Updating The Lang Factor And Testing Its Accuracy, Reliability And .
PM World JournalVol. III, Issue X – October 2014www.pmworldjournal.netUpdating the Lang Factor and Testing its Accuracy, Reliability andPrecision as a Stochastic Cost Estimating MethodFeatured Paperby Yosep Asro WainUpdating the Lang Factor and Testing its Accuracy, Reliability andPrecision as a Stochastic Cost Estimating MethodBy Yosep Asro Wain, CCPABSTRACTThe Lang factor is a one of the factored estimating techniques that is recommended AmericanAssociation of Cost Engineers (AACE) International for class 4 and class 5 estimates. Thismethod was proposed by Hans J. Lang in 1940’s use a simple formula: consist of a set offactor multiplied by the Total Equipment Cost (TEC) to obtain the Total Plant Cost (TPC).These factors are 3.10 for solid plant, 3.63 for solid-fluids plant and 4.74 for fluids plant.Over the ensuing decades, several people tried to calculate the Lang factor by using theircurrent data.In this paper, the fluid plant Lang factor was updated and tested its accuracy, precision andreliability by using historical project data from a major Indonesian national oil company.The result of updating and testing show that while the Lang factor is appropriate for use forClass 4-5 estimates, because it exhibits such a high degree of variability; it is notrecommended for creating high accurate, reliable or precise of cost estimates.Keywords: Lang factor, Factorized Estimating, Cost Estimating, Stochastic Cost Estimating,Parametric Cost Estimating, Equipment Factor Cost Estimating, Statistical Analysis, MonteCarlo Simulation.1. Introduction.1.1. Cost Estimation in the Project.Cost estimating is the predictive process used to quantify, cost, and price the resourcesrequired by the scope of an investment option, activity, or project [1]. In that regard, costestimating contains two thinks, namely resources quantification and resources pricing orcosting. In resources quantification, the project scope definition in the form of the workbreakdown structure (WBS) and the work statement may be used to identify the activities thatmake up the work, and further, each activity is decomposed into detailed items so that laborhours, material, equipment and subcontract are itemized and quantified [2]. In resourcespricing, any methodology such as stochastic, factored, or deterministic may be used to costthe resources.The cost estimating process is carried out during the entire of the project life cycle. Atbeginning of the project where scope definition is still roughly, the accuracy of the costestimation is low. As the project definition go to more detail, then the accuracy of the costestimation become higher. 2014 Yosep Asro Wainwww.pmworldlibrary.netPage 1 of 17
PM World JournalVol. III, Issue X – October 2014www.pmworldjournal.netUpdating the Lang Factor and Testing its Accuracy, Reliability andPrecision as a Stochastic Cost Estimating MethodFeatured Paperby Yosep Asro WainAmerican Association of Cost Engineers (AACE) International categorize cost estimationbecome 5 estimate classes, based on maturity level of project definition deliverable, endusage of the estimate, estimating methodology, expected accuracy range of estimate, andpreparation effort, as shown in table 1.Table 1 – Cost Estimate Classification for Process Industries [3].Primary CharacteristicESTIMATE CLASSDEGREE OF PROJECTDEFINITIONDELIVERABLESExpressed as % ofcomplete definitionSecondary CharacteristicEND USAGETypical purpose ofestimateMETHODOLOGYTypical estimating methodEXPECTEDACCURACY RANGETypical variation in lowand high range [a]Capacity factored,parametric models,judgment, or analogyL: -20% to -50%1% to 15%Study or feasibilityEquipment factored orparametric modelsL: -15% to -30%Class 310% to 40%Budget authorizationor controlSemi-detailed unit costswith assembly level lineitemsL: -10% to -20%Class 230% to 70%Control orbid/tenderDetailed unit cost withforced detailed take-offL: -5% to -15%Class 170% to 100%Check estimateor bid/tenderDetailed unit cost withdetailed take-offClass 5Class 40% to 2%Concept screeningH: 30% to 100%H: 20% to 50%H: 10% to 30%H: 5% to 20%Note :L: -3% to -10%H: 3% to 15%[a] The state of process technology and availability of applicable reference cost data affect the range markedly.The /- value represents typical percentage variation of actual costs from the cost estimate after application of contingency(typically at a 50% level of confidence) for given scope.Figure 1 provides an example of the variability in uncertainty ranges for a process industryestimate versus the level of project/scope definition. 2014 Yosep Asro Wainwww.pmworldlibrary.netPage 2 of 17
