Implementation Monte Carlo Simulation in Investment Evaluation:

Case Study - Gas Compressor Investment PLTG 4 x 25 MW

Maleo-Gorontalo

Abdollah Siregar

and Sudarso Kaderi Wiryono

PT XYZ, Finance Manager, Batam Indonesia

School of Business and Management, Institut Teknologi Bandung, Indonesia

Keywords: Capital Budgeting, Investment Evaluation, Sensitivity Analysis, Monte Carlo Simulation, uncertainty.

Abstract: Investment evaluation is a crucial part before investment decision in order to measure will the project generate

profit for the company. This study is investment evaluation Gas Compressor Investment PLTG 4 x25 MW

Maleo Gorontalo. There are two investment evaluation method in this project Capital Budgeting and Monte

Carlo Simulation. Capital Budgeting technique used to measure this investment evaluation in this project

consist of Net Present Value (NPV), Internal Rate Return (IRR), Probability Index (PI) and Payback Period

(PBP). From the evaluation, it was obtained that a positive NPV of 500,444, an IRR of 11.50% greater than

the WACC of 9%, while the PI of 1.08 and PBP of 1.57 years was faster than the duration of the 2 (two) year

contract. Monte Carlo simulation applied to predict the financial feasibility of a project by considering risks

and uncertainties use to calculate Probability NPV<0 in this project, with use Capital Expenditure (CAPEX),

Lifetime Project and Debt: Equity Portion as the input variables. Monte Carlo simulation result probability

NPV <0 is 10,32 % mean while probability NPV >0 is 89,68%.

1 INTRODUCTION

1.1 Background

Electricity is a basic human need, which is an

inseparable part from daily life. According to report

second quarter of 2020 the data shows that the

development of the national electrification ratio has

reached 99.09%, as explained in the graph below:

Figure .1. Electrification Ratio

The electrification growth mentioned above

resulted an increase in electricity production in GWh

with an average annual growth of 5.62% while the

https://orcid.org/0000-0002-6599-3379

average annual increase in electricity sales in GWh

was 5.86% per year. The comparison between

production and sales in GWh is presented in the graph

below:

Figure .2. Production and Sales in GWh 2011-2019.

The transformation of PLN towards new and

renewable energy (EBT) is still emphasizing the

energy mix in coal and placing natural gas energy

sources as a transition energy to EBT. The following

is the energy mix target to generate electricity starting

at the end of 2025.

Siregar, A. and Wiryono, S.

Implementation Monte Carlo Simulation in Investment Evaluation: Case Study - Gas Compressor Investment PLTG 4 x 25 MW Maleo-Gorontalo.

DOI: 10.5220/0010935100003255

In Proceedings of the 3rd International Conference on Applied Economics and Social Science (ICAESS 2021), pages 72-79

ISBN: 978-989-758-605-7

Figure 3: Target of energy mixing end of 2025.

PT XYZ as a subsidiary of PT Pelayanan Listrik

Nasional Batam (PLNB) a state-owned company

electricity provider in Batam island. PT XYZ has

contributing to Optimizing Cost Efficiency in PT

PLN (Persero) group especially for gas infrastructure

and the first subsidiary specialize in gas pipeline in

PT PLN (Persero).

PT XYZ has received an offering letter from PT

PLN Gas Geothermal (“PLNGG”) as the owner of the

project to offer Investment in Gas Compressor for

PLTG 4 x 25 MW Maleo-Gorontalo.

Management of PT XYZ need to ensure the in-

vestment decision according to offering letter will

generate a profit margin to the Company.

1.2 Problem Statement

As described above, will investment in Compressor

for PLTG 4 x 25 MW generate profit to PT XYZ.

And questions related to this statement are:

a. What return are expected and how can manage-

ment maximize the return?

b. How are investment performing evaluated by

Capital Budgeting?

c. Which scenarios greatest financial risk or benefit?

d. Percentage of probability Net Present Value > 0,

as indicate of project success?

1.3 Objective

The main objectives of this paper are as follow:

a. To analyze investment evaluation of the project

with to the Capital Budgeting technique.

b. To identify the most sensitives variable key

changes input to capital budgeting indicator.

c. To perform assessment on sensitivity of uncer-

tainties factors in the investment decision with

Monte Carlo simulation to get profitability of suc-

cess of project.

