Cost Estimating Relationships Research Articles

A weather-adaptive perspective to building cost estimation in Nigeria: exploring climate-induced cost variance

PurposeUsing Nigeria, as a point of reference, this study aims to explore the applicability of climatic variables as analytically valid factors for conceptual cost estimation. This is in view of the vastness and topographical alignment of Nigeria's landmass, which makes it a country of extreme climatic variability from north to south. As construction costs in Nigeria, similarly, tend to show a north-south alignment, the study's objective is to establish cost-estimating relationships (CERs) between the variability of climatic elements and the variance in construction cost, to arouse interest in the concept.Design/methodology/approachDeploying correlation analysis and multiple regression analysis, significant associations/relationships between meteorological variables and building cost for selected locations, following a North-South transect of the major climatic zones, are sought, to explain climate-induced construction cost variance. Validation of the regression model was carried out using variance analysis and the Mean Absolute Percentage Error of a different dataset.FindingsClimatic indices of atmospheric moisture exhibited strong direct and partial correlations with construction costs, while sunshine hours and temperature were inversely correlated. The study further establishes statistically significant CERs between climatic variables and building cost in Nigeria, which accounted for 47.9% of the variance in construction cost across the climatic zones.Practical implicationsThe study outcome provides a statistically valid platform for the development of more elaborate analytical costing models, for prototype buildings to be cited in disparate climatic settings.Originality/valueThis study establishes the statistical validity of climatic variables in constituting CERs for predicting construction costs in disparate climatic settings.

Inflation and Price Escalation Adjustments in Estimating Program Cost: F-35 Case Study

Applying price indexes presents a challenge in estimating the costs of new defense systems. An inappropriate price index—one not closely linked to the inputs to the systems being costed—can introduce errors in both development of cost estimating relationships (CER) and in development of out-year budgets. To help cost analysts understand the impacts of different price indexes, this article applies two sets of price indexes to the F-35 program. Using hedonic price indexes derived from CERs, the authors isolate changes in price due to factors other than changes in quality by developing a “Baseline” CER model using data on historical tactical aircraft programs available early in the F-35 program. The focus of the work is to improve estimates of acquisition costs. All the data used in the econometric analysis are acquisition cost data. Better cost estimates should improve projections of budget requirements.

The Development of an Ordinary Least Squares Parametric Model to Estimate the Cost Per Flying Hour of ‘Unknown’ Aircraft Types and a Comparative Application †

The development of a parametric model for the variable portion of the Cost Per Flying Hour (CPFH) of an ‘unknown’ aircraft platform and its application to diverse types of fixed and rotary wing aircraft development programs (F-35A, Su-57, Dassault Rafale, T-X candidates, AW189, Airbus RACER among others) is presented. The novelty of this paper lies in the utilization of a diverse sample of aircraft types, aiming to obtain a ‘universal’ Cost Estimating Relationship (CER) applicable to a wide range of platforms. Moreover, the model does not produce absolute cost figures but rather analogy ratios versus the F-16’s CPFH, broadening the model’s applicability. The model will enable an analyst to carry out timely and reliable Operational and Support (O&S) cost estimates for a wide range of ‘unknown’ aircraft platforms at their early stages of conceptual design, despite the lack of actual data from the utilization and support life cycle stages. The statistical analysis is based on Ordinary Least Squares (OLS) regression, conducted with R software (v5.3.1, released on 2 July 2018). The model’s output is validated against officially published CPFH data of several existing ‘mature’ aircraft platforms, including one of the most prolific fighter jet types all over the world, the F-16C/D, which is also used as a reference to compare CPFH estimates of various next generation aircraft platforms. Actual CPFH data of the Hellenic Air Force (HAF) have been used to develop the parametric model, the application of which is expected to significantly inform high level decision making regarding aircraft procurement, budgeting and future force structure planning, including decisions related to large scale aircraft modifications and upgrades.

Cost estimation relationships of a small scale linear Fresnel reflector

This paper presents a cost estimating relationship (CER) of a novel small scale linear Fresnel reflector (SSLFR). The CER has been developed analyzing in detail the manufacturing processes of the SSLFR, its parameters, and the possible sub-components. As a result of this analysis, the SSLFR has been divided into 8 sub-components: fixed and mobile structures, movement units, mirror units, secondary reflector system, tracking system, assembly, and foundation. These sub-components are described in detail and designed using Autodesk Inventor, specifying manufacturing materials and processes. The study includes a full stress analysis of the sub-components, considering self weight, snow loads, and wind loads. For each sub-component an estimate of the primary costs is presented and also a relationship between the cost and the geometric parameters of the SSLFR. The primary costs considered include material, labor, and tooling costs. A numerical example shows the suitability of the proposed approach.