PM World JournalVol. III, Issue X – October 2014www.pmworldjournal.netUpdating the Lang Factor and Testing its Accuracy, Reliability andPrecision as a Stochastic Cost Estimating MethodFeatured Paperby Yosep Asro WainFigure 2, Example of the Variability in Accuracy/Uncertainty Ranges for a Process Industry Estimate [4]A Class 5 estimate is associated with the lowest level of project definition or maturity, whilea Class 1 estimate, with the highest one. The estimating methodology tends to progress fromstochastic or factored to deterministic methods with increase in the level of project definition,which result the increase in accuracy. Meanwhile, preparation effort ranging from the loweston Class 5 estimate (0.005% of project cost) to the highest on Class 1 estimate (0.5% ofproject cost).1.2. Factored Cost Estimate [5].Factored estimating techniques are method recommended by AACE International for Class 4and Class 5 estimate. These factors are derived from historical data by using statisticalinferential or modeling. Several type of factored estimating that is used, especially in processindustries are capacity factored estimates, equipment factored estimates, and parametric costestimates.The capacity factored estimates using cost of the similar plant or equipment of knowncapacity to obtain cost of a new plant or equipment, by using equation :Cost B/CostA (CapB/CapA)r, where CostA and CostB are the costs of two similar plants or equipment, Cap Aand CapB are the capacities of the two plants or equipment, and r is the exponent or prorationfactor. 2014 Yosep Asro Wainwww.pmworldlibrary.netPage 3 of 17
PM World JournalVol. III, Issue X – October 2014www.pmworldjournal.netUpdating the Lang Factor and Testing its Accuracy, Reliability andPrecision as a Stochastic Cost Estimating MethodFeatured Paperby Yosep Asro WainThe equipment factored estimates is used to obtain total installation cost from equipmentcost. Several method are categorized as the equipment factored estimates are Lang factor,Happel, Hand, Hackney, and Guthrie.Parametric model estimates using parametric model to obtain equipment cost and further thetotal plant cost. Parametric model is derived from statistical analysis of equipment cost datafrom specific time duration.1.3. Problem Statement.As mentioned above, Lang factor is a one of the factored estimating techniques that isrecommended for Class 4 or Class 5 estimate. Lang factor was proposed by Hans J. Lang in1940’s, using a simple formula; consist of a set of factor multiplied by main equipment costto obtain total cost. These factors are 3.10 for solid plant, 3.63 for solid-fluids plant and 4.74for fluids plant.Over the ensuing decades, several people tried to calculate the Lang factor by using theircurrent data. Several of these studies and results are contained in the Fixed Capital CostEstimation chapter of Perry’s handbook; others include in books by Gerrard, Page, andDysert, also studied by Wolf, T.E [6].The updated Lang factor also is used in the AACE International Recommended Practice,which are 3.89 for solid plant, 5.04 for solid-fluids plant and 6.21 for fluids plant [7].During period from Lang Factor was introduced until now, many things have changed. Thereare now governmental rules and regulations in-place, which just did not exist in the 1940sand 1950s. There are materials and construction methods that are different. There are digitalprocess controls instead of pneumatic controls. The computer is used in lieu of the slide ruleand there