The application of engineering practices and scientific principles to the creation of cost estimates along a product life cycle is one of the basic aspects of Cost Engineering. Cost estimates are used as fundamental criteria to make design decisions in the development stage and also to make business decisions in collaboration between OEMs and their supply chain. The achievement of an estimate requires experience and knowledge of different techniques and methodologies. Key aspects on its creation are the adoption of a costestimating process, the availability of the needed data and the proper management of the information used during the process. The collaboration between the OEM and its supplier can be facilitated by having a better common understanding of how the cost estimates have been created. The costestimating process used is then a fundamental piece of trust. In this context, V-CES project has developed a set of virtual tools and services around costestimating processes to support the creation of cost estimates, the improvement of competences of the Cost Engineering Community, and the common understanding on cost between OEMs and their supply chain. The main purpose of this paper is to present the research conducted in the definition of costestimating processes and the virtual solutions developed around them.
Therefore the research is needed because in Malaysia the use of CostEstimating system was very limited. The most common and widespread method for taking-off was still the traditional method and spreadsheet method. In the mean time, although most of the contractors have their highly capable CostEstimating system, there are still many of them who still use the spreadsheets program. The reason is that spreadsheet programs actually save more time than the CostEstimating production system. For all this spreadsheet users, template or standard sheets were being developed to help out CostEstimating process. The users will only have a key in the dimension and then, they will have quantities for multiple items in one single sheet.
ABSTRACT: The models structure of construction cost and price forecasting underlie The models structure of construction cost and price forecasting underlie all the techniques being used and yet until quite recently, industry has still not getting familiar with their attributes and characteristics. Without a good comprehension of the various types of cost model, effective cost control and the development of future forecasting techniques are studied. The central theme of this paper is that costestimating financial and non-financial information includes all the information which quantity surveyors and estimators need to manage effectively to lead their firms to competitive success. However, the specific role of costestimating in the quantity surveying and contracting firms differ depending on the firm’s competitive strategy, organizations, and the management functions to which costestimating is applied. Meanwhile, changes in the business environment have amended the nature of competition and the types of techniques that quantity surveyors and estimators use to succeed in their businesses including globalization, advances in construction technologies, advances in information technologies, the internet and e-commerce, client centered, new forms of management organization, and changes in the social, political, and cultural environment of business. As a result, some of the relevant potential areas of construction costestimating have been recognized and being discussed further in this article.
The costestimating model used in design phase must be in coherence with the definition report of the project. It is then better to consider the concept of product architecture. We have not known yet how these products will be pro- duced, but we can access a number of physical charac- teristics or parameters like the mass, the volume, the en- ergy absorbed or the number of input-output. This is ob- tained for information purposes at the Beginning of de- velopment phase.
Recent BIM survey results reveal that many professions such as engineers and contractors are lagging behind the architects in adopting BIM (McGraw-Hill Construction, 2010; NBS, 2012). Notably, quantity surveyors are found to be slow to embrace the use of BIM. According to the RICS BIM survey (Matthews, 2011b), many quantity surveyors are still not aware of what BIM is and only small numbers (10%) claimed to have used BIM. There is also a little evidence to show that BIM is systematically introduced by the quantity surveying (QS) profession in UK. According to the initial background studies, the majority of literature available on BIM focus on the architects’ and designers’ interest at the utmost and only a very few connect BIM and the practice of quantity surveying. In addition, there are also limited case studies found within the UK to portray the integration of BIM into QS practice. This signifies an alarming need to gain quantity surveyors attention into this new technology and help them to keep up with the pace of other industry professionals to maintain their competitiveness within the industry. Hence, research about the integration of BIM into QS practice in the UK is a timely need. In this paper, the authors aims to bridge the knowledge gap of BIM based estimating or take-off technology and the UK standards and practices of measurement and costestimating, this research investigates how a BIM model to support the estimating process according to the RICS standard of NRM1 order of costestimating and elemental cost planning, and reviews the technical requirement for BIM based software tools to support NRM1. In order to achieve this aim, the following three objectives have been established:-
Thus, to improve the existing cost management practice, contractors should follow a detailed examination of all the factors related to submit tender offer; they need to have a bidding strategy which is directed towards the acquisition of sufficient volume of business at a sufficient profit level; they are advised to maintain records of actual data on material consumptions and resources’ productivity; overhead costs need to be identified, quantified and estimated item by item during the costestimating stage; all potential risks, economic conditions, and political situations should be assessed, forecasted, quantified and incorporated in to the tender sum to the extent possible. Also, they are recommended to use other estimating techniques in addition to the standard estimating technique; costestimating formats should be integrated with those used for budgeting and cost controlling purposes; their cost controlling system should be able to identify activities which are being carried out uneconomically and indicate the causes; they must be accustomed to the preparation of a budget for each activity; and it is highly recommended that contractors should use project works breakdown for facilitating the cost management and controlling process.
