Rodrigo Toneto de Melo
Ibmec Business School, Brazil
E-mail: rodrigotoneto@gmail.com
Luiz Flavio Autran Monteiro Gomes
Ibmec Business School, Brazil
E-mail: luiz.gomes@ibmec.edu.br
Fernando Filardi
Ibmec Business School, Brazil
E-mail: fernandofilardi@gmail.com
Submission: 7/16/2018
Revision: 8/10/2018
Accept: 12/20/2018
ABSTRACT
This theory driven study puts forward the
implementation of Multi-Criteria Decision Aid,
through the PROMÉTHÉE V method, to support a decision on prioritizing a
project portfolio on offshore oil and gas platforms,
with aims to extend its service life. The chosen method supports, in a
structured manner, the prioritization of a project portfolio which is
necessary to leverage the oil production in offshore facilities, especially when they have already surpassed the plateau
phase and presents production decline.
Although the most relevant issues for investors are related to return on
investments and the risks involved, the study suggests
that other criteria are considered in specific settings. The research used data from 12 main projects of an
oil and gas company and the criteria evaluations were made based on documents
retrieved from the organization's database. This
implementation represents a very important improvement for a well-known
problem, in which the result is found based on criteria and their respective
weights selected through a consensus. The results reinforce that any
organization, with a constraint similar to the one presented in this study, may
obtain relevant gains with the use of methods that clearly reflect the decision
process and its criteria, assisting the decision maker's job significantly.
Keywords: Portfolio
Prioritization Strategies, Offshore Platforms, PROMÉTHÉE V, Oil & Gas, MCDA
1. INTRODUCTION
Due to the constant increase on
changes in the global economic scenario, the decisions on how and where to
invest correctly have become more and more complex. Consequently, organizations
require more strategic decisions which are not only based on their managers'
experiences or intuitions that are not necessarily well-founded. In the Oil
& Gas industry, one of the decision makers main challenges is to allocate
resources according to the most valuable opportunities. For this reason, the
Multi-Criteria Decision Aid methodology may offer the necessary support, as
shown below.
According to Belton and Stewart (2002), it is important
to stress that the focus of this methodology is to support or aid decision
making, not to prescribe how decisions should be made or describe how decisions
are made without formal support. Gomes (2007) believes decision making is a
process that leads to the selection of at least one alternative among many
others to solve the problem.
Some methods were developed in the
last decades aiming at providing tools which are able to represent real
problems, with the use of models to obtain information and comprehension.
According to Clemen and Reilly (2001), decisions can
be strengthened through the use of this modelling process.
The quest for scientific methods to
support decision making, such as multi-criteria decision methods, meets the
need to support decision-making agents in identifying objectives, consequences
and potential bargains. This includes procedures that facilitate the
implementation of these concepts in a logical, transparent and organized way
(KEENEY, 2004).
In the oil industry, recognized by
high-risk investment decisions, any process optimization will bring financial
return of bigger proportions when compared to most of the others. When
decisions are made correctly, managers and investors receive the desired returns.
In this context, the aim of this
study is to offer the application of a well-structured approach which is able
to aid the solution of an existing problem, seeking to prioritize projects
implementation in order to execute the portfolio in a robust way. This aim is
aligned with the organization strategy, since the limitation of available
technical resources is real, and the projects cannot be developed
concomitantly.
For this purpose, this article
suggests the application of a multi-criteria method called PROMÉTHÉE
(Preference Ranking Organization Method for Enrichment Evaluations), more
specifically, PROMÉTHÉE V. This is a multi-criteria analytical method that
leads to the identification of a complete pre-order of alternatives, given a
set of restrictions and serving therefore as an alternative for the model which
already exists in the company.
The portfolio, composed of twelve
projects, will be divided into three smaller subportfolios
with four projects each, prioritized according to the criteria established by
the proposed model. Because of the reduced availability of top level oil &
gas staff resources to develop the projects, which is common in many
organizations during times of economic crisis. With the inclusion of this
constraint, it may be possible to execute this portfolio in the course of the
next three years.
2. THE DECISION THEORY FOCUS
2.1.
Problem
Formulation
At times, companies own a project portfolio with
potential for implementation due to the need for production extension in oil
production units, which is a common goal in oil fields. According to Câmara (2004), those oil
production units have exceeded their production peaks. However, they
come across the lack of a scientific methodology capable of supporting the
appropriate prioritization.
Companies have their own
methodologies that serve as
assistance for scenarios where this problem can be found. Usually, there are
few criteria, the weight for each criterion is given and, after that, an
individual alternatives analysis is made for each selected criterion. For instance, this type
of methodology reaches, as a result, a value from an alternative in each
criterion with its weight, the ranking is found through the obtained result,
from the highest to the lowest, as it can be seen in the case studied.
Alternatives are evaluated separately, according
to each criterion, and there are no comparisons between them. This is one of the reasons why the methodology,
usually applied in the problem, is considered overly simplistic. Another factor that contributes to this review is
the selection of criteria.
The number of criteria is too small and there are
different criteria associated with the same criterion in use, called "supercriterion" in this paper.
Thus, when a certain alternative has an extremely positive impact over at
least one of the criteria inserted in this "supercriterion",
this alternative's performance is overly high. This alternative's real
importance may be overlooked if the other criteria in the same "supercriterion" do not have a performance evaluation
as high as the first one.
2.2.
Decision
making process in the Oil & Gas Industry
The history of oil exploration dates back to the 19th
century, when the United States of America started its commercial exploitation.