is a three dimensional of computer design. Then there is material and labor costinflation (escalation) over the many decades [8]; therefore, updating the factor sometimesnecessary to adapt with the current condition.In addition, testing the accuracy, precision and reliability of the Lang factor also necessary toknow whether this method can be used for high accuracy of cost estimate or not.In this paper, Lang factor will be updated and tested its accuracy, precision and reliability byusing historical data from Refinery Directorate of a major Indonesian national oil company(“Company”).2. Lang Factor.Hans Lang introduced the concept of using the total cost of equipment to estimate the totalcost of a plant [9], by using the following formula:TPC f x TEC . (1)The TPC is a total plant cost, while the TEC is total (main) equipment cost.As mentioned above, several people have tried to calculate the Lang factor by using their current data, some ofthem as shown in table 2. 2014 Yosep Asro Wainwww.pmworldlibrary.netPage 4 of 17
PM World JournalVol. III, Issue X – October 2014www.pmworldjournal.netUpdating the Lang Factor and Testing its Accuracy, Reliability andPrecision as a Stochastic Cost Estimating MethodFeatured Paperby Yosep Asro WainTable 2 Lang factor f and its updatedDescriptionOriginal Lang factor [10]AACE International RP 59R-10 [11]Perry’s Chem. Eng. H.B. [12]Dysert L.R. [13]Wolf T.E. [14]Solid3.103.893.80-Solid - s approach was simple, utilizing a factor that varies only by the type of process. Today,many different methods of equipment factoring have been proposed. The Lang factor,however, is often used generically to refer to all the different types of equipment factors [15].3. Data Gathering.Data are collected from several refineries owned by Company which are located at severalareas in Indonesia. A total 29 project data sets is obtained, spanning from 2003 to 2013 (10years project data). Figure 2 shows data distribution based on refinery unit. All of the 29projects are for fluid plants; therefore the Lang factor to be obtained and tested in this paperis for process fluid only.20151050Unit AUnit BUnit CUnit DUnit EFigure 2, Data distribution based on refinery unitEach of the 29 sets of project data consists of the total plant cost (TPC) and the totalequipment cost (TEC).The data range from several tens of thousands up to hundreds of millions US Dollar of TEC.A factor f for each project data is derived by using the following equation:f TPC/TEC . . (2)Figure 3 shows scatter plot of data. 2014 Yosep Asro Wainwww.pmworldlibrary.netPage 5 of 17
PM World JournalVol. III, Issue X – October 2014www.pmworldjournal.netUpdating the Lang Factor and Testing its Accuracy, Reliability andPrecision as a Stochastic Cost Estimating MethodFeatured Paperby Yosep Asro 00,000150,000,000200,000,000Figure 3, Scatter Plot of f and TEC (USD)4. Data Analysis.It is necessary to do data analysis before is used for deriving the Lang factor. Data analysisincludes understanding of the data and relationship between the variables. Table 3 showsdescriptive statistics for factor f.Table 3 Descriptive Statistics for the factorf.DescriptionStatisticalMean3.298Standard Error0.293Median2.865Standard Deviation1.580Sample inimum1.221Maximum8.342Confidence Level (95%)0.601The factor, f range from 1.221 to 8.342, with the mean is 3.298 and standard deviation is1.580, at 95% confidential level. This information shows us the wide variability of the data.The value of Kurtosis and Skewness are 3.035 and 1.673 respectively indicate thatdistribution of data is not symmetries with wide tail to the right (right skew).Another aspect that necessary to analyze are accuracy, precision and reliability of the data.The standard deviation value of 1.580, also indicate that the precision of data is very low(wide spread). In this case, we could not analyze the accuracy of the data, unless we know thetrue value of the data. To know reliability of the data, outlier checking by using Q-test wasconducted for rightmost data, namely 8.342. The result was Qcalculated 0.214 Qcritical 0.298 at95% confidential level, which indicate there is no outlier data, so that the data is reliable.Due to data distribution is not symmetries, it is necessary to conduct Monte Carlo simulationfirst to find normal distribution data, and then to derive Lang factor. 2014 Yosep Asro Wainwww.pmworldlibrary.netPage 6 of 17