CLINICAL AND ECONOMIC EVALUATION OF ERIBULIN EFFECTIVENESS IN SOFT TISSUE SARCOMA: RESULTS OF META-ANALYSIS

Innovative drugs eribulin and trabectedin are effective in the treatment of soft tissue sarcomas (STS) – rare, but severe malignancies. Aim of the study . To perform the pharmacoeconomic evaluation of these two agents used with the second and subsequent lines of chemotherapy in common/metastatic STS, in theRussian Federation. Materials and Methods . Direct, indirect and meta-analytic comparisons were applied to the results of two multicenter, randomized phase III clinical studies, on effectiveness and safety of trabectedin and eribulin as compared with dacarbazine for the treatment of severe anthracyclines-resistant lipoand leiomyosarcoms. The 12-month survival rate and the occurrence of adverse events were the endpoint parameters in this study. The impact on the budget and the “cost-effectiveness” index were also analyzed. Results . The relative number of patients, who survived for 12 months and continued their treatment with the given drug, was 53% in the eribulin group, and 18% in the trabectedin group. The total number of recorded adverse events was not significantly different between eribulin and dacarbazine. The incidence of adverse events (including haematological) in the trabectedin group was significantly higher than that in the dacarbazine group (lg 95% CI> 1.0). The cost values of different treatment doses of eribulin got into a zone of 1-2 willingness to pay threshold (WPT). The cost of trabectedin treatment was higher than that of eribulin, and even at mid-optimal doses (1.5 mg/m2 ) exceeded 2xWPT. The cost estimating relationship (CER) was 0.69 for trabectedin, and 0.15 for eribulin. Conclusion . In the Health Care system ofRussia, eribulin has a more favorable pharmaco-economic profile than trabectedin. Since STS is viewed as an orphan disease, both drugs are considered important and promising for the Russian oncological services.

Engineered Resilient System Life Cycle Costing Model

In order to make the best use of the defense spending budget, it is critical that the Department of Defense (DoD) accurately predict the Research, Development, Test and Evaluation (RDT&E), Procurement, and Operation and Support (O&S) costs down to the third level of the Work Breakdown Structure for Major Defense Acquisition Project (MDAP) wheeled or tracked vehicles. This research utilizes historical data, extracted from government databases, to develop cost estimating relationships (CERs) that predict the life cycle cost of wheeled and tracked vehicles based on attributes. This research can also be leveraged for defense acquisition programs across the DoD portfolio. The model will be integrated into a tradespace analysis tool, ERS & CREATE-GV, which was developed by ERDC to predict the cost of each alternative created in the tradespace.

Cost Estimation Comparisons Between Least Square Regression and Quantile Regression on Fiber Reinforced Bridge Projects

This paper focuses on the Least Square (LS) regression using the mean and Quantile (M) regression analysis using median which is based on “well-Known” parametric estimation methodologies. Data from Oregon and California highway bridges were used for the comparison of the two methods. Relationships were developed to predict the unit cost of FRP repair work and FRP cost was found to have a high degree of correlation with FRP area for both Oregon and California. It was observed that the Cost Estimating Relationships (CERs) obtained by Quantile (M) regression method had the smaller Mean Absolute Deviation (MAD) values and lower Mean Absolute Percentage Error (MAPE) values than Least Square (LS) regression. The stuudy showed that Quantile Regression is much less sensitive to outliers than Least Squares Regression.

COST ESTIMATING RELATIONSHIPS IN ONSHORE DRILLING PROJECTS

DOI: 10.12957/cadest.2016.27711 Cost estimating relationships (CERs) are very important tools in the planning phases of an upstream project. CERs are, in general, multiple regression models developed to estimate the cost of a particular item or scope of a project. They are based in historical data that should pass through a normalization process before fitting a model. In the early phases they are the primary tool for cost estimating. In later phases they are usually used as an estimation validation tool and sometimes for benchmarking purposes. As in any other modeling methodology there are number of important steps to build a model. In this paper the process of building a CER to estimate drilling cost of onshore wells will be addressed. Keywords: Cost Estimating Relationships. Onshore wells. Upstream projects.

