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PM World Journal Updating the Lang Factor and Testing its Accuracy, Reliability and Vol. III, Issue X – October 2014 Precision as a Stochastic Cost Estimating Method www.pmworldjournal.net Featured Paper by Yosep Asro Wain

© 2014 Yosep Asro Wain www.pmworldlibrary.net of 17

Updating the Lang Factor and Testing its Accuracy, Reliability and

Precision as a Stochastic Cost Estimating Method

By Yosep Asro Wain, CCP

ABSTRACT

The Lang factor is a one of the factored estimating techniques that is recommended American

Association of Cost Engineers (AACE) International for class 4 and class 5 estimates. This

method was proposed by Hans J. Lang in 1940’s use a simple formula: consist of a set of

factor 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 their

current data.

In this paper, the fluid plant Lang factor was updated and tested its accuracy, precision and

reliability 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 for

Class 4-5 estimates, because it exhibits such a high degree of variability; it is not

recommended 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, 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]. In that regard, cost

estimating contains two thinks, namely resources quantification and resources pricing or

costing. In resources quantification, the project scope definition in the form of the work

breakdown structure (WBS) and the work statement may be used to identify the activities that

make up the work, and further, each activity is decomposed into detailed items so that labor

hours, material, equipment and subcontract are itemized and quantified [2]. In resources

pricing, any methodology such as stochastic, factored, or deterministic may be used to cost

the resources.

The cost estimating process is carried out during the entire of the project life cycle. At

beginning of the project where scope definition is still roughly, the accuracy of the cost

estimation is low. As the project definition go to more detail, then the accuracy of the cost

estimation become higher.

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American Association of Cost Engineers (AACE) International categorize cost estimation

become 5 estimate classes, based on maturity level of project definition deliverable, end

usage of the estimate, estimating methodology, expected accuracy range of estimate, and

preparation effort, as shown in table 1.

Table 1 – Cost Estimate Classification for Process Industries [3].

Primary Characteristic Secondary Characteristic

ESTIMATE CLASS

DEGREE OF PROJECT

DEFINITION

DELIVERABLES

Expressed as % of

complete definition

END USAGE

Typical purpose of

estimate

METHODOLOGY

Typical estimating method

EXPECTED

ACCURACY RANGE

Typical variation in low

and high range [a]

Class 5

0% to 2%

Concept screening

Capacity factored,

parametric models,

judgment, or analogy

L: -20% to -50%

H: +30% to +100%

Class 4 1% to 15% Study or feasibility Equipment factored or

parametric models L: -15% to -30%

H: +20% to +50%

Class 3

10% to 40%

Budget authorization

or control

Semi-detailed unit costs

with assembly level line

items

L: -10% to -20%

H: +10% to +30%

Class 2 30% to 70% Control or

bid/tender

Detailed unit cost with

forced detailed take-off L: -5% to -15%

H: +5% to +20%

Class 1 70% to 100% Check estimate

or bid/tender

Detailed unit cost with

detailed take-off L: -3% to -10%

H: +3% to +15%

Note : [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 industry

estimate versus the level of project/scope definition.





Figure 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, while

a Class 1 estimate, with the highest one. The estimating methodology tends to progress from

stochastic 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 lowest

on Class 5 estimate (0.005% of project cost) to the highest on Class 1 estimate (0.5% of

project cost).

1.2. Factored Cost Estimate [5].

Factored estimating techniques are method recommended by AACE International for Class 4

and Class 5 estimate. These factors are derived from historical data by using statistical

inferential or modeling. Several type of factored estimating that is used, especially in process

industries are capacity factored estimates, equipment factored estimates, and parametric cost

estimates.

The capacity factored estimates using cost of the similar plant or equipment of known

capacity to obtain cost of a new plant or equipment, by using equation :CostB/CostA =

(CapB/CapA)r, where CostA and CostB are the costs of two similar plants or equipment, CapA

and CapB are the capacities of the two plants or equipment, and r is the exponent or proration

factor.





