مدل تجزیه و تحلیل هزینه احتمال سلسله مراتبی با ترکیب اصل معلولیت برای برآورد هزینه پروژه EPC
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|23388||2011||12 صفحه PDF||سفارش دهید||9031 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 38, Issue 7, July 2011, Pages 8087–8098
Two new types of hierarchy probability cost analysis (HPCA) model incorporating money allocated is money spent (MAIMS) principle based on definite work breakdown structure (WBS) level for EPC (engineering, procurement and construction) projects are presented. The proposed models have skillfully solved dilemma to appropriate cost elements and maximize the efficiency of information for cost risk analysis. Macroscopic and microscopic risk analysis of the project cost elements are introduced for meaningful model input. The illustration of an actual bidding EPC project substantiates that proposed integrating HPCA-hierarchy MAIMS models have demonstrated effective and viable for EPC projects.
Real-world experience and intuition both suggest that project costs depend on many factors including technical, organizational, and behavioral considerations. Unfortunately, today’s typical probabilistic cost analysis assumes an “ideal” project that is devoid of the human and organizational considerations that heavily influence the success and cost of real-world projects. In the real world “money allocated is money spent” (MAIMS principle); cost under-runs are rarely available to protect against cost overruns while task overruns are passed onto the total project cost (TPC). Realistic cost estimates therefore require an integrated probabilistic cost analysis that simultaneously models all components of the cost management strategy including budget allocation, psychological influences (such as overconfidence in assessing uncertainties and dependencies among cost elements), unexpected events, and other important considerations that are generally not addressed. The following deficiencies in cost modeling and contingency management have been major contributors to both project high costs and overruns: (1) garbage input (Walker & Cox, 2003); (2) “money allocated is money spent” (MAIMS principle) (Gordon, 1997 and Kujawski et al., 2004); (3) invalid mathematics such as using statistical methods arithmetically summing uncertain cost elements instead of Gordon, 1997 and Kujawski et al., 2004. In today’s highly competitive business environment, it is therefore critical to improve the realism of cost estimation. Monte Carlo simulation is only a mathematical tool of PCA, it cannot compensate for “garbage in/garbage out” (GIGO) (Walker & Cox, 2003). Meanwhile, overconfidence is also commonly found in the assessment of probability distributions. Alpert and Raiffa (1982) say: in every case, the spread of the tails of the distributions was too small, regardless of the definition of the extremes, and although feedback did improve the spread, it did not completely eliminate the overconfidence bias. Winkler, Hora, and Baca (1992) suggest three reasons why it is useful to aggregate the judgments of multiple experts: (1) an aggregated distribution provide a better appraisal of knowledge than the individual distribution (a sample mean is better than one observation); (2) the aggregated distribution is sometimes thought of as representing some sort of consensus; (3) it is easier to use a single distribution for further analysis. The evidence shows that whether expert or naive, many factors affect the calibration and goodness of probability assessments. Studies of non-expert subjects answering almanac questions, while not providing meaningful probability assessments, do provide insight into the cognitive strategies used in making subjective probability assessments. While most researchers agree that feedback and training are necessary, there is little systematic evidence on what types of feedback improve calibration, discrimination, and other measures of goodness. Few studies assess how effective training is at overcoming the biases caused by cognitive simplification mechanisms (Wilson, 1999). As Hogarth mentions (Hogarth, 1987), “the success any judgmental strategy will necessarily depend on the extent to which it is suited to the characteristics of the tasks”. He suggests the development of taxonomy of assessment task characteristics that could be used to select appropriate elicitation techniques. For the EPC projects, work breakdown structure (WBS) is easily considered as such candidate taxonomy for similar techniques. In general, there are interrelationships among the cost elements because of their dependence on common factors such as state of technology, complexity, criticality, management, staff, and product development process (Browning & Eppinger, 2002). Risks faced in complex engineering projects on different cost elements are often correlated; ignoring correlation in statistical computations makes the spread of the cost distribution narrower than it should be (Kujawski et al., 2004). Failing to account for correlation therefore deceives the analyst by making an estimate