p–ISSN: 2723 - 6609 e-ISSN: 2745-5254

Vol. 5, No. 6 Juny 2024 http://jist.publikasiindonesia.id/

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 6, Juny 2024 2803

Analysis of Mark UP value decisions with a bidding strategy

model to win projects in government auctions

Nyoman Rowin Sinaya

, Parwadi Moengin

, Bambang Endro Yuwono

Darmawan Pontan

Universitas Trisakti, Indonesia

Email: [email protected]

, [email protected]

[email protected]

, [email protected]

*Correspondence

ABSTRACT

Keywords: Mark Up,

Expected Profit, Bid

Strategy, Auction, LPSE.

The purpose of this study is to create a bidding strategy to

determine the optimum markup value and maximum

expected profit to win a project auction. From the data

selection results, 24 project tenders and 12 large

qualification companies participated in the tender on the

LPSE page of the Ministry of PUPR, which will be used as

samples in this study. The bid strategy model used to

calculate the optimum markup value and maximum expected

profit are with 3 (three) bid strategy models, namely the

Friedman Model, Gates Model, and Ackoff & Sasieni

Model, and to calculate the probability of winning using the

statistical approach method of multi discrete distribution,

normal multi-distribution, and single normal distribution.

From the results of testing models with optimal mark-ups for

the 24 project tenders used in this study, the percentage for

each bidding strategy model that has the potential to win the

tender sequentially is the Friedman model by 80.56%, the

Gates model by 61.11% and the Ackoff &; Sasieni model by

43.06%. So, it can be concluded that the Friedman model

provides a fairly high chance of winning tenders in

government projects within the Ministry of PUPR.

Introduction

Construction projects are currently increasing in line with the rapid growth of

population, economy, industry, and tourism (Senduk, 2022). Rapid growth must be

balanced with the government's development of infrastructure that supports community

activities (Maharani, Hardiyati, & Subagyo, 2021). Infrastructure development in

Indonesia is one of the factors that is increasing the role of the construction sector in the

Indonesian economy. This can be seen from the large contribution of the construction

sector to the Gross Domestic Product (GDP) of 5.23% in the second quarter of 2023. This

causes the number of businesses in the construction sector to reach 203,403 companies in

2023, according to BPS 2023 data, so it can be interpreted that the market share and tight

competition in the construction services sector are getting higher (Oo, Lim, & Runeson,

Nyoman Rowin Sinaya, Parwadi Moengin, Bambang Endro Yuwono, Darmawan Pontan

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 6, Juny 2024 2804

2023). In its implementation, contractors must be ready to face competition from other

competitors when participating in auctions (Leśniak & Plebankiewicz, 2015). Therefore,

it is necessary to anticipate the problems of a contractor company in facing competitive

auctions where there are conditions in terms of decision-making to participate or not

participate in the auction and how big the bid will be submitted; an appropriate bidding

strategy is needed (Hardiyanti, Maharani, & Subagyo, 2022). The estimated markup value

implemented in bidding for construction projects can be used as a reference in submitting

bid prices; the approach model in calculating markups is a tool for contractors in

developing bid strategies in facing competitive bidding system tenders so that the best

chance of participating in tenders or getting the optimum chance to win the project

(Pemayun, 2018). A wide variety of bid strategy models that can be used to define bid

strategies (Ramdhan, 2021). This study will analyze the optimum markup value and

maximum expected profit using 3 (three) bidding strategy models, namely the Friedman,

Gates, and Ackoff &; Sasieni models, and using statistical approach methods of multi

discrete distribution, multi normal distribution, and single normal distribution to calculate

the probability of winning with case studies on government tenders found on the LPSE

page of the Ministry of PUPR (Melisa & Johny, 2019).

In bidding, the contractor will have to place a competitive bid price, which means

that the bid price cannot be submitted too high in the hope of getting a large profit or vice

versa. The contractor cannot submit a bid price too low in the hope of winning the greater

tender (Mahapatni & Dewi, 2020). There are many ways bidders try to win auctions by

applying various strategies. Strategy is an effort that users can use to bring problems

closer to real conditions. Some common strategies that are often used are:

1. Competitive strategy is the most ideal bidding strategy, assuming all competitors use

an honest strategy in the competition.

2. Price lowering strategy is a strategy used by bidders to win auctions by lowering prices

by getting minimal profits

3. A loss-making strategy aims to gain sympathy from the owner in the hope of getting

the next project.

4. A payment strategy with leeway aims to provide the owner with leeway in terms of

term payments.

5. The under-table negotiation strategy aims to obtain the value of the Owner Estimate

in an informal setting.

