Vinicius Maia de Jesus
IBMEC Business School, Brazil
E-mail: vinicius.maia@petrobras.com.br
Luiz Flavio Autran Monteiro Gomes
IBMEC Business School, Brazil
E-mail: luiz.gomes@ibmec.edu.br
Fernando Filardi
IBMEC Business School, Brazil
E-mail: fernando.filardi@ibmec.edu.br
Submission: 16/07/2018
Revision: 09/08/2018
Accept: 14/09/2018
ABSTRACT
The key objective of this study is to
present a model that uses the analytic network process (ANP) to select contract
strategies for oil and gas projects and discuss its benefits at the
organizational level. The literature review explores the concepts of contract
strategies and multicriteria decision-making methods, particularly the ANP. This
study conducted an analysis of a working group formed to recommend the most
adequate contract strategy for a particularly complex project, explaining its
drivers, criteria, and evaluation. Next, an ex post facto analysis was
performed, applying the model proposed to the same problem. The results showed
that the ANP improved the transparency of the selection process. A sensitivity
analysis was conducted, considering the independence of the clusters (group of
related elements), which did not change the ranking of the alternatives,
leading to a robust solution to the problem. The case study showed that the
candidate strategies had multiple, interrelated consequences and generated
technical and commercial impacts. This article’s main contributions are the
proposition of a model for selecting project contract strategies that provides
more transparency to the decision making process.
Keywords: contract strategy; oil & gas projects; multicriteria
decision making; analytic network process; ANP
1. INTRODUCTION
In project management, acquisition
management covers the processes required for buying or obtaining products,
services, or results that do not depend on the project team. This area of
knowledge also includes contract management and change control processes, which
are essential for developing and managing contracts or purchase orders (PROJECT
MANAGEMENT INSTITUTE, 2013).
The contract strategy is an initial
step of the contracting process that aims to define the optimum level of risk
allocation, delivery, and incentives to the contractor, as well as the degree
of integration between the engineering and the construction phases (OFFICE OF
GOVERNMENT COMMERCE, 2003).
Briefly, the contract strategy must
reflect the organizational policies required to execute and achieve the project’s
objectives. Moreover, other aspects should be defined, such as responsibilities
of parties, payment and contractual conditions, companies’ selection,
organizational structure for design and construction, and the tendering procedure
(PERRY, 1985; WRIGHT, 2002; DHANUSHKODI, 2012).
This article aims at presenting a
model to define the contract strategy for oil and gas (O&G) projects by
using the analytic network process (ANP) (SAATY, 2008; SAATY, 2009). Since this
process usually involves multiple and conflicting criteria, the use of a
multicriteria decision making (MCDM) method is adequate. Thus, based on a case
study, this article shows the benefits of using the ANP.
An ex post facto analysis of a
working group’s (WG’s) results was performed in this research. This group was
created to recommend the best contract strategy for a complex O&G project,
so the managers could make an informed decision. Thus, the contract strategies
described in this article are appropriate for huge industrial and construction
projects, such as those in the O&G industry.
Since the contract strategy has the
power to influence the project schedule and cost (BOWER, 2003), it is crucial
that it represents the most suitable commitment among the different evaluated
criteria, contributing to the project’s success and the achievement of its
goals. When an MCDM, such as the ANP, is used, the reasons for recommending an
alternative become clearer, leading to a more transparent decision and thus adding
value to the organization.
The literature review discusses the
concepts of contract strategies and the MCDM, particularly the ANP. The
articles about a contract strategy describe what it is, its main aspects, and
when each is applicable. Nonetheless, no research demonstrating how
organizations choose their contract strategies has been found. Therefore, this
study’s relevance lies not only in the proposed model but also in the
discussion on the analysis conducted by the WG, which represents how an O&G
company chooses each of its projects’ contract strategy. Some state-of-the-art
articles on the ANP are mentioned, indicating some of its applications. This
discussion highlights the originality of the study since no research that relates
projects’ contract strategies to MCDM methods has been found.
Contracts are formal agreements between two or more parties in which
conditions for performing a certain job are established
(DHANUSHKODI, 2012). Some of the contract objectives are defining the
work to be done, determining the amount to be paid and the payment method, as
well as establishing the responsibilities of the parties (DHANUSHKODI, 2012).
In turn, the contract strategy determines the level of integration across the
different stages of a project (from design to
operation). The aim is to attain the project’s
goals in terms of delivery, incentives, risk allocation, and so on (OFFICE OF GOVERNMENT COMMERCE, 2003).
