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238 I CHAPTER 9If you are much involved in the construction business, you must have experienced how difficult it is to decide on asuitable bidding strategy against the expectedcompetitors. This bidding strategy isbasically a fine-tuning of the bid by accountingfor the level of uncertainty associated with the project and as an allowance for profit.In general, contractors often have two main methods of assessing and accounting forproject risks: (1)estimating asingle percentage markup tobe added to the total cost;(2) detailed analysis of the risky components in the project, the probability of riskoc-currence, and the expected damages so as to assign an appropriate contingencyal-lowance for each of these components. The latter analysis, however, is lengthy andmay not suit competitive bids; as such, it is beyond the scope of this book. Specifictechniques that can account for the uncertainty associated with activities' durationsand cost will be sufficiently described in Chapter 12.Our bidding strategy, therefore,

will focus on estimating an optimum markup for aproject.Markup needs to be optimally decided for a project. Weneed to decide onthepercentage that makes thebid low enough towin and, at the same time, highenoughtomake areasonable profit. Despite the importance of these decisions toacostlycom-mitment, you might have to decide on them while alot of information isstill lackingand under pressures to speed up thebid preparation. Often, many construction prac-titioners are left to their own intuition and "gut feeling," with little or no helpfromavailable tools. In this chapter, therefore, wewill be introduced to the basics thatwillallow us toestimate an optimum markup value for theproject. In thenext chapter,wecan then deal with project financing so that the bid becomes ready for submission.

Our cost estimate (C = direct cost +indirect cost) for any past bid isknoto us. Because wecannot know the cost estimate ofother competitors, let'ssume that the cost estimates ofall bidders arethe same. Thisassumptionistrue but can be realistic if we assume that all bidders have accesstothesubcontractors and follow standard construction technology. The bid prices of competitors in past bids are known to us asapublic:mation published by most owners after the bid is let. Government agensuch as public works (largest owner organizations) make this informapublic. Therefore, the relationship between thebid price and thecostes.in any bid isas follows:

Bid Price (Bj) of competitor i = C * (1+markup)Thus, B;!C = 1+markupAnd, markup = B;!C - 1.

TheB;/ Cratio inEquations 9.2and 9.3isarepresentation ofthemarkup usedbypetitor i in one bid. For example, in a past bid that we lost, our cost estimate$1,000,000. For that project, we submitted atotal bid of $1,150,000 while ourkeypetitor, Company A, bid $1,100,000. Assuming cost estimate is constant, wemarkup of 15% (markup =B/C -1) while Company A used a10%markup.we lost that bid, it is important for us to analyze if the 10%markup usedbyCo

9.2 AnalyzingtheBiddingBehavior of KeyCompetitorsFor acontractor tosustain success in the construction business, heorshehastobelowest bidder for asufficient number of projects while that bid price isnot toolow,order to make a reasonable profit. It is important, therefore, to strike abalancetween profitability and the chances of winning. Toenable us to establish awi .bidding strategy, we need to keep track of our past bids, analyze their informatiand depict any bidding pattern our key competitors use. First, lees seewhatkindinformation wehave:


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BIDDING STRATEGY AND MARKUP ESTIMATION I 239

A isapolicy that is repeated in other bids. If this is true, then it is possible in the fu-ture tobeat them by bidding with amarkup less than 10%;otherwise we need to an-alyze how their markup policy changes from onebid to the other.Let's now expand our analysis of Company A'sbidding behavior by retrieving allour records of past bids inwhich we competed against them. Let's assume we found31past bids and wehave all the information regarding our cost estimates and thebidprices. From that information, we can create ahistogram as shown in Figure 9-1(a).The histogram in Figure 9-1(a) shows the frequency at which Company A bid at dif-ferent markup levels. From the histogram, we can answer the following questions:1. If the B/C ratio used by Company A in apast bid was 1.25, itmeans the com-pany used amarkup of __ %of cost.Answer: 25%because markup =B/C - 1=1.25 - 1=0.25=25%

2. If we decide to use a10%markup in anew bid against Company A, howmany times in the past did they underbid us at this level of markup?Answer: six times. From the histogram, the number of occurrences to the leftof B/C =1.1are 3+2+1=6.

3. What areour chances of winning Company A using 25%markup?Answer: 6/31. From the histogram, the number of occurrences to the right ofB/C =1.25are3+2+1=6.Then the probability =6out of the total 31past bids.