However, the beginning of the Brazilian offshore production sector was in the
70s. From then on, many oil fields went into production along Brazil's coast. However, the expansion
of offshore exploration and production took place in the 90s and the discovery
of pre-salt in the Tupi
field was only announced in 2007, which changed Brazil's history (MBP COPPE/UFRJ, 2014).
Due to the oil industry shrinkage (oil price fall), in
which the Brent value dropped from US$ 110 in June/2014 to less than US$ 30 in
the beginning of 2016, it was of extreme importance that companies from this
sector increased their diligence with investment decisions. Moreover, they
should enhance their level of efficiency, producing more and using less
resources (IEA, 2016). At the same time, oil companies are constantly
confronted with investment decisions in several projects, since investments in
hydrocarbons are of high risk (PARK et al., 2009; ZHANG, 2010; JAFARIZADEH,
2010; ZHANG; WANG, 2011; LIU et al., 2012).
2.3.
Project
Portfolio Management and Optimization
According to Barney and Hesterley
(2010), the concept of project portfolio management emerged from the need to
optimize resources use to ensure an efficient and effective investments return. Patanakul (2015) states that the
relevance of project portfolio management is related to the decision
makers' need to select, prioritize and control a set of initiatives, which takes into account the lack of resources as well as
the the need to reach strategic goals.
Alongside the portfolio management, its optimization is
also being developed. The portfolio theory suggests that this is characterized
by two indicators: the portfolio's return and its expected variation. The aim of the portfolio optimization is to
minimize the variation to a given return or maximize the expected return for a
certain risk (MARKOWITZ, 1959).
According to Cooper et al.
(1997), the project portfolio is a collection of projects and programs
of a particular organization with the same strategic aims, related or not to
each other, and that compete for resources use.
Cáñez and Garfias
(2015) state that the elaboration of a project portfolio is essential, since individual evaluation may lead to short or
long-term problems with results imbalance. It is
noteworthy to identify prominently financial criteria, such as: Net Present Value (NPV), Internal Rate of Return
(IRR) and Payback
Period. However, this assessment has
been imprecise. Brache and Bodley-Scott (2006) consider
the following categories of criteria used to prioritize projects: (a) alignment with strategy; (b) sales growth; (c) cost
reduction; (d) compliance
with regulatory requirements, among
others.
Accurate information about the projects must be available
to all committee members so that the results are grounded and aligned with the
organizational aims. According to Ghasemzadeh and Archer (2000), projects with
multiple and conflicting aims are an additional challenge to the selection of
project portfolio. Also, favorable environments for debate and
support to decision making must be accessible.
Kerzner
(2006) recognizes that the
senior management does not have enough information to evaluate possible
projects, especially when there is a probability of
deviation and failure, due to the degree of uncertainty and risk.
2.4.
Multi-Criteria
Decision Aid
The Multi-Criteria Decision Aid
(MCDA) field is,
according to Gomes and Gomes (2014), a dynamic
area of knowledge and research to support decision makers and
negotiators, giving assistance in problem structuring, which allows the
expansion of argumentation and learning and comprehension abilities.
MCDA helps decision
makers evaluate objectives and select alternatives through structured methods, in which several different
qualitative and quantitative criteria, at times contradictory, are considered and evaluated (VINCKE, 1992).
For Gomes (2007), decision making is divided into 3 broad
stages: problem structuring, decision analysis and synthesis; as described
below:
I.
Problem
structuring includes: relevant information gathering, problem identification, generation of the viable alternatives set, relationship
between the qualitative and quantitative objectives of decision making,
objectives unfolding into criteria and the definition of each alternatives
consequences for each criterion as well as the probability of these
consequences' occurrence.
II.
Decision
analysis includes: the use of at least one existing Multi-Criteria method to
select, classify, rank or describe alternatives through which decision will be
made and, also, the review of obtained results. Moreover,
the sensitivity analysis is carried out giving realistic modifications of
variables and parameters, verifying possible changes in the decision maker's
preferences.
III.
At last, there is a synthesis in which the decision maker receives
objective recommendations, including the proposals and how to implement
them.
According to Gomes, Araya and Carignano (2004), at least four types of problems may arise
during a decision analysis process, shown and
defined in Table 1 below.
Table 1: Multi-Criteria Decision Aid Types of
Problems
Type |
Objective |
Selection |
Select
the best alternative or best possible subset of satisfactory alternatives
which cannot be compared to each other. |
Classification |
Classify
each alternative in the most suitable category in a set of predefined
categories. |
Rank |
Rank the available alternatives. |
Describe |
Describe
alternatives, establishing their performances in selected criteria without
generating prescriptions or recommendations. |
Source: Adapted from Gomes (2007)
2.5.
The
PROMÉTHÉE methods
The PROMÉTHÉE V method belongs to the French school's
family of multi-criteria methods. It is a
ranking multi-criteria method which is simpler, compared to other methods, in
its conception and applications (BRANS; MARESCHAL,
1986). Its implementation is suitable for problems with restrict numbers
of alternatives which need to be ranked, taking into account a group of
conflicting criteria.
The method encompasses
two phases: i) outranking
relationship building, gathering
information about alternatives and criteria; and ii) explore this relationship in order to support
decision making.
The PROMÉTHÉE methods are non-compensatory methods
which require intercriterion information that
corresponds to the relative importance between criteria,
and intracriterion information, acquired through the comparison
between criteria pairs:
●
Intercriterion information: is obtained
through the attribution of weight to each criterion. These weights must be positive and the criterion
with the biggest weight is considered the most important one.
