Journal articles: 'Cost Estimation'

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Ekung, Samuel, Adeniran Lashinde, and Emmanuel Adu. "Critical Risks to Construction Cost Estimation." Journal of Engineering, Project, and Production Management 11, no. 1 (January 1, 2021): 19–29. http://dx.doi.org/10.2478/jeppm-2021-0003.

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AbstractThe prevalence of cost overrun in project delivery suggests an acute dearth of inclusive understanding of the effect of risks on construction cost estimation. In aberrant to the generic assumptions, customary to inquiries in construction risk researches, this paper appraised critical construction estimating risks. The study evaluated the sources, frequency and significance of construction estimating risks, using data from a questionnaire survey of 206 quantity surveyors in Nigeria. The data were analysed using factor analysis, Fussy Set Theory, Terrell Transformation Index (TTI), and Kruskal Wallis H tests. The results showed that estimating risks are correlate seven principal sources, namely: estimating resources, construction knowledge, design information, economic condition, the expertise of estimator, geographic factor, cost data, and project factors (λ, > 0.70 <1.0). Twenty-nine risk factors likewise emerged critical construction estimation risks (TTI, 69-87 > 65 percent) and the top three were low construction knowledge, inaccurate cost information and changes in government regulations (factor scores > 0.60 > 0.50). The awareness and accurate assessment of these risks into project cost estimation would reduce cost overrun. The study, therefore, recommends synergies between projects’ internal/ external environments for proper scoping of these risks into project estimates.

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Kahandawa, Ravindu, Niluka Domingo, Gregory Chawynski, and S. R. Uma. "Factors impacting post-earthquake damage repair cost estimations." International Journal of Disaster Resilience in the Built Environment 12, no. 1 (June 4, 2020): 29–49. http://dx.doi.org/10.1108/ijdrbe-10-2019-0071.

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Purpose Reconstruction processes after an earthquake require estimating repair costs to decide on whether to repair or rebuild. This requires an accurate post-earthquake cost estimation tool. Currently, there are no post-earthquake loss estimation models to estimate repair costs accurately. There are loss assessment tools available, namely, HAZUS, performance assessment calculation tool (PACT), seismic performance and loss assessment tool (SLAT) and seismic performance prediction tool, which have not been specifically used for post-earthquake repair cost estimation. This paper aims to focus on identifying factors that need to be considered when upgrading these tools for post-earthquake repair cost estimation. Design/methodology/approach The research was conducted as an exploratory study using a literature review, document analysis of the PACT, SLAT and HAZUS software and 18 semi-structured interviews. Findings The research identified information sources available for estimation and factors to be considered when developing estimations based on the information sources. Research limitations/implications The data was collected from professionals who were involved mostly in housing repair work in New Zealand. Therefore, impact of these repair work factors might vary in other forms of structures such as civil structures include bridges and the country as a result of varying construction details and standards. Practical implications The identified factors will be used to improve the loss estimation tools are such as PACT and HAZUS, as well as to develop a post-earthquake repair cost estimation tool. Originality/value Currently, the identified factors impacting post-earthquake damage repair cost estimations are not considered in loss estimation tools. Factors identified in this research will help to develop a more accurate cost estimation tool for post-earthquake repair work.

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Kurasova, O., V. Marcinkevičius, V. Medvedev, and B. Mikulskienė. "Early Cost Estimation in Customized Furniture Manufacturing Using Machine Learning." International Journal of Machine Learning and Computing 11, no. 1 (January 2021): 28–33. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1010.

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Accurate cost estimation at the early stage of a construction project is a key factor in the success of most projects. Many difficulties arise when estimating the cost during the early design stage in customized furniture manufacturing. It is important to estimate the product cost in the earlier manufacturing phase. The cost estimation is related to the prediction of the cost, which commonly includes calculation of the materials, labor, sales, overhead, and other costs. Historical data of the previously manufactured products can be used in the cost estimation process of the new products. In this paper, we propose an early cost estimation approach, which is based on machine learning techniques. The experimental investigation based on the real customized furniture manufacturing data is performed, results are presented, and insights are given.

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Saif, Huda. "Software Cost Estimation." Global Sci-Tech 8, no. 1 (2016): 15. http://dx.doi.org/10.5958/2455-7110.2016.00003.3.

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Chatzipetrou, Panagiota. "Software Cost Estimation." International Journal of Service Science, Management, Engineering, and Technology 10, no. 3 (July 2019): 14–31. http://dx.doi.org/10.4018/ijssmet.2019070102.

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Software cost estimation (SCE) is a critical phase in software development projects. A common problem in building software cost models is that the available datasets contain projects with lots of missing categorical data. There are several techniques for handling missing data in the context of SCE. The purpose of this article is to show a state-of-art statistical and visualization approach of evaluating and comparing the effect of missing data on the accuracy of cost estimation models. Five missing data techniques were used: multinomial logistic regression, listwise deletion, mean imputation, expectation maximization and regression imputation; and compared with respect to their effect on the prediction accuracy of a least squares regression cost model. The evaluation is based on various expressions of the prediction error. The comparisons are conducted using statistical tests, resampling techniques and visualization tools like the regression error characteristic curves.