Development of reliability-corrected cost model for Small Earth Observation satellites

In this study, a novel reliability-corrected cost model for estimating the development cost of highly agile small EO (Earth Observation) satellites is presented. In order to develop the cost model of highly agile small EO satellites, a database has been constructed consisting of 49 satellites carrying electro-optical payloads and having a launch mass between 100kg and 1,000kg that have either been developed or being developed within a time frame from 1991 to 2011. The cost model of top-down type was developed by analyzing the database statistically. The reliability-corrected CER (Cost Estimating Relationships) developed in this study implement multiple parameters-based complexity indexes. In addition, the Cost Correction Factor (CCF) and Low Cost Small Satellite (LCSS) adjustment factor were newly introduced as additional parameters for cost estimation. The reliability-corrected CERs for 26 EO satellites were used for verification of the cost model developed in this study. It was observed that there are approximately 7% of absolute average errors in the reliability-corrected CER. It is concluded that this cost model can provide cost estimates with a higher accuracy, as compared to conventional cost models such as USCM and SSCM. Finally, this paper also describes the results of cost estimation obtained by applying the developed cost model to highly agile small electro-optical satellites having specific performance requirements.

유도무기 획득단가 추정 모델 개발에 관한 연구

Cost estimates are necessary for government acquisition program to support decisions about funding, to develop annual budget requests and to validate resource requirements at key decision points. Many researches have been done about cost estimating technique recently. Parametric cost estimating models based on CERs(Cost Estimating Relationships) have been mainly used using regression method with historical data. However, there are many restrictions in developing Korean version CERs because the number of data points are too small. Specially, data collection and data management system are unstable in Korean defense environment, when developing CERs. In this research, we analyzed the historical data, and found some cost drivers in guided weapon system area. We developed the Acquisition Unit Cost CER using the regression to remove multicollinearity in the historical data. So we could overcome the restriction of the insufficient sample number. This research as a first attempt is meaningful in terms of obtaining our own Acquisition Unit Cost CER using historical cost and physical characteristic in Korean development environment.

Statistical Foundations of Adaptive Cost-Estimating Relationships

Traditional development of cost-estimating relationships (CERs) has been based on “full” data sets consisting of all available cost and technical data associated with a particular class of products of interest, e.g., components, subsystems or entire systems of satellites, ground systems, etc. In this article, we review an extension of the concept of “analogy estimating” to parametric estimating, namely the concept of “adaptive” CERs—CERs that are based on specific knowledge of individual data points that may be more relevant to a particular estimating problem than would the full data set. The goal of adaptive CER development is to be able to apply CERs that have smaller estimating error and narrower prediction bounds. Several examples of adaptive CERs were provided in a presentation (Book & Broder, 2008) by the first two authors to the May 2008 SSCAG Meeting in Noordwijk, Holland, and the June 2008 SCEA/ISPA Conference in Industry Hills, CA. This article focuses on statistical foundations of the derivatio...

R2versus r2

Abstract Cost estimating relationships (CERs) with multiplicative-error assumptions are commonly used in cost analysis. Consequently, we need to apply appropriate statistical measures to evaluate a CER's quality when developing multiplicative error CERs such as minimum-unbiased-percentage error (MUPE) and minimum-percentage error under zero-percentage bias (ZMPE) CERs. Generalized R-squared (GRSQ, also denoted by the symbol r2) is commonly used for measuring the quality of a nonlinear CER. GRSQ is defined as the square of Pearson's correlation coefficient between the actual observations and CER-predicted values (see Young 1992). Many statistical analysts believe GRSQ is an appropriate analog to measure the proportion of variation explained by a nonlinear CER (see Nguyen and Lozzi, 1994), including MUPE and ZMPE CERs; some even use it to measure the appropriateness of shape of a CER. Adjusted R2 in unit space is a frequently used alternative measure for CER quality. This statistic translates the sum of squares due to error (SSE) from the absolute scale to the relative scale. This metric is used to measure how well the CER-predicted costs match the actual data set, adjusting for the number of estimated coefficients used in the model. There have been academic concerns over the years about the relevance of using Adjusted R2 and Pearson's r2. For example, some insist that Adjusted R2, calculated by the traditional formula, has no value as a metric except for ordinary least squares (OLS); others argue that Pearson's r2 does not measure how well the estimate matches database actuals for nonlinear CERs. This article discusses these concerns and examines the properties of these statistics, along with pros and cons of using each for CER development. In addition, this article proposes 1) a modified Adjusted R2 for evaluating MUPE and ZMPE CERs and 2) a modified GRSQ to account for degrees of freedom (DF).

Establishing expert system for prediction based on the project-oriented data warehouse

Many studies have generated cost estimating relationships (CERs) for transportation projects via data analysis. Some studies collected data from databases, while others sourced data from conventional paper-based formats. When cost data were not in a consistent format, many studies failed to discuss the streamlining of pattern recognition, ranging from generating a problem statement, data warehouse and prediction modeling to information management. This study adopts a standard procedure of identifying CERs for transportation projects. For the proposed dimensional data warehouse, a pavement maintenance and rehabilitation project was selected as a case study for extracting data and concealed prediction rules. Linear and log-linear statistical approaches were adopted to create most advantageous models, defined based on their explanatory power and mean absolute prediction error. The resulting favorable estimation models created from the proposed cost data warehouse were integrated into an expert system to facilitate information management and generate preliminary budgets for transportation agencies.