The equipment factored estimates is used to obtain total installation cost from equipment

cost. 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 the

total plant cost. Parametric model is derived from statistical analysis of equipment cost data

from specific time duration.

1.3. Problem Statement.

As mentioned above, Lang factor is a one of the factored estimating techniques that is

recommended for Class 4 or Class 5 estimate. Lang factor was proposed by Hans J. Lang in

1940’s, using a simple formula; consist of a set of factor multiplied by main equipment cost

to obtain total cost. 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 their

current data. Several of these studies and results are contained in the Fixed Capital Cost

Estimation chapter of Perry’s handbook; others include in books by Gerrard, Page, and

Dysert, 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. There

are now governmental rules and regulations in-place, which just did not exist in the 1940s

and 1950s. There are materials and construction methods that are different. There are digital

process controls instead of pneumatic controls. The computer is used in lieu of the slide rule

and there is a three dimensional of computer design. Then there is material and labor cost

inflation (escalation) over the many decades [8]; therefore, updating the factor sometimes

necessary to adapt with the current condition.

In addition, testing the accuracy, precision and reliability of the Lang factor also necessary to

know 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 by

using 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 total

cost 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 of

them as shown in table 2.





Table 2 Lang factor f and its updated

Description Solid Solid - Fluid Fluid

Original Lang factor [10] 3.10 3.63 4.74

AACE International RP 59R-10 [11] 3.89 5.04 6.21

Perry’s Chem. Eng. H.B. [12] 3.80 4.10 4.80

Dysert L.R. [13] - - 5.10

Wolf T.E. [14] - - 5.12

Lang’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 several

areas in Indonesia. A total 29 project data sets is obtained, spanning from 2003 to 2013 (10

years project data). Figure 2 shows data distribution based on refinery unit. All of the 29

projects are for fluid plants; therefore the Lang factor to be obtained and tested in this paper

is for process fluid only.

Figure 2, Data distribution based on refinery unit

Each of the 29 sets of project data consists of the total plant cost (TPC) and the total

equipment 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.

0

5

10

15

20

Unit A Unit B Unit C Unit D Unit E





Figure 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 analysis

includes understanding of the data and relationship between the variables. Table 3 shows

descriptive statistics for factor f.

Table 3 Descriptive Statistics for the factorf.

Description Statistical

Mean 3.298

Standard Error 0.293 Median 2.865

Standard Deviation 1.580

Sample Variance 2.495

Kurtosis 3.035

Skewness 1.673

Range 7.121

Minimum 1.221

Maximum 8.342

Confidence Level (95%) 0.601

The factor, f range from 1.221 to 8.342, with the mean is 3.298 and standard deviation is

1.580, 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 that

distribution 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 the

true value of the data. To know reliability of the data, outlier checking by using Q-test was

conducted for rightmost data, namely 8.342. The result was Qcalculated 0.214<Qcritical 0.298 at

95% 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 simulation

first to find normal distribution data, and then to derive Lang factor.

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

0 50,000,000 100,000,000 150,000,000 200,000,000





Further analysis is to identify whether there is any relationship between variables, in this case

is relation between TEC as the independent variable and the factor (f) as the dependent

variable. For this purpose, the Pearson Correlation test was conducted with result Pearson

correlation 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, it

should first be conducted data transformation from logarithmic to linear by using following

equation:

LNTEC = ln (TEC)

…………………………………………………………………………………………………………………...(3)

LNf = ln (f)

……………………………………………………………………………………………………………

……………………(4)

The Pearson Correlation test was conducted on transformation data with result Pearson

correlation coefficient, r = -0.40, that shows medium correlation.

Another possibility of relationship between TEC and f is polynomial form. The Pearson

Correlation test was conducted for some polynomial forms as shown in Table 4.