-l.theBehavior ofltorNo. ofpastbidsagainstthecompetitor

76 6

3 32

Competitor's bid(8)Our costestimate (C).1 S/C=.2 1.3 1.4Competitor bidbelow cost 0 10% 20% 30% 40% Markup = S/C-1(a) Analyzing PastBidsagainst One Key Competitor

Calculate the mean (m) and standard deviation (s) of B/e ratio of thiscompetitor, assuming a normal distribution and, repeat the analysis for akev competitors.

tDesiredmarkup=mThen, B/C = (m+1)__ ,--SIC

(b) Calculating the Probability ofWinning ThisCompetitor Usinga Given Markup Value


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240 I CHAPTER 94. If we bid right at cost (no profit), then our B/C becomes what?Answer: 1because B=C, then B/C =1.0and markup =B/C - 1=o .

5. How many times did Company A bid below cost?Answer: 1as read from the left part of the histogram.6. What is the average markup used by Company A and how much does itvary?Answer: Wecan calculate the J .. L and 0 - of the B/C ratio from the histogram:Mean ( J .. L ) = [1X 1.375+2 X 1.325+3 X 1.275+6 X 1.225+7 X 1.175+6 X 1.125+3 X 1.075+2 X 1.025+1 X 0.975]131 =1.175Standard Deviation (0 - ) =Sqrt [(n (kX2 - (kX?)/n(n - 1)] =0.0931The J . .L and 0 - of B/C ratio, therefore, represent the competitor's behavior andcan be used to evaluate the probability of beating our competitor using anymarkup value, as shown in Figure 9-1(b).

9.3 Estimating OptimumMarkup9.3.1

Figure 9-2. BiddingStrategy Formulation

What to Optimize?In order to optimize our markup decision, weneed to definewhat optimum meansbyproviding ameasure ofoptimality. Notice that wehave two conflicting objectives:tore-ducemarkup toimprove theprobability ofwinning; and toincreasemarkup toimproveprofitability. If you recall what we did in Chapter 8,we were trying to optimize time-cost tradeoff (TCT) decisions. Wealso had two conflicting forces: direct cost and indi-rect cost. When wecrashed theproject, direct cost increased while the indirect costde-creased. In that case, weused asimple measure of optimality that is asummation ofthetwo components, which is the total project cost. That measure of optimality is accept-able to usebecause both components have the same units (cost)and thus canbeaddedtogether. Notice here that for TCT decisions, weneeded tominimize themeasure ofop-timality and determine the decision that brings minimum total cost for theproject.Themarkup case is apparently different. The probability of winning is unit-less,whereas profit is in dollars. In this case, it is logical to use multiplication instead o fsummation, and thus use what is called the expected profit as ameasure of optimality.The expected profit can be viewed as a fictitious profit value that is weighed bythechances of attaining that profit. Certainly, in this case, our bidding strategy should fo -cus onmaximizing the expected profit and we can define our optimum markup astheone that maximizes it, as formulated in Figure 9-2.

Expected profitof a given markup =I (~~~~:)$)tMarkup (%) x Cost x (Probability of winning ~0/1 competitors using thespecified markup (%)t1. Calculate the probabilityof winning individualcompetitors, thenRepeat thecalculations usingvarious markupvalues and findthe optimummarkup as the oneassociated withmaximumexpected profit. 2. Combine theseprobabilities to determinethe probability of winningall of them simultaneously.


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Youhave kept good records of thebidding behavior of one competitor, Com-pany A. Themean and standard deviation of thecompany's B/C ratio arecal-culated to be 1.1and 0.1, respectively. Answer the following:

1.1 1.2 SICa. What is the probability of winning Company A in anew bid, using a20%.markup? Your cost estimate for the new project is $1,000,000.b. What is the expected profit at this markup?Solution:a. At 20%markup, B/C =1+markup =1.2Wethen use the standardized normal distribution table:Z = (X - 11-)/0' = (1.2 - 1.1)/0.1 = 1.0and from the table (provides left side area), probability = 0.8413Then, the probability of beating Company A at 20%markup = shadedarea = 1 - 0.8413= 0.1587b. Expected profit Probability of winning X ProfitProbability of winning X Cost X Markup0.1587 X $1,000,000X 0.2= $31,740