2 LITERATURE REVIEW

2.1 Capital Budgeting

Capital budgeting is the process of evaluating and

selecting long term investment. This process intended

to achieve the firm’s goal of maximizing

shareholder’s wealth (Gitman, Lawrence, J_Zutter,

Chad J :2014).

2.1.1 Discounted Cash Flow

According to the Chan S. Park (2007: p.216)

Discounted Cash Flow (DCF) is a method of

evaluating an investment by estimating future cash

flow and taking into consideration of money. The

discounted cash flow (DCF) formula is as below:

𝑫𝑪𝑭𝒕 =

𝑵𝑪𝑭

𝒕

(𝟏 + 𝒓)

𝒕

Where:

DCFt = Net cash flow at the year of year t

r = Discount rate

t = Number of the year t = 0,1,2,3….t

2.1.2 Weight Average Cost of Capital

(WACC)

A method to calculating MARR is WACC approach

with assuming source of investment capital from debt

and equity. The expected return for equity investor

name cost of equity, the expected that lenders hope to

make on their investment named cost of debt. All

financing that the company takes on, the composition

of cost of financing will be a weighted average of the

cost of equity and debt, this weight cost name Weight

Average Cost of Capital (Damoradan 2011). WACC

formula as below:

𝑾𝑨𝑪𝑪 =

[

𝒓𝒅 ∗

(

𝟏−𝒕𝒂𝒙

)

∗𝑫/(𝑫+𝑬)

]

+[𝒓𝒆∗𝑬/(𝑫+𝑬)]

Where:

rd = cost of debt

re = cost of equity

D = Debt

E = Equity

2.2 Capital Budgeting Technique

2.2.1 Net Present Value (NPV)

NPV determines whether project is an acceptable

investment, NPV is the difference between the

present value of cash inflow and the present value of

Gas Bumi;

22,00%

EBT; 23,00%

BBM; 0,40%

Batubara;

54,60%

Implementation Monte Carlo Simulation in Investment Evaluation: Case Study - Gas Compressor Investment PLTG 4 x 25 MW

Maleo-Gorontalo

cash outflow (Chan S. Park 2007: p.216). NPV

formula as below:

𝑵𝑷𝑽 =  −

𝒕𝑵

𝒕𝟏

𝑵𝑪𝑭𝒕

(𝟏 + 𝒓)

𝒕

− 𝑰𝒏𝒊𝒕𝒊𝒂𝒍 𝑰𝒏𝒗𝒆𝒔𝒕𝒎𝒆𝒏𝒕

Where:

NCFt = Net Cash flow in t period

r = discount rate

N = life of the project

Decision criteria for NPV as follows:

If NPV ≥ 0, accept the project

If NPV ≤ 0, reject the project

2.2.2 Internal Rate of Return (IRR)

According to Gitman (2018: p.454) Internal rate of

return (IRR) is the discount rate that equates the NPV

of an investment opportunity with 0 (because present

value of cash inflow equals the initial investment).

IRR formula show as below:

𝟎=



𝑪𝑭𝒕

(𝟏 + 𝒓)

𝒕

𝒏

𝒕𝟏

− 𝑪𝑭𝒐

−—

Where:

CFt = Cash flow in period t

r = Discount rate

N = lifetime of the project

IRR = Internal rate of return of the project

When IRR is used to make accept – reject decision,

the decision are as follows:

If the IRR > Discount Rate, accept the project.

If the IRR < Discount Rate, reject the project.

2.2.3 Profitability Index (Pi)

According to Ross, et. AI, (2010) profitability index

is ratio accumulation net present value, net cash flow

after initial investment divided by initial investment.