According to Perry (1985),
the four decisions associated with choosing the contract strategy are the
project’s characteristics, the organizational structure for design and
construction, the type of contract, and the tendering procedure. These four aspects are usually correlated,
resulting in interactive decision processes. The
project characteristics (scope, quality, cost, etc.) are inputs to the contract
strategy and should be defined prior its selection.
As for the organizational structure, an appropriate
selection of the work size and scope should be made, as well as the allocation
of duties to the client and the contractor. The main
organizational structures used in projects are
turnkey, separation between design and implementation, build-own-operate-transfer (BOOT), and management
agreements (WRIGHT, 2002; DHANUSHKODI, 2012).
In turnkey contracts, one contractor is responsible for
all the stages of the project, from design to completion. Usually, they are lump-sum contracts, that is, the price is known and
fixed from the beginning. This structure is widely used in process
industries, such as the chemical industry, the O&G
sector, and so on. A slight variation of the turnkey type is the engineering, procurement,
and construction (EPC) structure. The difference
between EPC and turnkey is that the first has distinct stages (engineering, procurement, and construction), and each may have a specific payment method (PERRY, 1985; WRIGHT, 2002).
However, the client may opt to divide
the stages of design and implementation by assigning them to two
different companies or even create the design with its own resources. A third possibility is the BOOT contract, which is more
commonly used in public-private partnerships or concessions. In this case, the
contractor builds and operates the asset, owning its property while the
concession contract lasts; subsequently, the property returns to the client (WRIGHT, 2002).
There are also management agreements in which a client
hires a company to manage the project. However, the project’s execution is usually subdivided into
smaller contracts. Typically, the party
responsible for the management plays a collaborative role with the client. These contracts are normally implemented when the
client lacks the resources to manage the project or when flexibility is
necessary. This model allows an easier negotiation of scope alterations and
some overlapping between the stages of design and construction (PERRY, 1985; WRIGHT, 2002).
Another aspect of contract strategies is the type of
contract, which varies according to the payment method.
Contracts can be divided into two main categories, based on price and
based on cost. Contracts based on price can set the
lump sum or the unit price. In lump sums, contractors stipulate fixed
prices to perform the services. In unit price contracts, a price per measuring unit is established for each item of
service.
In contrast, contracts based on cost
can consist of reimbursable cost or the target cost. Regarding the first type,
the client reimburses the
contractor for the cost of goods and services,
plus a profit margin. As for the second type, the
deviations from the previously defined and established target cost are shared
between the client and the contractor. Each type of
contract has its advantages and disadvantages. The choice of the most
suitable type depends on the nature of the parties involved, the available resources for the contract management, as
well as incentive alignment, responsibility delegation,
and risk allocation (PERRY, 1985; WRIGHT, 2002).
On one hand, contracts based on price require strong
discipline from the contractor. The contractor must be efficient in handing
over the deliverables because it bears the financial
risk. Anyway, the fixed price serves as an
incentive for cost reduction since any saved amount represents an increase in the
contractor’s profit margin.
On the other hand, cost-reimbursable contracts do not
entail any financial risk for the contractor, requiring less discipline in
expenditure control. Regarding target cost
contracts, the financial risk is shared
between the parties. In this model, the client also
reimburses the contractor for the costs
incurred by the latter; both agree on the possible value of the service
cost (target cost).
This value is defined as the target on which a contractual mechanism of
cost sharing is established. In other words, the
difference between the target cost and the final cost (whether positive or negative) is shared between the parties according to the
proportion agreed on the contract.
Finally, as for the tendering procedure, a few
definitions must be established: the parties who will be responsible for design
and construction, the need for prequalified
suppliers, and the appropriate contractual conditions.
There are several procedures for selecting proposals. The first pertains to the
competitive process (bidding). The
second involves a two-stage process, where the
initial proposal is based on the conceptual (basic) project according to
the plan, and the final proposal is based on the cost and the price indicated in
the initial proposal. The third entails direct negotiation,
usually involving up to three companies. The fourth is contracting for several
similar projects, based on the initial proposal.
The fifth involves serial contracting, in
which a proposal for a contract package is
presented. The sixth refers to overall contracting, in which the
contracted object and the contract term are defined, but the amount of work is
not noted (WRIGHT, 2002).
Given its importance, some strategic decisions about the
contracting process must be carefully evaluated, for
instance, involving bid document preparation, bid
proposal preparation, and proposal analysis.