9 . 3 . 2 Beating All Competitors SimultaneouslySimilar to the above example, it is simple to calculate the probability of winning eachcompetitor separately from all others. Todetermine our probability of winning themall, however, is still simple but controversial and many formulations are availablewith various assumptions.Friedman, in 1956,was the first to suggest amodel that predicts the probabilityof winning abid knowing the previous performance of other competitors (mean andstandard deviation of B/C distributions). Friedman employed abasic assumption inhis bidding model that different competitors' probability distributions aremutuallyindependent. Accordingly, he suggested amultiplicative model to combine theprob-abilities of winning individual competitors, at agiven markup, as follows:

a. Probability of winning (n ) known competitors is:(9.4)

b. Probability of winning (n ) unknown competitors is:P(Winall)=P(WinTypicalCompetitort (9.5)

Where, P(Win;) is the probability of winning competitor i.Also, the typical competi-tor is onewho represents the average bidder who isexperienced in the type of bid be-ing analyzed.The most notable model proposed a decade after Friedman's is that of Gates in1967.Gates has criticized Friedman's basic assumption of independence and offered


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242 I CHAPTER 9

his own assessment. According toGates, the probability of winning all competitorsagiven markup is as follows:a. Probability of winning (n) known competitors is:

1- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ( 9[(1- P(Win1 / P(Win1)]+...+[(1- P(Winn / P(Winn)]+1

b. Probability of winning (n ) unknown competitors is:1P(Winall)=--------------------n[(l - P(WinTypicalCompetitor / P(WinTypicalCompetitor)] +1

Friedman's and Gates's models give different results, and debate over theyhas not been able to resolve this conflict. Instead, these models have generatedctroversy and confusion about their application in the construction industry. A nber of studies concluded that Friedman's model ismore correct when the variabiof bids is caused only by markup differences, while Gates's model ismorecowhen the variation in bids is caused only by variations in cost estimates. A comhensive study of a contractor's application of both models over aperiod of sevyears showed that Gates's model produces higher markups than that of FriedmIn this sense, Friedman's model could represent a pessimistic approach wheGates's represents an optimistic one. Despite their differences, however, ovestudy period, both models have led, approximately, to the same total of poteprofits.

9.4 TheOptimum-MarkupEstimation ProcessLet's now look at the detailed process for optimum markup estimation and apto an example. The following four steps will be followed:

1. Assume apercentage markup in the range from 1-20%, with 1% incremdLater wecan repeat this process with finer increments to refine the calcutions.2. At eachmarkup, we calculate the expected profit, as follows: Profit =cost X markup (%). Probability to win each competitor (from his past history); Combined probability P(winall), using Friedman's or Gates' models; Calculate expected profit = profit x P(winall). Tabulate themarkup and expected profit values Increment markup and repeat the calculations in this step.3. Plot the tabulated markup versus expected profit values, as shown, wht(X=markup; Y =expected profit).

OptimumL- .L -M_a_rk_u_ p Markup

(%)

4. Choose the optimum markup from the plot.


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ABC

568

],08]],032],067

0,0520,0440,06]


A contractor wants to determine the optimal bid to submit for ajobwith es-timated cost $1,000,000, bidding against three key competitors with the fo l-lowing historical data.Competitor No. of Occurrences B/C Mean(fL) B/C StandardDeviation (0')

Solution1. Let us assume arange of markups from 1%to 7%with 1%increments.2. Atmarkup = 1%, we calculate the following:a. Probability of beating the first competitor, A:

X = B/C = 1+markup = 1+0.01= 1.01ZA = (X - fLA)laA = (1.01 - 1.081)/0.052 = -1.365Then, from the table of standardized normal distribution, theprobability P (WinA) at 1%markup =1 - Fz (-1.365)= 1 - 0.086= 0.914

b. Probability of beating the second competitor, B:X = B/C = 1+markup = 1+0.01= 1.01ZB = (X - fLB)/aB = (1.01 - 1.032)/0.044 = --;0.500Then, from the table of standardized normal distribution, theprobability P (WinB) at 1%markup = 1 - Fz (-0.500)

=1- 0.309=0.691c. Probability of beating the third competitor, C:X = B/C = 1+markup = 1+0.01= 1.01Zc = (X - t-tdlac = (1.01 - 1.067)10.061 = -0.934Then, from the table of standardized normal distribution, theprobability P (WinA) at 1%markup =1 - F, (-=0.934)

=1 - 0.175=0.825d. Probability of beating A, B, and C, simultaneously and the expectedprofit:Using Friedman's model and 1%markup:

P(Winall)-F =0.914 X 0.691 X 0.825=0.521EP-F (expected profit) =$1,000,000X 0.01 X 0.521=$5,213.3Using Gates's model and 1%markup:

1P(Winall)-G=-----------[(1- 0.914)/0.914 +(1- 0.691) 10.691+(1- 0.825) 1 0.825+1]=0.571

EP-G (expected profit) =$1,000,000 X 0.01 X 0.571=$5,705.9e. Incrementing markup and repeating the calculation in a, b, c, and dabove, as tabulated in Table 9-1.