Table 4: Pearson Correlation Test for Polynomial Relationship

TEC TEC^2 TEC^3 TEC^4 TEC^5 TEC^6 TEC^7 … TEC^13

r -0.244 -0.186 -0.172 -0.167 -0.165 -0.164 -0.164 -0.164 -0.164

As shown in the table, the correlation between TEC and f in polynomial forms are very low

also.

5. Obtaining of Lang factor.

From the descriptive statistical of factor f on Table 3, at first glance we may conclude that the

Lang 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 normal

distribution, then it is better to first doing Monte Carlo simulation to the data, and then using

it for obtaining the Lang factor.

The table 5 and figure 4 show result of Monte Carlo simulation for the data.





Table 5.Descriptive Statistics for the Factor, f after Monte Carlo Simulation.


Mean 3.264

Standard Error 0.049

Median 3.299

Standard Deviation 1.560 Sample Variance 2.435

Kurtosis 0.096

Skewness 0.003

Range 10.418

Minimum -1.726

Maximum

Count

8.691

1000


Figure 4, Histogram for the Factor, f after Monte Carlo Simulation

As shown in Table 5 and Figure 3, the range of the data is from -1.726 to 8.691. In regard to

the factor f, the value less than 1 has no meaning, therefore it is better to be excluded from

the 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 and

right.

The table 6 and figure 5 show the descriptive statistics and histogram of data after doing

7.3% tail cutting.

Table 6. Descriptive Statistics for the Factor, f after 7.3% left and right tail cutting.


Mean 3.282 Standard Error 0.038

Median 3.323

Standard Deviation 1.123

Sample Variance 1.261

Kurtosis -0.849

Skewness -0.036

Range 4.521

Minimum 1.005

Maximum

Count

5.526

855


0%

20%

40%

60%

80%

100%

120%

0

20

40

60

80

100

120

-1.7

3

-0.7

2

0.2

9

1.3

0

2.3

1

3.3

1

4.3

2

5.3

3

6.3

4

7.3

5

8.3

6

Fre

qu

ency

Bin





Figure 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, with

0.038 of standard error that means there is a significant improvement on standard error. The

significant improvement also occurred in the data distribution, as indicted by value of the

Kurtosis 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 model

of Lang factor from logarithmic relationship as mentioned above. To this purpose, regression

analysis was conducted on LNTEC on equation (3) as the independent variable and LNf on

equation (4) as the dependent variable, with result as shown in table 7a, 7b, and 7c.

Table 7a. Linear Regression: Regression Statistics

Multiple R R Square Adjusted R Square Standard Error

0.399 0.160 0.128 0.393

Table 7b. Linear Regression: ANOVA

df SS MS F Significance F

Regression

Residual

Total

1

27

28

0.792

4.173

4.965

0.792

0.155

5.13 0.032

Table 7c. Linear Regression: Coefficients

Coefficients Standard Error t Stat P-value

Intercept

X Variable 1

2.469

-0.090

0.608

0.040

4.062

-2.264

0

0.03

From the regression statistics table, model variance is low, as indicated by R Square value of

0.160, which is means only 16% variance in dependent variables can be explained by the

0%

20%

40%

60%

80%

100%

120%

0

10

20

30

40

50

60

Fre

qu

en

cy

Bin





regression model. Based on ANOVA table, this model is significant at 0.05 level. From

Coefficients table, linear model is obtained as follow:

LNf = 2.469 – 0.09 * LNTEC

…………………………………………………………………………………………………..…(8)

The 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)

or

ln f = ln (11.807 x TEC-0.09

)………….…………….………………………………………………………………………..…(9b)

Hence, the second model Lang factor will be:

f = 11.807 x TEC-0.09

……………………………………………………………………………………………………………….(9c)

Another model for Lang factor, namely the third model is obtained from the polynomial

relationship. Table 8 contains several polynomial model are obtained from regression

analyses.

