BIDDING & ESTIMATING - engr.usask.ca 5 05-10-07 STUDENT.pdfchapter 5 bidding & estimating. 165 ... 1 3 5 7 9 111315 1719 2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0. bidding & estimating

164

October 2007

CE 420

Chapter 5

BIDDING & ESTIMATING

165

5.1 DECISION TO BID

The decision to bid – major financial gamble

Decision requires input: A)______________B) _______________C) _________________

Bid Preparation Costs ≈ ___________of total cost of project


166BIDDING & ESTIMATING5.2 PRE-QUALIFICATION

On many projects, bidders are required to pre-qualify

Firms submit their “resumes” describing:

A. B. C. D.

167BIDDING & ESTIMATING5.3 STRATEGIC BIDDING FOR CONTRACTORS

What to expect from strategic bidding

“EXPECT A MIRACLE” – Oral Roberts

“WE JUST DON’T EXPECT MIRACLESWE DEPEND ON MIRACLES” – Anonymous Contractor

168BIDDING & ESTIMATING5.3 STRATEGIC BIDDING (Con’t)

To be successful – competitive bidding strategy – contractor needs to bid high enough to assure himself a profit on each job, yet low enough to get the job

Problem: If bid is high enough to get a sure profit__________________________________

Only way to be sure of having the low bid is to bid below cost

Under these conditions, it seems that a contractor is faced with two extremely unpleasant alternatives

1) An excellent chance of making no profit with a low bid2) No chance at all of making a high profit with a high bid

However, somewhere between these there is an opportunity for a contractor to make a reasonable profit

Every job has an optimum or “best bid” which will, in the long run, result in the highest possible profit obtainable under the existing competitive situations.

169BIDDING & ESTIMATING5.3 STRATEGIC BIDDING (CON’T)

The objective of strategic bidding is simply to identify this optimum markup for a job before it is bid, or better yet, before incurring the time and expense of preparing a detailed cost estimate.

WHAT IS A FAIR PROFIT?

A typical general contractor should earn a ________________ on his total sales volume and a return on his net capital investment of ________________

By applying a few strategic principles to his bidding practices, he can make about half as much profit as he could if he knew all his competitors bids in advance!

If this much profit still fails to bring him his 4 – 5% profit margin and his 20 –25% return on investment, then he is either

1) ________________________2) ________________________

170

5.3.1 MEASURING BIDDING EFFICIENCY


Efficient bidding is a key to success

Efficient Bidding = amount of profit made/amount of profit that could have been made if the competitors bid had been known in advance

171

5.3.1 MEASURING BIDDING EFFICIENCY (CON’T)BIDDING & ESTIMATING

Table 5.1: Summary of bid tabulations on nine contracts

Bidding Efficiency = __________________________________

Job Number

Number of Competitors

Competitors Bid

Estimated Cost

Your Actual Bid

Profit Potential

Profit Potential

Actual Profit

1 2 $99,800 $92,300 $98,100 $7,500 8.1% $6,1002 5 $37,500 $33,200 $38,600 $4,300 13.0% $03 4 $376,600 $350,500 $385,400 $26,100 7.4% $04 2 $692,800 $680,000 $702,500 $12,800 1.9% $05 6 $132,600 $118,300 $126,800 $14,300 12.1% $8,5006 3 $13,400 $12,700 $15,100 $700 5.5% $07 1 $31,500 $26,900 $30,200 $4,600 17.1% $3,3008 3 $121,400 $108,900 $118,400 $12,500 11.5% $9,5009 5 $501,900 $478,500 $534,400 $23,400 4.9% $0

$1,901,300 $106,200 $27,400Total

172BIDDING & ESTIMATING5.3.2 EFFECTS ON DIFFERENT MARKUPS

Table 5.2: Results of applying various percentage markups to bidsApplied to

JobNumber of Jobs Won

Cost of Jobs Won

Gross Profit on Jobs Won

Bidding Efficiency

0% 9 $1,901,300 $0 0.0%1% 9 $1,901,300 $19,013 17.9%2% 8 $1,221,300 $24,426 23.0%3% 8 $1,221,300 $36,639 34.5%4% 8 $1,221,300 $48,852 46.0%5% 7 $742,800 $37,140 35.0%6% 7 $742,800 $44,568 42.0%7% 6 $730,100 $51,107 48.1%8% 5 $379,600 $30,368 28.6%9% 4 $287,300 $25,857 24.3%

10% 4 $287,300 $28,730 27.1%12% 3 $178,400 $21,408 20.2%15% 1 $26,900 $4,035 3.8%20% 0 $0 $0 0.0%

173BIDDING & ESTIMATING

Figure 5.1: Cost of Contracts and Profit as a Function of Markup

Bidding Efficiency =_________________________

Est

. Cos

t of C

ontra

ct ($

)

2,000,000

1,800,000

1,500,000

1,300,000

1,000,000

800,000

500,000

300,000

00 % 5 % 1 0

%1 5 %

2 0 %Markup

160,000

120,000

80,000

40,000 Pro

fit o

n C

ontra

ct

174

5.3.3 COMPETITIVE STRATEGYBIDDING & ESTIMATING

Developed from an analysis of 50 recent jobs.