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244 I CHAPTER 9Table 9-1. Markup versus Expected Profit: Friedman and Gates Modelsarku~ P(winau) EP P(winau)(% ZA P(WinA) ZB P(WinB) Zc P(Winc) Friedman Gates1.0 -1.365 0.914 -0.500 0.691 -0.934 0.825 0.521 $5,213.32.0 -1.173 0.880 -0.273 0.607 -0.770 0.779 0.417 8'330.33.0 -0.981 0.837 -0.045 0.518 -0.607 0.728 0.316 $9M6-64.0 -0.788 0.785 0.182 0.428 -0.443 0.671 0.225 89.012.15.0 -0.596 0.724 0.409 0.341 -0.279 0.610 0.151 87,5S7.06.0 -0.404 0.657 0.636 0.262 -0.115 0.546 0.094 $5,640.27.0 -0.212 0.584 0.864 0.194 0.049 0.480 0.054 $3,806.23.1 -0.962 0.832 -0.023 0.509 -0.590 0.722 0.306 $9,484.23.2 -0.942 0.827 0.000 0.500 -0.574 0.717 0.296 9,486.33.3 -0.923 0.822 0.023 0.491 -0.557 0.711 0.287 $9,473.54.1 -0.769 0.779 0.205 0.419 -0.426 0.665 0.6154.2 -0.750 0.773 0.227 0.410 -0.410 0.659 0.3084.3 -0.731 0.768 0.250 0.401 -0.393 0.653 0.301

Figure 9-3.Optimum MarkupPlot$ 1 5 , 0 0 0 . 0 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

4.2%$1 3, 00 0. 0 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -From this plot, we canevaluate ourprobability of winningthe bid at any markupvalue.P(Win)= Expected ProfitMarkup X Cost

- $11,000.0~Il."C $9,000.0~Q)Q,>< $7,000.0w

$5,000.0

$3,000.00 2 3 4 5 6 7

Markup (%)

Notice that the top part of Table9-1 shows that the highest expectedprofit for Friedman's model occurs around amarkup of 3%,whereasitisaround 4%for Gates model. Therefore, the second and thirdpartsof Table9-1show refined calculations in which themarkup isinere-mented by small values around the expected optimum. Accordingly,it isseen from the calculations that optimum markup isas follows:Using Friedman's model, optimum markup = 3.2%.Using Gates' model, optimum markup = 4.2%.

3. Plotting themarkup versus expected profit relationship and confirmingoptimum markup values, as shown in Figure 9-3:


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9.4.1BIDDING STRATEGY AND MARKUP ESTIMATION I 245

Important Bidding RelationshipsFromtheprevious discussion and thesolved example given, let's discuss someof theobserved reationships:

When the(J' of theB/C ratio of acompetitor issmall, it indicates that thiscom-petitor uses aconsistent markup policy. Itispossible in this casetoestablish amarkup towin over himorher.

Friedman's mode, in most cases,determnes alower optimum markup thanthat of Gates's (asshown in thesolved example). In this sense, Gates's modeismoreoptimstic asitassumes that youcanstill win thebid atahighmarkup.

If you haveentered only onebid against acompetitor in thepast, the (J' of hisorher BICratio becomes zeroandtheuseof probability tables isnot possible.Therefore, one bid against a competitor is not sufficient to determne yourcompetitor's bidding behavior. Inanew bid against this competitor, therefore,it is advisable toreplace it with atypical onewhose behavior is closeto thetypeof projectbeing analyzed.

In caseof high project risk, the chances of cost and schedule variations arehigh, thus their potential impact onprojectcostishigh. Inthis case,therefore,itiswisetousehigher markup asanallowanceforunforeseen conditions. Theuseof Gates's mode in this caseismoreadvisable than Friedman's mode.

When theleve of competition ishigh (largenumber of bidders) and theeco-nomc conditions arenot favorable, winning bids becomes difficult and bid-ders reduce their bids tobecomemorecompetitive. In construction, an averagebidder behavior isexhibited ashaving abid/ cost

ratio mean of 1.06and astandard deviation of 0.065.For building construc-tion, markup may vary from2to 10%,whereas for highway and heavy civilconstruction, it canreachupto20%.Theaveragenumber of competitors bid-ding for a job is around six. Accordingly, if no information is availableregarding typical competitor behavior, numbers around thesevalues can beassumed.

