APPLICATION OF THE ANALYTICAL HIERARCHICAL PROCESSES (AHP)
ALGORITHM IN PROJECT MULTI-CRITERIA DECISION-MAKING: A CASE OF
PLAN INTERNATIONAL ZIMBABWE
By
Marshal Paradzai Machikiche Kumire
C18135814V
A Research Proposal Submitted in Partial Fulfilment of the
Requirements for the Degree of
Master of Science in Data Analytics
Graduate Business School
Chinhoyi University of Technology
Zimbabwe
Engineer Mastara
1.0 Introduction
The study advocates the application of the AHP algorithm in project multi-criteria decision
making. Specifically, the study sought to apply the analytical hierarchical processes in project
multicriteria decision making using a case study of Plan International Zimbabwe. This
chapter presents the background of the study and the main problem that motivated the study.
The objectives as well as the questions of the study are as well laid out in the chapter. The
chapter also justifies why conducting such a study is of importance to the nation at large as
well as specifically to organisations such as Plan International.
1.1 Background
Multi-Criteria Decision Analysis (MCDA) is a broad framework for assisting complicated
decision-making circumstances with various and frequently competing outcomes evaluated
differently by stakeholders, groups and/or decision-makers. A project is defined as "a
temporary initiative conducted to provide a one-of-a-kind product or service." The
deployment of information, skills, tools, and procedures to project activities to achieve
project requirements is therefore defined as project management. This knowledge application
necessitates the efficient administration of suitable procedures' (PMBOK Guide 2008). The
reason of at least one of the conditions such as project goals, resources, and/or environment,
each project is made unique and outstanding. This makes project management a difficult task
(Vidal, 2011), with several phases and procedures that are inextricably linked to multicriteria
decision-making. Various scenarios and challenges arise during the project that need a project
manager (or other person responsible for the particular subject) to select the optimal decisionmaking option from a collection of options while weighing a number of essential factors
(criteria). Purchasing fixed assets, accepting a tender, and accepting an investment choice are
just a few examples.
Because of its direct influence on corporate profitability and maintaining a competitive
position, systematic selection is thus regarded as one of the most crucial criteria for every
organization (Memari, Dargi, Jokar, Ahmad & Rahim, 2019). According to Ding, Dong, Bi,
and Liang (2015), in the modern economy, which is characterized by intense competition,
low profit margins, shifting consumer preferences, high-quality products, high delivery
reliability, and short lead times, among other factors, it is apparent that optimized decisionsupport systems, rather than the companies themselves, are the de facto competitors. Given
the importance of decision support systems, organizations may be compelled to seize every
chance to improve their overall performance. Materials expenses often account for a bigger
share of total costs for organizations and projects that invest major sections of their budgets
in raw material supply. Furthermore, excellent judgments, in addition to cost, time, and
quality management, are critical in overall project management, necessitating the need to
optimize selection in project management decisions (Luthra, Govindan, Kannan, Mangla,
Chandra & Garg, 2017).
The decision-making process in project management is considered complex, consisting of
several tasks such as problem definition (identification of needs and specifications),
formulation and selection of evaluation criteria, evaluation and ranking of potential choices
relative to specific criteria and importance, and finally evaluation and final selection of the
decision (Petrović, Mihajlović, Ćojbašić, Madić & Marinković, 2019). The rigor of all the
different phases in the screening process influence the nature or caliber of the final choice.
Establishing and choosing an effective assessment criterion that incorporates all necessary
parts of the screening process, as well as the selection of techniques for ultimate decision
making (selection), are two such processes that are extremely important (Petrovi et al., 2019).
The decision-making process is thus depicted as a multi-criteria decision-making issue due to
its complexity.
Decision-making in project management is characterized by relatively rigid regulations,
necessitating methodical selection. Transparency, impartiality, independence, and nondiscrimination, for example, need the decision-maker openly stating the selected selection
technique, decision criteria, and their relative importance (Dotoli, Epicoco & Falagario,
2020). One of the key goals of optimum selection is to reduce costs through competition,
promote transparency, and reduce corruption in order to improve service delivery
effectiveness. In fact, most project routines are created for problem-free and smoothly
working settings, despite the relevance of optimal selection in every organization and the
requirement for cautious selection (Malik & Sarkar, 2020). However, differences in the
socio-political context, underlying economic circumstances, and technical environment of
distinct nations, as well as project legal frameworks, have a significant impact on project
deployment and outcomes (Dzuke & Naude, 2017).
Other than the multi-criteria challenge in project management, poor choices in unpredictable
economic settings like Zimbabwe might result in a loss of return on investment or other
obstacles like project interruptions. For example, numerous service providers in Zimbabwe
have lately been criticized for failing to implement programs and initiatives (Dzuke & Naude,
2017). Project interruptions are expensive, and recovering from them may incur additional
costs for the organization (Malik & Sakar, 2020). The additional effects of COVID-19 on
operations need prudent decision-making. Procurement is expected to account for 18 percent
of global GDP, or USD 5.8 trillion on average, yet an estimated USD 400 billion is lost due
to inefficient project portfolio managers. Furthermore, approximately 70% of projects in SubSaharan Africa are impacted by corruption, which drives up contract prices by 20% to
30% (Bawole & Adjei-Bamfo, 2020).