𝑫𝑷𝑰 =



𝑵𝑪𝑭𝒕

(𝟏 + 𝒓)

𝒕

𝒕𝑵

𝒕𝟏

−

−𝒃 ±

√

𝒃

𝟐

−𝟒𝒂𝒄

𝑰𝑶

−

Where:

NCFt = Net Cash flow in period t

r = Discount Rate

IO = Initial Investment

N = project lifetime

DPI criteria for independent project as follow:

If DPI ≥ 0, then project can be accepted

If DPI ≤ 0, then reject the project

2.2.4 Payback Period Analysis (PBP)

Payback period analysis is when the period of time

over which cash flow from investment are expected

to recover the initial outlay (Erich A. Helfert: 2001

p.444), with formula as follow:

𝑷𝑩𝑩 =  𝑵𝑪𝑭𝒕

𝒕𝑵

𝒕𝟏

≥𝟎

Where:

PBP = Payback period (PBP)

NCFt = Net Cash flow in t period

N = life of the project

Criteria in PBP indicator:

If the PBP < cut off time the project, accept the project

If the PBP > cut off time the project, reject the project

2.3 Sensitivity Analysis

Sensitivity analysis is the process of tweaking one

key input or driver in a financial model and seeing

how sensitive model is to the change in that variable

(Danielle Stein Fairhurst: 2017: p.160). In this

sensitivity analysis used to identify how significant

each variable impact to investment analysis

parameter of the project. The main uncertainty factors

in this project are:

1. Capital cost

2. Inflation

3. Interest

4. Capital expenditure (CAPEX)

5. Operational expenditure (OPEX)

6. Lifetime project (month)

3 RESEARCH MODEL

3.1 Conceptual Framework

Proper decision in investment will generate benefit to

company. The future is certainly not exact, however

capital budgeting technique will be making better

decision in investment evaluation as economic

decision. This paper project output will be used as an

input to company management in order to investment

evaluation.

The framework of this final project shown as

below:

ICAESS 2021 - The International Conference on Applied Economics and Social Science

Figure 4: Conceptual Framework.

4 ANALYSES

4.1 Defining Assumption

Project cooperation concept use in this final project is

Build, Operate, Own (BOO), with project lifetime 2

(two) years according to the Letter of Intent (LOI)

from user with an option will be extended until 5 (five)

years.

4.2 Project Investment Cost

Total investment cost of for this project USD

7.118.174, -, according to bill of quantity and

engineering team calculation consist of:

Table 1: Investment Cost.

4.3 WACC

WACC calculation for this project using Regulation

of Badan Pengatur Migas No. 8 tahun 2013, article 14.

Calculation and Reference and show as below:

Table .2. WACC.

Authors use PT Perusahaan Gas Negara (PGN)

with listed code in Jakarta Stock Exchange PGAS as

data to calculated ꞵ(beta) detail provided in Appendix

3-1, assume Gas Compressor use for PLTG 4 x 25

MW in Maleo is part of Gas Infrastructure. Then

WACC output calculation below:

Table .3. WACC Calculation.

4.4 Revenue

Projected revenue of this project divided in 2 (two)

streams:

• Cost Capital Recovery (CCR)

• Operation Maintenance Recovery (OMR)

1 PROJECT MANAGEMENT 1 LS 115.468

2 ENGINEERING 1 LS 156.970

3 MAIN EQUIPMENT 1 LS 4.255.619

4 PIPING & VALVES 1 LS 547.430

C. INSTRUMENT CONTROL, SAFETY

DEVICE & ELECTRICAL

1 LS 465.038

6 CIVIL WORKS 1 LS 575.596

7 SITE CONSTRUCTION 1 LS 277.978

8 PRE COMMISSIONING & COMMISIONING 1 LS 34.302

9 INSURANCE 1 LS 42.667

6.471.067

647.107

7.118.174

PRICE (USD)

SUB TOTAL

PPN 10%

TOTAL

NO ITEM DESCRIPTION QTY UNIT

Implementation Monte Carlo Simulation in Investment Evaluation: Case Study - Gas Compressor Investment PLTG 4 x 25 MW

Maleo-Gorontalo

Total CCR and OMR during lifetime project show

as table below:

Table .4. revenue projection.

4.5 Operating Expenditure (OPEX)

To operating a gas compressor facility, required

operating cost that consist of fixed cost and variable

cost:

Table .5. fixed and variable cost/annum

4.6 Capital Budgeting Analysis

Analysis result according to the investment

evaluation analysis rule show as table below:

Table .6. Investment Analysis Result.