The correation between markup and number of competitors and betweenmarkup and project sizehas been studied by many researchers and can beexpressed in thefollowing simplereationship:

M2 = (N1)O.7 (9.8)M1 N2where, N1andN2arethenumber of competitors onjobs1and 2;M1 andM2arethemarkup on jobs 1and 2, respectivey. According to this inverse rea-tionship, markup is reduced with increase in theleve of competition associ-ated with alarger number of competitors.

Projectsize, as indicated by itscostestimate (C),has an important impact onthemarkup value, asexpressed in thefollowing reationship, which indicatesthat thepercentage markup islesswhen project sizeincreases.

~~=(~~r2 (9.9)Theuseof this reationship becomes handy sothat last-mnute adjustmentscanbemade tothebid at thenegotiation table. If, forexample, you receivedinformation that thewinning bid has thepotential for additional work tobeawarded later,you may bewillingtoreduceyour markup sothat your bid be-comes more competitive. In this case, this reationship can giveyou aroughfigureontherevised markup touse.

With Friedman's and Gates's modes being viewed as pessimstic and opti-mstic, respectivey, amoderate bidding strategy is toconsider theaverageoftheir optimum markups.


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246 I CHAPTER 9

9.5 BiddingStrategyProgramon ExcelAs seen in the earlier solved example, optimum markup calculations canbetparticularly if a large number of competitors are involved. Let's now try ansheet, Bidding.xIs, developed to automate the calculations involved. Forthepof using this spreadsheet, we will consider the following case study.

The previous records of past bids against four key competitors isinthe ol -lowing table. Using Friedman's and Gates's models, determine themark upneeded to optimize expected profit in bidding against competitors A , B , andC inanew job with an estimated total cost of $4,000,000.J ob Contractor's Bid Price of Competitors ($)No. Cost Estimate ($) A B1 1,550,000 1,900,000 1,700,0002 2,000,000 2,000,0003 1,300,000 1,500,000 1AOO,0004 1,200,000 1,600,000Solution:The solution of this example isprovided in the Bidding.xIs spreadsheet. D e-tailed explanation on the use of this sheet as it applies to theexample is madein Figures 9-4to 9-6.

Names of Other Competitors Here(Sheet is Set up for a total of 40)CAnalysis: r---:;":";':';""7'L..:..;.--::"':=~"'--"'::':':~;:'


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t: Ci .J i K ~ICompetition in a New Bid Select competitors:C os t E s timate: I$1 000,000.001 CompelHor1 ComponyA -CompelHor2 Compony8 {No. of B idders: 3 Compelttor 3 ComponyC

$60.000.000 Compelttor4 i -f! CompelHor5 1 He : ~.. $50.000.000 : : : : : : : : : : ) i -'K' C ompelttor6j ~.. ..J !u $40,000.000. . " - Compelltor7Q. C ompe tttor8 1 R " $30.000.000 ... , .... _ .- _ . _ - . -~~w /$20.000.000 l CompelHor9CompetHor10 $10,000.000 II Com pelHor11 I f i$0.000 CompetHor12 ~0 004 008 012 016 02M arkup CompelHor13 I : -

Fr ied ma n M od el C ompetHor14 C ompetttor 15 .;;Friedman's O ptimum M arkup is =~ %G ates' O ptim um M a rk up is = 11 .20 %

. . ~ ~ ~ - t t - . u f 1~ 1 ant .. v .=Jding Sheet with Markup Calculationsly the cost estimate and select the competitors (up to 15) from user-friendly combo boxes. Accordingly,is calculated automatically (10%and 11.2%for Friedman and Gates, respectively) .

__ :...._J_.J..1 J..!_---11 Behavior of .j. P 5 1 E~ectedBiddera:J ~ __t--~ - = ~ ! ~+-"

competitors: t IMarkup V.lues: from IX to20X--r-.,-.mB98!an263,JOS.ot35.Oe980v" _ ~ - rl---+...c=;o:O::.o::-,-=O .:.:.::;1='-;:.0.0:::t-=2:..:....:;I;.;:.c.o"".0"'17T I - - - - = o 'H l- PI ) I 1.000 + 1000 I ,.(XX}+- ..'cOOO_ IH _ _ 1.126mfV246229 ~ Comoetitor 2 1 0.845 0.B26-rL082~8!7 (H 1.1662324 0.0639911t _ i -- _ .:.N ", O :=t. _.:::3_...,1_ 0.995 0.993, 0.992 ! 0.991 (8 I ~- - ~~~ - - ~- - - - ~ - - -_~- - - - ~- - - - +- _Id 5 I __ 'n -::-- _ r = - 6 - - . _ _rJ _ , _ - - - - ,- < 18 8l : J 1 9