The study observed that in the majority of cases, contractors’ efforts to manage projects costs are centred on management of project resources. This resource-centric strategy premised on materials, plant and labour resources management is founded on the principle that resources consumption stimulates building project costs. Statistical analysis performed through chi-square test at 95% shows that: cost reports, costestimating and budgeting, variance analysis, cost value reconciliation and resources management are the significant factors employed by contractors to manage costs on building projects in Zimbabwe. However, despite the existence of these strategies, most contractors admit that their projects would, in most cases, end with a cost overrun. This may be inferentially explained by a host of challenges affecting cost management practice. Whereas several challenges were observed with respect to cost management, it is labour related challenges, materials related challenges and organisational problems that impact significantly on cost management practice. The study also observed that the cost management process in Zimbabwe, like in other developing countries in Africa and elsewhere is still dominated by the traditional paper based system or ‘prematurely’ developed automated systems of managing project cost information. In a nutshell, the problem in Zimbabwe is not about having the strategies to manage costs but relates to ‘flawed’ implementation. The findings of this research suggest that the strategies instituted to manage project cost and, the challenges to such practice are not significantly different from the ones in other developing countries including but not limited South Africa, Botswana, Nigeria and Uganda. To improve on the effectiveness of contractors’ cost management strategies in Zimbabwe, the research recommends that contractors pay particular attention to problem areas that significantly affect cost management (labour, material and organisational related challenges), engagement of experienced cost management personnel or upgrading of staff’s professional ingenuity through conventional training, refresher courses or participation in seminars; and regular review of cost management systems and strategies. The industry can also take advantage of advancement in ICTS to automate their cost information management systems. This study has provided baseline information on the strategies used to manage project cost and related challenges; however, further research is needed to assess the effectiveness of these strategies to manage project cost.
A summary of the component costs making up the total cost of cervical cancer screening and their percentage con- tribution to the total is shown in Figure 2. Similar esti- mates from a public health system payer perspective (i.e, excluding patient time and transport) are shown in Figure 3. The direct medical costs of cervical cancer screening with cervical cytology excluding laboratory transport and laboratory sample processing were I$2.34, I$2.67, I$3.65, I$16.27, and I$2.21 for India, Kenya, Peru, South Africa, and Thailand respectively. With HPV DNA testing using Hybrid Capture 2, these costs were I$4.22, I$5.60, I$6.21, I$21.21, I$4.71. Based on primary data, expert opinion on quantity, productivity, and depreciation, and interna- tional prices, we produced detailed cost estimates of sam- ple processing in cervical cytology laboratories and in HPV DNA laboratories that illustrate the relative contribu- tions of component costs. Table 2 shows staff, supply, and equipment quantity, price, and depreciation data as well as the resulting cost estimates. Cervical cytology is more labor intensive, requiring a broader range and quantity of labor inputs with less reliance on equipment. HPV DNA laboratories rely on automated processing thus requiring less staff, although requiring specific equipment. Because of the uncertainty inherent in these estimates, Table 2 also
A common feature of G&H and M&M models is the fact that the risk premium for the cost of equity is determined based on a certain number of factors which in total characterize a degree of risk rate of business activities of an evaluated company. It is true, however, that these two models are rather general. To determine the risks more precisely, build‑up method may include specific risks of different lines of business, or of the company itself whose discount rate is to be determined.
The EU generally, and the UK, Belgium, Netherlands and Germany specifically, have ambitious plans for the large scale installation of offshore wind-power capacity. However, the cost of energy from offshore wind is much higher than that from land-based generation and anything between 15% and 30% may be due to the cost of Operation and Maintenance (O&M) [### Ref], largely driven by delays in access and repair caused by adverse weather and sea-state, high vessel costs, higher wage costs, and lost revenue from extended down-time.
Whilst value on the whole became accepted, the size effect has been unclear. Hsu and Kalesnik (2014) claimed that size has not been observed in the United States since the early 1980s and does not exist outside the U.S. This highlights a danger with anomalies; once discovered they frequently stop working (we discuss this in more detail in part ii of this appendix). Shumway and Warther’s (1999) examination of the size effect, led them to conclude that it was likely driven by failure to account for the performance of small stocks that delisted (i.e. there was survivorship bias in the results). Despite this mixed academic evidence, practitioners often argue that size is a good “enabler” of other factors, e.g. for value stocks, the outperformance is more pronounced in smaller companies. Another criticism of size is that any excess returns disappear once higher relative transaction costs (from lower liquidity) are accounted for. Whilst this might mean that net expected returns from investing in smaller illiquid companies may be no higher, the pre-transaction cost return, which will be equal to the cost of equity for the issuer, will be. Small companies may therefore have a higher of cost of equity to reflect the higher transaction costs. This might impact the cost of equity of the very smallest independent utility companies. However, in a competitive market companies should find their optimum size. It would be perverse for regulators to reward inefficient scale. Furthermore, whilst higher relative transaction costs might apply to public listed companies it might not apply for other ownership structures.