The client must evaluate the potential suppliers’
commitment before beginning the bidding process to increase the probability of
participating companies. The client should also assess competitiveness, whether
the risk allocation on the contract is appropriate to the size and the characteristics
of potential bidders, the deadline for the
proposals’ elaboration, as well as potential clarifications throughout the process.
Regarding the proposal analysis, it should be determined whether
there is a need for prequalified suppliers and the evaluation criteria. Several criteria are used to choose the contractor,
including expertise, technical or managing ability, use of the supplier’s
specific resources, capacity to manage certain
risks, development of a future supplier base, or hiring a well-known supplier,
whose competence has been attested.
The supplier selection may occur before the bidding process—the invitation
is restricted to a group of companies—or an open competition may be implemented,
where companies should demonstrate that they meet the qualification
requirements throughout the tendering procedure
(WRIGHT, 2002).
As for proposal analysis and classification, it is
necessary to elaborate on a detailed estimation to compare the proposals. In the documentation sent to the bidders, it is also common to specify the criteria for
determining the winner although it is not mandatory.
These criteria may vary from the best price to the evaluation of
qualitative factors (PERRY, 1985).
3. MULTICRITERIA DECISION MAKING AND THE ANALYTIC NETWORK
PROCESS METHOD
Decisions are part of everyone’s daily life. At times,
alternatives and their consequences are clear and intuitive, making the
decision easy and fast. However, it is usual
to face situations in which the problem itself is not straightforward, let
alone the alternatives’ consequences. In these cases,
it is necessary to undertake an in-depth analysis of the results that a
client wishes to achieve (objectives) and what is or is not acceptable (preferences and restrictions) so that
viable alternatives are established and compared. This
thorough and structured analysis is the basis for MCDM,
which involves multiple and conflicting objectives (BELTON; STEWART, 2002).
According to Belton and Stewart (2002), MCDM can be divided into three distinct
stages: problem identification and structuring, model construction, and
development of action plans. The first stage consists
of gathering the parties interested in the problem—decision makers,
technicians, and facilitators—to promote a mutual understanding of it, the
decisions to be made, and the criteria and the assessments used for deliberation. Subsequently, a model is elaborated—representing
the decision makers’ preferences in terms of objectives, criteria, and
tradeoffs—which allows the comparison of different alternatives, instilling
transparency in the process. Finally, action
plans are developed and associated with the chosen alternative, destined to
solve the initial problem.
Typically, problems addressed by multicriteria analysis
can be classified into six categories: choice, classification, ordination,
description, design, and portfolio (BELTON; STEWART,
2002).
In the analytic hierarchy
process (AHP), a particular case of ANP, relative
measures of tangible and intangible criteria are calculated through the construction of the criteria hierarchy
where the alternatives are placed at the lowest level. An element of the
hierarchy can only influence elements at the next higher level.
After the hierarchy elaboration, pairwise comparisons are
made among the different elements of the same level.
The pairwise comparison uses Saaty’s (1980) fundamental scale as a reference, presented
in Table 1. The resulting evaluations
of these comparisons are arranged in a reciprocal and positive square matrix.
Table 1: Fundamental
scale of AHP evaluations
Importance Strength |
Definition |
1 |
Equal importance |
3 |
Moderate importance |
5 |
Strong
importance |
7 |
Very
strong importance |
9 |
Extreme importance |
2, 4, 6, and 8 |
Intermediary
values |
Reciprocal
values above |
If
the comparison between criterion i and criterion j is one of
the values above, then the comparison between criterion j and
criterion i
will be reciprocal. |
Source: SAATY, (1980)
From
this matrix, it is possible to obtain an eigenvalue
of priorities that represents each criterion’s relative priority. According to Saaty
(1980), the eigenvector of priorities w must be calculated, becoming reciprocal to the sum of the elements of each column and
dividing it by the sum of the reciprocals of each column. Next, the consistency ratio (CR) is calculated, according to Equations (1), (2),
and (3).
(1)
(2)
(3)
In Equations (1), (2), and (3), n denotes
the number of criteria, A refers to the matrix of comparisons by pairs, λmax
signifies the highest eigenvalue of matrix A, CI
represents the consistency index, and RI pertains to the random index.
Inconsistency is admitted to a certain extent since the AHP
is not based on the transitivity principle. For this
reason, the determination of the CR is fundamental. Saaty (1980) presents the values of the RI to
some values of n,
calculated by the National Laboratory of Oak
Ridge. Ideally, the CR should be zero,
but an inconsistency of up to 10% may be tolerated.