Table 8, Polynomial Model for Lang Factor

Order Equation R

Squared

1st

2nd

3rd

4th

5th

6th

7th

8th

9th

10th

f = 3.487 – 9.9 * 10-9 * TEC

f = 3.598 – 2.4 * 10-8 * TEC + 9.0 * 10-17 * TEC2

f =3.865 – 9.4 * 10-8 * TEC + 1.5 * 10-15 * TEC2 - 5.7 * 10-24 * TEC3

f =4.163 – 2.2 * 10-7 * TEC + 6.8 * 10-15 * TEC2 - 6.9 * 10-23 * TEC3 + 2.1 * 10-31 *

TEC4

f =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 * TEC5

f =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 * TEC6

f =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

f =4.960 – 8.5 * 10-7 * TEC + 9.2 * 10-14 * TEC2 – 3.2 * 10-20 * TEC3 – 1.4 * 10-29 *

TEC4 +

3.0 * 10-36 * TEC5 – 6.0 * 10-44 * TEC6 + 4.6 * 10-52 * TEC7 – 1.2 * 10-60 * TEC8

f =4.987 – 9.1 * 10-7 * TEC + 1.1 * 10-13 * TEC2 – 6.2 * 10-21 * TEC3 + 1.9 * 10-28 *

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 * TEC9

f =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 –

0.060

0.076

0.143

0.198

0.243

0.225

0.338

0.343

0.343

0.343





Order Equation R

Squared

4.4 * 10-67 * TEC9 + 8.7 * 10-76 * TEC10

The table above also shows that the models variance is low, as indicated by the value of R

Square 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 as

shown 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 the

models have low variance, however it is necessary to compare the calculation error of each

other by using 29 pairs fit data on following equation:

Individual Calculation Error (%) = ((Exact Value – estimated Value) / Exact Value)*100%……………….……(10)

and Average 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 Error

Data No Constant (%) Logarithmic function

(%)

Polynomial function (%)

1 24 44 37

2 -70 -45 -75

3 -169 -188 -193

4 -15 -31 -35

5 -68 -9 -16

6 24 28 19

7 -18 5 -16

8 -10 11 -8

9 -6 7 -11

10 -34 -23 -43

11 -7 -41 -17

12 26 28 20

13 -42 -27 -50

14 -59 -23 -46

15 -14 5 -16

16 -95 -46 -67

17 -28 -22 -38

18 -22 -12 -30

19 21 15 14

20 -29 -21 -38

21 45 40 40

22 -24 -12 -32

23 52 46 47





Data No Constant (%) Logarithmic function

(%)

Polynomial function (%)

24 0 2 -8

25 -36 3 0

26 31 34 26

27 -7 26 30

28 -57 -55 -70

29 61 55 57

The results of the average calculation error are 38%, 31% and 38% for Constant Lang factor,

Logarithmic function and 2nd

Polynomial 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 limitation

of the data, factor that derived only for the first model. By using same procedure used to

obtain Lang factor for whole refinery units, the first model of Lang factor for each refinery

unit were derived as shown in Table 9.

Table 10, Lang Factor for Each Refinery Unit

Unit A Unit B Unit C Unit D Unit E

Lang factor, f 2.497 3.167 3.273 3.423 5.260

Data in Table 9 shows us that the Lang factor is different between the refinery units. The

smallest Lang factor is for Unit A is equal to 2.497, while the largest is for Unit E is equal to

5.260.

8. Conclusion.

By using the company own data, the fluid plant Lang factor has been obtained with value of

3.282 (ref equation (7)). This value is derived from 29 pairs of data with wide range of

factor, 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 and

polynomial function, however the result shows that model correlation and variant are very

low, 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 average

about 30% to 40%. As shown in figure 6, the individual calculation errors (for constant Lang

factor) 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 were

located. This VALIDATES using Lang Factors for Class 4 and 5 estimates, however based

on this research; the original Lang Factor is no longer valid and needs to be revised and the

use of a single Lang Factor is not recommended.





Figure 6, Putting The Calculation Error on Accuracy/Uncertainty Ranges of Estimate Graph

1

Proposed Revisions.