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Heemstra, F. J. "Software cost estimation." Information and Software Technology 34, no. 10 (October 1992): 627–39. http://dx.doi.org/10.1016/0950-5849(92)90068-z.

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Sekhar, R. Poorna Chandra, and Dr G. Anjan Babu. "Comparison of Software Cost Estimation Techniques: An Overview." International Journal of Trend in Scientific Research and Development Volume-1, Issue-5 (August 31, 2017): 26–32. http://dx.doi.org/10.31142/ijtsrd2248.

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Gupta, Sanjali. "A Comparison between Various Software Cost Estimation Models." International journal of Emerging Trends in Science and Technology 03, no. 11 (November 22, 2016): 4771–76. http://dx.doi.org/10.18535/ijetst/v3i11.08.

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Jiang, Shao Fei, and Cong Da Lu. "Cost Estimation Model for Few Samples and Information in Product Early Design Stage." Materials Science Forum 628-629 (August 2009): 311–16. http://dx.doi.org/10.4028/www.scientific.net/msf.628-629.311.

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Product cost estimation in early design stage puzzles researchers for long-time. A new product cost estimation model is proposed, which is applicable for few estimating samples and product information in early design stage. Similarity between the estimating product and the samples are calculated by model of Euclidean distance. Both of utilization ratio of estimating samples and reliability of estimation result are improved based on samples’ sequencing for effect of product cost. The cost estimation model is applied in design of claw of brake disc. The estimation result indicates that the presented model can be used for condition of few estimating samples and product information in early design phase.

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Talla Ouambo, Steve Alan, Alexandre Teplaira Boum, Adolphe Moukengue Imano, and Jean-Pierre Corriou. "Enhancement of the Moving Horizon Estimation Performance Based on an Adaptive Estimation Algorithm." Journal of Control Science and Engineering 2021 (December 29, 2021): 1–14. http://dx.doi.org/10.1155/2021/3776506.

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Although moving horizon estimation (MHE) is a very efficient technique for estimating parameters and states of constrained dynamical systems, however, the approximation of the arrival cost remains a major challenge and therefore a popular research topic. The importance of the arrival cost is such that it allows information from past measurements to be introduced into current estimates. In this paper, using an adaptive estimation algorithm, we approximate and update the parameters of the arrival cost of the moving horizon estimator. The proposed method is based on the least-squares algorithm but includes a variable forgetting factor which is based on the constant information principle and a dead zone which ensures robustness. We show by this method that a fairly good approximation of the arrival cost guarantees the convergence and stability of estimates. Some simulations are made to show and demonstrate the effectiveness of the proposed method and to compare it with the classical MHE.

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Oyewo, Toyese, Odunayo Magret Olarewaju, Melanie Bernice Cloete, and Olukorede Tijani Adenuga. "Environmental costs estimation and mathematical model of marginal social cost: A case study of coal power plants." Environmental Economics 12, no. 1 (August 20, 2021): 90–102. http://dx.doi.org/10.21511/ee.12(1).2021.08.

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An increase in electricity production is proportional to environmental risks due to continuous energy production. The paper aims to quantitatively estimate the environmental costs and mathematically model the marginal social cost associated with the lifespan of the coal power plants. Results revealed South Africa Tier 1 company optimum level of electricity production per annum at around 2.15 gigawatts, considering the emission costs and reasonable profit. 85% of the total emissions during the combustion phase average cost of the C02 emission discharged by coal is calculated as 0.23c/KWh, 0.085c/kWh is calculated for NO2, while SO2 is 0.035c/KWh. Total emission cost represents 69.2% of the total cost of producing 1 MGW of electricity. The results confirmed the company losses to be insignificantly considerable to the evaluated environmental costs and capital investment. However, the use of this newly developed mathematical model depends on the source of energy production to confirm the feasibility and profitability of investment in coal-powered stations using environmental management accounting and marginal social cost approaches. AcknowledgmentThe authors would like to acknowledge the National Research Foundation and Durban University of Technology for financial support.

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Yang, Hai. "Research on Improved Staged Software Cost Estimation Method Based on COCOMO Model." Advanced Materials Research 989-994 (July 2014): 1501–4. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1501.

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The accuracy of software cost estimation is essential for software development management. By introducing and analyzing the estimation methods of software cost systematically, the paper discussed the necessary of considering the software maintenance stage and estimating the software cost by separating the procedure of software development into several small stages. Then a staged software cost estimation method based on COCOMO model was proposed. The use of the new software cost estimation method proposed by this paper not only contributes to the cost control of software project, but also effectively avoids the bias problem due to using by single cost estimation method so that the accuracy of cost estimation could be improved.

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Yang, Ming, Yuan Li, and Yong Chao Wang. "Using Gray Forecasting Estimates Cost of Aviation Project." Materials Science Forum 532-533 (December 2006): 725–28. http://dx.doi.org/10.4028/www.scientific.net/msf.532-533.725.