Table 5.3Optimum percentage markups for different job characteristics

Bidding Efficiency = __________________

“Ultimate A Contractor Could Hope For”

Profits of $ __________________________________________

The real payoff: “________________________________”

Competitors Under$50,000

$50,000 to$200,000

Over$200,000

1 to 2 21% 15% 11%3 to 4 11% 8% 6%5 to 6 8% 6% 4%

175

PROFIT MARGIN


176

CAPITOL ACCOUNTS AND CAPITAL TURNOVER RATE


Plus Plus

Minus

Divided By

177

RETURN ON INVESTMENTBIDDING & ESTIMATING

Figure 5.4: Graphical Description of Net Return Rate on Investment

ProfitSales

XSales

Investment(Net Worth)

ProfitInvestment

=

178

TYPICAL PROFIT VALUESBIDDING & ESTIMATING

Type of Contractor High Typical Low

Profit Margin (%) 2.5 1.5 1.0Capital Turnover Rate 12.0 8.0 5.0Return on Capital (%) 30.0 12.0 5.0




General (Buildings)

General (Highway-Heavy)

Mechanical

Electrical


Total dollar amount above direct job costs that must be recovered through markup – to generate a specific net profit after taxes

The NRR must include1)__________________2)__________________3) _________________

5.4 NET REVENUE REQUIREMENTS

NR

R

Net Profit After TaxesIncome Taxes

Total Overhead Costs


Profitability Objective - based on the balance sheet

Profits – earned on income statement

Example : Contractor having net worth of $1,000,000 and overhead of $300,000, annually. His profitability objective is to earn 20% on his net worth, which translates into a profit objective of

_____________________________________

In this situation, net after-tax earnings of $200,000 would place him in the 50% income tax bracket, so his net pre-tax return would have been $400,000. Adding the $300,000 in overhead costs to his $400,000 gives him a net revenue requirement of $700, 000.

5.4 NET REVENUE REQUIREMENTS (CON’T)


The $700,000 (from example) can only be obtained by applying a low markup on a large volume, or high markup on a low volume.

The amount of work needed to return any given amount is a function of markup, and can be expressed as follows:

Total Sales (S): NRR [(100+M)/M]


Figure 5.5: Development of NRR relationship

100

+ M

Tota

l

Sal

es

NR

R

100 + M

M

M

Dir e

ct C

ost

100


Our Example: The contractor would require the following Sales to meet his or her profitability objectives (NRR)

•$24,030,000 @ 3% markup•$14,700,000 @ 5% markup•$7,700,000 @ 10% markup•$5,370,000 @ 15% markup•$4,200,000 @ 20% markup•$3,500,000 @ 25% markup•$2,100,000 @ 50% markup

Using this equation, it can be shown that for a markup (M) of 1%, $101.00 of sales is required to generate $1.00 of NRR.

This information is shown for markup between 0 & 40% of direct costs in Figure 5.6


(S) =_______________________


Figure 5.6: Relationship between sales and Markup to produce $1 NRR

SA

LES

RE

QU

IRE

D P

ER

D

OLL

AR

NR

R

100 + MM

% MARKUP ON DIRECT COSTS0 10 20 30 40

$26.00$24.00$22.00$20.00$18.00$16.00$14.00$12.00

$10.00$8.00$6.00$4.00$2.00$0.00


The price/volume graph shown in Figure 5.6 illustrated a typicalprice/volume relationship for a general contractor.

Unfortunately there is no such thing as a “typical” contractor

The relationship between percentages and sales volume needed to reach a specified NRR are developed mathematically and apply universally to all types of contractors

5.4.1 THE PRICE VOLUME RELATIONSHIP


Figure 5.7 shows the combining of NRR and volume of sales

The sales volume was taken from Table 5.1 used earlier to discuss bidding efficiency (NRR for this example arbitrarily chosen as $30,000)

5.4.1 THE PRICE VOLUME RELATIONSHIP (CON’T)

Figure 5.7: Relationship between Sales as a function of Markup and NRR

Tota

l Sal

es V

olum

e

($ M

illio

ns)

% Markup on Estimated Costs1 3 5 7 9 11 13 15 17 19

2

1.8

1.6

1.4

1.2

1

0.8

0.6

0.4

0.2

0


Figure 5.8 illustrates how the change in NRR influences the range and availability of satisfactory NRR

5.4.1 THE PRICE VOLUME RELATIONSHIP (CON’T)

Figure 5.8: Relationship between Sales as a function of Markup and Different levels of NRR

Tota

l Sal

es V

olum

e

($ M

illio

ns)

% Markup on Estimated Cost

0 5 10 15 20

2.01.81.61.41.2

1.00.80.60.4

0.20.0


Many different theoretical approaches have been used and tested with varying

results.