PM World JournalVol. III, Issue X – October 2014www.pmworldjournal.netUpdating the Lang Factor and Testing its Accuracy, Reliability andPrecision as a Stochastic Cost Estimating MethodFeatured Paperby Yosep Asro WainFurther analysis is to identify whether there is any relationship between variables, in this caseis relation between TEC as the independent variable and the factor (f) as the dependentvariable. For this purpose, the Pearson Correlation test was conducted with result Pearsoncorrelation coefficient, r -0.244, that shows very low correlation between both variables.Sometimes it is necessary to identify possibility of another relationship between variables,one of the relationship may exist is logarithmic. Before applied Pearson Correlation test, itshould first be conducted data transformation from logarithmic to linear by using followingequation:LNTEC ln(TEC) .(3)LNf ln(f) (4)The Pearson Correlation test was conducted on transformation data with result Pearsoncorrelation coefficient, r -0.40, that shows medium correlation.Another possibility of relationship between TEC and f is polynomial form. The PearsonCorrelation test was conducted for some polynomial forms as shown in Table 4.Table 4: Pearson Correlation Test for Polynomial RelationshiprTEC-0.244TEC 2-0.186TEC 3-0.172TEC 4-0.167TEC 5-0.165TEC 6-0.164TEC 7-0.164 -0.164TEC 13-0.164As shown in the table, the correlation between TEC and f in polynomial forms are very lowalso.5. Obtaining of Lang factor.From the descriptive statistical of factor f on Table 3, at first glance we may conclude that theLang factor, f is:f 3.298 . .(6)A value of 3.298 is mean of the data. However, due to the data is slightly far from normaldistribution, then it is better to first doing Monte Carlo simulation to the data, and then usingit for obtaining the Lang factor.The table 5 and figure 4 show result of Monte Carlo simulation for the data. 2014 Yosep Asro Wainwww.pmworldlibrary.netPage 7 of 17
PM World JournalUpdating the Lang Factor and Testing its Accuracy, Reliability andPrecision as a Stochastic Cost Estimating MethodFeatured Paperby Yosep Asro WainVol. III, Issue X – October 2014www.pmworldjournal.netTable 5.Descriptive Statistics for the Factor, f after Monte Carlo 100-0.72120-1.73FrequencyDescriptionMeanStandard ErrorMedianStandard DeviationSample nfidence Level (95%)BinFigure 4, Histogram for the Factor, f after Monte Carlo SimulationAs shown in Table 5 and Figure 3, the range of the data is from -1.726 to 8.691. In regard tothe factor f, the value less than 1 has no meaning, therefore it is better to be excluded fromthe data. The best way to exclude this data is by cutting both tail left and right of the data.Due to probability the factor value of 1 is7.3%, so that the data will be cut on 7.3% left andright.The table 6 and figure 5 show the descriptive statistics and histogram of data after doing7.3% tail cutting.Table 6. Descriptive Statistics for the Factor, f after 7.3% left and right tail cutting.DescriptionMeanStandard ErrorMedianStandard DeviationSample nfidence Level (95%) 2014 Yosep Asro e 8 of 17
PM World JournalUpdating the Lang Factor and Testing its Accuracy, Reliability andPrecision as a Stochastic Cost Estimating MethodFeatured Paperby Yosep Asro WainFrequencyVol. III, Issue X – October 40%1020%00%BinFigure 5, Histogram for the Factor, f after 7.3% left and right tail cutting.As shown in Table 6, the mean of the data is 3.282 and standard deviation is 1.123, with0.038 of standard error that means there is a significant improvement on standard error. Thesignificant improvement also occurred in the data distribution, as indicted by value of theKurtosis and Skewness. Hence the Lang factor f will be:f 3.282 . . .