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China aviation project has little sample and the traditional method can not estimate the project cost accurately. This paper proposes a method estimating aviation project cost based on gray systematic theory. We divide aviation product into systems or parts and regard them as cost estimation unit. The gray forecasting model GM (1, 1) was set up based on historical cost data of cost estimation unit. If the model’s precision doesn’t meet with the requirement of aviation project cost estimation, the revision model GM (1, 1) must be set up to raise the estimation precision The experimental results show the method can get the more high-accuracy estimation value using the little sample and is suitable for the cost estimation of the our country aviation project.

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Kutlu, Levent, and Ran Wang. "Estimation of cost efficiency without cost data." Journal of Productivity Analysis 49, no. 2-3 (March 30, 2018): 137–51. http://dx.doi.org/10.1007/s11123-018-0527-9.

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Ba’abbad, Ibrahim Mohammad, and M. Rizwan Jameel Qureshi. "Quality Extended Use Case Point (QUCP): An Improved Cost Estimation Method." International Journal of Computer Science and Mobile Computing 10, no. 6 (June 30, 2021): 1–9. http://dx.doi.org/10.47760/ijcsmc.2021.v10i06.001.

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The quality of a product is one of the major interests of the manufacturing process in all industries. The software industry imposes to construct a project with several phases to ensure producing high-quality software. A software development company estimates time, effort and cost of the project during planning phase. It is important to have accurate estimations to reduce the risks of project failure. Several cost estimation methods are practiced in the software development companies such as Function Point (FP), Use Case Points (UCP), Constructive Cost Model I and II and Story Points (SP). UCP cost estimation method is taken in this research to improve the accuracy of its estimation. UCP estimation depends on the use case diagram of the proposed system. A use case diagram describes the main functional requirements of the proposed system. UCP partially considers non-functional requirements through the technical and environmental factors. There is a lacking in the UCP method to consider the importance of quality attributes in the estimating process. This paper proposes an extended version of the existing UCP method named Quality Extended Use Case Point (QUCP) method in which quality attributes are included to improve the accuracy of cost estimation. A questionnaire is used to validate the proposed QUCP method. It is found after data analysis that seventy five percentages of the participants are agreed that the proposed method will not only help to improve the accuracy of cost estimation but it will also enable a software development company to deliver high-quality products.

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Nicolaou, Panicos, Deborah L. Thurston, and James V. Carnahan. "Machining Quality and Cost: Estimation and Tradeoffs." Journal of Manufacturing Science and Engineering 124, no. 4 (October 23, 2002): 840–51. http://dx.doi.org/10.1115/1.1511169.

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Simultaneous improvement of machining cost, quality and environmental impact is sometimes possible, but after the Pareto optimal frontier has been reached, decisions must be made regarding unavoidable tradeoffs. This paper presents a method for formulating a mathematical model for first estimating quality, cost and cutting fluid wastewater treatment impacts of two machining operations (end milling and drilling), and then for tradeoff decision making. The milling quality estimation model is developed through virtual experimentation on a simulation model, while the drilling quality estimation model is developed through physical experimentation. Cost is estimated through an activity based costing approach. Cutting fluid wastewater treatment impacts (BOD and TSS) are estimated through stoichiometric analysis of cutting fluids. Input decision variables include material choice, design, manufacturing and limited lubrication parameters. The contribution of this paper is the integration of activity based cost estimation, machining quality estimation via statistical analysis of data from virtual and physical experiments, cutting fluid wastewater treatment impact estimation and formal decision theory. A case study of an automotive steering knuckle is presented, where decision variables include material choice (cast iron versus aluminum), feed rate, cutting speed and wet versus dry machining.

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Turin, Raymond, Oguz Dagci, and Man-Feng Chang. "Low-Cost Air Estimation." SAE International Journal of Engines 2, no. 1 (April 20, 2009): 357–69. http://dx.doi.org/10.4271/2009-01-0590.

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Zhukov, V. E. "Frequent Flyer Cost Estimation." World of Transport and Transportation 19, no. 4 (January 13, 2022): 74–80. http://dx.doi.org/10.30932/1992-3252-2021-19-4-8.

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The marketing activity of a modern airline is quite diverse. Under the conditions of an oligopoly, airlines develop their business in competition for a passenger.In modern conditions in Russia, the use of dumping methods of the early 1990s is very ineffective. There are no weak airlines left on the long-distance air transportation market, and in regional markets large companies lose to small regional carriers due to the high cost of performing flights on large-capacity aircrafts of the airline’s fleet.Generally, non-price methods of competition come out on top. Flexible tariff policy, advertising, and high service in servicing passengers remain leading traditional methods of competing for a passenger.This article is devoted to the study of another marketing method for attracting passengers, or rather retaining passengers on the airline’s flights, which is development of bonus programs, frequent flyer programs. PJSC Russian Airlines (Aeroflot) was chosen as the object for the study. The subject of the research is the «Aeroflot Bonus» program.The objective of the study is to study the cost of the program. For research purposes, this is the value of the frequent flyer program point. The problem proposed to be solved is to determine the amount of expenses for implementation of the bonus program of frequent flyers. When solving the problem in its staging part, the assessment is not limited to direct costs associated with the costs of marketing efforts in the form of costs for organising a special advertising department, issuing bonus cards, software, and wages. The estimation refers also to indirect costs in the form of unreceived proceeds from free bonus tickets. Besides, a rough estimate has been made of the airline’s hidden costs due to an unpaid seat on the plane. The study conclusions indicate that hidden costs will be taken into account in calculating the cost of a flight and the bonus program has a right to exist.