●
Intracriterion information: pairwise comparisons are made, observing
the differences between the alternatives values inside each criterion. For small differences, the decision maker will
have to give a weak preference for the best alternative.
For big differences, a stronger preference.
These preferences will take a real number between 0 and 1, which means that for each
criterion fj(.), the decision maker will make use of the
function in (i):
Pj(a,b) = Pj [dj(a,b)] a,b ∈ A, onde: dj(a,b) = fj(a) - fj(b) e 0 ≤ Pj(a,b) ≤ 1 (i)
The pair {fj(a), Pj(a,b)} is called
generalized criterion associated with criterion Pj(.). That
is, it represents the degree of preference of a over b according to dj(a,b), which is the
difference between the alternatives a
and b performances
in criterion j, thus,
for dj(a,b) ≥ 0:
I.
If Pj(a,b) = 0 there is no preference of a
over b
in criterion j.
II.
If Pj(a,b) ≈ 0 there is weak preference of a over b
in criterion j.
III.
If Pj(a,b) ≈ 1 there is strong preference of
a over b in
criterion j.
IV.
If Pj(a,b) = 1 there is close preference of a
over b
in criterion j.
According to Brans et al (1986), six types of preference functions
are contemplated in the PROMÉTHÉE method, as show in
Table 2:
Preference Functions |
Parameters |
|
I. Usual Criterion |
0 if indifferent
or worst; |
None |
II. U-shape function |
0 if d ≤ q; |
q |
III. V-shape / Linear function |
0 f indifferent or worst; |
p |
IV. Level criterion |
0 if |d| ≤ q; |
q, p |
V. Linear with
indifference preference |
0 if |d| ≤ q; |
q, p |
VI. Gaussian criterion |
0 if d < 0; |
𝜎 (standard deviation) |
Source: ferreira, (2013)
In the preference functions on Table 2 above, p
and q parameters represent:
●
qj (indifference threshold) – the highest value for dj(a,b), under which there is a preference indifference
between a and b; and
●
pj (preference threshold) – the lowest value for dj(a,b), above which there is a close preference
of a in
relation to b.
Still with respect to the preference functions
on Table 2:
●
Type I: must be chosen in
radical situations in which a minimum deviation justifies close preference.
●
Types II and IV: are particularly suitable for cases of
qualitative data in a discrete scale.
●
Types III or V: must be selected for cases of real numbers
evaluations on a continuous scale with or without indifference zone.
●
Type VI: is preferred when
the decision maker considers a positive degree of preference for weak
deviations, this degree is increased as the deviation decreases.
For
this case study, the
limitation of staff resources to execute the project portfolio, will be
the restriction used.
A
subset of alternatives which satisfies the restrictions, providing as many net
flows as possible, will be obtained by the solution of the linear programming (0-1).
3. CASE STUDY
3.1.
Methodology
Now that the problem has been defined, the
scientific method and objective have also been established. Alternatives were selected based on the company's
database and the criteria were defined through a process of improving existing
criteria in the same organization. The model
structuring was made based on the available data, qualitative and quantitative
ones, which were adjusted to the proposed model.
The result was found through computer
processing, using the Visual PROMETHEE software in
its academic version, available free of charge to this end.
3.2.
The
Motivation behind choosing the PROMÉTHÉE V method
According to the literature review carried out
and acknowledged by Vetschera
and Almeida (2012), the PROMÉTHÉE method is
one of the analysis and surpassing methods more widely used in
applications involving portfolio selection issues.
The main problem in the application of surpassing
methods for portfolio issues is that they require alternatives pairwise
comparison - which may limit the number of alternatives considered due to the
heavy mathematical work involved. Moreover, in
portfolio issues, each item combination that fulfills certain constraints is a
potential alternative. This leads to a high number of
potential alternatives - different portfolios.
Therefore, the typical methods of selecting portfolio do not explicitly
generate all possible portfolios, but they try to create the ideal portfolio
based on the set of available items (VETSCHERA;
ALMEIDA, 2012).
The PROMÉTHÉE V method
was chosen based on the literature review which has been mentioned.
Besides that, it perfectly applies to the problem identified. Its applicability in the research problem analysis
has the following characteristics: (i) The method is suitable for the portfolio creation; (ii)
The method uses linear mathematical programming to create portfolios, integrating the PROMÉTHÉE II method and the
optimization technique; (iii) The method has support
computational tools, which eliminate the need to repeat manual
calculations.
3.3.
Objectives
and Alternatives
According to Keeney
(2004), the foundation for any analysis is the objective or set of objectives,
and the set of alternatives to reach this objective.
The alternatives, shown by the labels Project 1 as (P1), Project 2 as (P2), Project
3 as (P3), ..., Project 12 as (P12),
represent the twelve modification projects established by the
organization as the most important ones to be implemented in the next three
years. They are ranked according to the common model, as it was mentioned
before.
As an objective, the portfolio composed of these
twelve projects will be divided into three smaller portfolios with four
projects each, since there is a lack of staff resources to develop these
projects. They seek to make the portfolio
execution possible over the next three years and they were prioritized based on
the established criteria and identified constraint.
3.4.
Criteria
Composition
Miller (1956), recommends the number of evaluated
criteria to be seven, more or less two. This is due to the psychometrics
studies, which demonstrate that the human brain is limited when comparing more
than seven attributes at the same time.