Despite these facts, the selection process followed in project decision-making are unclear in
Zimbabwean academic literature. In the context of a multi-criteria problem and
environmental uncertainty, project interruptions indicate the use of ad-hoc decision-making
methods, which are biased (Memari et al, 2019). While literature from advanced economies
has established successful multi-criteria decision-making procedures, Zimbabwean literature
on multi-criteria decision-making and uncertainty remains scarce. For example, studies of
Mutava (2012), and Owuoth & Mwangagi (2015) have established the procurement
efficiency impacts on supplier service delivery, Musanzikwa (2013) and Tsabora (2014) have
focused on implementation of government projects and service delivery, Dzuke & Naude on
procurement challenges in Zimbabwean public sector, and Dzuke & Naude (2017) on
operational procurement processes. Matters pertaining to optimal decision-making in project
management in Zimbabwe however remain unaddressed. It is against this backdrop that the
study seeks to apply the AHP algorithm in multi-criteria decision making for project
management
In academic literature, the AHP is regarded as one of the most effective and widely used
(Govindan, Rajendran, Sarkis, & Murugesan, 2015; Zimmer, Fröhling, & Schultmann, 2016).
Saaty (1986) invented the AHP as a decision-making tool. It seeks to quantify relative
priorities for a given set of alternatives on a ratio scale, based on the decision-opinion,
maker's and emphasizes the relevance of the decision-intuitive maker's judgements as well as
the consistency of alternative comparisons in the decision-making process. The AHP method
aligns well with decision-maker behavior because a decision-maker relies judgements on
knowledge and experience before making decisions. The merit of this technique is that it
integrates physical and intangible aspects in a systematic manner, resulting in a structured yet
relatively straightforward solution to decision-making difficulties. Furthermore, by breaking
an issue down logically from the huge to the smaller and smaller, one may relate the little to
the large using basic paired comparison judgements.
1.3 Research Problem
Plan International Zimbabwe (PIZ) has had poor project implementation and project overruns
in recent years, despite high procurement costs. There have been major delays, inability of
contractual suppliers to deliver as per service level agreements, project budget overspends,
and projects failing to meet goals as planned in the project plan. PIZ's project decisionmaking process frequently takes into account a variety of opposing considerations, and as a
result, uncertainty influences the whole decision-making process. The procedure is deemed
complicated since it involves various tasks and multiple selection criteria, some of which are
contradictory. Some of its project management decisions may result in a loss of return on
investment (ROI). Apart from the aforementioned issues, the multi-criteria decision-making
challenge in the Plan International project is exacerbated by the nature of the business
environment, which is marked by unpredictability (CZI, 2020). Despite the obvious
requirement for a systematic project multi-criterion decision making, the use of optimisation
algorithms such as AHP at PIZ and in Zimbabwean literature is yet unknown.
1.4 Research Objectives
Aim
The aim of the study is to apply the AHP algorithm in project multi-criteria decision making
using a case of Plan International Zimbabwe
Research objectives
1. To identify the multi-criteria decision-making process at Plan International Zimbabwe
2. To demonstrate how the AHP algorithm can be applied at Plan International
Zimbabwe in multi-criteria decision making
3. To evaluate the performance of the AHP algorithm in multi-criteria decision-making
relative to currently available methods at Plan International Zimbabwe
Research Questions
Main Research Question
Can the AHP algorithm help resolve the multi-criteria decision-making problem in project
management in organisations such as Plan International Zimbabwe?
Secondary Research Questions
1. What is the multi-criteria decision-making processes at Plan International Zimbabwe?
2. How can the AHP Algorithm be applied at Plan International Zimbabwe in project
multi-criteria decision making?
3. To what extent is the AHP algorithm effective in multi-criteria decision-making?
1.6 Significance of the Study
1.6.1 Significance to Practice
Because optimum selection is directly related with profitability and competitiveness, Plan
International Zimbabwe can expect optimal decision-making to boost project return on
investment (ROI) and effective processes (Memari et al., 2019) after the successful
completion of this study. Ding et al. (2015) argue that optimized processes, rather than
companies, are the de facto competitors, especially in uncertain economic environments like
Zimbabwe's, which are marked by high competition, low profit margins, changing consumer
preferences, high-quality products, good delivery reliability, and short lead times, among
other things. According to the resource-based view theory, if organizations have valuable,
uncommon, unique, and non-substitutable decision support systems through project optimum
selection, they become a source of competitive advantage that may lead to positive
operational results and long-term value (Ding et al., 2015). Problems such as incorrect project
selections or supply chain interruptions can be avoided by selecting projects and providers in
a methodical manner and therefore eliminating project risks (Malik & Sakar, 2020).
Significance to Project Managers
If numerous project or firm objectives are adequately incorporated in project management
choices, project managers and decision support systems managers can save precious project
resources such as time and money (Luthra et al., 2017). Multiple stakeholder objectives such
as environmental sustainability, legal compliance, and corporate social responsibility may as
well be met using multi-criteria decision analysis (Memari et al, 2019), hence boosting
company value as envisaged under the stakeholder theory of good governance.
1.6.2 Significance to Academia
The work contributes to the field by putting the optimal project decision making under multicriteria circumstances in a Zimbabwean setting. The selection criteria employed in project
management are unclear in Zimbabwean academic literature. While research from rich
nations has proven useful methodologies, the application of the AHP algorithm in project
multi-criteria decision making in Zimbabwe is restricted. Studies of Mutava (2012), and
Owuoth & Mwangagi (2015) have established the procurement efficiency impacts on
supplier service delivery, Musanzikwa (2013) and Tsabora (2014) have focused on
implementation of government projects and service delivery, Dzuke & Naude on
procurement challenges in Zimbabwean public sector, and Dzuke & Naude (2017) on
operational procurement processes. Matters pertaining to optimal project management
decision-making in Zimbabwe however remain unaddressed.