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Lotfi, Ehsan, M. Darini, and M. R. Karimi-T. "Cost estimation using ANFIS." Engineering Economist 61, no. 2 (February 22, 2016): 144–54. http://dx.doi.org/10.1080/0013791x.2015.1104568.

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Sparrius, Ad. "Tutorial: Cost estimation techniques." INCOSE International Symposium 18, no. 1 (June 2008): 2367–446. http://dx.doi.org/10.1002/j.2334-5837.2008.tb00925.x.

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Yang, Seung-Won, Seong-Wan Moon, Hangyeol Jang, Seungyeon Choo, and Sung-Ah Kim. "Parametric Method and Building Information Modeling-Based Cost Estimation Model for Construction Cost Prediction in Architectural Planning." Applied Sciences 12, no. 19 (September 23, 2022): 9553. http://dx.doi.org/10.3390/app12199553.

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Economic feasibility and cost analysis in the preliminary planning stage of early-phase construction projects have a significant impact on project management and implementation. However, while estimating the construction cost per unit area, the existing approaches do not account for factors other than the area-related information, causing estimation error. Therefore, a construction cost estimation model that can be utilized in the early phase of a construction project is developed in this study based on BIM in the architectural planning stage. Moreover, goodness of fit and accuracy of the model were verified through a validation method considering the BIM design process. The proposed model showed higher accuracy than the conventional models in terms of the floor area. Furthermore, it was possible to confirm the model performance based on the cost estimation accuracy range presented by the AACE. In addition, the developed model can generate estimation results corresponding to Class 1–3, a subsequent construction project stage. The findings of this study emphasize the importance of jointly considering the processes related to construction cost estimation and indicate that parameters other than the floor area need to be considered for construction cost estimation in the early phase of a construction project.

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Le, Hang Thu Thi, Veerasak Likhitruangsilp, and Nobuyoshi Yabuki. "A BIM-DATABASE-INTEGRATED SYSTEM FOR CONSTRUCTION COST ESTIMATION." ASEAN Engineering Journal 11, no. 1 (March 16, 2021): 45–59. http://dx.doi.org/10.11113/aej.v11.16666.

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This paper presents a building information modeling (BIM)-database-integrated system for estimating the construction costs of building projects. The proposed system consists of four main modules: (1) the relational database management module, (2) the visualized BIM-integrated module, (3) the cost estimation module, and (4) the BIM-integrated report module. The relational database management module is designed to store and update the necessary data, which are extracted from BIM models. The visualized BIM-integrated module assists users in visualizing the complex building elements while performing cost estimating. The cost estimation module computes construction cost components. It can also automatically adjust to the change of the building element parameters while estimating costs. This module can minimize human errors associated with manual data input and calculation. The BIM-integrated report module allows users to access and comprehend the results conveniently. As compared to traditional 2D CAD drawings, the proposed system offers a more efficient methodology for construction cost estimating through 3D models. It can also minimize time, costs, and errors in the cost estimating process for building procurement.

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Munguía, J., J. Ciurana, and C. Riba. "Neural-network-based model for build-time estimation in selective laser sintering." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 223, no. 8 (April 24, 2009): 995–1003. http://dx.doi.org/10.1243/09544054jem1324.

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Cost assessment for rapid manufacturing (RM) is highly dependent on time estimation. Total build time dictates most indirect costs for a given part, such as labour, machine costs, and overheads. A number of parametric and empirical time estimators exist; however, they normally account for error rates between 20 and 35 per cent which are then translated to inaccurate final cost estimations. The estimator presented herein is based on the ability of artificial neural networks (ANNs) to learn and adapt to different cases, so that the developed model is capable of providing accurate estimates regardless of machine type or model. A simulation is performed with MATLAB to compare existing approaches for cost/time estimation for selective laser sintering (SLS). Error rates observed from the model range from 2 to 15 per cent, which shows the validity and robustness of the proposed method.

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Silas, Faki Agebee, Musa Yusuf, and Anah Hassan Bijik. "Hybridization of Class Responsibility Collaborators Model (HCRCM) with Function Point to enhance Project Estimation Cost in Agile Software Development." Circulation in Computer Science 2, no. 6 (July 20, 2017): 20–24. http://dx.doi.org/10.22632/ccs-2017-252-32.

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Estimating software cost in an agile system in terms of effort is very challenging. This is because the traditional models of software cost estimation do not completely fit in the agile development process. This paper presents a methodology to enhance the cost of project estimation in agile development. The hybridization adopts Class Responsibility Collaborators models with function point thereby boosting the agile software development estimation process. The study found out that adopting the Hybridized Class Responsibility Collaborator with function point has great improvement on cost estimation in agile software development.