The criteria can be gathered into a "supercriterion", in three different components: Production, Compliance and Safety. In this way, each one of
them is evaluated separately, constituting a set of five criteria, next to Cost and Ease.
As a result, the model
is formed by the following criteria: (i) Security; (II) Compliance; (iii) Production; (iv) Cost and (v) Ease. The definitions are presented on Table 4, in the
Criteria Structuring and Weights Attribution section.
3.5.
Data
Collection
The research is limited to the 12 main projects
identified in an oil & gas company. The projects information and their
evaluation in the studied criteria were gathered based on the documents from
the organization's database. The data obtained can be seen on Table 3:
Table 3: Original projects, their criteria and weights
DATA
COLLECTION |
||||||||
PROJECTS |
Criterion 1 |
Criterion 2 |
Criterion 3 |
TOTAL |
||||
Weight |
5 |
Weight |
2 |
Weight |
3 |
|||
ID |
Area of application |
Security / Compliance / Production |
Cost |
Ease |
||||
P1 |
Technical Safety |
10 |
10 |
8 |
94 |
|||
P2 |
Electrical |
8 |
10 |
10 |
90 |
|||
P3 |
Process Safety |
10 |
6 |
7 |
83 |
|||
P4 |
Technical Safety |
10 |
5 |
7 |
81 |
|||
P5 |
Process Safety |
8 |
8 |
8 |
80 |
|||
P6 |
Utilities |
10 |
4 |
7 |
79 |
|||
P7 |
Electrical |
8 |
7 |
8 |
78 |
|||
P8 |
Electrical |
8 |
7 |
8 |
78 |
|||
P9 |
Naval |
7 |
7 |
8 |
73 |
|||
P10 |
Electrical |
6 |
9 |
8 |
72 |
|||
P11 |
Corrosion Management |
7 |
6 |
8 |
71 |
|||
P12 |
Utilities |
8 |
5 |
7 |
71 |
|||
3.6.
Data
Processing
The data from Table 3 was revised and processed
alongside the group responsible for the method structuring which already exists
in the organization, across meetings with experts from the areas of Operations,
Integrity assurance of Offshore installations and Offshore Modification
Projects Management.
This work was necessary to organize the existing
data in order to adjust them into the established criteria and weights and,
also, for them to be processed by the PROMÉTHÉE method.
The multidisciplinary team, conducted by the
Decision Analyst, was consulted for the criteria structuring with due
preference, types, weights and preference and indifference thresholds
functions. The evaluation of these parameters was carried out based on the
company's Decision Analyst and Decision Maker's knowledge. The other group components were: a modification projects
manager, the platforms integrity manager, an operations engineer and a
project cost control coordinator.
3.7.
Criteria
Structuring and Weights Attribution
Following the criteria adopted by the
organization and adapting them as described above, the criteria structuring and
their weights for the method's implementation are the following:
Safety: It is a
type I (Usual) and maximization criterion, in which the highest value has preference over the
lowest one. It will be evaluated according to
a qualitative scale of impact of five elements (1 to 5):
•
5 for projects with
very high positive impact over the degree of safety;
•
4 for projects with high impact;
•
3 for projects with moderate positive impact;
•
2 for projects with low positive impact;
•
1 for projects without any impact over the degree of
safety.
Since
safety is a basic value for the industry at hand, the weight attributed to the
criterion will be 25.
Compliance: It is a type I (Usual) and maximization criterion. It will be evaluated in the simplest qualitative
way, with a binary scale. "Yes" for
projects that meet some compliance requirements, and "No"
for the ones that do not have compliance to any requirements.
To be in compliance with rules and regulations is
mandatory, the weight attributed to the criterion
will also be 25. It is important to say that requirements, which fit
into this criterion, usually have a deadline for implementation and the Company
will not fail to fulfill any deadlines because of portfolio prioritization
matters. The method's implementation seeks to
provide inputs on when the project will be executed, since it respects any
limits imposed by the specific requirement.
Product: It is a type III (Linear
Preference – V-Shape) and maximization
criterion. It will be evaluated according to a
quantitative Likert scale of five elements (1 to 5):
•
5 for projects with potential
for increased production over 2 kBOE/day;
•
4 for projects with potential for increased production between 1 and 2 kBOE/day;
•
3 for projects with potential for increased production
between 0.5 and 1 kBOE/day;
•
2 for projects with potential for increased production
between 0.1 and 0.5 kBOE/day;
•
1 for projects with potential for increased production
between 0 and 0.1 kBOE/day.
Since this criterion is connected with the
revenue-generating activity, its weight will be 22.5.
Cost: It is a type V (Linear
Preference with indifference area) and minimization criterion. It will be based
on a monetary scale, using American dollars as a reference. The values correspond
to the total cost foreseen for the project's implementation.
Projects that belong to the portfolio at hand, require
considerable low investments for the industry, therefore, the weight of this
criterion will be 12.5.
Ease: It is a type IV (Levels) and maximization criterion.
It will be evaluated according to a qualitative Likert scale of five
degrees (1 to 5):
•
5 for
projects with very low degree of complexity;
•
4 for projects with low degree of complexity;
•
3 for projects with moderate degree
of complexity;
•
2 for projects with high degree
of complexity;
•
1 for projects with very high degree of complexity.
Although this criterion is extremely important, its
weight will be the least relevant one comparing to the three first ones,
reflecting its real importance to the company. Thus, its
weight will be 15, making the sum of all criteria weights be 100.
