeriodicalId":390877,"journal":{"name":"Journal of Cost Analysis and Parametrics","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128782645","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}
Pub Date : 2012-07-01DOI: 10.1080/1941658x.2012.756765
{"title":"EOV Ed Board","authors":"","doi":"10.1080/1941658x.2012.756765","DOIUrl":"https://doi.org/10.1080/1941658x.2012.756765","url":null,"abstract":"","PeriodicalId":390877,"journal":{"name":"Journal of Cost Analysis and Parametrics","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128014185","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}
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