~-- - '--- -t--

: - 1 1 - . . : ~ - - - +-----:-=.~ I ~ __ -+- 1 _1",2_+- __ -- ---- t---+-< ~.I 1 ~ +_1"..3_-,-----1-~ I.d --+ 15

IFriedman,.I!-!P1...!.",:::",nJ,-+1-,0",.84:::I_+'_O::::.820~jI_~0.8:::1:...5j'-,...:::0.8:::.1O:........+...::.(r I Elp) I I $8.195.533 SS.m613 I $11.~1644 $12,IOptimum I

nn

iled Optimum Markup Calculations

247


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9.6 IncorporatingQualitativeFactorsProbability-based bidding models such asFriedman's and Gates's areusefulvide aguideline onmarkup estimation, instead of shooting in the dark. Fromatical point of view, however, the soleuse of such probabilistic methods isinadProbabilistic models do not account for anumber of important factors,suchkeenness of the contractor to win the job, prevailing economic conditions,Iproject complexity, and owner's attitude, that govern the determination ofmarcurrent practice. Theresults ofvarious surveys among construction practitionseem tosupport this argument.One survey among the top 400general contractors in theUnited Stateshavetified the top-ranked factors that govern the contractors' markup decisions.top-ranked factors are:

1. Degree of hazard2. Degree of difficulty3. Type of job4. Uncertainty in estimate5. Historical profit6. Current work load7. Risk of investment8. Rate of return9. Owner10. LocationNoted that competition and profitability, which arethe only two factorscoin the formulation of probabilistic models, were not among the top-rankedOther surveys have identified similar factors but with a different ranking0which the contractor's workload and desirability of the jobareat thetop.dependence of Friedman's and Gates's models on quantitative lac.tOts N . 1 ,ment of their inapplicability isnot true. Their underlying analysis providesapoint for markup estimation and their analyses of past bids could disclosetheof the factors that are implicit in themarkup decided by acontractor.The subject of incorporating qualitative factors into markup estimationtributed to the development of nontraditional decision-support systemsbtificial intelligence. One such system, ProBIO, has been included with thebook for your experimentation. ProBIO is acomprehensive systembasedoncepts of artificial neural networks, which is capable of learning the insandreal-life projects to become able to predict the outcomes ofnew projects.Inaworks as asort of complex regression model that has good interpolativeandolative performance. In addition, ProBIO organizes the contractor's historimation regarding past projects and past bids and analyzes the performancompetitors. Therefore, in addition to suggesting a markup value, ProBgently recognizes the risk pattern of your upcoming project and thenmatproject environment with a number of stored cases of successful andprojects. Accordingly, ProBIO predicts some indicators of theproject'spotcess or failure. ProBIO predictions direct your attention to potential problthat you may consider to adjust your estimate, think of alternative decisions,early countermeasures to help assure asuccessful bid.One benefit of ProBIO is that it isnot apurely theoretical model. Rather,

veloped based on the experience of alarge number of real-life projectsthatlected from general contractors in the United States and Canada. Althoughwas initially intended for building projects, it is designed with apowerful"tion" option that builds on your own past projects' experience andenablesvelop custom predictors that suit your particular work environment, Itypes of projects.


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F i g u re9 -7 . ProBIDMa inSc reen andHelpTopicsY o u canfollowthehelp topics toget ag o o d idea aboutP ro B I D features.

F i g u re 9 -8 . Loadingan E xample P rojec t


Toexperiment with ProBID, you need to install it from the CD to your hard disk.Afterwards, you can activate the PB.bat file to run the program. After the introduc-tory screens, the main menu appears. Figures 9-7to 9-13show the main features ofProBID and its use.

P r o B I D . A B I D ANA L V S I S S OF T WAR E F OR CONS T RUCT I ON P ROJ E C T S- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -,::=====-,P r oBI r - - - - - - - - - - - - - - - - - - - - - - - - - ~Cus t om H&l p T op i c s : =Co nt .P r o j e c tA dv i s e r

- Abou t P r oBI DPr o j e c t Da t aOr 9 an i z e r

T UT ' I AL S :1 - L o a di n g E xamp l e' o j ec t .2 - P r . i c t i ng P r oj ec t Out c ome.3- 0 e r i v i ng a ar k up St r a t egy .~- Or g ani i ng Pas t Exper i enc &.5- 0 eve l op i ng us t o. P r e d i c t or s .6 - U s i Cu s t o m P r e di c t o r s .