With the criteria now defined and their types established
according to preference functions and attributed weights, Table 4 is given:
Table 4: Criteria Definitions, their types and weights
Criterion |
Definition |
Typo |
Min/Max |
Weight |
Safety |
It measures the project's positive impact on
the installations safety. |
I |
Maximize |
25 |
Compliance |
It measures whether the project has or not the
aim to meet an existing requirement, internal
or external to the organisation. |
I |
Maximize |
25 |
Production |
It measures the potential increase of the
installation's production efficiency with the project's implementation. |
III |
Maximize |
22.5 |
Cost |
It measures the cost of investment needed for
the project's implementation. |
V |
Minimize |
12.5 |
Ease |
It measures the degree of easiness for the
project's implementation. |
IV |
Maximize |
15 |
Table 5 was established as a
result of this work of adequacy of data collected and structured by the
multidisciplinary team.
Table 5:
Parameters input on the PROMÉTHÉE Application
THE PROMÉTHÉE V METHOD APPLICATION |
||||||||||
|
C1 |
C2 |
C3 |
C4 |
C5 |
|||||
Safety |
Compliance |
Production |
Cost |
Ease |
||||||
Preference |
Maximize |
Maximize |
Maximize |
Minimize |
Maximize |
|||||
Type |
I |
I |
III |
V |
IV |
|||||
Thresholds |
P:
- |
Q:
- |
P:
- |
Q:
- |
P:
1 |
Q:
- |
P:
0.5 |
Q:
0.25 |
P:
2 |
Q:
1 |
Weights |
25 |
25 |
22.5 |
12.5 |
15 |
|||||
Projects |
||||||||||
P1 |
5 |
No |
1 |
0.8 |
4 |
|||||
P2 |
3 |
No |
1 |
0.6 |
5 |
|||||
P3 |
4 |
Yes |
1 |
2.3 |
3 |
|||||
P4 |
4 |
Yes |
1 |
2.2 |
3 |
|||||
P5 |
4 |
Yes |
2 |
1.2 |
4 |
|||||
P6 |
2 |
No |
4 |
3 |
3 |
|||||
P7 |
3 |
Yes |
1 |
1.7 |
4 |
|||||
P8 |
3 |
No |
2 |
1.1 |
4 |
|||||
P9 |
1 |
No |
3 |
1.3 |
4 |
|||||
P10 |
3 |
Yes |
1 |
1 |
4 |
|||||
P11 |
3 |
No |
1 |
2.3 |
4 |
|||||
P12 |
1 |
No |
4 |
1.9 |
3 |
|||||
3.8.
The
PROMÉTHÉE Method Computer Processing
The Visual PROMÉTHÉE software was
used, in its academic version and free of charge, to
apply this method. The data for Table 5 were inserted into the software and
result is shown below:
Picture 1: Data
inserted in the Visual
PROMÉTHÉE software
After
data entry, the model was executed and the following results of outranking positive (f+ or Phi+), negative (f- or Phi-) and net flows (f or Phi) were obtained. Table 6 presents this
result ranked by PROMÉTHÉE II.
Alternatives |
f+ |
f- |
f |
Project 5 |
0.5523 |
0.1023 |
0.4500 |
Project 4 |
0.3523 |
0.2205 |
0.1318 |
Project 3 |
0.3523 |
0.2250 |
0.1273 |
Project 1 |
0.3386 |
0.2159 |
0.1227 |
Project 10 |
0.2977 |
0.2000 |
0.0977 |
Project 7 |
0.2727 |
0.2568 |
0.0159 |
Project 8 |
0.2795 |
0.2795 |
0.0000 |
Project 2 |
0.2318 |
0.3068 |
-0.0750 |
Project 9 |
0.2477 |
0.4068 |
-0.1591 |
Project 12 |
0.2318 |
0.4227 |
-0.1909 |
Project 6 |
0.2500 |
0.4568 |
-0.2068 |
Project 11 |
0.0795 |
0.3932 |
-0.3136 |
Picture 2 presents
the contributions of each criterion to the alternative in the net flow result. Criteria with positive impact on the alternative's
net flow in the ranking appear in the chart's
upper area and criteria with negative impact appear in the chart's bottom area.
Picture 2:
Disintegrated vision of f - PROMÉTHÉE
II
With
the ranking now established, it is easy to compare the obtained results from
the model's data collection, which already exists in the organization, with the
PROMÉTHÉE II's ranking. This comparison is expressed
below:
Ranking of the
organization's original method:
P1 – P2
– P3 – P4 – P5 – P6 – P7
– P8 – P9 – P10 – P11 – P12
Ranking of the PROMÉTHÉE
II method:
P5 – P4
– P3 – P1 – P10 – P7 – P8
– P2 – P9 – P12 – P6 – P11
Picture 3: Results comparison – Original model
and PROMÉTHÉE II
The ranking from PROMÉTHÉE II was completely different
from the original model. Among the six first projects
ranked in the organization’s model, only four – P1,
P3, P4 and P5
- remained in the first six positions of PROMÉTHÉE
II ranking. Even so, three of them appeared in positions different from
the original ones, with the exception of Project 3, which remained in the third
position.
3.9.
Inclusion
of Constraint – PROMÉTHÉE V
The portfolio composed of twelve projects needs
to be divided into three subportfolios and aligned
with the organization's strategy to plan the execution of this portfolio in the
next three years. It is also important to take into account the lack of staff
resources to develop these projects, which is a common fact for these
companies, especially during economic crisis.