Expected profit is the difference between the bid price and the estimated cost, so

the bid price is the estimated cost of the project plus the markup. The greater the bid price,

the less likely it is to become the lowest bidder, so this potential profit must be made

optimal known as the expected maximum profit in order to be the lowest bidder (Citra,

Wibowo, Malinda, & Apdeni, 2022). The value of Expected profit is obtained using the

following equation:

E (P) = P x Mark up………………….. (1)

Dimana :

Analysis of Mark UP value decisions with a bidding strategy model to win projects in

government auctions

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 6, Juny 2024 2805

E (P) = Expected profit

P = Probabilitas menang

Mark up

Markup is the difference between the bid price and the budget plan for work costs

(direct costs) plus indirect costs. In addition, Markup is the bid price divided by the

estimated cost in percent (Markup = Bid Price / Estimated Cost). The formula for finding

the markup value is the bid price divided by the estimated cost in percent.

Mark up = (B-C)/C x 100% ………………………….(2)

Where:

B = Bid Price

C = Direct Cost

Multi Discrete Distributed

Multi-discrete distribution is a histogram-shaped distribution in which data from

each known competitor is calculated individually for the probability of winning. This

calculation uses the ratio of each company, which is then grouped by the lowest ratio of

each auction and the highest ratio of each auction. The probability seen using a histogram

is the amount of data with certain markup limits divided by the total data of contractors

who participated in the auction so that the opportunity is obtained.

Multi Normal Distribution

This method uses the equation

Z = ( R – Mr) / Dr …………………………. (3)

where:

Z = Normal probability of a random variable

R = ( 1 + Mark up )

Mr = Mean Bid ratio from contractor data

Dr = Standard deviation from contractor cost quote

After Z is calculated, the probability of winning can be found in the normal

distribution table, which can be seen in the statistical book by looking at the area on the

right of the figure below.

Nyoman Rowin Sinaya, Parwadi Moengin, Bambang Endro Yuwono, Darmawan Pontan

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 6, Juny 2024 2806

Figure 1 Cost quote normal distribution graph

Normal Single Distribution

The formula for calculating the probability of winning is the same as Equation 3.

The difference with normal multi-distribution is that in a normal single distribution, the

probability of winning is calculated against the average of all competitors (Average

Bidders) or only on one bid data set, namely the lowest bid data set.

Method

Data Collection Methods

This study used primary data collection methods and secondary data. Primary data

was obtained from the construction company PT Nindya Karya, which had bidding data

in the period 2021 to 2023 on infrastructure projects with project values between IDR 100

billion and IDR 300 billion, as well as competitor bid data. Meanwhile, secondary data

are data obtained from literature sources such as course materials, websites, the internet,

scientific papers/journals, books, Electronic Procurement Services (LPSE) of the

Ministry of PUPR, and other sources that have something to do with this research.

Data Processing Method with Statistical Approach

The initial step in data processing with statistical approach methods is to determine

the method used, namely by using three methods: multi-discrete distribution method,

normal multi-distribution, and normal single distribution. The data converted into ratios

is then grouped from smallest ratio and largest ratio. After that, the mean, standard

deviation, and variance for normal multi-distribution and normal single distribution,

while discrete multi-distribution uses the initial ratio that has been analyzed, are found.

The result of this data processing is the probability of each contractor winning. In the

multi-discrete distribution method, a histogram or analysis from the Microsoft Excel

program is used, which is the same. In contrast, in the multi-normal distribution method,

a single normal distribution is used in the Z cumulative normal distribution table.

Bidding Strategy Model Data Processing Method

After finishing calculating all probability of winning using the statistical approach

of multi-discrete distribution, multi-normal distribution, and single normal distribution,

then the next step is to calculate the optimum Mark up and maximum Expected Profit

using three bid strategy models, namely the Friedman model, Gates model, and Ackoff

&; Sasieni model. After that, a comparison chart is made between the Expected Profit

against the Mark of each model.

Model Testing With Optimum Mark-Up

The optimum markup obtained from the calculation process will be tested against

the bid prices by seeing whether it will be lower (which means winning) or higher

(meaning losing) than the lowest bid price. The bid hypothesis is obtained by multiplying

the estimated cost of the contract by the markup of the calculation result and then

comparing it with the lowest bid from the winning contractor. The data used in this test

is the data of 24 project tenders used by the sample in this study, which will be tested for

Analysis of Mark UP value decisions with a bidding strategy model to win projects in

government auctions

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 6, Juny 2024 2807

bid value based on the optimum markup value; the model is then analyzed to determine

which model provides a chance of winning as one of the decision-makers to submit price

bids in an auction, especially within the Ministry of PUPR.