1.6.3 Significance to Policy
The study is relevant to policy makers in that systematic decision-making methods eliminates
human bias and thus decisions become more likely to be selected on the basis of merit rather
than bribes or nepotism. This is consistent with national development strategies as
documented in Zimbabwe’s vision 2030 aimed at increasing national transparency. One of
the most common complaints about ad-hoc project decision-making is that it allows for
prejudice (Memari et al., 2019). As Bawole and Adjei-Bamfo show, this leads to a lack of
openness, which is likely the major driver of corruption in public procurement in
impoverished nations like Zimbabwe (2020). Procurement is expected to account for 18
percent of global GDP, or USD 5.8 trillion, yet it is claimed that an estimated USD 400
billion is lost due to wasteful project decisions. Furthermore, approximately 70% of projects
in Sub-Saharan Africa are impacted by corruption, which drives up contract prices by 20% to
30%. (Bawole & Adjei-Bamfo, 2020).
1.7 Scope of the Study
The study aimed at applying the AHP algorithm in multi-criteria decision making for project
management. The study was thus delimited by geography, time, theory and methodological
scope.
Geographical Delimitation
Geographically, the study was mainly focused on a developing context specifically
Zimbabwe. All data used in this study is of a Zimbabwean origin. Survey data was obtained
from project managers and supply chain managers of Plan International in Zimbabwe.
Time Delimitation
In relation to time, the study is cross-sectional case based.
Theoretical Delimitations
Theoretically, the study paid particular attention to project management literature and theory
under multi-criteria decision-making.
Methodological Delimitation
While there a plethora of methods and algorithms that can be applied in a multi-criteria
problem, the study pays particular attention to the popularly used algorithm in literature –
which is the AHP algorithm.
1.8 Dissertation Outline
The study is arranged into chapters. The present chapter introduced the study and offered
background information. The research problem was established, the study objectives and
questions were identified, and the research justification was discussed in this chapter. The
review of literature and relevant work by other scholars is included in the second chapter. The
approach and philosophy used in the research exercise are described in Chapter 3. The
sample strategies and data gathering methodologies are also described. The fourth chapter
examines and summarizes the research findings. The dissertation concludes with Chapter 5,
which summarizes the findings from Chapter 4 and offers management advice as well as
proposals for further research.
1.9 Chapter Summary
The history of the study, the statement of the problem, the research objectives, the research
questions, and the importance of the study in relation to the development of the AHP
algorithm that systematically selects choices in a project multi-criteria decision-making
problem were all covered in this chapter. The assumptions, delimitation, and limitations of
the research were also illustrated in the chapter, which also included a glossary of words. The
empirical and theoretical literature review on project multi-criteria decision making is the
subject of the next chapter.
CHAPTER 2: LITERATURE REVIEW
2.0 Introduction
This chapter presents theories, hypotheses, methods and results obtained in prior academic
literature on project multi-criteria decision making. The main aim was to identify gaps in
literature in relation to the use of multi-criteria decision-making models in project decisions.
Further, it explains the proposed machine learning model which is the Analytic Hierarchy
Processes (AHP) algorithm, with the aim of contextualising it to a case study of Plan
International Zimbabwe.
2.1 Multi-Criteria Decision-Making Concepts
This section of the study starts by explaining the term decision-making and later on other
concepts.
2.1.1 Decision Making
In general, decision-making refers to the process through which a person, group, or
organization comes to decisions regarding future actions based on a set of priorities and
resource constraints. This is often an iterative process comprising issue formulation,
intelligence collecting, reaching conclusions, and learning from past mistakes. In project
management, it is an iterative process in which the project manager considers several courses
of action from which to pick in order to meet the project's defined objectives while working
within resource restrictions (Project Management Institute, 2017). The Project Management
Institute (2017) defines it as tools and procedures for converting project inputs into project
outcomes.
Figure 2.1 Project Management Process
As shown by the figure, there are three decision making techniques that can be applied in
project management – voting, autocratic decision-making, and multicriteria decision analysis.
The main focus in this study is multicriteria decision making which is regarded as the most
complex.
2.1.2 Multi-Criteria Decision Making
The process of determining the best or optimum option from a group of viable options is
known as multi-criteria decision making (Dalalah, Hayajneh & Batieha, 2011). Munier,
Hontoria, and Jiménez-Sáez (2016) define decision-making as a human activity in which the
decision maker's alternatives are not restricted to a few, but rather include various elements
that must be considered. The pervasiveness of the multitude of elements utilized to grade or
appraise the available options is always the difficulty in a multi-criteria decision-making
dilemma. To determine the worth of each choice and the proportional significance of each
parameter in relation to the overall goal of the organization or the problem, the decisionmaker may need to offer qualitative and/or quantitative assessments (Dalalah et al., 2011).
Multi-criteria decision-making is complicated and difficult since it frequently takes place in a
hazy environment with unclear, imprecise, infinite, and subjective data, which can lead to
confusion among decision-makers (Dalalah et al., 2011).
2.2 Multi-Criteria Decision-Making Complexity in Project Management
The disparities associated with decision making and goal achievement that appear to be
connected to complexity propels project managers to have a good grasp of project complexity
and how it may be handled (Bjorvatn & Wald, 2018). Ever since projects have gotten more
complicated, there has been a growing worry over project complexity, and standard tools and
procedures created for small projects have been shown to be ineffective for large projects
(Bjorvatn & Wald, 2018). The value of complexity in the project management process is
widely recognized for several reasons. First, it aids in determining planning, coordination,
and control requirements; second, it obstructs the clear identification of major project goals
and objectives; third, it can influence the selection of an appropriate project organization
form and management personnel experience requirements; and finally, it can be used as a
criterion in the selection of a suitable project manager (time, cost, quality, safety, etc.)