appear less uncertain than it really is. So, the correlation between project-item costs is a critical factor in the estimation of total project cost uncertainty. It is standard practice for EPC projects to allocate definite budgets to cost elements and maintain a budget contingency for dealing with unforeseen in-scope events. The MAIMS principle captures the fact that given this situation, cost under runs are rarely available to protect against cost overruns while task overruns are passed onto the total project cost. Gordon’s (1997) numerical studies strongly indicate that a realistic PCA needs to account for the MAIMS principle. To deliver a successful project at an optimal cost, project management needs to allocate “reasonable” budgets to the cost elements and dynamically manage the contingency funds as a risk portfolio at the project level (Kujawski, 2002a and Kujawski, 2002b). Building on all above consideration, the practical yet realistic and mathematically valid hierarchy integrated probability cost analysis models are proposed to remedy several shortcomings that are prevalent in today’s PCAs and adversely impact project management. In Section 1 we reviewed some findings on cost overruns and potential solution for complex EPC projects. In Section 2 the work breakdown structure (WBS) model is advocated for EPC projects cost taxonomy. In Section 3 macroscopic and microscopic risk analysis of the project cost elements are provided, to make sure that input to the cost model is meaningful and realistic. In Section 4 integrating HPCA-hierarchy MAIMS models are proposed. Correlation matrix that accounts for correlations among cost elements by rank and subject, and its feasible verification procedure are recommended. Some mathematical properties of multi-variable statistical product and sums are reviewed, after that, improved hierarchy cost estimation models based on WBS and MAIMS principle are proposed. In Section 5 illustration of an actual bidding EPC project substantiates that proposed integrating HPCA-hierarchy MAIMS models have been demonstrated and verified. In Section 6 summary and conclusions are introduced.
نتیجه گیری انگلیسی
The practical and theoretically valid hierarchy PCA-hierarchy MAIMS models among WBS-item cost elements have been developed to solve skillfully the dilemma of typical PCA. The key elements include: 1. The use of an appropriate WBS for cost hierarchical structure. Subdividing the project costs into too many bite-size pieces is likely lead to erroneous results and a false sense of confidence. Analysts should be wary of the pitfalls of performing a probabilistic cost analysis that consists of hundreds of cost elements that are subordinate to WBS-level 3. 2. Macroscopic and microscope risk analysis of project cost elements in order to obtain accurate model input and maximize efficiency of information. Monte Carlo simulation method is recommended for historical data of WBS level 4 (discipline level) in order to obtain percentile of preliminary PDF. Real estimate of Cmin; Cm; Cmax and reasonable budget will be approached via discipline experts’ calibration. 3. Incorporation of the “money allocated is money spent” (MAIMS principle) with budget management practices and hierarchy. The assessment of the cost elements, correlation effects, budget allocation, and project management consideration items all influence each other and have a significant impact on the total project cost and/or probability of success. For enhanced credibility and realism, HIPCA-hierarchy MAIMS considers these influences simultaneously rather than individually. The proposed approach provides a cost estimation and analysis framework for EPC project. It avoids the impact of high number of cost elements and maximizes efficiency of historical data and experts’ judgment. And it not only makes demands upon the cost estimator, but also provides benefits to project management, particularly when it comes to recommending a prudent management reserve. Having in hand a probability distribution of total WBS-item cost, rather than just a single best estimate, project management can propose, for example, that the basic cost estimate can be budgeted at the 50% confidence level, but that sufficient management reserve can be included to bring the success probability up to 70%. Project managers can develop more viable plans and make better decisions during bidding stage and execution stage, so that projects are delivered for a lower cost and higher probability of success. The magnitude of the cost overrun problem is no excuse for accepting the status quo; the benefits from proposed approaches are likely to be significant. Our experience is that the single greatest challenge to the development and use of hierarchy probabilistic cost analysis is the implementation of systems thinking. Further development of a tracking system that identifies the assumptions for the high, medium, and low (or percentiles) three points estimate and tracks their evolution are necessary, so as to develop and implement more refined cost models substantially.