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Bhawana Verma, Satish Kumar Alaria. "Design & Analysis of Cost Estimation for New Mobile-COCOMO Tool for Mobile Application." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 1 (January 31, 2019): 27–34. http://dx.doi.org/10.17762/ijritcc.v7i1.5222.

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Software cost estimation is a resource forecasting method, which is required by the software development process. However, estimating the workload, schedule and cost of a software project is a complex task because it involves predicting the future using historical project data and extrapolating to see future values. For cost estimates for software projects, several methods are used. Among the various software cost estimation methods available, the most commonly used technology is the COCOMO method. Similarly, to calculate software costs, there are several cost estimating tools available for software developers to use. But these released cost estimation tools can only provide parameters (i.e. cost, development time, average personnel) for large software with multiple lines of code. However, if a software developer wants to estimate the cost of a small project that is usually a mobile application, the available tools will not give the right results. Therefore, to calculate the cost of the mobile application, the available cost estimation method COCOMO II is improved to a new model called New Mobile COCOMO Tool. The New Mobile COCOMO tool developed specifically for mobile applications is a boon for software developers working in small software applications because it only includes important multipliers that play a vital role in estimating the cost of developing mobile applications. Therefore, the objective of this paper is to propose a cost estimation model with a special case of COCOMO II, especially for mobile applications, which calculates the person-month, the programmed time and the average personnel involved in the development of any mobile app.

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Gharehchopogh, Farhad Soleimanian, Akbar Talebi, and Isa Maleki. "Analysis of use case points models for software cost estimation." International Journal of Academic Research 6, no. 3 (May 30, 2014): 118–24. http://dx.doi.org/10.7813/2075-4124.2014/6-3/a.16.

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Ma, Y. S., N. Sajadfar, and L. Campos Triana. "A Feature-Based Semantic Model for Automatic Product Cost Estimation." International Journal of Engineering and Technology 6, no. 2 (2014): 109–13. http://dx.doi.org/10.7763/ijet.2014.v6.676.

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Aprianti, E., S. Hamzah, and M. A. Abdurrahman. "The Analysis of Cost Estimation using Cost Significant Model on Bridge Construction in South Sulawesi." IOP Conference Series: Earth and Environmental Science 921, no. 1 (November 1, 2021): 012073. http://dx.doi.org/10.1088/1755-1315/921/1/012073.

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Abstract One of the fundamental problems faced by the province of South Sulawesi is the factor of accessibility, so the role of bridges is quite important. For this reason, the budget planning for standard bridge construction projects also needs to be efficient in terms of preparation and accurate in terms of budget. The Cost Significant Model is one of the total construction cost estimation models that relies more on the prices that have the most influence on the total project cost as the basis for estimation. In general, this study uses data from steel frame bridge construction projects in South Sulawesi Province to formulate a mathematical model with linear regression analysis so that it can be used in the process of estimating similar projects going forward. The Estimation Model which is formed from the regression analysis and the Cost Significant Model in this study, namely; Y = 3.884 (X7) + 0.989 (X8) - 65515.372. With; Y = Estimated Total Cost (Rp/m); X7 = Reinforcement Work Cost (Rp/m); X8 = Steel Frame Structure Work Cost (Rp/m). Where this model can explain 99.7% of the total project cost with a cost model factor of 1.038. The level of accuracy (percentage error estimate) of the estimation results of the Cost Significant Model in this study ranges from - 1.46% to +2.45%.

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Mendes, Emilia, Steve Counsell, and Nile Mosley. "Web hypermedia cost estimation: further assessment and comparison off cost estimation modelling techniques." New Review of Hypermedia and Multimedia 8, no. 1 (January 2002): 199–229. http://dx.doi.org/10.1080/13614560208914741.

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Sonmez, Rifat. "Conceptual cost estimation of building projects with regression analysis and neural networks." Canadian Journal of Civil Engineering 31, no. 4 (August 1, 2004): 677–83. http://dx.doi.org/10.1139/l04-029.

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Conceptual cost estimates play a crucial role in initial project decisions, although scope is not finalized and very limited design information is available during early project stages. In this paper, the advantages and disadvantages of the current conceptual cost estimation methods are discussed and the use of regression, neural network, and range estimation techniques for conceptual cost estimation of building projects are presented. Historical cost data of continuing care retirement community projects were compiled to develop regression and neural network models. Three linear regression models were considered to identify the significant variables affecting project cost. Two neural network models were developed to examine the possible need for nonlinear or interaction terms in the regression model. Prediction intervals were constructed for the regression model to quantify the level of uncertainty for the estimates. Advantages of simultaneous use of regression analysis, neural networks, and range estimation for conceptual cost estimating are discussed.Key words: conceptual cost estimation, regression analysis, neural networks, range estimation.

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Relich, Marcin. "Computational Intelligence for Estimating Cost of New Product Development." Foundations of Management 8, no. 1 (January 1, 2016): 21–34. http://dx.doi.org/10.1515/fman-2016-0002.

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AbstractThis paper is concerned with estimating cost of various new product development phases with the use of computational intelligence techniques such as neural networks and fuzzy neural system. Companies tend to develop many new products simultaneously and a limited project budget imposes the selection of the most promising new product development projects. The evaluation of new product projects requires cost estimation. The model of cost estimation contains product design, prototype manufacturing and testing, and it is specified in terms of a constraint satisfaction problem. The illustrative example presents comparative analysis of estimating product development cost using computational intelligence techniques and multiple regression model.