(Overwijk, 2021).
In project contexts, there is a lack of agreement on what complexity truly is. Even yet, it
appears that there is no one definition of project complexity that encompasses the entire idea
(Morcov, Pintelon, Kusters, 2020). The number of various components in a system alone is
referred to as complexity, whereas the number of elements in a system plus the conceivable
relationships between these elements is referred to as complexity (Morcov et al., 2020).
Complexity is defined as the sum of the following components in Luhmannian system theory,
first, the distinction of tasks in a project amongst customers, providers, subcontractors,
suppliers, banks, and so on, or the company's internal differentiating (degree of
manifoldness); second, the interdependencies within supersystems, systems, and subsystems,
or among the latter (interrelatedness); and lastly, the decision field's subsequent influence or
procedures (Overwijk, 2021). Complexity can be made up of several different interconnected
aspects or the quantity and interdependencies of affecting factors (Locatelli, Greco,
Invernizzi, Grimaldi, & Malizia, 2021). Tatikonda & Rosenthal (2000) as cited in Zheng, Gu,
Luo, Zhang, Xie, & Chang (2022) consider complexity to include interdependencies between
product and process technologies, innovation, and goal difficulties. Complexity may be
characterized as information deficiency when too many variables interact, as well as a quality
of the system that makes it difficult to comprehend. Some writers regard complexity as a
component of uncertainty, and vice versa. With recent technological advancements and the
expansion of data, big data has become more complicated (Zheng et al., 2022).
2.2.1 Multi-Criteria Decision-Making Problem
Project management decision-making has arguably advanced (Munier et al., 2016). Project
management used to be focused on cost criteria (or economy), but in today's competitive
world, it's more likely to be based on multi-criteria judgments with trade-offs - conflicting
and competing options (Kaviani, Yazdi, Ocampo, & Kusi-Sarpong, 2019). For example, one
project may have a highly consistent delivery time but low-quality returns, while another may
have a very consistent delivery time but an unpredictable delivery time (Kaviani et al., 2019).
Other external issues, such as business environment uncertainties, may also interfere with
project particular issues. In fact, Dweiri, Kumar, Khan, & Jain (2016) suggest that uncertainty
must now be included as an intrinsic element of the decision-making process. Most projects
have always been planned for trouble-free, smooth-running settings. Natural catastrophes,
raw material shortages, transportation issues, and labor strikes are all examples of unforeseen
occurrences that can disrupt project cycles (Malik & Sarkar, 2020). The covid-19 pandemic,
which prompted numerous enterprises and manufacturing companies to suspend business and
operations in order to stop the virus from spreading, is the most recent project interruption.
Emerging trends also emphasize the importance of considering sustainable environmental
practices in project execution, as environmental issues have become increasingly important in
the corporate world (Memari et al., 2019). Given these considerations, project management is
regarded as a complicated process including numerous duties, which will be detailed in the
sections below (Petrovi et al., 2019).
2.3 Multicriteria Factors Affecting Project Decision Making
Projects exist and function in settings that have the potential to affect them. These factors
might have a positive or negative impact on the project (Scholl, LaRussa, Hahlweg, Kobrin,
& Elwyn, 2018). Enterprise environmental variables and organizational process assets are
two primary areas of effects (Alizadehsalehi & Yitmen, 2019). The environment outside of
the project, and frequently outside of the organization, is the source of enterprise
environmental elements. At the organizational, portfolio, program, or project level, enterprise
environmental variables may have an influence (Alizadehsalehi & Yitmen, 2019).
Organizational process assets are owned by the company. These might come from the
company itself, a portfolio, a program, a different project, or a mix of the above.
Organizational systems, in addition to business environmental variables and organizational
process assets, play an important part in the project's life cycle. People's power, influence,
interests, skills, and political capacity to act inside the organizational system may be
influenced by system elements (Scholl et al., 2018).
2.3.1 Enterprise Environmental Factors
Enterprise environmental variables are conditions that impact, restrain, or steer the project
that are outside the project team's control. These circumstances might be both internal and
external to the company (Vrchota, Řehoř, Maříková & Pech, 2020). Many project
management procedures, particularly most planning processes, use enterprise environmental
elements as inputs. These variables may help or hinder project management alternatives.
Furthermore, these variables may have a favorable or negative impact on the result. The types
and nature of enterprise environmental variables vary greatly. If the project is to be
successful, several considerations must be addressed (Vrchota et al., 2020). The internal and
external elements outlined below are examples of enterprise environmental factors.
2.3.1.1 Enterprise Environmental Factors Internal to The Organization
There are internal environmental factors within the organisation that the project manager
should take into consideration. The first factor that can be considered is corporate
governance, culture and structure. Examples include management style, hierarchical and
authoritative relations, corporate style, values, and standards of practice (Owuori, Ngala &
Obwatho, 2020). Secondly, a project manager may consider how the amenities and resources
are distributed geographically. For example, factory sites, remote teams, connected
infrastructure, and cloud computing (Sadeghfam & Abadi, 2021). The third aspect that can be
considered by the project manager is infrastructure. Infrastructure, for example includes
facilities provided, machinery, corporate communication systems, IT systems, accessibility,
and scalability (Urazova & Kotelnikov, 2020). IT Software for project use can also be taken
into consideration with aspects such as, computer programs for scheduling, system
integration, web connections to other online automation machines, and work authorisation
systems (Urazova & Kotelnikov, 2020). Another factor that can be considered is the
availability of resources taking considerations such as, contractual and procurement
limitations, permitted suppliers and subcontractors, and partnership contracts. Finally, a
project manager may take into consideration the human resources aptitudes, such as, existing
HR competencies, skills, competences, and expert training (Urazova & Kotelnikov, 2020).