In case the percentage is over 10%, the
pairwise comparisons should be reviewed (SAATY,
1980).
Finally, after the hierarchy construction, the acquisition
of relative weights through the pairwise comparison and the verification of
model consistency, the overall value of each alternative
can be calculated by the sum of grades in each criterion multiplied by
the relative weight in each criterion (SAATY, 1980).
The ANP is a
generalization of the AHP, in which the problems of the decision are
represented through networks composed of elements in clusters that are interconnected
internally or externally. There may be an interaction or a dependency among
those elements. This method uses pairwise
comparisons and a fundamental scale, such as the AHP
(SAATY, 1980; SAATY, 2009).
In the real world, almost everything is interdependent to
a higher or a lower degree. This means that the ANP
structure is more associated with reality.
Nonetheless, the difficulties with feedback—inherently cyclical—exceed the
problem’s structuring, which makes the calculation of priorities in the ANP
more complex, requiring much effort to justify the results’ validity (SAATY, 2009).
The ANP uses the concept of the supermatrix
to synthesize the impact
of the elements on each other. Consider a
network with N
clusters (CN), in which each cluster i contains ni elements (ei).
Figure 1 represents the supermatrix (W) resulting
from the model, which synthesizes the relative influence of one element from
the left on one element from the top, in terms of a certain control criterion. Each element of the supermatrix is a submatrix (Wij),
whose columns represent the relative influences of the elements in cluster Ci
on each element in cluster Cj,
in terms of a certain control criterion.
Figure 1: Graphic representation of the
supermatrix generated in the ANP
Source: SAATY (2008).
The pairwise comparisons of the clusters’ elements result in an unbalanced supermatrix. The clusters’ matrix is then obtained from pairwise
comparisons among the clusters. Next, the
balanced supermatrix is obtained by multiplying each element (Cij)
of the clusters’ matrix by the corresponding elements from the unbalanced
matrix (Wij).
This operation ensures that the balanced
supermatrix is stochastic, that is, the sum of the elements of each column is
one (SAATY, 2008).
Subsequently, the limit supermatrix is calculated by raising the
balanced supermatrix to the umpteenth power to obtain convergence in the values. The previous balancing stage guarantees the limit
supermatrix convergence. The last stage involves a sensitivity analysis
to verify the results of the variations in the assessments.
To
identify the state-of-the-art articles on the ANP and some relevant
applications in organizations, two databases were searched: Ebsco and Science
Direct. The key phrase used was “analytic network process,” which generated
over 250 articles (combining both databases). The results were filtered by
relevance, and nine were chosen among the first fifty results after scanning
the abstracts and identifying relevant application to the organizations.
The
articles’ topics ranged from the stakeholders’ influence on project management
to the assessment of wastewater treatment alternatives, including logistics
site selection and international contractor rating, among others. As mentioned,
no research relating contract strategy to MCDM was found. (For further
information on the applications, refer to HSU et al., 2012; ERGU et al., 2014;
ÖLÇER; AKYOL, 2014; BOATENG et al., 2015; NEUMÜLLER et al., 2015; SENANTE et
al., 2015; OCAMPO; SEVA, 2016; PEKER et al., 2016; BELTRAN et al., 2017.)
4. METHODOLOGY
This study
employed both a qualitative and an analytical approach. The research method used
was the case study, which was carried out through document analysis and focused
on the participants’ perspectives on a certain problem,
not on the researchers’ or the literature’s interpretation (STAKE, 1995; CRESWELL, 2010).
The
ANP was chosen for several reasons. These included the network approach, which
allowed the modeling of problems where many elements influenced one another and
entailed interactions among the decision
makers for the model structuring, enabling adaptations to the problems. Lastly, Saaty’s (1980) fundamental scale was also
useful because the existing verbal correspondence was adequate to assess the
elements involved in the contract strategy definition.
This
section shows the proposed network to define the project’s contract strategy
using the ANP. This model is the outcome of the
literature review and the authors’ experience
in this subject. It is used in the case study presented in the following
section, with the aid of the Super Decisions software
(CREATIVE DECISIONS FOUNDATION, unknown date).
The
created model has
six clusters: objective, project characteristics, organizational
structure, contract types, tendering procedure, and alternatives. The control criterion in this network is the organization
responsible for contracting. Specifically, all the comparisons among different
elements and clusters are about the influence of one over the other in the
organization. Figure 2 presents the clusters and the elements
of the proposed network.