From table 5 above, it shown that Investment in

Compressor for PLTG 4 x 25 MW Maleo, Gorontalo

has positive value of NPV USD 500.444 with IRR

11,50% greater than WACC 9% and Payback Period

1,57 years. This parameter described that investing in

Gas Compressor for PLTG 4 x 25 MW feasible and

will generated profit for PT XYZ.

4.7 Sensitivity Analysis

According to Stein Fairhurst, Danielle (2017:140)

Sensitivity analysis is the process of tweaking one

key input variable which lead to the greatest decrease

or increase of the output variables when changes. In

this project sensitivity analysis used to identify how

significant each variable changes impact to

investment analysis parameter of the project NPV,

IRR, PI and PBP. The main uncertainty factors in this

project shown in this table below and output will be

presented in bar chart comparison to evaluate

sensitivity level

Table .7. Sensitivity Parameter.

4.7.1 NPV Sensitivity Analysis

Changes in variable CAPEX, LIFETIME PROJECT

and DEBT: EQUITY PORTION are the most

sensitive parameter to NPV indicator show in the

NPV Sensitivity analysis chart below:

Figure .5. NPV Sensitivity analysis

From figure 5 shows above explain that:

A1). If CAPEX low under budget -10% in this project

will impact to increase NPV USD 968.869,

meanwhile if CAPEX high over budget +10% NPV

value will decrease to USD 32.019,20 or -93,60%

from BASE assumption.

B1). If LIFETIME PROJECT (MONTH) high with

extended 6 (six) month from 24 (twenty-four) month

become 30 (thirty) month NPV will increase to USD

879.563 or increase 75,76% from BASE assumption,

No. Years CCR (USD) OMR (USD)

Total Revenue

(USD)

2021 1.829.001 438.973 2.267.974

2 2022 5.472.010 1.314.336 6.786.346

3 2023 3.643.009 876.368 4.519.377

10.944.020 2.629.676 13.573.697

Total

A Operation & Maintenance

1 Man Power 1 LS 599.825 per annum

2 Supporting 1 LS 160.089 per annum

3 Spare Parts & Consumable 1 LS 546.010 per annum

4 O&M all risk Insurance 1 LS 7.397 per annum

Total 1.313.322

PRICE (USD) NoteNO ITEM DESCRIPTION QTY UNIT

No. Sensitivity Focus Low Base High

1 Capital Expenditure (CAPEX) -10% Base +10%

2 Operating Expenditures (OPE

-10% Base +10%

3 Interest Rate 4,53% 5,24% 5,92%

4 Inflation Rate 1,32% 2,77% 3,61%

5 Life Time Project (month) 18 24 30

6 Debt : Equity Portion 80% : 20% 70% : 30% 60% : 40%

968.869

500.444

522.355

500.444

93.955

655.908

32.019

500.444

479.459

500.444

879.564

350.429

500.444

CAPEX OPEX INTEREST

RATE

INFLATION

RATE

LIFE TIME

PROJECT

(MONTH)

DEBT :

EQUITY

PORTION

Low High Base

ICAESS 2021 - The International Conference on Applied Economics and Social Science

meanwhile if with shortened 6 (six) month NPV will

drop to USD 93.995,36 (-81,23%) from BASE

assumption.

C1). DEBT: EQUITY PORTION, with low

assumption 80% Debt: 20% Equity will impact to

NPV project USD 655.907,88 or increase 31,07%

from NPV BASE assumption as impact of changes in

WACC from 9% become 7,31%, meanwhile with high

assumption 60% Debt: 40% Equity NPV become

USD 350.428,60 or decrease 29,98% as impact

changes in WACC from 9% become 10,70%. Other’s

parameter has less sensitive to NPV calculation

4.7.2 IRR Sensitivity analysis

Changes in variable CAPEX and LIFETIME

PROJECT are the most sensitive parameter to IRR

indicator as show in the IRR Sensitivity analysis chart

below:

Figure .6. IRR Sensitivity analysis

From figure 6 above explain that:

A2). If CAPEX low under budget -10% will impact

to IRR 19,91%, meanwhile if CAPEX high over

budget +10% will impact to decrease IRR 2,85%

lower than WACC 9% or decrease -75,24% from

BASE assumption with IRR 11,50%.