(7)Just call this factor as a first model of Lang factor. Further, we tried to obtain second modelof Lang factor from logarithmic relationship as mentioned above. To this purpose, regressionanalysis was conducted on LNTEC on equation (3) as the independent variable and LNf onequation (4) as the dependent variable, with result as shown in table 7a, 7b, and 7c.Table 7a. Linear Regression: Regression StatisticsMultiple R0.399R Square0.160Adjusted R Square0.128Standard Error0.393Table 7b. Linear Regression: 65MS0.7920.155F5.13Significance F0.032Table 7c. Linear Regression: CoefficientsInterceptX Variable 1Coefficients2.469-0.090Standard Error0.6080.040t Stat4.062-2.264P-value00.03From the regression statistics table, model variance is low, as indicated by R Square value of0.160, which is means only 16% variance in dependent variables can be explained by the 2014 Yosep Asro Wainwww.pmworldlibrary.netPage 9 of 17
PM World JournalVol. III, Issue X – October 2014www.pmworldjournal.netUpdating the Lang Factor and Testing its Accuracy, Reliability andPrecision as a Stochastic Cost Estimating MethodFeatured Paperby Yosep Asro Wainregression model. Based on ANOVA table, this model is significant at 0.05 level. FromCoefficients table, linear model is obtained as follow:LNf 2.469–0.09* . (8)LNTECThe Coefficient table also indicate that the LNTEC variable also significant at 0.05 level.Equation (8) can be simplified as:LNf ln(11.807) LNTEC-0.09 . .(9a)orlnf ln(11.807xTEC-0.09) . . . (9b)Hence, the second model Lang factor will be:f 11.807xTEC-0.09 .(9c)Another model for Lang factor, namely the third model is obtained from the polynomialrelationship. Table 8 contains several polynomial model are obtained from regressionanalyses.Table 8, Polynomial Model for Lang FactorOrderEquation1stf 3.487 – 9.9 * 10-9 * TEC2ndf 3.598 – 2.4 * 10-8 * TEC 9.0 * 10-17 * TEC2rd-8-152RSquared0.0600.076-2433f 3.865 – 9.4 * 10 * TEC 1.5 * 104thf 4.163 – 2.2 * 10-7 * TEC 6.8 * 10-15 * TEC2 - 6.9 * 10-23 * TEC3 2.1 * 10-31 *TEC4th56th7th8th9th10th* TEC - 5.7 * 10* TEC0.143f 4.455 – 3.9 * 10-7 * TEC 2.0 * 10-14 * TEC2 – 4.1 * 10-22 * TEC3 3.3 * 10-30 *TEC4 8.8 * 10-39 * TEC5f 4.602 – 5.0 * 10-7 * TEC 3.5 * 10-14 * TEC2 – 1.0 * 10-21 * TEC3 1.5 * 10-29 *TEC4 9.3 * 10-38 * TEC5 2.2 * 10-46 * TEC6f 5.074 – 1.0 * 10-6 * TEC 1.5 * 10-13 * TEC2 – 8.9 * 10-21 * TEC3 2.6 * 10-28 *TEC4 3.7 * 10-36 * TEC5 2.5 * 10-46 * TEC6 – 5.9 * 10-53 * TEC7-7-142-203-29-7-132-213-28f 4.960 – 8.5 * 10 * TEC 9.2 * 10 * TEC – 3.2 * 10 * TEC – 1.4 * 10 *TEC4 3.0 * 10-36 * TEC5 – 6.0 * 10-44 * TEC6 4.6 * 10-52 * TEC7 – 1.2 * 10-60 * TEC80.1980.2430.2250.3380.3430.3430.343f 4.987 – 9.1 * 10 * TEC 1.1 * 10 * TEC – 6.2 * 10 * TEC 1.9 * 10 *TEC4 4.5 * 10-36 * TEC5 9.1 * 10-44 * TEC6 – 1.2 * 10-51 * TEC7 8.4 * 10-60 * TEC8 –2.0 * 10-67 * TEC9f 4.996 – 9.3 * 10-7 * TEC 1.3 * 10-13 * TEC2 – 8.7 * 10-21 * TEC3 4.3 * 10-28 *TEC4 1.8 * 10-35 * TEC5 5.0 * 10-43 * TEC6 – 8.8 * 10-51 * TEC7 8.7 * 10-59 * TEC8 – 2014 Yosep Asro Wainwww.pmworldlibrary.netPage 10 of 17
PM World JournalUpdating the Lang Factor and Testing its Accuracy, Reliability andPrecision as a Stochastic Cost Estimating MethodFeatured Paperby Yosep Asro WainVol. III, Issue X – October 2014www.pmworldjournal.netOrderEquationRSquared4.4 * 10-67 * TEC9 8.7 * 10-76 * TEC10The table above also shows that the models variance is low, as indicated by the value of RSquare range from 0.06 to 0.343.6. Model Comparison.Up to now, we have obtained three models for Lang factor; the Lang factor as a constant asshown in equation (7); Lang factor as logarithmic function of TEC as shown in equation (9c)and Lang factor as polynomial function of TEC as shown in Table 8. Even though themodels have low variance, however it is necessary to compare the calculation error of eachother by using 29 pairs fit data on following equation:Individual Calculation Error (%)Value)*100% . (10) ((ExactValue–estimatedValue)/ExactandAverage Calculation Error (%) ((( Exact Value – estimated Value / Exact Value)/n)*100% (11)The “ ” symbol mean absolute value, and n is number of data, that is 29.The results of individual calculation error as shown in Table 9.Table 9, Individual Calculation ErrorData NoConstant (%)1234567891011121314151617181920212223 2014 Yosep Asro 8-2221-2945-2452Logarithmic 22-1215-2140-1246www.pmworldlibrary.netPolynomial function 8-3014-3840-3247Page 11 of 17