In cluster 2, “Project characteristics,” the elements’ cost, time,
and performance represent the project’s approved budget, the deadline for
starting the operations, and the scope and quality demands, respectively.
In cluster 3, “Organizational structure,” the ease in identifying the responsible party
represents the time and the necessary resources to identify the company responsible for
failures or flaws. Flexibility is associated with the contractual tolerance to changes,
which indicates client favorability when negotiating
scope alterations. Control refers to the client’s need or desire to control the design or the
execution activities.
The
use of internal resources is related to the availability and the extension of allocating internal resources for the project
activities. The number of interfaces is about dividing
the project scope into contract packages.
Competitiveness is associated with the number of companies that are able
to execute the contract’s scope.
Cluster 4, “Contract types,”
comprises six elements. The contractor’s discipline pertains to the level of commitment expected from
this company in terms of fulfilling the contract.
Risk transfer refers to the client’s tendency to pass on the project’s
main risks to the contractor, either due to the inability to manage these risks
or to strategic decisions. Established price represents the client’s inclination
to sign a contract, knowing its price.
Experience
in contract management denotes the level of experience of the team responsible for managing the contract, which may be
relevant for contracts with a high competition among bidders. Experience in project management measures the client’s
level of knowledge on this matter, especially the experience in managing
activities executed by third parties. Incentives are associated with the intention of inserting elements
in the contract that represent motivations for the contractor to finish the project or meet a certain
deadline.
Cluster 5, “Tendering procedure,” has only two elements: the
contractor’s prequalification and the evaluation procedure. The first refers to the need to limit the participation
in the tendering procedure to the companies selected based on specific
requirements. The second represents the way that
these proposals will be classified (best price, best technique, the combination
of these two criteria, or others) and defines
how this analysis will be conducted.
Lastly, cluster 6, “Alternatives,”
presents the possible options for the contract strategy. The traditional possibilities for organizational
structures in different contract strategies are included in this cluster (turnkey, EPC, two-stage contracting, management, BOOT, design,
build and operate [DBO], and design, build, finance,
and operate [DBFO]), instead of the alternatives themselves.
Nevertheless, the specific alternatives are presented in the case study.
Figure 2: Representation of the network proposed using the Super Decisions
software
5. CASE STUDY AND DISCUSSION OF RESULTS
The
case study involves the analysis of the process of choosing the contract
strategy for a project by a Brazilian O&G company. It is important to
highlight the times faced by the O&G companies upon seeing the price of oil
plummet from more than US$100 a barrel in 2014 to US$70 a barrel in 2018, with
registered prices lower than US$30 a barrel some time in 2016 (U.S. ENERGY INFORMATION ADMINISTRATION, 2018).
This
dramatic slump in the oil price required the
companies’ revision of their projects’ portfolios (among other actions),
suiting these to the new reality and preparing
for a long period of low oil prices.
The
definition of the contract strategy was developed in this environment of
investment revision. For this purpose, a WG
was created to recommend the best contract strategy for a project to the
decision makers. According to the document that
formally created the group, its responsibility was to “analyze the
supply record, future demands and suppliers, as well as develop actions and
levers for value generation, considering maximum adherence to the business plan, minimum consumption obligation, local content,
procurement alternatives, risk evaluation and other actions/levers
for value generation.” The document also highlighted
the urgency of reducing the investment in the current business plan and
the need to study alternatives that considered partnerships and better tax solutions, reinforcing
cost as a fundamental variable in the contract strategy.
A
previous WG had been created—with some members also belonging to the second WG—and its work had already been concluded and
evaluated two alternatives, A and B, for the contract strategy of the given project. In alternative A, the
asset belonged to the client, whereas in the
other alternative, the asset belonged to the contractor.
Alternative B was a leasing. However, alternative A was discarded in this second WG for involving a considerable
disbursement over a short period, which was not included in the company’s
business plan at that time. Alternative B was also
eliminated for impacting the company’s leverage.
Therefore, after evaluating the project and the restrictions,
this second team dedicated itself to describing and analyzing two other
alternatives, C and
D. They represented a provision of a service contract and a rent
contract, respectively.
On
one hand, alternative C was considered the base case and represented the status quo.
