• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.33 no.3
• /
• pp.55-62
• /
• 2010

Currently, cost estimation is very important to the government acquisition programs to support decisions about funding and to evaluate resource requirement as key decision points. Parametric cost estimating models have been used extensively to obtain appropriate cost estimates in early acquisition phase. However, they have many restrictions to ensure the cost estimating result in Korean defense environment because they are developed in the U.S.A. environment. In order to obtain a good R&D cost estimate, developing our own CERs (Cost Estimation Relationships) using historical R&D data is essential. Nevertheless, there has been little research to develop our own CERs. In this research, we established a CER development process and found some cost drivers in the historical movement weapon system R&D data. The R&D CER is developed using the PCR(Principle Component Regression) method to remove multicollinearity among data and to overcome the restriction of the insufficient number of sample. At least, this research is meaningful as a first attempt in terms of defining the CER development process and obtaining our own R&D CER based on the historical data in Korean weapon system R&D environment.

• Journal of the military operations research society of Korea
• /
• v.27 no.1
• /
• pp.10-27
• /
• 2001

This paper deals with the application of PRICE model in estimating the proper acquisition cost for weapon budgeting phase. The PRICE(Parametric Review of Information for Costing and Evaluation) Hardware model is a computerized method for deriving cost estimates of electronic and mechanical hardware assemblies and systems. The model can be used in obtaining not only initial cost estimates in conceptual phase, but also detailed cost estimates in budgeting phase depending on available historical and empirical data. We analyzed first step cost estimate parameters and derived cost equations using PRICe output dta. Using weight and complexity, We can find cost variation. Sensitivity analysis shows that cost increases exponentially as complexity increases exponentially as complexity increases. We estimated KAAV\`s (Korea Amphibious Assault Vehicle) production cost using the PRICE model and compare with engineering cost estimates which is based on actual production data submitted by the production company. The result shows that tow estimates are close within $\pm2%$ differences.

• IE interfaces
• /
• v.25 no.3
• /
• pp.347-356
• /
• 2012

The acquisition cost of defense weapon system has been continuously increasing because of art-of-technology of it. This phenomenon requires efficiency and transparency in the weapon system acquisition process through cost estimation. Therefore cost estimation is very important to the government acquisition programs to support decisions about funding and to evaluate resource requirement as a key decision point. The Commercial parametric cost estimating models have been using extensively to obtain appropriate cost estimates in early acquisition phase. These models have many restrictions to ensure the cost estimating result in Korean defense environment because they are developed based on foreign R&D data. Also estimation results are different from Korean defense industry accounting system. So, some studies have been tried to develop a CER (Cost Estimation Relationship) based on the Korean historical data. However, there are some restrictions to improve the predictability and ensure the stability of the developed singular CERs which consider the following data characteristics individually. The the abnormal conditions of data that is multicollinearity, outlier and heteroscedasticity under rack of the number of observations. In this paper, a CER's Linear Combining Model is proposed to overcome those limitations which guarantee more accurate estimation (25.42% higher precision) than other singular CERs. At least, this study is meaningful as a first attempt to improve the predictability of CER with insufficient data. The methodology suggested in this study will be useful to develop a complex Korean version cost estimating model development in future.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.31 no.4D
• /
• pp.595-601
• /
• 2011

There is currently applied to the unit cost per a distance (KRW/km) for estimating the conceptual cost of civil work on basic planning stage of high speed railroad. This unit cost is an arithmetic average value based on historical data, which could be in big error. It also is difficult to explain the deficiency comparing the estimated cost derived from next basic design stage. This study provides the conceptual estimation model using by the parametric method and regression analysis. Independent variables are the distance and the geological materials (earth, weathered rock, soft-rock, hard-rock), extracting from the actual data to 36 contracts. The deviation between the unit costs estimated using the developed model and the actual cost data is presented in the range from -0.4% to +31%. This range is acceptable compared the typical range "-30% to + 50%". This model will improve the accuracy of existing method and be expected to contribute to effective total cost management and the economic aspects, reduce the financial expenditure.

• Proceedings of the KSR Conference
• /
• 2009.05a
• /
• pp.331-340
• /
• 2009

This study is objected by suggesting rationalization method of management and maintenance cost for railway investment assessment. The estimate of rational benefit and cost are a work of vital importance to decide railway investment as preliminary feasibility investigation is institutionally reinforced since January 2007. In particular, railway management and maintenance cost have to be applied to realistic and detail cost as railway investment assessment guide. For example, types of railway, construction of new line, improvement of conventional line, double tracking, railway electrification. However, railway investment assessment is inconsistency because of estimating the railway management and maintenance cost using existing unrealistic management and maintenance cost. Therefore, this study is performed parametric analysis effecting on the railway management and maintenance cost considered new technique, enhanced facilities and improved standard. Also, it suggests the itemized standard management and maintenance cost. Finally, it will be helped to establish the base of railway investment through the rationalization method of management and maintenance cost.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.7
• /
• pp.1-8
• /
• 2009

In this paper, make a study decision problem called an optimal release policies after testing a software system in development phase and transfer it to the user. The optimal software release policies which minimize a total average software cost of development and maintenance under the constraint of satisfying a software reliability requirement is generally accepted. The Bayesian parametric inference of model using log Poisson execution time employ tool of Markov chain(Gibbs sampling and Metropolis algorithm). In a numerical example by T1 data was illustrated. make out estimating software optimal release time from the maximum likelihood estimation and Bayesian parametric estimation.