2.3.1.2 Enterprise Environmental Factors External to The Organization
The macroeconomic factors should also be taken into consideration in project planning by the
project manager. The factors that may be taken into consideration include market
circumstances such as competition, market share, brand awareness, and trademarks (Hussain,
Fangwei, Siddiqi, Ali & Shabbir, 2018). Social and cultural effects such as political
atmosphere, norms of behaviour, ethics, and perceptions can also be taken into consideration
(Hussain et al., 2018). Other factors include the legal constraints such as security, data
protection, commercial behaviour, employment, and procurement; access to commercial
databases that contain comparative data, normalized cost estimate data, information from
industrial risk studies, and risk databases; academic studies such as Industry research,
publications, and comparative results (Mohammadi, Tavakolan & Khosravi, 2018). The
Industry or government norms such as state regulatory norms and standards in the areas of
products, production, the environment, quality, and craftsmanship; monetary considerations
such as currency fluctuations, interest and inflation rates, and levies.; as well as the
environmental physical factors such as working circumstances, weather, geographical
location, and restrictions should all be taken into consideration for successful project
implementation (Durdyev & Hosseini, 2019).
2.3.2 Organizational Process Assets
Organizational process assets are the strategies, processes, rules, protocols, and learning
structures relevant to and employed by the performing organization. These assets have an
impact on project management (Formby, Medlin, Chen, Shou & Charoen, 2019). They also
consists of any object, technique, or expertise from any or all of the performing organizations
engaged in the project that may be utilized to execute or control the project (Formby et al.,
2019). Lessons learned from prior projects and historical information, as well as completed
timelines, risk data, and created value data, are all part of the organizational process assets.
Many project management methods use them as inputs (Kumar, Kumar &Chand, 2019). Due
to the fact that organizational process assets are internal to the company, project team
members may be able to edit and add to them as needed all through the project. Processes,
policies, and procedures are classified into two parts: organizational knowledge bases and
methods, guidelines, and protocols (Winch & Cha, 2020).
2.3.3 Organizational Systems
Projects are constrained by the firm's structure and governance system. The project manager
should know where responsibilities, accountability, and authority are located throughout the
company in order to work successfully and efficiently (Winch & Cha, 2020). This knowledge
will aid the project manager in making efficient use of his or her authority, influence,
expertise, leadership, and political talents to execute the project successfully. Multiple
elements interacting inside a single company form a unique system that has an influence on
the projects that operate within that system (Winch & Cha, 2018). The authority, influence,
interests, expertise, and political capacities of the persons who may act inside the system are
determined by the organizational system. Management aspects, governance mechanisms, and
organizational structure types are only a few of the system variables (Cha, Newman &
Winch, 2018).
A system is a collection of distinct components that, when combined, may accomplish effects
that the individual components could not achieve on their own. A component is a
distinguishable piece inside a project or organization that performs a specific function or set
of related functions. The organizational culture and capabilities are created by the interplay of
the different system components (Winch & Cha, 2018). System concepts include the
following: Systems are dynamic, they can be optimized, their components can be optimized,
but they can't be streamlined simultaneously, and their reactivity is non - linear (The output is
not predictable given an alteration in the input) (Cha et al., 2018).
Numerous changes can happen inside that system as well as between the system and its
surroundings. Adaptive behaviour happens within the elements as a result of this shift, which
adds to the system's dynamism. The interactions between the elements, depending on the
interconnections and interactions that exist between them, establish the system's dynamics.
The administration of an organization is usually responsible for its systems (Winch & Cha,
2018). The organization's management analyses the optimum transfer between the elements
and the system so as to choose the best action for the organization's best results. The findings
of this investigation will have an influence on the project in question. As a consequence, it is
also critical that the project manager considers these findings when deciding how to achieve
the project's goals. Furthermore, the project manager must consider the governance system of
the organization (Cha et al., 2018).
2.3.3.1 Organizational Governance Frameworks
Administrative or architectural frameworks at all levels of an organization aimed to decide
and affect the conduct of the organisational stakeholders are referred to as governance
(Derakhshan, Turner & Mancini, 2019). This implies that governance is multifaceted,
requiring taking into cognisance people, responsibilities, frameworks, and rules, as well as
giving direction and monitoring through communication and insights. The structure under
which organizations exert authority is known as governance (Derakshan et al., 2019). Rules,
Regulations, Processes, Morals, Interactions, Systems, and Operations are all part of this
framework. This framework has an impact on how the organization's goals are created and
met, risk is managed and evaluated, and performance is improved (Derakshan et al., 2019).
2.3.3.3.2 Management Elements
Management elements are the features that characterize the organization's primary operations
or concepts of program management (Martens, Machado, Martens & de Freitas, 2018). The
general management components are distributed throughout the organization based on the
governance system and organization design type chosen (Stanitsas, Kirytopoulos &
Leopoulos, 2021). Management's core roles or principles include, but are not limited to:
Employment division based on specific skills and work availability; Authority to conduct
work is granted; responsibility for performing work is suitably assigned based on skills and
expertise. Action discipline (dutifulness, individuals, and rules, for example); Unity of
command (e.g., just one person delivers commands to an individual for any action or
activity); Directional consistency (– for example, one plan and one leader for a collection of
operations with the same goal); Individual ambitions take second place to the organization's
overall aims. Fairly compensated for task completed (Martens et al., 2018; Stanitas et al.,
2021).