B2). Additional LIFETIME PROJECT (MONTH) 6

(six) month from BASE assumption 24 month

become 30 months will increase IRR to 15,13%,

meanwhile with decrease LIFETIME PROJECT

(MONTH) 6 (six) from 24 month to 18 month will

decrease IRR to 4,78%.

Other’s parameter has less sensitive to IRR

calculation.

4.7.3 Profitability Index (PI) Sensitivity

Analysis

Changes in variable CAPEX and LIFETIME

PROJECT are the most sensitive parameter to PI

indicator as show in the PI Sensitivity analysis chart

below:

Figure .7. PI sensitivity analysis

From figure 7 above explanation that:

A3). If CAPEX low under budget -10%, this project

show PI 1,17 meanwhile if CAPEX high over budget

+10% PI will be decrease to 1,00.

B3). Additional LIFETIME PROJECT (MONTH) 6

(six) month from BASE assumption 24 month

become 30 months will impact to PI from 1,08 to 1,14

meanwhile decrease LIFETIME PROJECT

(MONTH) 6 (six) month become 18 months will be

decrease PI to 1,02.

Other’s parameter has less sensitive to PI calculation.

4.7.4 Payback Period (PBP) Sensitivity

Analysis

Changes in variable CAPEX and LIFETIME

PROJECT are the most sensitive parameter to PBP

indicator as show in the PBP Sensitivity analysis

chart below:

Figure .8. PBP sensitivity analysis

From figure 8 above explain that:

A4). if CAPEX lower under budget -10%, this

project show PBP 1,43 years (equal to 1 year 6 month)

but when the CAPEX high over budget +10% PBP

will be decrease to 1,72 year (equal to 1 year 9 month).

19,91%

11,50%

11,36%

11,50%

4,78%

11,65%

2,85%

11,50%

11,64%

11,50%

15,13%

11,35%

11,50%

CAPEX OPEX INTEREST

RATE

INFLATION

RATE

LIFE TIME

PROJECT

(MONTH)

DEBT :

EQUITY

PORTION

Low High Base

1,43

1,57

1,58

1,57

1,45

1,57

1,72

1,57

1,72

1,58

1,57

CAPEX OPEX INTEREST

RATE

INFLATION

RATE

LIFE TIME

PROJECT

(MONTH)

DEBT :

EQUITY

PORTION

Low High Base

1,17

1,08

1,02

1,11

1,00

1,08

1,14

1,06

1,08

CAPEX OPEX INTEREST

RATE

INFLATION

RATE

LIFE TIME

PROJECT

(MONTH)

DEBT :

EQUITY

PORTION

Low High Base

Implementation Monte Carlo Simulation in Investment Evaluation: Case Study - Gas Compressor Investment PLTG 4 x 25 MW

Maleo-Gorontalo

B4). High assumption with additional LIFETIME

PROJECT (MONTH) 6 (six) month from BASE

assumption 24 month become 30 months will impact

to PBP from 1,45 years (equal to 1 year 7 month) to

1,72 years (equal to 1 year 9 month) as impact of

extended period which impact to second higher NPV

USD 879.563,73 meanwhile decrease LIFETIME

PROJECT (MONTH) 6 (six) month become 18-

month impact to PBP 1,45 years (equal to 1 years 6

month), with negative impact to NPV USD 93.955,36.

Other’s parameter has less sensitive to PBP

calculation.

According to the sensitivity analysis Capital

Expenditure (CAPEX), Lifetime Project and Debt:

Equity Portion are the most sensitive variable key

input.

4.8 Scenario Analysis

According to the result of sensitives analysis

calculation above, author provide Scenario analysis

as below:

Table .8. Scenario Analysis.

4.9 Monte Carlo Simulation

After getting the results of investment decisions using

the deterministic method as described in table 7 above,

there are still weaknesses in predicting future

conditions, because cash flows are built based on

input from certain estimated values, when in fact

there are uncertainties that may differ in future values.