These three subportfolios
are limited by the sum of scores in the "Ease" criterion of each one
of the projects. When this sum is 45 on Table 5, the restriction will be
established in a way that each subportfolio is
composed of four projects and the sum for each of the three portfolios is 15,
the aim is to balance the complexity between them.
With the constraint is imposed, the software's
setting is presented as below:
Picture 4: Inclusion of the first constraint
into the model – First subportfolio
With
the constraint, the first subportfolio is composed by: Project 5, Project 4, Project 1 and Project 10. It is possible to see that Project 3, which was
in the third position of the complete ranking, was not selected to integrate
the first subportfolio.
This is due to the fact that Project 3 has a low evaluation in its ease, the constraint allowed the inclusion of
alternatives with higher values to form the
group of projects. The
results are presented in Picture 5:
Picture 5: The Result of the first project subportfolio with the inclusion of constraint
In order to establish a second subportfolio, a second constraint was added to the software
in the same way the first one was. However, the projects selected to the first subportfolio were deactivated. The configuration is presented in Picture 6:
Picture 6: Inclusion of constraint into the
model – Second subportfolio
The modelling was executed and the second subportfolio was composed by: Project 3, Project 7, Project
8 and Project 9.
This time, Project 2, which was in the eighth
place of the complete ranking, was not select for the second subportfolio. This is due
to the fact that Project 2 has the best evaluation in the criterion ease, the constraint hindered its inclusion on the
second group of projects. The
results are displayed in Picture 7:
Picture 7: The Result of the second project subportfolio with the inclusion of constraints
To define the third and last subportfolio,
it was not necessary to include the constraint again, since the four remaining
projects form the group. Following the PROMÉTHÉE
II ranking, the third subportfolio is composed of
Project 2, Project 12, Project 6 and Project 11, ranked according to their
outranking performances.
As expected, the result obtained by the
PROMÉTHÉE II ranking was changed in order to meet the constraint needed for the
PROMÉTHÉE V application.
In this way, the final
results of the three subportfolios,
with the alternatives ranked according to the prioritization made by PROMÉTHÉE V, were:
•
Subportfolio 1: P5
– P4 – P1 – P10
•
Subportfolio 2: P3
– P7 – P8 – P9
•
Subportfolio 3: P2 –
P12 – P6 – P11
•
Complete Portfolio: P5 –
P4 – P1 – P10 – P3 – P7
– P8 – P9 – P2 – P12 – P6
– P11
As a matter of reference for comparison, this is
the result of the PROMÉTHÉE II ranking:
•
P5
– P4 – P3 – P1 – P10 – P7
– P8 – P2 – P9 – P12 – P6
– P11
By
comparing both results, it is possible to see that projects P3, P9 and P2 changed
positions in the ranking in order to respect
the subportfolios prioritization, given their easiness to be executed.
3.10.
The
PROMÉTHÉE II Sensitivity Analysis
The
aim of the following sensitivity analysis was to evaluate how sensitive the
proposed model is, when some of its parameters are altered.
The purpose of this stage is to distribute the
weights equally, 20 for each of the five criteria.
This simulation intends to demonstrate how the result of the model can be affected when weights attribution is not given the appropriate importance.
After levelling, Table 7 presents the ranking provided
by the software:
Table 7: Ranking for criteria with equal weights,
provided by PROMÉTHÉE II
Alternatives |
f+ |
f- |
f |
Project 5 |
0.5091 |
0.1018 |
0.4073 |
Project 1 |
0.3418 |
0.1818 |
0.1600 |
Project 10 |
0.2945 |
0.1745 |
0.1200 |
Project 8 |
0.2909 |
0.2400 |
0.0509 |
Project 2 |
0.3018 |
0.2545 |
0.0473 |
Project 4 |
0.2909 |
0.2582 |
0.0327 |
Project 3 |
0.2909 |
0.2655 |
0.0254 |
Project 7 |
0.2545 |
0.2655 |
-0.0110 |
Project 9 |
0.2655 |
0.3491 |
-0.0836 |
Project 12 |
0.2255 |
0.4000 |
-0.1745 |
Project 6 |
0.2182 |
0.4727 |
-0.2545 |
Project 11 |
0.0727 |
0.3927 |
-0.3200 |
The software also provides
the ranking output in a visual way, where the result of net flow can be
observed in the chart, Picture 8.
Picture 8: Chart ranking of alternatives with
equal weights provided by PROMÉTHÉE II
There were considerable changes of projects
position when compared to the model proposed by the study. Although Projects 5 and 11 are still on the first
and last positions, respectively, all projects between the second and eighth
changed their positions in the ranking with equivalent weights. These changes of position are presented below:
Pre-ranking of the
PROMÉTHÉE II method – proposed base model:
P5 – P4
– P3 – P1 – P10 – P7 – P8
– P2 – P9 – P12 – P6 – P11
Pre-ranking of the
PROMÉTHÉE II method – weights levelling between criteria:
P5 – P1
– P10 – P8 – P2 – P4 – P3
– P7 – P9 – P12 – P6 – P11
Besides demonstrating that the choice of weights
is fundamental to the adequate ranking result, it is
also possible to conclude that Projects 9, 12, 6 and 11, placed in last positions
of the ranking, are in fact the alternatives with worst evaluations according
to the selected criteria, since this subportfolio kept the same elements.
3.11.
The
PROMÉTHÉE V Sensitivity Analysis for weights levelling
The same constraint applied in the model was
established for the weights levelling between the five criteria and was
inserted into the software, Picture 9.