• International conference on construction engineering and project management
• /
• 2011.02a
• /
• pp.534-541
• /
• 2011

Risk evaluation approaches for bidding on international construction projects are typically partitioned into three stages: country selection, project classification, and bid-cost evaluation. However, previous studies are frequently under attack in that they have several crucial limitations: 1) a dearth of studies about country selection risk tailored for the overseas construction market at a corporate level; 2) no consideration of uncertainties for input variable per se; 3) less probabilistic approaches in estimating a range of cost variance; and 4) less inclusion of covariance impacts. This study thus suggests a three-staged risk evaluation model to resolve these inherent problems. In the first stage, a country portfolio model that maximizes the expected construction market growth rate and profit rate while decreasing market uncertainty is formulated using multi-objective genetic analysis. Following this, probabilistic approaches for screening bad projects are suggested through applying various data mining methods such as discriminant logistic regression, neural network, C5.0, and support vector machine. For the last stage, the cost overrun prediction model is simulated for determining a reasonable bid cost, while considering non-parametric distribution, effects of systematic risks, and the firm's specific capability accrued in a given country. Through the three consecutive models, this study verifies that international construction risk can be allocated, reduced, and projected to some degree, thereby contributing to sustaining stable profits and revenues in both the short-term and the long-term perspective.

• Journal of Digital Convergence
• /
• v.12 no.5
• /
• pp.139-148
• /
• 2014

In recent years, computational models using parametric estimation method have been developed and used widely for efficient cost analysis. In this research, by applying experienced data from Guidance and Control Systems in Missile System field, the cost analysis for engineering model and commercial computational model(Price H, HL, M, S) are conducted and its result is analysed, so that the difference between two models and its grounds are apprehended. Comparing the calibrated value of computational model based on the data base of similar equipment and the cost from the engineering estimation, the two results are very close. It means that the credibility of data is enhanced through calibration. Also, for cost analysis of similar components in the future, the method for calibration of the computational models is also examined. When estimating development cost in this research, although many parts have been estimated through uncertain elements, the reliability could have been enhanced by applying computational model which secures objectivity. It is a very reasonable estimation method by utilizing calibration of the computational models based on existing accumulated development data.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.24 no.1
• /
• pp.133-143
• /
• 2021

In government procurement programs, cost estimation and analysis support funding decisions and are the basis for other major decisions, too. Such estimating and analyzing the cost of the weapon systems are crucial in execution of the defense budget. However, existing cost estimations and analyses have focused on domestic R&D projects, thus those are not valid in application to foreign weapon acquisitions. This study aims at foreign weapon systems that are acquired from Direct Commercial Sales. Because the data for price estimation of a foreign weapon is usually not available, we suggest a price estimation model based on performance factors of the weapon. In this study, the proper price of the weapon system is estimated using the parametric cost estimating model. Using the data of helicopter-launched anti-tank guided missiles worldwide, we analyze the effect of each performance factor on the weapon system price by regression analysis, and use step-wise and ridge regression analysis to remove multi-collinearity. This study hopefully contributes to more reasonable decision making on proper price of weapons.

• Journal of the Korean Society for Precision Engineering
• /
• v.21 no.9
• /
• pp.85-94
• /
• 2004

As over 70% of the total life cycle cost (LCC) of a product is committed at the early design stage, designers are in an important position to substantially reduce the LCC of the products they design by giving due to life cycle implications of their design decisions. During early design stages, there may be competing concepts with dramatic differences. In addition, the detailed information is scarce and decisions must be made quickly. Thus, both the overhead in developing parametric LCC models fur a wide range of concepts, and the lack of detailed information make the application of traditional LCC models impractical. A different approach is needed, because a traditional LCC method is to be incorporated in the very early design stages. This paper explores an approximate method for providing the preliminary LCC, Learning algorithms trained to use the known characteristics of existing products might allow the LCC of new products to be approximated quickly during the conceptual design phase without the overhead of defining new LCC models. Artificial neural networks are trained to generalize product attributes and LCC data from pre-existing LCC studies. Then the product designers query the trained artificial model with new high-level product attribute data to quickly obtain an LCC for a new product concept. Foundations fur the learning LCC approach are established, and then an application is provided.