P r o B I D : A B I D ANA L V S I S S OF T WARE F OR CONS T RUCT I ON P ROJ E CT S- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

- I d h P r oBI D?- What ec hno l o gy i s Us ed ?- How does P r oBI O o r k?- What i s P r oBI D , t p u t ?- Who ar e P r oBI O e r s?- I d her e t o F i n d Mor e nf o . ?

.

Us ef u l He l pT op i c sPr o j ec t Dat aOr g a ni z e r P as s wo r d ~Utili t i e sP r o BI DCu s t o m z e r

Ne w/ L o a d P r o ' e c t


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(b)

Figure 9-9. Data Inputs for a ProjectYouneed to input various factors that describe the project in terms of: a. General information about thejobty pe ,etc. b. Jobuncertainty and complexity levels. c. Market condition. d. Your company's experience and needfor

Figule 9-\ O. ProBIDPredictionsPredictions include: (%) Markup. Chance towin/lose. ($) left on thetable. Change orderslevel. Claims level. Actual duration\months) . Actualptohtability.

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[ SENSI T I VI T Y ANAL YS I S OP T I ONS

Ba c k t o I ' i a i nI ' i e nu .Mo s t i k e l y P L e d i c t i o n s .MaLkup i s t o g L a m.Ma L k u p v s c h a nc e s o f wi n n i ng - i s c Le t e .Ma L k u p v s c h a nc e s o f wi n n i ng - o L ma l .

Figure 9-11. Sensitivity Analysis OptionSensitivity analysis examnes how ProBIDpredictions may vary with changes inyourassessment of theproject factors. Thesimulation generates anumber of scenarios(simulations) that aremnor random variations of theassessment you provided during theediting of theproject data. All simulations arethen input totheprediction mode youseect,and predictions for all scenarios areproduced. Asaresult, themean and standarddeviation inall scenarios will bereported asthemost likey predictions for theprojectoutcomes. Refertothemanual for guideines on thenumber of simulations touseand howtointerpret theresults.

~hnCompetitorsop t i m s t i c St r a t egy .

ingFriedman's and Gates's Models to Establisha Winning Strategy

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(a)

What-if ?

Win COI l J l .ti tors

Rlcolmendat ion9

(b)

(c)

(d)

Summary of ProBID Output" for: (TES'r)1st Aspect ,)hat happenedi n past pr oJects?

Figure 9-13. ASetof Recommendations for the Example Project (a) Summary of all project results;(b), (c), and (d) Guidelines for fine-tuning the markup and for providing a more attractive bid,252

ProBID has oompered the project descrl.p't.l.ol1 .ith stored Past-BIDS andconducted a t(lhat-if anaysis on lGD pcoject, scenarl0S. It seems that:Using B bid markup of 4.9 % above the job's $ 25 ml. estmat.ecCCS_t you ar e l i ~e: y t o ~ N t he bi d, w_t h 0. 033 m l . l ef t on t 5bl ~}l.lso, based 0:1 tne v/ha-:-if analysis, varaat Lons a.n PCO]ct cor.dit.:..onsseem to mandate an average markup of 3.33% wi.h a st. dev_ of 1.02%_Al s o, execut i on out comes of t he pi : o j e c t st:'e p r e d . l . c t e d t o be:-Potent' ..ia change ocdera: High. -. unLikeLy to differ ~l/conditJ .on.~ lPotentia clams L(.1W, ':;;'3LncLinedvto d.trf~r: w/conditioll:!:.-Execution time (months): 10, but may not exceed 11.-Profitability leve H'OldiLun, & unlikey 'to d.1.tfec w/conctit.1.0n3

Recommendat l ons for ProJ ect {TEST)As ~art of ~he overal l sel l l ng strategy,Pro?. I O gi ves you some r ecomMendat i ons tohep increase your chances cf w~nning.