Results and Discussion

The bidding data collected is bidding data from 2021 to 2023, with an evaluation

of the lowest price knockout auction, the auction of which has been completed and is

available on the LPSE website of the Ministry of PUPR. The project provisions taken are

projects with a budget ceiling value between Rp 100 billion to Rp 300 billion, followed

by ≥ 3 (three) contractors with their active participation in ≥ 5 (five) tenders

simultaneously. In the first phase of data collection, 32 projects were obtained, and the

number of competitors was 66 large qualifying contractor companies. Then, a selection

of bid data was carried out, with the provision that each project tender was followed by ≥

3 (three) contractors who actively participated in ≥ 5 (five) tenders simultaneously. From

the selection of data then collected and obtained, 24 projects with 12 competitors will be

examined for bidding behavior.

Multi Discrete Distributed

The first step of this method is to calculate the direct cost of each tender in this

study using the direct cost reference of PT Nindya Karya. After obtaining the direct cost,

the ratio of supply to direct cost is calculated. After the ratio results are obtained, the ratio

data is grouped by R-value with intervals of 0% to 60%. After that, it is done compulsively

by reducing each data from the total data of each company. Furthermore, the probability

of winning is calculated at each ratio by dividing the cumulative ratio by the total offer

made by each company.

Multi Normal Distribution

The first step in this distribution analysis is to calculate the mean, standard

deviation, and variance of each competitor, the results of which are presented in the table

below.

Table 1

Mean Value, Standard Deviation, and Normal Multi Distribution Variance

COMPETITOR

MEA

TOTAL X

(ΣX)

TOTAL

X^2

STANDARD

DEVIATIO

VARIANT

PS1

1,218

29,222

35,717

0,0770

0,0059

PS2

1,242

12,421

15,573

0,1272

0,0162

PS3

1,305

14,350

18,847

0,1120

0,0125

PS4

1,268

6,342

8,084

0,0982

0,0096

PS5

1,268

19,014

24,266

0,1085

0,0118

PS6

1,246

7,475

9,336

0,0680

0,0046

PS7

1,220

6,099

7,500

0,1234

0,0152

Nyoman Rowin Sinaya, Parwadi Moengin, Bambang Endro Yuwono, Darmawan Pontan

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 6, Juny 2024 2808

To find the probability of winning in a normal multi-distribution, you must first find

the value of Z (equation 3), which serves to determine the probability number in the

normal distribution table.

Normal Single Distribution

The initial step in analyzing a single normal distribution is to calculate the mean,

standard deviation, and variance against the highest ratio and the lowest ratio. The results

of the analysis of the calculation of mean, standard deviation, and variance can be seen in

the table below.

Table 2

Mean Value, Standard Deviation, and Single Variance of Normal Distribution

The statistical results above are divided into two parts, namely bid/cost, and low

bid/cost, where bid/cost is the highest cumulative ratio of the contractor's bid. In contrast,

low bid/cost is cumulative of all the lowest ratios. The mean and standard deviation values

used are bid/cost values. This is because it is possible in the hope of getting the largest

ratio value to obtain a large Z value and will obtain a large probability value as well. The

probability of winning with a single normal distribution is obtained from the cumulative

distribution table Z, just like the normal multi-distribution.

Data Processing with Supply Strategy Model

a. Friedman's Strategy Model

PS8

1,228

6,142

7,585

0,1010

0,0102

PS9

1,285

6,423

8,334

0,1434

0,0206

PS10

1,197

5,987

7,202

0,0925

0,0086

PS11

1,179

10,607

12,593

0,1071

0,0115

PS12

1,151

8,058

9,325

0,0917

0,0084

STATISTICAL

RESULTS

2021-2023

1. Bid / Cost

Mean

1,29362

Total X

31,04690

Total X^2

40,40303

Standard Deviation

0,10217

Variant

0,01044

2. Low Bid / Cost

Mean

1,16536

Total X

27,96876

Total X^2

32,73465

Standard Deviation

0,07825

Variant

0,00612

Analysis of Mark UP value decisions with a bidding strategy model to win projects in

government auctions

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 6, Juny 2024 2809

Figure 1 Relationship between expected profit and markup for discrete multi-distribution

using the Friedman model

Figure 2 Relationship between Expected Profit and Mark Up for Multi-Normal

Distribution using the Friedman Model

Figure 3 Relationship between Expected Profit and Mark Up for a Normal Single

Distribution using the Friedman model

0,0373

0,0000

0,0050

0,0100

0,0150

0,0200

0,0250

0,0300

0,0350

0,0400

0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 22% 24%

Expected Profit

Mark up (%)