The company used to work with it, so there was an advantage in contract
management compared with alternative D, the rent
contract. Nonetheless,
alternative C incurred a much higher cost than alternative D did. Alternative C’s cost was also higher compared with
those of alternatives A and B. However, disbursements would occur some years
ahead and did not affect the company’s debt, which was why alternative C was
not discarded.
On
the other hand, alternative D was new to the company. Therefore, the decision
makers requested an in-depth risk evaluation of this strategy when the support of experts from the legal, tax,
and risk departments was needed.
On
another front, the second WG contacted potential suppliers to map the market’s
interest in the project and its preferences. It would
thus be possible to compare the two alternatives in terms of contract
attractiveness/competitiveness.
Furthermore, the difference between the contract models
(service provision and rent) imposed operational differences. The reason was that the service provision model entailed
the definition of service-level agreements
(SLAs) with the establishment of the main conditions for the service provided. Nevertheless, since it was a
long-term contract (more than 10 years), there was
considerable uncertainty about what this operating
model would become at the end of the contract. Many disruptive applications
could arise and would not be covered in the original
SLA. Thus, alternative C involved less
operational flexibility compared with
alternative D. In the latter, the client was responsible for controlling the asset, without the need for SLAs.
The
project schedule was another aspect evaluated by the group. In this case, the rent
implied a deadline extension (one month more
than that of the service provision) since it would be necessary to elaborate
new and specific contractual documentation.
After
observing the above-mentioned aspects, the following criteria were established
to evaluate the most suitable contract strategy: legal risks, updated expenditure, attractiveness, number of contracts,
operational flexibility, and project completion.
A
color-code table (Table 2) was used
to illustrate the comparison between the alternatives, indicating each one’s
favorability. The colors green, red, and yellow
indicated high, low, and medium favorability, respectively. Some information in Table 2 was slightly altered
to maintain confidentiality. Based on these
pieces of information, the WG recommended alternative D, mainly because of the
significant difference in cost. The alternatives had no disparities in the
other criteria.
The
evaluation on the WG’s methodology and conclusions indicated that although
there were six evaluated criteria, the cost had the most considerable influence. Furthermore,
the fact that the two alternatives showed similar results when evaluated in the
other criteria reduced these criteria’s importance relative to the cost. Despite the group’s coherent conclusion, the tool it
used did not point directly to the recommended alternative.
Table 2: Representation of the
color-code table elaborated by the WG
Criteria |
Service provision |
Rent |
Legal risks |
No risk |
Fine equivalent to 6.5% of
the cost |
Cost |
151% |
100% |
Attractiveness |
9 interested
companies |
6 interested
companies |
Contracts |
1 bidding (2 contracts) |
3 biddings (5 contracts, 2 of
which must be open for a new bidding every 5 years) |
Operational flexibility |
Need
for contractual negotiation in case of SLA
deviation |
Easy to upgrade and increase capacity,
besides synergy with existing assets |
Project completion |
28 months |
29 months |
Next,
the same problem was assessed with the model proposed in this article. It showed
that the transparency stemming from the use of a
system that would aid decision making could help the group reach its conclusions,
as well as promote third-party visualization and understanding of the decision
process. To verify the criteria’s
applicability, the authors identified (from the available documents) the basis
for associating the criteria with each cluster, as well as the identified
alternatives (C and D). Table 3 describes the main characteristics of the four
mentioned alternatives, in conjunction with the pertinent alternatives used in the case study.
Figure 3 illustrates the model applied in the case study.
The authors relied on their own experience to make
pairwise comparisons between the criteria and the alternatives. Relying on the
authors’ expertise limited the analysis since it could not be implied
that the WG would produce the same comparisons. In any case, this approach is
still valid when showing the benefits of its use.
Table 3: Description of alternatives
in the case study
Alternatives |
Description |
A:
Own the asset |
Standard turnkey contract. This alternative was rejected
because it needed investments that were not included in the company’s
business plan. |
B:
Leasing |
In
this model, the contractor is the asset’s owner and responsible for its
construction and maintenance. It is similar to
the build, own, and operate (BOO) model, but the
client controls the operation. This
alternative was eliminated for affecting the company’s leverage. |
C:
Service provision contract |
This
model resembles the design, build, finance, and
operate (DBFO) type. A multi-user model, it
has well-defined and strict service-level agreements (SLAs). |
D:
Rent contract |
In
this model, the contractor is the asset’s owner and responsible for its
construction and maintenance, similar to leasing.