In your bi.d proposa, you can outsel the conpetitors by: Adding teatut:es val ued by the customer to ~ncrease youc bid '5 wcrt.h, Emphos i : : : i n g t o t he customer t he features i n which you have an edge. Dnwnpl1.ying features in which you have a disadvantage_

Thngs youneedto strengthen andemphasize: C01~PANY:performance, reputation, LoyaLr.y, morals, wor-kmanah ap. RELTAPI:,TTY: dependab iLir.y, accuracy, consistency, ercor-free. PUNr'TUALT"'Y: compLetion t".itr.e, del i ver y t i ne, r esponse time. COCoPF.RATIVSNESS:suppier loyalty, good rapport., peas"nt. RI~HT~BJECTIVE: corr ect goas, proper scope, qual i ~YI hel p: ul nes5/

RecomOend"tlens ~Ot P"o]ecl iTE3T) IYet, ProBID has given you amazin'J insight mt.o the bid si.uetaonand before you size up and fine tune you" bid you need to consider:

~ THE PRICING BALANCEIMotIves for LOWERbids t1o::ives fer HIGHERbids Compet~tion Follow-on business Good Experience Avoiding layoffs cus coner good"ill

P:cofit Risk of cost overruns Bid prepara::ion cost Tying up resources Chance of cancelatIon

RecolnlOendatlOos for ProJect (TEST)Gudeines for fine-tuning your bid

Bid on jobI:1crease bi dCost to pcepare the b,d ? ~ Low-med.LH'ghCos~ of ceser vl ng your r esourcesfor Lh_::Ibid until ::re.::'ec:.ion? -- Low-HlghIs there valuable ownec qoodwillto gain by submtting t.h i.s bid ? -- Low-High Dec: ' eSge bi dI s t her e a val uabl e ex. peri ence 7~ Low- med*t.o gain (new imoulledge, pr"gtlge\ LHigh Bid on JobDecreasebidChances of j ob beng canceled Low-med.High Bid on JobDo not b~dWill you have to shut down/lay-off peope .1.f you lose ? Low-Hlgh De e r - e a s e bi d


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9.7 BacktoOur CaseStudyProjectWecan easily apply the concepts presented in this chapter to our case study project.For simplicity we will assume a5%markup ismost suited to the project at hand. Inthe next chapter, we will use this percentage in finalizing our bid proposal, consider-ing the expected project cash flow.

9.8 SummaryBidding strategy models are, basically, methodologies designed to maximize con-tractor's expected profit in acompetitive environment, where expected profit is, for agiven bid amount, the product of the profit that would be realized from the bid andthe probability of winning the job with that bid. These models enable the contractorto organize his past experience on bids and use this experience to establish winningstrategies against key competitors. Collectively, all bidding strategy models compro-mise between acontractor gaining amaximum profit and being the lowest bidder. Inboth Friedman's and Gates's models, optimum markup is determined in an iterativemanner, within a practical range of markup. Incremental variations in markup areplotted against the expected profit and the optimum markup is determined as themarkup corresponding to peak expected profit.Despite the differences in assumptions and basic formulations between these models,they generally provide answers to three questions:

1. What is the probability of winning at adesired markup?2. What is the optimum markup value?3. What is the probability of winning at optimum markup?In this chapter, aspreadsheet model, Bidding.xls, isused toautomate the calculationsinvolved in probability-based bidding strategies. A more sophisticated program, Pro-BID, is also used to consider the qualitative factors that affect markup decisions andprovide guidelines on fine-tuning themarkup estimate. After amarkup is estimated,our bid for aproject becomes close tobeing ready for submission. In thenext chapter,wewill consider project financing options and the finalyreparation of abid proposal.

9.9 BibliographyAhmad, I., and Minkarah, I. (1988,July). "Questionnaire Survey on Bidding in Con-struction," J ournal ofM anagement in Engineering, American Society of Civil Engineers,Vol.4, No.3, pp. 229-243.Benjamin, N. B.H., and Meador, R. C. (1979,March). "Comparison of Friedman andGates Competitive Bidding Models," J ournal of theConstruction Division, American So-ciety of Civil Engineers, Vol. 105,No. C01, pp. 25-40.Friedman, L. (1956). "A Competitive Bidding Strategy," Operations Research, Vol. 4,pp.104-112.Gates, M. (1967, March). "Bidding Strategies and Probabilities," J ournal of the Con-struction Division, American' Society of Civil Engineers, Vol.93, No. C01, pp. 75-107.Ioannou, P.G. (1988, June). "Bidding Models-Symmetry and State of Information,"J ournal of Construction Engineering and Management, American Society of Civil Engi-neers, Vol. 114,No.2, pp. 214-232.Morin, T. L., and Clough, R. H. (1969, July). "OPBID: Competitive Bidding StrategyModel," J ournal of the Construction Division, American Society of Civil Engineers, Vol.95,No. C01, pp. 85-106.


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