0,0280

0,0000

0,0050

0,0100

0,0150

0,0200

0,0250

0,0300

0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 22% 24%

Expected Profit

Mark up (%)

0,0709

0,0000

0,0100

0,0200

0,0300

0,0400

0,0500

0,0600

0,0700

0,0800

0% 2% 4% 6% 8% 10%12%14%16%18%20%22%24%26%28%30%

Expected Profit

Mark up (%)

Nyoman Rowin Sinaya, Parwadi Moengin, Bambang Endro Yuwono, Darmawan Pontan

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 6, Juny 2024 2810

Model Strategi Gates

Figure 4

Relationship between Expected Profit and Mark-Up for Multi-Discrete Distribution Using

Gates Model

Figure 5 Relationship between Expected Profit and Mark Up for Multi-Normal

Distribution using Gates Model

Figure 6 Relationship between Expected Profit and Mark Up for Normal Single

Distribution using Gates Model

0,0515

-0,0100

0,0000

0,0100

0,0200

0,0300

0,0400

0,0500

0,0600

0% 4% 8%12%16%20%24%28%32%36%40%44%48%52%56%60%64%

Expected Profit

Mark up (%)

0,0375

0,0000

0,0050

0,0100

0,0150

0,0200

0,0250

0,0300

0,0350

0,0400

0% 4% 8% 12% 16% 20% 24% 28% 32% 36% 40% 44% 48% 52% 56%

Expected Profit

Mark up (%)

0,1684

0,0000

0,0200

0,0400

0,0600

0,0800

0,1000

0,1200

0,1400

0,1600

0,1800

0% 4% 8% 12%16%20%24%28%32%36%40%44%48%52%56%60%

Expected Profit

Mark up (%)

Analysis of Mark UP value decisions with a bidding strategy model to win projects in

government auctions

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 6, Juny 2024 2811

Analysis of Optimum Mark-Up and Maximum Expected Profit

From the overall analysis above, it is concluded that the optimum markup value

with maximum expected profit for the three models is as follows:

Table 3

Optimum Mark Up Results and Maximum Expected Profit

Types of

Distribution

Strategy Model

Mark Up

Optimum

Expected

Profit

Multi Discrete

Distributed

Friedman

12,00%

0,0373

Gates

12,00%

0,0515

Ackoff & Sasieni

18,00%

0,1425

Multi Normal

Distribution

Friedman

7,00%

0,0280

Gates

9,00%

0,0375

Ackoff & Sasieni

17,00%

0,1245

Normal Single

Distribution

Friedman

11,00%

0,0709

Gates

23,00%

0,1684

Ackoff & Sasieni

17,00%

0,1245

Model Testing With Optimum Mark-Up

The markup obtained from the calculation analysis of the three methods above was

tested against the bid price on 24 project tenders to see if the bid price obtained would be

lower or higher than the lowest bid. From this test, it will be known whether you win or

lose when using variations in the markup generated from the previous count analysis.

Figure 7 Percentage probability of winning the tender of each bidding strategy model

Conclusion

It can be seen from the results of the analysis with the Friedman model that produces

the smallest markup of the three statistical approaches used, namely 12% for multi-

discrete distribution with an expected profit of 0.0373, 7% for normal multi-distribution

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

80,00%

90,00%

100,00%

Friedman Gates Ackoff & Sasieni

80,56%

61,11%

43,06%

Nyoman Rowin Sinaya, Parwadi Moengin, Bambang Endro Yuwono, Darmawan Pontan

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 6, Juny 2024 2812

with an expected profit of 0.0280 and 11% for a normal single distribution with an

expected profit of 0.0709 The results of the analysis of the Gates model produce an

optimum mark up of 12% for multi discrete distribution with an expected profit of 0.0515,

9% for normal multiple distributions with an expected profit of 0.0375, and 23% for

normal single distributions with an expected profit of 0.1684. At the same time, the results

of the analysis using the Ackoff & Sasieni model produced an optimum markup of 18%

for multi-discrete distribution with an expected profit of 0.1425, 17% for normal multi-

distribution with an expected profit of 0.1245 and 17% for a single normal distribution

with an expected profit of 0.1245.

From the results of testing models with optimal mark-ups for the 24 project tenders

used in this study, the percentage for each bidding strategy model that has the potential

to win a fairly high tender sequentially is the Friedman model of 80.56%, the Gates model

of 61.11% and the Ackoff & Sasieni model of 43.06%. So, it can be concluded that

Friedman's model provides a fairly high chance of winning tenders in government

projects within the Ministry of PUPR.

Analysis of Mark UP value decisions with a bidding strategy model to win projects in

government auctions

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 6, Juny 2024 2813

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