R. Flage et al (2013) and J. Li et al (2017) explained

that to mitigate uncertainty, it can be done by making

uncertainty values in certain probability distributions.

S. Grey at all (1995) explained that the simulation

method is a method that can accommodate

quantitative risk and uncertainty assessments in

determining project investment. Monte Carlo

simulation is one of the most applicable methods.

According to M. Marseguerra and E. Zio (2009:

180-186) Monte Carlo simulation method is based on

iteration of repetition of random numbers and is

usually used to obtain forecasts of certain probability

models in solving a problem. Referring to the

explanation above, the Monte Carlo simulation can be

applied to predict the financial feasibility of a project

by considering risks and uncertainties.

4.9.1 Monte Carlo Simulation Steps

The risk analysis simulation steps in the project's

financial feasibility based on the Monte Carlo

simulation technique are as follows:

1. Identification of input variables key changes with

the greatest impact to the NPV. According to the

sensitivity analysis Capital Expenditure

(CAPEX), Lifetime Project and Debt: Equity

Portion are the most sensitive variable key input.

2. Identify the NPV output calculation.

3. Define related assumptions to assign probability

distribution to the input variables. Authors use

probability distribution for the input variables:

Table .9. Probability distribution.

No.

Input

Variables

Probabilit

Distribution

Explanation

1 CAPEX Uniform

distribution

With the

minimum and

maximum value

2 Lifetime

Project

Uniform

distribution

With the

minimum and

maximum value

3 Debt:

Equity

Portion

Triangular

distribution

With the

minimum, most

likely and

maximum value

4.9.2 Monte Carlo Simulation Result

Monte Carlo simulation result with 1.000 iteration as

follows:

Table .10. Statistic data from NPV Simulation Result.

According to the table 10 above, Probability NPV<0

in this project is 10,32% meanwhile NPV>0 is

89,68%, NPV Monte Carlo Normal Distribution

shown as below:

Worst Case

Scenario

Base Case

Scenario

Best Case

Scenario

Payback Period (PBP) - Years 1,62 1,57 1,68

Net Present Value (NPV) 199.076,29 500.444,15 1.239.436,89

Profitability Index (PI) 1,03 1,08 1,20

Internal Rate Return (IRR) 9,1% 11,50% 19,91%

Criteria

Statistic Value

Mean 474.346,30

Median 481.496,58

Standar Deviation 375.428,74

Skewness (0,03)

Kurtosis (0,56)

Minimum (449.661,21)

Maximum 1.434.177,00

Probability NPV <0 10,32%

ICAESS 2021 - The International Conference on Applied Economics and Social Science

Figure .9. NPV Monte Carlo Normal distribution Simula-

tion.

5 CONCLUSIONS

5.1 Capital Budgeting

Based on Investment Analysis calculation use Capital

budgeting technique in table 5 above, it shown that

Investment in Compressor for PLTG 4 x 25 MW

Maleo, Gorontalo has positive value of NPV USD

500.444 with IRR 11,50% greater than WACC 9%

and Payback Period 1,57 years. These parameters

described that investing in Gas Compressor for PLTG

4 x 25 MW recommended to executed assume project

will generate profit for PT XYZ.

5.2 Sensitivity Analysis

Sensitivity analysis shown that Capital Expenditure

(CAPEX), Lifetime Project and Debt: Equity Portion

are the most sensitive variable key input changes with

the greatest impact to the NPV.

5.3 Monte Carlo Simulation

In this paper, for performing Monte Carlo simulation

author use Capital Expenditure (CAPEX), Lifetime

Project and Debt: Equity Portion as the input

variables. The result shown that Probability NPV<0

is 10,32% and project success is NPV>0 is 89,68%

as explain in figure 9.

This paper is still limited in getting the right

variable input data. Expert opinion and historical data

review methods should be applied, not just based on

theoretical calculations

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Peraturan BPH MIGAS Nomor 8 tahun 2013, pasal 14 Ap-

pendix

Implementation Monte Carlo Simulation in Investment Evaluation: Case Study - Gas Compressor Investment PLTG 4 x 25 MW

Maleo-Gorontalo