Any of these strategies should improve the contractors bidding efficiency

The best known and most widely accepted theoretical approaches are known as the

1) Fieldman’s model2) Gates model

Elements of the Gates model are presented below.

5.5 THEORY OF BIDDING STRATEGY

188BIDDING & ESTIMATING5.5.1 BASIC CONCEPTS

The lower limit of bids is generally set by the estimated direct cost of the work.

The relationship between the bid price and the estimated cost depends on several factors, such as:

1) The contractors need for work2) The minimum acceptable markup3) The maximum the contractor thinks he can get.

The basic concept underlying the competitive bidding strategy consists simply in recognizing that there is some one bid which results in the best possible combination of two factors:

1)The profit resulting from obtaining a contract at a specified bid price2) The probability of getting the job by bidding that amount

189

Bid as % of Estimated

Cost

Probability of Being Lower Bidder

Expected Profit

(%)

Probability of Being Lower

Bidder

Expected Profit

(%)


Expected Profit

(%)


Expected Profit

(%)

100.0 100 0.0 100 0.0 100 0.0 100 0.0102.5 90 2.3 81 2.0 73 1.8 59 1.5103.8 85 3.2 72 2.7 61 2.3 44 1.7105.0 80 4.0 64 3.2 51 2.6 33 1.6106.8 73 4.9 53 3.6 39 2.6 21 1.4108.4 66 5.5 44 3.7 29 2.4 13 1.1110.0 60 6.0 36 3.6 22 2.2 8 0.8111.0 56 6.2 31 3.4 18 1.9 6 0.6112.0 52 6.2 27 3.2 14 1.7 4 0.5112.5 50 6.3 25 3.1 13 1.6 3 0.4113.0 48 6.2 23 3.0 11 1.4 3 0.3114.0 44 6.2 19 2.7 9 1.2 2 0.2115.0 40 6.0 16 2.4 6 1.0 1 0.2116.6 34 5.6 12 1.9 4 0.7 0 0.1118.4 26 4.8 7 1.2 2 0.3 0 0.0120.0 20 4.0 4 0.8 1 0.2 0 0.0122.5 10 2.3 1 0.2 0 0.0 0 0.0

One Competitor Two Competitor Three Competitor Five Competitor

5.5.2 BIDDING AGAINST A SINGLE COMPETITORBIDDING & ESTIMATING

Table 5.4: Data Associated with an analysis of bidding success and expected profit for single and multiple competitors

190BIDDING & ESTIMATING5.5.2 BIDDING AGAINST A SINGLE COMPETITOR (CON’T)

Figure 5.9 illustrates the effect of the bid price on the chances of being the low bidder, when bidding against a single competitor. In this figure, the contractor can be certain of being the low bidder only if he bids the job at cost. By bidding 12.5% above costs, he can expect to be the low bidder on 50% of the jobs

Figure 5.9: Probability of Success for Single Bidder

BID AS A PERCENTAGE OF ESTIMATED COST

PRO

BA

BIL

ITY

OF

BEI

NG

LOW

BID

DER

(%)

100

90

80

70

60

50

40

30

20

10

0100 104 108 112 116 120 124 128

191BIDDING & ESTIMATING5.5.2 BIDDING AGAINST A SINGLE COMPETITOR (CON’T)

Figure 5.10 shows the expected profit associated with each combination of markup and probability of being low bidder. In this example, the contractor can make more money in the long run bidding 12.5%

Figure 5.10: Relationship between Expected Profit and Probability of Success for Single Bidder

Optimum Bid at 12.5% Markup

Exp

ecte

d P

rofit

(%)

BID AS PERCENTAGE OF ESTIMATED COST

7

6

5

4

3

2

1

0100 104 108 112 116 120 124 128

192BIDDING & ESTIMATING5.5.3 BIDDING AGAINST MORE THAN ONE COMPETITOR

The top line of Figure 4.11 is the same as for Figure 4.9, for a single competitor.

If the probability of winning against a single competitor is 50% (at 12.5% markup), then the probability of winning against two competitors at the same level of markup is 0.5 x 0.5 = 0.25%. In a similar manner, the probability of winning against three competitors is 12.5% and for five competitors is 3%.

Figure 5.11: Probability of Success for Multiple Bidders

PRO

BA

BIL

ITY

OF

BEI

NG

LO

W B

IDD

ER (%

)

BID AS A PERCENTAGE OF ESTIMATED COST

100

90

80

70

60

50

40

30

20

10

0100 104 108 112 116 120 124 128

1 Competitor

23

5


Figure 5.12: Relationship between Expected Profit and Probability of Success for Multiple Bidders

Expe

cted

Pro

f i t (%

)

Bid as a Percentage of Estimated Cost100 104 108 112 116 120 124 128

77

66

55

44

33

22

11

00

1 Competitor

6.3%

12.5

%

Documents

BIDDING & ESTIMATING - engr.usask.ca 5 05-10-07 STUDENT.pdfchapter 5 bidding & estimating. 165 ... 1 3 5 7 9 111315 1719 2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0. bidding & estimating