PM World JournalUpdating the Lang Factor and Testing its Accuracy, Reliability andPrecision as a Stochastic Cost Estimating MethodFeatured Paperby Yosep Asro WainVol. III, Issue X – October 2014www.pmworldjournal.netData NoConstant (%)242526272829Logarithmic function(%)0-3631-7-5761Polynomial function (%)233426-5555-802630-7057The results of the average calculation error are 38%, 31% and 38% for Constant Lang factor,Logarithmic function and 2ndPolynomial Lang factor respectively.7. Lang Factor for Each Refinery Unit.In addition, it is necessary to derive the Lang factor for each refinery unit. Due to limitationof the data, factor that derived only for the first model. By using same procedure used toobtain Lang factor for whole refinery units, the first model of Lang factor for each refineryunit were derived as shown in Table 9.Table 10, Lang Factor for Each Refinery UnitLang factor, fUnit A2.497Unit B3.167Unit C3.273Unit D3.423Unit E5.260Data in Table 9 shows us that the Lang factor is different between the refinery units. Thesmallest Lang factor is for Unit A is equal to 2.497, while the largest is for Unit E is equal to5.260.8. Conclusion.By using the company own data, the fluid plant Lang factor has been obtained with value of3.282 (ref equation (7)). This value is derived from 29 pairs of data with wide range offactor, namely 1.221 to 8.342.Calculated fluid plant Lang factor also variety between refinery units as shown in Table 10.Lang factor as a function of TEC also was tried to be derived, for logarithmic andpolynomial function, however the result shows that model correlation and variant are verylow, as indicated by low value of both, correlation factor and R-square.The individual calculation errors of the obtained Lang factor are spread, with the averageabout 30% to 40%. As shown in figure 6, the individual calculation errors (for constant Langfactor) are spread on all classes of estimate, even there are go out from class 5 estimate,however most of them lie on area of class 4 and class 5 estimates, as their average werelocated. This VALIDATES using Lang Factors for Class 4 and 5 estimates, however basedon this research; the original Lang Factor is no longer valid and needs to be revised and theuse of a single Lang Factor is not recommended. 2014 Yosep Asro Wainwww.pmworldlibrary.netPage 12 of 17
PM World JournalVol. III, Issue X – October 2014www.pmworldjournal.netUpdating the Lang Factor and Testing its Accuracy, Reliability andPrecision as a Stochastic Cost Estimating MethodFeatured Paperby Yosep Asro WainFigure 6, Putting The Calculation Error on Accuracy/Uncertainty Ranges of Estimate Graph1Proposed Revisions.The comparison of the company owned Lang factor of 3.282 with the Lang factors whichobtained previously, shows that all of previously obtained Lang factor is higher than thiscompany owned Lang factor, as shown in Table 11.Table 11, Benchmarking of Company Owned Lang Factor and OthersDescriptionCompany owned Lang FactorOriginal Lang factorAACE International RP 59R10Perry’s Chem. Eng. H.B.Dysert L.R.Wolf T.E.Solid3.284.746.214.805.105.12Difference44% higher89% higher46% higher55% higher56% higherAll of these indicate that the Lang factor exhibits a high degree of variability; therefore it isnot recommended for creating highly accurate, reliable or precise cost estimates other thanClass 5 or Class 41It is assumed that the Lang factor is used during project initiation phase, where degree of project definition below 10%. 2014 Yosep Asro Wainwww.pmworldlibrary.netPage 13 of 17