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Galli, Brian J. "Cost Estimation Methods in Quality Management and Continuous Improvement." International Journal of Service Science, Management, Engineering, and Technology 12, no. 1 (January 2021): 38–61. http://dx.doi.org/10.4018/ijssmet.2021010103.

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This paper investigated cost estimation methods and continuous improvement in terms of project management planning, as well as the factors that influenced these actions. Estimating the cost was one of the most imperative tasks to be done by the managers for a project. Key factors, such as cost, size, schedule, quality, people resources, maintenance costs, and complexity, were usually estimated in the beginning of project development. The techniques used for cost estimation included data composed from past projects that were combined with mathematical formulae to get the closest estimation. In regards to continuous improvement, the four-step quality model (PDCA cycle) was used as an ongoing effort to improve products or services. PDCA stands for plan, do, check, and act, which were the steps to successfully implement change. Notably, project management, when tasked with cost estimation and continuous improvement, was challenged to cope with evolving and situational alterations, which required a different set of skills.

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Gkika, Despina, Efstathios Liakos, Nick Vordos, Christina Kontogoulidou, Lykourgos Magafas, Dimitrios Bikiaris, Dimitrios Bandekas, Athanasios Mitropoulos, and George Kyzas. "Cost Estimation of Polymeric Adsorbents." Polymers 11, no. 5 (May 27, 2019): 925. http://dx.doi.org/10.3390/polym11050925.

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One of the most promising techniques of recent research is adsorption. This technique attracts great attention in environmental technology, especially in the decontamination of water and wastewaters. A “hidden” point of the above is the cost of adsorbents. As can be easily observed in the literature, there is not any mention about the synthesis cost of adsorbents. What are the basic criteria with which an industry can select an adsorbent? What is the synthesis (recipe) cost? What is the energy demand to synthesize an efficient material? All of these are questions which have not been answered, until now. The reason for this is that the estimation of adsorbents’ cost is relatively difficult, because too many cost factors are involved (labor cost, raw materials cost, energy cost, tax cost, etc.). In this work, the first estimation cost of adsorbents is presented, taking into consideration all of the major factors which influence the final value. To be more comparable, the adsorbents used are from a list of polymeric materials which are already synthesized and tested in our laboratory. All of them are polymeric materials with chitosan as a substrate, which is efficiently used for the removal of heavy metal ions.

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Wagh, Bhagyashree. "Construction Site Cost Estimation System." International Journal for Research in Applied Science and Engineering Technology 7, no. 5 (May 31, 2019): 2839–47. http://dx.doi.org/10.22214/ijraset.2019.5468.

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Lane, Jo Ann, and Barry Boehm. "System-of-Systems Cost Estimation." Information Resources Management Journal 20, no. 2 (April 2007): 23–32. http://dx.doi.org/10.4018/irmj.2007040102.

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Qin, Xiaotie, and Miao Fang. "Summarization of Software Cost Estimation." Procedia Engineering 15 (2011): 3027–31. http://dx.doi.org/10.1016/j.proeng.2011.08.568.

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Baser, O., J. Gardiner, C. Bradley, and C. Given. "PCN32 CENSORED MEDICAL COST ESTIMATION." Value in Health 7, no. 3 (May 2004): 255–56. http://dx.doi.org/10.1016/s1098-3015(10)62174-2.

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Koonce, David, Robert Judd, Dusan Sormaz, and Dale T. Masel. "A hierarchical cost estimation tool." Computers in Industry 50, no. 3 (April 2003): 293–302. http://dx.doi.org/10.1016/s0166-3615(03)00016-2.

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McGough, Keith. "Cost Estimation in Software Engineering." Journal of Parametrics 8, no. 2 (June 1988): 5–11. http://dx.doi.org/10.1080/10157891.1988.10472824.

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Kitchenham, Barbara A., and N. R. Taylor. "Software project development cost estimation." Journal of Systems and Software 5, no. 4 (November 1985): 267–78. http://dx.doi.org/10.1016/0164-1212(85)90026-3.

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Jursch, S., S. Bischoff, E. Hauck, P. Flachskampf, K. Henning, and S. Jeschke. "Value Oriented Cost-effectiveness Estimation of an Innovative Intermodal Loading Unit." International Journal of Trade, Economics and Finance 1, no. 3 (2010): 271–76. http://dx.doi.org/10.7763/ijtef.2010.v1.49.

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Stubelj, Igor. "The Cost of Equity Capital on Developing Equity Markets: Estimations for Selected Slovene Companies." South East European Journal of Economics and Business 5, no. 1 (April 1, 2010): 67–74. http://dx.doi.org/10.2478/v10033-010-0006-3.