The comparison of the company owned Lang factor of 3.282 with the Lang factors which

obtained previously, shows that all of previously obtained Lang factor is higher than this

company owned Lang factor, as shown in Table 11.

Table 11, Benchmarking of Company Owned Lang Factor and Others

Description Solid Difference

Company owned Lang Factor 3.28 -

Original Lang factor 4.74 44% higher

AACE International RP 59R-

10

6.21 89% higher

Perry’s Chem. Eng. H.B. 4.80 46% higher

Dysert L.R. 5.10 55% higher

Wolf T.E. 5.12 56% higher

All of these indicate that the Lang factor exhibits a high degree of variability; therefore it is

not recommended for creating highly accurate, reliable or precise cost estimates other than

Class 5 or Class 4

1 It is assumed that the Lang factor is used during project initiation phase, where degree of project definition below 10%.





Finally, due to wide variety of Lang factor as mentioned above, it is better to express it as a

range, instead of as a single value.

Suppose the range is 1 x standard deviation, so that:

f = 3.282 ± 1.123

………….……………………………………….…………………………………………….………………….(1

2)

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 of

2.693 to 4.721.





References

1. AACE International, Recommended Practice No. 46R-11. (2013). Required Skills And

Knowledge of Project Cost Estimating, Page 1 of 21, AACE International, Morgantown, WV.

2. US Government, Department of Energy (DOE). (2011). DOE G 413.3-21 Cost Estimation Guide, Page 18.

3. US Government, Department of Energy (DOE). (2011). DOE G 413.3-21 Cost Estimation

Guide, Page 15.

4. US Government, Department of Energy (DOE). (2011). DOE G 413.3-21 Cost Estimation Guide, Page 16.

5. AACE International. (2011). Recommended Practice No. 59R-10, Development of Factored

Cost Estimates – As applied In Engineering, Procurement, And Construction For The Process Industries. AACE International. Morgantown, WV.

6. Wolf, T.E. (2013). Lang Factor Cost Estimates. Retrieved

from http://prjmgrcap.com/langfactorestimating.html. 7. AACE International. (2011). Recommended Practice No. 59R-10, Development of Factored

Cost Estimates – As applied In Engineering, Procurement, And Construction For The Process

Industries. AACE International. Morgantown, WV.

8. Wolf, T.E. (2013). Lang Factor Cost Estimates. Retrieved from http://prjmgrcap.com/langfactorestimating.html.

9. Dysert, L.R., (2003). Sharpen Your Cost Estimating Skills. Cost Engineering Vol. 45/No.6 June

2003, page 25. 10. Dysert, L.R., (2003). Sharpen Your Cost Estimating Skills. Cost Engineering Vol. 45/No.6 June

2003, page 25.


Cost Estimates – As applied In Engineering, Procurement, And Construction For The Process Industries. AACE International. Morgantown, WV.

12. 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. 13. Dysert, L.R., (2003). Sharpen Your Cost Estimating Skills. Cost Engineering Vol. 45/No.6 June

2003, page 25.


15. Dysert, L.R., (2003). Sharpen Your Cost Estimating Skills. Cost Engineering Vol. 45/No.6 June

2003, page 25.



http://prjmgrcap.com/langfactorestimating.html





Bibliography

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About the Author

Yosep Asro Wain

Jakarta, Indonesia

Yosep Asro Wain is a professional in oil and gas, especially in

refinery fields with 20 years experiences. The most of his job are in the project management field, namely project engineering design, project budgeting, project cost estimating, project cost control, project scheduling and contract engineering. He is currently a cost engineering specialist at Engineering Center, Refinery Directorate of Pertamina. Yosep holds a bachelor degree in Electrical Engineering (in Control Engineering field) from Bandung Institute of Technology (ITB), and is a Certified Cost Professional (CCP-AACEI). He is also a senior process control and instrumentation engineer. He lives in Jakarta, Indonesia and can be contacted at [email protected].



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updating the lang factor and testing its accuracy ......pm world journal updating the lang factor...

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