Single-variable parametric cost models for space telescopes

Parametric cost models are routinely used to plan missions, compare concepts, and justify technology investments. Unfortunately, there is no definitive space telescope cost model. For example, historical cost estimating relationships (CERs) based on primary mirror diameter vary by an order of magnitude. We present new single-variable cost models for space telescope optical telescope assembly (OTA). They are based on data collected from 30 different space telescope missions. Standard statistical methods are used to derive CERs for OTA cost versus aperture diameter and mass. The results are compared with previously published models

A Distribution-Free Measure of the Significance of CER Regression Fit Parameters Established Using General Error Regression Methods

Abstract General error regression methods (GERM) have given rise to a wide variety of functional forms for cost-estimating relationships but have so far lacked a means for evaluating the “significance” of the individual regression fit parameters in a way that is analogous to the roles played by the t-statistic and associated p-value in ordinary least squares (OLS) regression. This article attempts to remedy that situation by developing and describing an analogous “significance” metric for GERM regression fit parameters that is independent of the nature of the underlying error distribution. Significance metrics developed herein are comparable across CERs, regardless of the functional form of the regression equation or the underlying error specification. Moreover, they are developed heuristically, require no distributional assumptions, and provide a collection of simple metrics by which to judge the “significance” of the individual regression fit parameters. These metrics will be of benefit to anyone who us...

What Do a Space Shuttle and an Anti-Cancer Drug Have in Common? Their Development Costs Can Be Estimated Using Parametric Cost Analysis: A Literature Review

The existing literature on Parametric cost analysis (PCA) has been reviewed focusing on methodology, application, shortcomings, suggested remedies and alternatives. Parametric cost analysis (PCA) is a method of estimating the cost of a product using mathematical relationships to other parameters of the product, such as parts and labor cost. These models or cost estimating relationships (CERs) are often obtained by regressing the total cost of a product on a set of variables that represent the product's performance. Throughout this review, emphasis has been placed on why these techniques are used, their practical applications, and explanation of statistical methodology in layman's terms. PCA is found to be useful in large scale projects and in situations where little is known about the new product.

The Trouble withR2

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.

Quantitative and qualitative cost estimating for engineering design

This paper describes the development of a cost-estimating methodology for predicting the cost of engineering design effort during the conceptual stages of product development. The research was carried out within a large European aerospace manufacturer whose traditional costing practices had become outdated. The main objective was to generate a suite of technical Cost Estimating Relationships (CERs) that integrate both quantitative and qualitative non-recurring airframe engineering input for the design process. Both the quantitative and qualitative design activities were separated during the CER development. At the end of the development process, they were integrated to produce a final CER. The results demonstrate that these newly generated CERs can predict the future design effort required, based on the typically limited product definition at the conceptual design stage.

Process cost modelling using neural networks

One method of establishing cost-estimating relationships (CERs) that appears to offer advantages is that of neural networks as they are 'model-free estimators', hence providing key advantages over traditional techniques, e.g. regression analysis. However, the complexity of neural network (NN) structures and the availability of a wide range of structural alternatives may make it difficult to select an appropriate network for a specific cost-modelling requirement. The current work is aimed at resolving this issue by examining the use of the Taguchi methodology for identifying NN structural elements. The Taguchi methodology has been used to identify 'best' and 'poorest' NN structures and the models developed using these structures compared with regression-based models in terms of their estimating accuracy. The robustness of these models is then examined in terms of the effects on their estimating accuracy of varying the number of data points used in their development and varying the number of predictor variables within the models.

Safe and Simple Software Cost Analysis

Simple software cost-analysis methods are readily available, but they aren't always safe. The simplest method is to base your cost estimate on the typical costs or productivity rates of your previous projects. That approach will work well if your new project doesn't have any cost-critical differences from those previous projects, but it won't be safe if some critical cost driver has degraded. Simple history-based software cost-analysis methods would be safer if you could identify which cost driver factors were likely to cause critical cost differences and estimate how much cost difference would result if a critical cost driver changed by a given degree. In this article, I provide a safe and simple method for doing both of these by using some cost-estimating relationships. COCOMO II is an updated and re-calibrated version of COCOMO (COnstructive COst MOdel). I also show how the COCOMO II cost drivers let you perform cost sensitivity and tradeoff analyses, and discuss how you can use similar methods with other software cost-estimation models