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The Cost of Equity Capital on Developing Equity Markets: Estimations for Selected Slovene CompaniesThe article sheds light on the estimation of the cost of equity capital on a developing equity market. The cost of equity is important; it is crucial in capital budgeting decisions and performance evaluation. It determines the minimum yield the investors require on the invested capital and we use it as a discount rate to calculate the present value of the expected free cash flows to equity. The aim of this paper is to tackle the estimation of the cost of equity capital on developing markets with the example of estimation for ten Slovene publicly traded companies. The estimated cost of capital for the selected Slovene companies is between 9,7% and 13,7%.

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Heitger, Dan L. "Estimating Activity Costs: How the Provision of Accurate Historical Activity Data from a Biased Cost System Can Improve Individuals' Cost Estimation Accuracy." Behavioral Research in Accounting 19, no. 1 (January 1, 2007): 133–59. http://dx.doi.org/10.2308/bria.2007.19.1.133.

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An integral component of effective cost control and performance evaluation is the ability to accurately estimate relationships between activities and overhead costs (i.e., activity costs). Individuals using a single cost pool system often have to rely on memory of historical activity data when estimating activity costs. If individuals' recall of data is representative of the historical data, then reliance on memory should not be detrimental to cost estimation accuracy. However, individuals often possess incorrect initial beliefs about activity costs. These incorrect beliefs are expected to serve as an anchor from which individuals make insufficient adjustments when estimating activity costs based on memory of historical activity data. Multiple cost pool systems frequently provide biased standard rates; however, such systems also provide accurate historical activity data when individuals estimate costs. I extend prior accounting research by experimentally examining whether a multiple cost pool system's provision of accurate historical activity data improves activity cost estimation for individuals with incorrect cost beliefs even when the cost system also provides biased standard rates. The main contribution of the study is its finding that the multiple cost pool system's provision of historical activity data improves individuals' adjustments from their incorrect initial cost beliefs when estimating activity costs, thereby increasing their estimation accuracy. The results suggest that this improved adjustment from incorrect initial cost beliefs occurs because the provision of historical activity data improves individuals' recognition of how wrong their initial cost beliefs were in reality. This result is achieved even though the cost system provides biased standard rates. The ability of flawed cost systems to improve individuals' activity cost estimation in other such ways has received little research attention and is important because of its potential for improving managerial decision making.

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Windmark, Christina, and Carin Andersson. "Cost modelling as decision support when locating manufacturing facilities." International Journal of Production Management and Engineering 4, no. 1 (January 15, 2016): 15. http://dx.doi.org/10.4995/ijpme.2016.4177.

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This paper presents a methodology for cost estimation in developing decision supports for production location issues. The purpose is to provide a structured work procedure to be used by practitioners to derive the knowledge needed to make informed decisions on where to locate production. This paper present a special focus on how to integrate cost effects during the decision process. The work procedure and cost models were developed in close collaboration with a group of industrial partners. The result is a structure of cost estimation tools aligned to different steps in the work procedure. The cost models can facilitate both cost estimation for manufacturing a product under new preconditions, including support costs, and cost simulations to analyse the risks of wrong estimations and uncertainties in the input parameters. Future research aims to test the methodology in ongoing transfer projects to further understand difficulties in managing global production systems. In existing models and methods presented in the literature, cost is usually estimated on a too aggregated level to be suitable for decision support regarding production system design. The cost estimation methodology presented here provides new insights on cost driving factors related to the production system.

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Ahiaga-Dagbui, Dominic D., and Simon D. Smith. "Rethinking construction cost overruns: cognition, learning and estimation." Journal of Financial Management of Property and Construction 19, no. 1 (April 7, 2014): 38–54. http://dx.doi.org/10.1108/jfmpc-06-2013-0027.

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Purpose – Drawing on mainstream arguments in the literature, the paper presents a coherent and holistic view on the causes of cost overruns, and the dynamics between cognitive dispositions, learning and estimation. A cost prediction model has also been developed using data mining for estimating final cost of projects. The paper aims to discuss these issues. Design/methodology/approach – A mixed-method approach was adopted: a qualitative exploration of the causes of cost overrun followed by an empirical development of a final cost model using artificial neural networks. Findings – A conceptual model to distinguish between the often conflated causes of underestimation and cost overruns on large publicly funded projects. The empirical model developed in this paper achieved an average absolute percentage error of 3.67 percent with 87 percent of the model predictions within a range of ±5 percent of the actual final cost. Practical implications – The model developed can be converted to a desktop package for quick cost predictions and the generation of various alternative solutions for a construction project in a sort of what-if analysis for the purposes of comparison. The use of the model could also greatly reduce the time and resources spent on estimation. Originality/value – A thorough discussion on the dynamics between cognitive dispositions, learning and cost estimation has been presented. It also presents a conceptual model for understanding two often conflated issues of cost overrun and under-estimation.

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Ning, Fangwei, Hongquan Qu, Yan Shi, Maolin Cai, and Weiqing Xu. "Feature-Based and Process-Based Manufacturing Cost Estimation." Machines 10, no. 5 (April 28, 2022): 319. http://dx.doi.org/10.3390/machines10050319.