2.4 Big Data and Multi-Criteria Project Management Problem
Big data (BD) is a successful approach for businesses to extract value from the growing
amounts of data created both internally and externally, which is defined by volumes,
variation, velocity, and value (Arvidsson, Jonsson & Kaipia, 2020). Big data analytics may be
defined as technology (database and data-mining tools) and procedures (analytical processes)
for analyzing massive amounts of complex data in order to affect corporate performance.
Under this concept, big data analytics includes high-tech data storage, management,
analytical capabilities, and visual technology (Arvidsson et al., 2020).
Big data is considered to have significant opportunities to aid project management, which
includes project activities, project selection, stakeholder relationship management, and supply
and logistics (Russo, Confente, & Borghesi, 2015). For instance, big data may be used to
assess different project risk scenarios, enhance data-driven project resource demand forecasts
for project planning, and better align internal and external project goals and procedures to
improve project management processes (Russo et al., 2015). Nevertheless, the volume of
information and data generated by, obtainable to, and accumulated, as well as the pace at
which data accrues and the various forms of such data, presents an obstacle for businesses, as
it makes it more difficult to identify and retrieve the most relevant data required for project
management (Kache & Seuring, 2015). Lamba, Singh, & Mishra (2018) present a framework
for integrating project management with Big Data analytics. The suggested framework
provides several approaches for incorporating both inter and intra heterogeneity in data to
ensure that it meets the core 3V's of Big Data, namely volume, variety, and velocity.
To extract value from Big Data, a suitable number of project criteria are recommended to be
integrated. As a function of time, the suggested paradigm should be multi-period, multiproduct, and multi-project, encompassing various elements such as costs, capacity, and
demand, and therefore introducing a diversity of features to the model under consideration.
Figure 2.1 The Framework for Big Data in Supplier Selection
Adopted from Lamba et al, 2018 p.13
Lastly, in order to account for data velocity, the model's parameters must be adjusted for each
project and time period (Lamba et al., 2018). The essential strength of such a framework, and
its importance in this study, is that it considers the project problem when considering many
criteria. Real-time analytics is a frequent way for dealing with the nature of Big Data and
avoiding issues linked with it (Tiwari, Wee & Daryanto, 2018). Real-time analytics allows
businesses to get insights and take action on data as soon as it enters their system. Real-time
analytics responds to queries in seconds. They handle massive quantities of data with high
velocity and quick reaction times (Tiwari et al., 2018). Thus, real-time decision-making is
possible.
2.5 Theoretical Framework: Multi-Criteria Decision-Making Models
MCDM approaches have been used to identify the optimum solution for many applications
and to determine the best option. The hierarchical picture of MCDM techniques and types is
shown below. The frequently used MCDM approaches are covered under the sections below.
Several academic research have looked at the multi-criteria decision-making dilemma to see
which techniques are the most appropriate. Kannan (2013), Igarashi et al. (2013), Govindan,
Rajendran, Sarkis & Murugesan (2015), and Simi, Kovaevi, Svirevi, & Simi (2017), among
other research, performed detailed literature reviews on similar methodologies. According to
the research, one model may be used with other tactics to improve the effectiveness of the
tools. Holistic single model strategies, such as the analytical hierarchy processes (AHP),
analytical network process, interpretive structural modelling (ISM), case-based reasoning,
data envelopment analysis, genetic algorithm, case-based reasoning, Fuzzy TOPSIS, Fuzzy
extent analysis, and mathematical programming, as well as their hybrids, have been proposed
for project multi-criteria decision-making. Multiple source approaches have been used by a
few academics, including linear programming, mixed integer linear programming, multiobjective programming, and target programming (Kannan, 2013).
Analytical hierarchy processes (AHP), analytical network processes (ANP), interpretive
structural modelling (ISM), case-based reasoning, data envelopment analysis, genetic
algorithm, case-based reasoning, Fuzzy TOPSIS, Fuzzy scale analysis, and mathematical
programming, as well as their hybrids, have all been proposed as comprehensive single
model approaches. Various MP approaches, such as linear programming (LP), mixed integer
LP, multi-objective programming (MOP), and target programming, were utilized by a few
researchers (GP). By differentiating the estimates of elements in the objective function, an
MPm model expresses the decision issue in terms of a mathematical objective function that
should be enhanced or limited (Kannan, 2013). The following diagram summarizes the multicriteria decision-making models:
Source: Kannan, 2013 p.357
The Analytical Hierarchy Process as well as the Fuzzy TOPSIS will receive a great attention
in this chapter because of their well-known capabilities (Simi et al., 2017). This will help to
justify the study's use of the analytical hierarchy process (AHP) algorithm.
2.5.1 The Fuzzy TOPSIS Algorithm
The TOPSIS method (Technique for Order of Preference by Similarity to Ideal Solution) is
based on the idea that the best choice should be the one with the shortest Euclidean distance
from the ideal solution (positive ideal solution – PIS) and the greatest distance from the antiideal solution (negative and ideal solution – NIS). The program employs a compensatory
aggregation approach, which compares a collection of choices by assigning weights to each
criterion. The TOPSIS has gained popularity since its creation by Hwang & Yoon (1981), and
has been developed to cope with ambiguous numbers. An optimal alternative in the Fuzzy
TOPSIS technique is the one that is closest to the Fuzzy Positive Ideal Solution (FPIS) and
farthest from the Fuzzy Negative Ideal Solution (FNIS). The best performance values for
each alternative make up the Fuzzy Positive Ideal Solution, while the lowest performance
values make up the Fuzzy Negative Ideal Solution.