The Capital Asset Pricing Model (CAPM) was originally developed by Treynor (1961, 1962), Sharpe (1964) and Lintner (1965a, b) in the mid 60s. It is the most commonly applied model by practitioners. This may be explained by the simplicity of its implementation, to the extent that it establishes a linear relationship between risk and return. The fact is still that the CAPM raises serious empirical problems which enabled several researchers, such as Fama and French (1993) and Carhart (1997), to propose extensions to the CAPM. The problem of estimating the cost of equity is even more serious in the context of emerging countries as the stock market is known to be inefficient and where little research has examined it.
In the present study, all values have been indexed by the National Civil Construction Index (INCC), a Brazilian official index, published monthly by Fundação Getúlio Var- gas (FGV). The INCC index was chosen due to two reasons: 1) in the Brazilian real es- tate market it is common to update the revenues with the INCC during the construc- tion period and it seems to be very convenient to keep cash flow inputs and outputs on the same basis; and 2) in this particular situation, considering the studied projects, INCC has been the official index that showed the cost variations most similar to those effectively observed by the company during the same period.
We use both Root Mean Square (RMS) error and Correlation Coefficient (CRCoef) to evaluate our model, since the two metrics evaluate different as- pects of an estimate. RMS is a way to quantify the amount by which an estimator differs from the true value of the quantity being estimated. It tells us how ‘off’ our estimate is from the truth. CRCoef on the other hand measures the strength and direction of a linear relationship between two random variables. It tells us how well correlated our estimate is with the actual annotation time. Thus, for evaluating how ac- curate our model is in predicting annotation times, RMS is a more appropriate metric. For evaluating the utility of the estimated annotation cost as a cri- terion for ranking and selecting examples for user’s annotation, CRCoef is a better metric.
This report has estimated the likely long-run cost to government of the current undergraduate and proposed new postgraduate loan schemes. We have also estimated the variation in these estimates that might result from changes to long-run real earnings growth or loan take-up behaviour, and in particular shown how sensitive they are to the choice of discount rate (the way in which we value future repayments in the present). While there is a range of plausible estimates for the long-run cost of the loan scheme to government – and the true cost, even for the 2012 cohort, will not be known for decades to come – it seems likely that, for the undergraduate system at least, in the absence of further policy reform, the long-run cost to government of issuing student loans will be at least £10,000 per student (or £3 billion per year for a cohort of 300,000 students). Recent policy debate has focused heavily on whether this figure is ‘too high’. It is, of course, important to bear in mind that the loan subsidy is just one element of the support government provides for undergraduate higher education: it also provides teaching and maintenance grants, for example, as well as funding for things such as ‘widening’ access (although we have not considered these latter elements in this report). Any debate about whether the current system is ‘too expensive’ should thus consider the full range of support on offer and not just the loan subsidy (commonly referred to as the RAB charge).
A modification of the method of predicting the cost of an enterprise on the basis of Shalanov’s chains is suggested; this modification enables implementing the concept of system predicting. The represented method differs from the known methods in using principles of system dynamics when growth of private cost parameters is considered together with other factors of financial and economic activity of an enterprise. The structure of fundamental cost of an enterprise is taken into account in the suggested approach, and also principles of dynamic compatibility of the parameters used for evaluating the quality of the enterprise development on the basis of its cost are observed.
The low-cost "SPod" air quality sensors output a time series that includes a slowly varying baseline, the sensor response to pollutants, and high frequency random noise. These sen- sors record measurements every second and are used to monitor pollutant concentrations at the perimeter of industrial facilities. Time points with high concentrations are iden- tified and compared with concurrent wind direction and speed. Ideally, three co-located and time aligned sensors (as shown in Figure 3.1) responding to a pollutant plume would result in the same signal classification after baseline trend removal and proper threshold choice. We first illustrate the difference between our detrend_eBIC method, hereafter referred to as detrendr , and qsreg using data from 13:10 to 15:10 from Figure 3.1 (Sec- tion 3.5.1). We then compare the methods on the complete day shown in Figure 3.1, estimating trends by applying the qsreg method to 2 hour non-overlapping windows of the data and Algorithm 1 to the entire day. We focus on this day because data from three sensors was available. Finally, we examine an entire week of measurements from two co-located sensors (Section 3.5.2).
Cloud computing has become widely used method for providing various types of services for users and consumers in all around of the world. It is providing facilities for saving users’ favorite files, documents or resources in cloud computing servers and these servers can be locate in various areas or states in a country or in world. Further, users can be access other users’ data or documents which are shared with others from anywhere or share their own data, files or documents through the cloud computing servers. The other usage of cloud computing services is, providing possibility for users that can run their own applications though the software which is installed in the server of cloud computing, therefore, these users can be save cost of purchasing or installing this software in their own personal computers.