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The demand for mass custom parts is increasing, estimating the cost of parts to a high degree of efficiency is a matter of great concern to most manufacturing companies. Under the premise of machining operations, cost estimation based on features and processes yields high estimation accuracy, but it necessitates accurately identifying a part’s machining features and establishing the relationship between the feature and the cost. Accordingly, a feature recognition method based on syntactic pattern recognition is proposed herein. The proposed method provides a more precise feature definition and easily describes complex features using constraints. To establish the relationships between geometric features, processing modes, and cost, this study proposes a method of describing the features and the processing mode using feature quantities and adopts deep learning technology to establish the relationship between feature quantities and cost. By comparing a back propagation (BP) network and a convolutional neural network (CNN) it can be concluded that a CNN using the “RMSProp” optimizer exhibits higher accuracy.

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Kale, Prof Amit. "Comparative Cost Estimation of Residential Building." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 2735–39. http://dx.doi.org/10.22214/ijraset.2021.37847.

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Abstract: A construction project of any building is mainly based on 3 important steps that are planning, Cost Estimation of the building and proper execution of construction of the building. Construction cost estimation has the lion’s share of the research effort in construction management. The Objective is to analyze the effectiveness of various cost estimation methods by comparing traditional and various online websites. This study will provide more accurate estimates that save time and minimize errors. The research conducted will be helpful for estimation of construction, also proving how the introduction of IT sector in construction industry is turning out to be beneficial. Keywords: Estimation, Construction Management, Online Websites, minimize errors

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Dashti, Maedeh, Taghi Javdani Gandomani, Dariush Hasanpoor Adeh, Hazura Zulzalil, and Abu Bakar Md Sultan. "LEMABE: a novel framework to improve analogy-based software cost estimation using learnable evolution model." PeerJ Computer Science 7 (January 3, 2022): e800. http://dx.doi.org/10.7717/peerj-cs.800.

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One of the most important and critical factors in software projects is the proper cost estimation. This activity, which has to be done prior to the beginning of a project in the initial stage, always encounters several challenges and problems. However, due to the high significance and impact of the proper cost estimation, several approaches and methods have been proposed regarding how to perform cost estimation, in which the analogy-based approach is one of the most popular ones. In recent years, many attempts have been made to employ suitable techniques and methods in this approach in order to improve estimation accuracy. However, achieving improved estimation accuracy in these techniques is still an appropriate research topic. To improve software development cost estimation, the current study has investigated the effect of the LEM algorithm on optimization of features weighting and proposed a new method as well. In this research, the effectiveness of this algorithm has been examined on two datasets, Desharnais and Maxwell. Then, MMRE, PRED (0.25), and MdMRE criteria have been used to evaluate and compare the proposed method against other evolutionary algorithms. Employing the proposed method showed considerable improvement in estimating software cost estimation.

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The, Jaya Suteja, Prasad K. D. V. Yarlagadda, M. Azharul Karim, and Cheng Yan. "A Framework for Life Cycle Cost Estimation of a Product Family at the Early Stage of Product Development." Advanced Materials Research 605-607 (December 2012): 222–27. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.222.

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A cost estimation method is required to estimate the life cycle cost of a product family at the early stage of product development in order to evaluate the product family design. There are difficulties with existing cost estimation techniques in estimating the life cycle cost for a product family at the early stage of product development. This paper proposes a framework that combines a knowledge based system and an activity based costing techniques in estimating the life cycle cost of a product family at the early stage of product development. The inputs of the framework are the product family structure and its sub function. The output of the framework is the life cycle cost of a product family that consists of all costs at each product family level and the costs of each product life cycle stage. The proposed framework provides a life cycle cost estimation tool for a product family at the early stage of product development using high level information as its input. The framework makes it possible to estimate the life cycle cost of various product family that use any types of product structure. It provides detailed information related to the activity and resource costs of both parts and products that can assist the designer in analyzing the cost of the product family design. In addition, it can reduce the required amount of information and time to construct the cost estimation system.

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Sari, F. A. K., and Y. Latief. "Safety Cost Estimation of Building Construction with Fuzzy Logic and Artificial Neural Network." Journal of Physics: Conference Series 1803, no. 1 (February 1, 2021): 012020. http://dx.doi.org/10.1088/1742-6596/1803/1/012020.

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Abstract The number of cases of construction work accidents continues to increase every year. To prevent work accidents, especially the fatality rate, Occupational Health and Safety (OHS) must be implemented in the construction project. Implementation of OHS can be a success if the availability of the budget is allocated explicitly for implementing OHS in construction projects. However, respondents’ current OHS budget is currently insufficient when referring to the guidelines regarding OHS costs applied in Indonesia. This condition will increase the initial budget and cause financial losses. So it is necessary to develop a cost estimation model that can estimate costs quickly and accurately. The results from this study will help estimator make OHS cost estimates quickly and accurately so that an estimator does not need a long time to do cost estimations at the beginning of the project, and the results of the estimated cost estimates are accurate. The fuzzy method uses to classify the contract value. The output of the fuzzy process will use as input in learning with the neural network. The Mean Absolute Percentage Error of tested data set for the adapted model is highly accurate (9.906%). The model obtained has a better MAPE value than the estimated cost by using regression analysis.

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Journal articles on the topic 'Cost Estimation'

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