2.5.1.1 A Critique of the Fuzzy TOPSIS Algorithm
The paper by Mari, Garibaldi, and Wagner (2016) provides an overview of the criticisms
related with the Fuzzy TOPSIS approach. Overall, the Fuzzy TOPSIS is panned due to
difficulties with the algorithm's dependability, complexity, and certainty. The concept has
relied on triangular fuzzy numbers defined in fuzzy sets to map out a decision-preference
maker's from its beginnings. The fuzzy sets are used to handle any linguistic uncertainty (also
known as the fuzzy environment or fuzziness in information) that may exist in the
preferences of the decision-makers. More changes to the algorithm were done in order to
increase its capacity to deal with linguistic ambiguity. According to Mari et al. (2016), such
fuzziness is not inherent in real-world knowledge, which is instead defined by "partial
dependability." As a result, relying on fuzzy numbers to address fuzziness is insufficient,
necessitating the addition of further information to characterize the partial dependability of
real-world data. Another difficulty is that, in addition to language ambiguities, there are other
uncertainties in information, such as missing data. As a result, a rather sophisticated idea of
limitation in the form of probability distributions would have to be applied. Nevertheless, in
addition to utilizing such probability distributions on real-world data, they must also be
applied to an event. This world would aid in overcoming concerns of rating uncertainty, i.e.,
the scope of a "Medium Poor" rating would be conveyed with confidence (Mari et al, 2016).
In this study, the Fuzzy TOPSIS and the AHP algorithms will be evaluated for their
appropriateness in a real-world Zimbabwean setting.
2.6 Conceptual Framework: The Analytic Hierarchy Processes (AHP) Algorithm
The AHP algorithm provides a thorough and reasonable framework for constructing a
decision issue, expressing and quantifying its pieces, linking them to overarching goals, and
evaluating alternate solutions (Jain et al., 2016). It is used to aid decision-makers in
determining the optimum option for their goal and knowledge of the challenge. The AHP
begins by breaking down the decision issue into a hierarchy of clearly understandable
subproblems, each of which is examined separately (Jain et al., 2016). The decision objective,
criteria for evaluating alternatives, and options for reaching the decision objective make up
the hierarchy. The AHP technique uses a Saaty scale for judging.
Table 2.3 Saaty Scale used in AHP (Saaty, 1987)
Intensity
Importance
of Definition
Explanation
1
Equal Importance
3
Somewhat
Important
Two things are equally important in achieving the
goal.
more Experience and judgment favor one over another
slightly.
5
Much
Important
more Experience and judgement strongly favour one over
the other
7
Very much
Important
more Experience and judgement very strongly favour one
over the other
9
Extremely Important
2, 4, 6, 8
Intermediate Score
The evidence favouring one over the other is of the
highest possible affirmation
Source: Jain, Sangaiah, Sakhuja, Thoduka & Aggarwal, 2016 p.2
In implementing the AHP, first the decision matrix A is formulated consisting of m x m
number of evaluation criteria as shown in the matrix below.
 a11

A
a
 61
a16 


a66 
Each entry ajk of the matrix represents the importance of the jth criterion relative to the kth
criterion. If ajk > 1, then the jth criterion is more important than the kth criterion, while if ajk >
1, then the jth criterion is less important than the kth criterion. If two criteria have the same
importance, then the entry ajk = 1. In principle aik x aki = 1 (Jain et al., 2016).
j 1
After the first step, the equation w   aij  [w1 ,
j 6
, w6 ] is then used for finding the operator
equation of matrix for normalising. After the pair-wise comparison matrices are complete, the
next step is to normalize the matrix using operator equation determined by w above. This is
done using:
After normalising the matrix, the normalised principal eigenvector is thus determined using:
Finally, the final weights of the alternatives are then determined using the matrix product of
two arrays (MMULT) as:
2.6.1 A Critique of the AHP Algorithm
Despite its popularity, many authors have voiced reservations about some aspects of the AHP
technique. In some circumstances, the model has been linked to ranking abnormalities known
as rank reversal, which is most likely to happen when an existing option is added to the set of
alternatives being considered. This can be overcome by employing a multiplicative variation
of the AHP; however, this introduces an issue with interpreting criterion weights (Ishizaka &
Labib, 2011). Another issue with the AHP is that, while it is considered a full aggregation
technique for additive types, excellent scores may be rewarded on certain criteria while bad
scores are compensated on others. As a result of this aggregation, specific and frequently
critical information may be lost (Ishizaka & Labib, 2011). Another issue highlighted is that
the decision problem under AHP is split into a number of subsystems, each of which requires
a large number of pairwise comparisons to be performed inside and between. This may result
in a high number of pairwise comparisons, making the entire procedure intimidating. Lastly,
the decision-maker may find it challenging to employ the artificial 9-point scale (Ishizaka &
Labib, 2011).
2.7 Empirical Literature Review
The goal of this portion of the study is to give a broad overview of how multi-criteria
decision approaches like the AHP algorithm have been used in various sorts of studies in
various circumstances.