However, only a part of the constructed asset is rented to the client, who is responsible for operating this rented
portion. |
The
influence between the elements and the pairwise comparisons are valid only when
done by experts with recognized experience and competence in the subject. Ideally, the decision-making
agents should have experience and technical knowledge about the subject
matter. The more experienced the group is, the
better and more trustworthy the results will be.
After
defining the dependence between the elements, a pairwise comparison between the
elements and the clusters was made. It was necessary
to make 286 pairwise comparisons due to the number of network elements
and their dependence. All consistency indexes of the submatrices were all below 10%,
according to Saaty’s (2008) recommendation. The unbalanced, balanced, and limit
supermatrices are listed in Tables 4, 5, and 6, respectively.
Figure 3: Representation
of the contract strategy selection model using the
Super Decisions software with the ANP applied
in the case study
In
Table 4, the last two columns of the unbalanced
supermatrix (alternatives under evaluation) show that for service provision,
the most relevant criteria of the clusters “project characteristics,” “organizational structure,”
“contract types,” and “contracting process” are time (74.47%), competitiveness (38.79%), established price
(28.86%), and criterion of evaluation (66.67%),
respectively. For the rent alternative, the
most relevant criteria are cost (71.47%),
flexibility (34.81%), established price (29.37%), and contractor’s prequalification (75%).
Of
the six criteria used by the WG, five are listed in the preceding paragraph: time, competitiveness, costs, risks, and
flexibility. This
finding indicates that the criteria chosen by the group are the most
relevant for these alternatives.
In
its last rows, the unbalanced supermatrix also reveals the comparisons between
the two alternatives in each criterion. For
example, these data show a preference of over
75% for rent in terms of the cost, performance, flexibility, and control
criteria. Likewise,
service provision has a preference of over 75% in the following
criteria: ease in identifying the responsible party, use of internal resources,
number of interfaces, competitiveness, and experience in contract management. This first evaluation demonstrates a certain
balance between the alternatives since service provision is preferable in seven
criteria, rent is preferable in six, and there is no difference among the other
four criteria.
The
last two columns of the balanced supermatrix (Table 5) indicate that for
service provision, the most relevant criteria
are time (46.98%),
performance (9.40%), and cost (6.71%). As for rent, the
most relevant criteria are cost (45.09%),
performance (13.78%), and established
price (5.61%).
Lastly, in Table 6 the limit supermatrix columns (where it
is possible to prioritize the alternatives) show that
the criteria with higher relative weights are cost (15.32%), performance (11.30%), time (7.14%), control (6.53%), number of interfaces (5.76%), incentives
(5.64%), risk transfer (5.14%), ease in
identifying the responsible party (4.76%), flexibility (4.09%), established price
(3.55%), competitiveness (3.42%), experience
in contract management (2.97%), contractor’s
prequalification (2.89%), contractor’s
discipline (2.79%), experience in project
management (2.10%), use of internal resources (2.00%), and evaluation criterion (1.28%).
The
sum of the six criteria used by the WG—cost, number of interfaces, ease in
identifying the responsible party, performance, control, flexibility, time, and
competitiveness—represents 58% of the total
preference. This figure demonstrates the relevance of the criteria used
by the WG. However, the remaining percentage (42%) cannot be overlooked. Therefore, the results demonstrate that the other
criteria also contribute to the selection of the contract strategy.
The
limit supermatrix’s last two rows present the final prioritization
of the alternatives: service provision (0.056323) and rent (0.077008). When the values are normalized, the
final prioritization vector can be obtained. Its values are 57.7569% preference for rent and 42.2431% preference for service provision, illustrated in Figure 4.
Thus, the simulation indicates that the
preferred alternative is rent contract, supporting
the WG’s recommendation. The simple fact that the ANP produces this
clear recommendation to the decision makers already indicates an improvement in
the organization’s decision-making process since the color-code table used by
the WG does not directly express its recommended alternative.
Figure 4: Prioritization of alternatives obtained with
the Super Decisions software
Subsequently, a sensitivity analysis
was carried out, removing the influence among clusters but keeping the
previously defined internal dependencies. In
this way, the model would be close to a hierarchy, similar
to the AHP. Therefore, it would be possible to
evaluate the results of both the ANP and the AHP applied to the case study.
Figure
5 presents the sensitivity analysis results. Although
the results shown in Figure 4 are not altered, there is a considerable
change in the prioritization of the alternatives. In
the base case, the preference for rent is approximately 58%, whereas in
the hierarchy setting (sensitivity analysis), this preference is approximately 64%.