Picture 9: Inclusion of constraint for the
PROMETHÉE V sensitivity analysis with equal weights to prioritize the first subportfolio
Based on the prioritization, the first subportfolio was composed by: Project 5, Project 1, Project
10 and Project 4. The results produced by the
software are shown in Picture 10.
Picture 10: Result of the first subportfolio - PROMÉTHÉE V Sensitivity Analysis
The first prioritized subportfolio
presented the same result of the constraint in the base model. However, projects changed their positions in the
ranking, as shown by PROMÉTHÉE II new ranking. This result did not alter the creation of the
first subportfolio, but it shows that constraint may
alter formulation in case the weights change the ranking established by the
PROMÉTHÉE II method significantly.
In order to prioritize the second subportfolio,
the constraint was inserted into the software again.
Modelling was executed and the second selected subportfolio was composed by: Project 8, Project 3, Project
7 and Project 9. Just as the first subportfolio, the second one did not alter in terms of
alternatives, although the PROMÉTHÉE II ranking has been altered
significantly. The four prioritized projects for the
second subportfolio are shown in Picture 11.
Picture 11: Results of the second subportfolio - PROMÉTHÉE V Sensitivity Analysis
It was not necessary to insert the constraint
again into the software in order to define the third and last subportfolio, since the four remaining projects integrate
the last subportfolio.
According to the ranking established by PROMÉTHÉE
II for sensitivity analysis, the third subportfolio
was composed by: Project 2, Project 6, Project 12 and Project 11. Therefore, the three
resulting subportfolios were:
•
Subportfolio 1: P5
– P1 – P10 – P4
•
Subportfolio 2: P8
– P3 – P7 – P9
•
Subportfolio 3: P2 –
P12 – P6 – P11
•
Complete Portfolio: P5 –
P1 – P10 – P4 – P8 – P3
– P7 – P9 – P2 – P12 – P6
– P11
Based on the final result of sensitivity
analysis with weights levelling between criteria, the imposed constraint did
not alter the final result. However, from this
analysis it is possible to conclude that the PROMÉTHÉE
II ranking is of utmost importance for the final stage of prioritization.
4. CONCLUSIONS
The proposal described in this study puts
forward a relevant theme. Through the use of a structured methodology, which is
scientifically established, it is possible to improve an existing process of the
company. It can be applied in a relatively simple manner, if the organization is ambitious
enough to optimize its way of work with the use of the methodology presented.
The
aim of portfolio optimization, described by Brache and Bodley-Scott (2006), was achieved
in the proposed model. The authors state that the criteria used to prioritize
projects of a certain portfolio should be aligned with the organization's
strategy. The result sought by the organization can be found in a structured
manner.
This
work can be an important step towards the use of the MCDA methodology in oil
companies, in which the size of their project
portfolio struggles with available resources. The proposed
method has the necessary elements to add to the projects ranking in a portfolio,
being able to adequate them to the existing constraint,
considering lack of professionals to develop these projects.
The
sensitivity analysis was made, and the impacts were basically on the portfolio
ranking itself. Therefore, the prioritization of subportfolios,
which resulted from the inclusion of constraint, did not change, keeping the result stable when compared to the base
model. One of the possible evaluations of the result is that the
constraint imposed a certain limitation. Since the number of projects of great
difficulty is similar to the amount of proposed subportfolios, the model kept the level of difficulty distributed among subportfolios, which allowed the
result the company wanted to be reached.
With
that, it is understood that organizations with a
constraint similar to the one proposed may have relevant gains with the
use of scientific methods which clearly mirror the decision process and its
criteria and assist the decision maker. As this study suggests, this is possible as long
as the model structuring is made in a transparent way, representing its
preferences clearly.
REFERENCES
ARCHER, N. P.;
GHASEMZADEH, F. (1999) An Integrated Framework for Project Portfolio
Selection. International Journal of
Project Management, v. 17, n. 4, p. 207-216.
BARNEY, J. B.;
HESTERLY, W. S. (2010) Strategic
Management and Competitive Advantage: Concepts. Upper Saddle River: Pearson Prentice Hall.
BELTON, V.; STEWART,
T. (2002) Multi-Criteria Decision Analysis: An Integrated Approach. Doirdrecht: Kluwer Academic Publishers.
BRANS, J. P.;
MARESCHAL, B. (1988) Geometrical representations for MCDA. European Journal of Operational Research, V. 34, n. 1, p. 69-77.
BRANS, J. P.;
MARESCHAL, B. (1992) PROMÉTHÉE V: MCDM problems with segmentation constraints. INFOR Information Systems & Operational
Research, v. 30, n. 2, p. 85-96.
BRANS, J. P.;
MARESCHAL, B.; VINCKE, P. (1984) PROMÉTHÉE:
a new family of outranking methods in Multi-Criteria analysis. In: BRANS,
J. P. (Ed.). Operational Research.
Amsterdam: North-Holland,
BRANS, J. P.;
VINCKE, P. H. (1985) A preference ranking organization method the PROMÉTHÉE
method for MCDM. Management Science,
v. 31, n. 6, p. 647-656,
BRANS, J. P.;
VINCKE, P. H.; MARESCHAL, B. (1986) How
to select and how to rank projects: The PROMÉTHÉE method. European Journal of Operational
Research, v. 24, n. 2, p. 228-238.
CÂMARA, R. J. B. (2004) Campos maduros e campos marginais:
definições para efeitos regulatórios. Dissertação de Mestrado em Regulação da
Indústria de Energia. Salvador: Universidade de Salvador.