Chen, Lin, & Huang (2016) used the fuzzy TOPSIS technique to pick suppliers in an
advanced technology manufacturing business. Three decision-makers examined five potential
vendors based on the five identified supplier criteria. Near the conclusion of the test,
alternative providers were guided by their coefficients of proximity. Chan & Chan (2014)
used AHP, which included six core evaluation criteria and 26 distinct criteria, the relative
importance of which was decided by the rates of client demands. Shahanaghi & Yazdian
(2019) used the fuzzy TOPSIS method to select the best decision for the purchase of key
components from alternative suppliers in an automotive company based on the selected
criteria; the best choice was chosen to complete the calculations carried out by three decisionmakers after the evaluation of four alternatives. Buyukozkan & Ersoy (2019) used the fuzzy
TOPSIS approach to choose external source providers for a corporation in the Turkish
informatics industry. Boran, Genc, Kurt, and Akay (2019) employed the TOPSIS approach in
conjunction with the intuitionist fuzzy set to pick a supplier for a vital component in an
automotive company's manufacturing process. IFWA (intuitive fuzzy weighted average) 18
(86 percent) 2 (9%) 1 (5%) AHP ANP ANN 165 administrator was used to assess the
importance of criteria and alternatives in decision-makers' individual viewpoints. Alternative
options were categorized by their closeness coefficients at the end of the test. Muralidharan,
Anantharaman, and Deshmukh (2012) evaluated group decisions while selecting suppliers.
Individual decisions in the group were assessed using the AHP, and the obtained findings
were used to calculate the confidence intervals for each option. Ten decision-makers assessed
decisions based on their quality, technical activity, and delivery criteria. In the Wang, Cheng,
& Kun-Cheng (2019) experiment, three suppliers were evaluated and arranged by three
decision-makers in line with four supplier selection criteria using the fuzzy TOPSIS-based
hierarchical TOPSIS technique. At the conclusion of the research, it was demonstrated that
this approach was more rational than the other ways and that it could be used to compute
loads in future studies or in other decision-making areas. Awasthi, Chauhan, & Goyal (2020)
employed the fuzzy TOPSIS approach for supplier selection; three master decision-makers
assessed selection criteria for four alternative suppliers, which were resolved based on a wellqualified evaluation, and then vendors were organized by their proximity index. The test was
completed with the help of a sensitivity analysis. Chen (2020) developed a two-phase strategy
comprising of DEA and fuzzy TOPSIS algorithms for textile supplier selection in Taiwan.
Chan (2013) employed AHP and an interactive selection model to help with decision-making
processes during supplier selection. Chan's investigation employed AHP to establish total
ratings for alternative providers, which were based on relative significance rates. Liu and Hai
(2015) employed the AHP to choose suppliers in the furniture industry. Supplier weights and
scores were calculated based on 60 administrators' selection of criteria and sub-criteria. In the
electrical-electronics business, Hou and Su (2016) applied AHP for web-based supplier
selection. Five different providers chose their priority weights in this inquiry. The supplier
selection criteria of Chan, Chan, Ip, & Lau (2017) research, which likewise employed the
AHP technique to address the issue, were 14 in number. Chan et al. used a sensitivity analysis
at the conclusion of the inquiry to vary the relative significant rates of each model and
analyze the alternatives' reactions. In a pharmaceutical manufacturing business in Ghana,
Asamoah, Annan, and Nyarko (2019) used the AHP approach for supplier assessment and
selection. Bruno, Esposito, Genovese, and Passaro (2019) utilized the AHP approach to select
the top supplier in the Italian railway business.
2.7.1 Discussion of the Current Literature
With a massive number of approaches and researchers, the multi-criteria decision-making
sector is quite concentrated. However, there is a lot of dependence on multi-criteria decisionmaking approaches in vendor selection for project management, and there is a paucity of such
literature, especially in poor countries like Zimbabwe. Mushanyuri (2012) looked at supplier
selection in Zimbabwe, however his research was confined to Zimbabwean universities, thus
his conclusions can't be applied to the economy's primary producing sectors. Despite his
emphasis on the need of including multi-factors, his study failed to suggest an acceptable
solution in this situation. Numerous approaches based on various multi-criteria decisionmaking procedures, implemented singly or in combination with a wide variety of techniques
in an unexpected way, were noticed in the published literature in the context of supplier
selection. Different types of multi-criteria decision-making approaches were identified.
Compensatory techniques are used in the great majority of approaches, such as AHP,
TOPSIS, and ANP. The strategy was chosen after research on how suitable compensation is
in the multi-criteria decision problem at hand. The incorrect interpretation of "weights" of
criteria raises concerns regarding compensating strategies' application (the proper term is
scaling constants). Non-compensatory constants represent relative relevance between criteria,
but compensatory constants represent extra information beyond relative value of criteria,
namely trade-off information, which embodies the notion of criteria compensation.
Differences like this have ramifications for how decision-makers get information. The
elicitation of weights is one of the most important issues when using a multi-criteria decision
technique, especially for compensating procedures. In practice, if the multi-criteria technique
is not effectively implemented, the answer may not match decision-makers' preferences, and
they are likely to reject the approach's suggestions, leading to its demise. Another key factor
that influences the acceptability of a technique for aiding decision-makers is the cognitive
effort required by them to grasp the ideas and parameters inherent in the method. The
effectiveness of multi-criteria techniques can be harmed by a complicated preference
modelling procedure.
2.8 Chapter Summary
The goal of this chapter was to present a theoretical and empirical review of the multi-criteria
decision-making literature. The study found that multicriteria decision-making is difficult due
to the necessity to balance several features that qualify projects beyond economic
considerations. Project management must take into account a variety of aspects, including
resource availability, external influences, governance issues, and the requirement to align
choices with the organization's overall goal. Although several strategies may be used in
multicriteria decision-making, the AHP is the most often used. The study will concentrate on
the AHP approach since, despite its robustness, it has never been used in the Zimbabwean
setting. The following part of the research outlines a thorough approach on how the strategy
will be used.