PM World JournalVol. III, Issue X – October 2014www.pmworldjournal.netUpdating the Lang Factor and Testing its Accuracy, Reliability andPrecision as a Stochastic Cost Estimating MethodFeatured Paperby Yosep Asro WainFinally, due to wide variety of Lang factor as mentioned above, it is better to express it as arange, instead of as a single value.Suppose the range is 1 x standard deviation, so that:f 3.282 1.123 . . . .(12)That’s means the Lang factor f will be in the range of 2.159 to 4.405.Or, suppose the range is from P30 to P90, so that the Lang factor f will be in the range of2.693 to 4.721. 2014 Yosep Asro Wainwww.pmworldlibrary.netPage 14 of 17
PM World JournalVol. III, Issue X – October 2014www.pmworldjournal.netUpdating the Lang Factor and Testing its Accuracy, Reliability andPrecision as a Stochastic Cost Estimating MethodFeatured Paperby Yosep Asro AACE International, Recommended Practice No. 46R-11. (2013). Required Skills AndKnowledge of Project Cost Estimating, Page 1 of 21, AACE International, Morgantown, WV.US Government, Department of Energy (DOE). (2011). DOE G 413.3-21 Cost EstimationGuide, Page 18.US Government, Department of Energy (DOE). (2011). DOE G 413.3-21 Cost EstimationGuide, Page 15.US Government, Department of Energy (DOE). (2011). DOE G 413.3-21 Cost EstimationGuide, Page 16.AACE International. (2011). Recommended Practice No. 59R-10, Development of FactoredCost Estimates – As applied In Engineering, Procurement, And Construction For The ProcessIndustries. AACE International. Morgantown, WV.Wolf, T.E. (2013). Lang Factor Cost Estimates. Retrievedfrom E International. (2011). Recommended Practice No. 59R-10, Development of FactoredCost Estimates – As applied In Engineering, Procurement, And Construction For The ProcessIndustries. AACE International. Morgantown, WV.Wolf, T.E. (2013). Lang Factor Cost Estimates. Retrievedfrom ert, L.R., (2003). Sharpen Your Cost Estimating Skills. Cost Engineering Vol. 45/No.6 June2003, page 25.Dysert, L.R., (2003). Sharpen Your Cost Estimating Skills. Cost Engineering Vol. 45/No.6 June2003, page 25.AACE International. (2011). Recommended Practice No. 59R-10, Development of FactoredCost Estimates – As applied In Engineering, Procurement, And Construction For The ProcessIndustries. AACE International. Morgantown, WV.Perry, Robert H, Don W. Green, and James O. Maloney. (1997).Perry's Chemical Engineers'Handbook, New York: McGraw-Hill, Seventh Edition April 1997., page 9-68.Dysert, L.R., (2003). Sharpen Your Cost Estimating Skills. Cost Engineering Vol. 45/No.6 June2003, page 25.Wolf, T.E. (2013). Lang Factor Cost Estimates. Retrievedfrom ert, L.R., (2003). Sharpen Your Cost Estimating Skills. Cost Engineering Vol. 45/No.6 June2003, page 25. 2014 Yosep Asro Wainwww.pmworldlibrary.netPage 15 of 17
PM World JournalVol. III, Issue X – October 2014www.pmworldjournal.netUpdating the Lang Factor and Testing its Accuracy, Reliability andPrecision as a Stochastic Cost Estimating MethodFeatured Paperby Yosep Asro 5.16.AACE International, Recommended Practice No. 46R-11. (2013). Required Skills AndKnowledge of Project Cost Estimating, AACE International, Morgantown, WV.AACE International, Recommended Practice No. 17R-97. (2011). Cost Estimate ClassificationSystem, AACE International, Morgantown, WV.AACE International. (2011). Recommended Practice No. 59R-10, Development of FactoredCost Estimates – As applied In Engineering, Procurement, And Construction For The ProcessIndustries, AACE International. Morgantown, WV.US Government, Department of Energy (DOE). (2011). DOE G 413.3-21 Cost EstimationGuide.Wolf, T.E. (2013). Lang Factor Cost Estimates. Retrievedfrom f, T.E. (2013). Escalation Treatment Validation. Retrievedfrom ips.htmlW. Andreas, W. Wahyu. (2007). Capacity Factor Based Cost Models for Buildings of VariousFunctions. Civil Engineering Dimension, Vo.9, No. 2, 70-76, September 2007.Harrison R. (2010). Capital Project Cost Estimation in the Phosphate Industry, Presented June12, 2010
Parametric Cost Estimating, Equipment Factor Cost Estimating, Statistical Analysis, Monte Carlo Simulation. 1. Introduction. 1.1. Cost Estimation in the Project. Cost estimating is the predictive process used to quantify, cost, and price the resources required by the scope of an investment option, activity, or project [1].
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