CÁÑEZ, L.;
GARFIAS, M. (2015) Portfolio Management at The Mexican Petroleum
Institute. Research-Technology
Management, v. 49, n. 4, p. 46-55
CLEMEN, R. T.;
REILLY, T. (2001) Making hard decisions
with decision tools. California: Duxbury.
COOPER, R.;
EDGETT, S. J.; KLEINSCHMIDT, E. J. (1997) Portfolio Management in New Product
Development: Lessons from the Leaders I. Research-Technology
Management, v. 40, n. 5, p. 16-28.
COOPER, R.;
EDGETT, S. J.; KLEINSCHMIDT, E. J. (1997) Portfolio Management in New Product
Development: Lessons from the Leaders II. Research-Technology Management,
v. 40, n. 6, p. 43-52.
COOPER, D. R.; SCHINDLER, P. S.
(2003) Métodos de pesquisa em administração.
7a ed. Porto Alegre:
Bookman.
COOPER, R.
G.; EDGETT, S. J.; KLEINSHMIDT, E. J. (1999) New product portfolio management:
practices and performance. Journal of Product Innovation Management,
v. 16, n. 4, p. 333-351.
FERREIRA, R. G. (2013) Priorização de portfólio de projetos de
geração de energia renovável utilizando o método PROMÉTHÉE V. Dissertação
de Mestrado Profissional em Administração. Rio de Janeiro: Faculdades IBMEC.
GHASEMZADEH,
F.; ARCHER, N. P. (2000) Project portfolio selection through decision
support. Decision
Support Systems, v. 29, n. 1, p. 73-88.
GOMES, L. F. A. M. (2007) Teoria da Decisão. São Paulo: Thomson
Learning.
GOMES, L. F. A. M.; ARAYA, M. C. G.;
CARIGNANO, C. (2004) Tomada de decisões
em cenários complexos: introdução aos métodos discretos do apoio
multicritério à decisão. São Paulo: Pioneira Thomson Learning.
GOMES, L. F. A. M.; GOMES, C. F. S. (2014)
Tomada de decisão gerencial: enfoque
multicritério. 5. ed. São
Paulo: Atlas.
JAFARIZADEH
B. (2010) Financial factor models for correlated inputs in the simulation of
project cash flows. Journal of Petroleum
Science and Engineering, v. 75, n. 1-2, p. 54-57.
KEENEY, R.
L. (2004) Making better decision makers. Decision
Analysis, v. 1, n. 4, p. 193-204.
KEENEY, R.
L.; RAIFFA, H. (1976) Decisions with
Multiple Objectives preferences and valur tradeoffs.
New York: Wiley.
KERZNER, H.
(2006) Project Management Best Practices:
Achieving Global Excellence. New Jersey: John Wiley & Sons.
LIU, M. M.;
WANG, Z.; ZHAO, L.; PAN, Y.; XIAO, F. (2012) Production sharing contract: An
analysis based on an oil price stochastic process. Petroleum Science, v. 8, n. 3, p. 408-415.
MARKOWITZ,
H. (1959) Portfolio selection: Efficient
Diversification of Investment. New York: John Wiley & Sons.
MAVROTAS,
G.; DIAKOULAKI, D.; CALOGHIROU, Y. (2006) Project prioritization under policy
restrictions: a combination of MCDA with 0-1 programming. European Journal of Operational Research, v. 171, n. 1, p. 296-308.
MBP COPPE/UFRJ (2014) História do Petróleo. Disponível em: http://www.petroleo.coppe.ufrj.br/historia-do-petroleo/.
Captured in August 15th, 2017.
PATANAKUL,
P. (2015) Key attributes of effectiveness in managing project portfolios. International Journal of Project Management,
v. 33, n. 5, p. 1084-1097.
PARK, C.;
KANG, J. M.; AHN, T. (2009) A stochastic approach for integrating market and
technical uncertainties in economic evaluations of petroleum development. Petroleum Science, v. 6, n. 3, p.
319-326.
ROY, B.
(2005) Paradigms and challenges. In: FIGUEIRA, J.; GRECO, S.; EHRGOTT, M. (Ed),
Multi-Criteria Decision Analysis:
state of the art surveys. p. 3-26. New York:
Springer Science.
THOMAS, J. E. (2001) Fundamentos de Engenharia de Petróleo.
Petrobras. Rio de Janeiro: Interciência.
VETSCHERA,
R.; ALMEIDA, A. T. (2012) A PROMÉTHÉE-based approach to portfolio selection
problems. Computer & Operations
Research, v. 39, n. 5, p. 1010-1020.
VINCKE, P. (1992)
Multicriteria Decision-Aid. New York: Wiley.
XUE, Q.;
WANG, Z.; LIU, S.; ZHAO, D. (2014) An improved portfolio optimization model for
oil and gas investment selection. Petroleum
Science, v. 11, n. 1, p. 181-188, March.
ZELENY, M.
(2005) Human systems management:
Integrating knowledge, management and systems. Singapore: World Scientific.
ZHANG, J.;
SUN, Z. D.; ZHANG, Y. W.; NAFI, T. (2010) Risk-opportunity analyses and
production peak forecasting on world conventional oil and gas perspectives. Petroleum Science, v. 7, n. 1, p.
136-146.
ZHANG, B.
S.; WANG Q. (2011) Analysis and forecasts of investment scale and structure in
upstream sector for oil companies based on system dynamics. Petroleum Science, v. 8, n. 1, p. 120-126.