Figure 5. Prioritization of alternatives obtained with
the sensitivity analysis
Based
on the exposed information, it is possible to state
that the model for selecting contract strategies is valid for this case
study since it supports the WG’s conclusions.
Besides, the use of the ANP facilitates the visualization of the WG’s
recommendation, as well as which criteria have exerted more influence on the
WG’s selection decision. Moreover, when comparing the criteria used by
the WG with those from the model, the latter set is more complete. The proposed
model thus enables the analysis of different cases, where the alternatives may
differ from those in this case study.
Furthermore, the WG recommended the rent contract mainly “due
to the significant difference of cost, since there
were no great disparities in the criteria between alternatives.” The results of the simulation also support this fact.
Additionally, the WG’s report mentions that for the rent contract, the “operational flexibility of this model reveals
itself as a great advantage in this setting,” which
is also reflected in the simulation. These
facts indicate that the simulation supports the WG’s
main conclusions.
It is
also important to highlight that the prioritization of the alternatives may
initially indicate a weak preference for the rent contract instead of the service
provision since the values shown in Figure 4 are close.
However, the alternatives are equivalent in four criteria (contractor’s discipline,
risk transfer, incentives, and evaluation procedure), and there is a weak preference for the other four (time, established price, experience
in project management, and contractor’s prequalification).
Certainly,
it balances the results of both alternatives.
Moreover, it indicates that the simplification proposed by the WG has no
influence on the decision in this specific case study since the
alternatives are similar in eight criteria, which represents approximately 30% of the total preference, according to the limit
supermatrix.
Moreover, the sensitivity analysis allows the verification
of the impact extension in the results, in
case hierarchical modeling is adopted. Although it
does not alter the recommendation, this change results in different priorities
between the alternatives, which could influence the decision analysis.
The
implementation of a multicriteria method, such as the ANP, allows in-depth
conclusions, explicitly indicating which criteria have more influence on the selection
decision, as can be extracted from the supermatrices. Thus, this selection
becomes clearer and stronger. The proposed model also minimizes the possibility
of excluding relevant criteria compared with the simplified model used by the
WG.
Table 4. Unweighted
supermatrix obtained from the ANP
Table 5. Weighted supermatrix
obtained from the ANP
Table 6. Limit
supermatrix obtained from the ANP
Finally,
it is important to note that the generalization of the model for selecting
contract strategies for projects in any context does not belong to this article’s
scope. It is suggested that further studies implement the model in different
cases, with varying alternatives to test its
validity. Moreover, research can be carried out
by adapting the proposed model to investigate the effects of including or
excluding specific criteria.
6. CONCLUSIONS
As previously stated, the selection of a contract
strategy for a project is fundamental to its success. Therefore, it is a
decisive factor for its completion in a timely and cost-effective manner with
high-quality outcomes. Thus, the development of methods that improve this
process is relevant for organizations.
The theoretical background has emphasized the influence
of the contract strategy on project management and risk management. Furthermore, it has defined
the main organizational structures in current contracts, which directly affect how the stages of design and
project implementation will turn out. Two types of
contracts have been identified, based on price (e.g., lump sum) or on
cost (e.g., cost-reimbursable contracts), which differ according to the payment
method. Lastly, the
main stages of the process that should be defined have been revealed, including the importance of the bidder’s
prequalification and the definition of a clear evaluation
criterion.
Moreover, the basis of the ANP has been presented. It
allows the representation of dependencies and feedback across different
criteria and alternatives, which are divided into clusters. Some of the cited state-of-the-art articles show a few
relevant applications of this method for organizations.
The presented methodology involves the proposed model for
selecting contract strategies for projects, using the ANP and its
implementation in a case study. The model’s criteria are in accordance with the aspects discussed in
the theoretical background and presented in the literature review. As for the case study, it aims to verify if the
implementation of the model would support the decision made and if there
would be benefits of its use.
The criteria used by the WG responsible for analyzing the
problem in the case study and the proposed criteria are correlated. The results
of the simulation have led to the same recommendation, with the advantage of
facilitating its visualization. The proposed model is also more complete than
the one used by the WG, but it does not affect the
results because both alternatives are equivalent in the additional
criteria. Therefore, the results show that the
model is valid for this case study and beneficial for the decision process in
terms of improving transparency.
For future studies, it is suggested
that case studies with different alternatives be analyzed to test the validity
of the model. The model could also be modified to investigate the effects of
including or excluding some criteria.
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