Critically evaluate the importance of Project Life Cycle to the.docx
ResearchTopicDevelopment_21Aug2014
1. RESEARCH PROPOSAL
Optimisation of project cost outcome through alignment of project and
project team objectives: An application of Self similarity in Fractals
Candidate
Enyonam AGBODO
Student Number: 09211438J
Doctor of Philosophy (Project Management)
School of Natural and Built Environments
Division of Information Technology, Engineering and the Environment
University of South Australia
Principal Supervisor
Dr Tony MA
Co-Supervisor
Mr Simon EMMS
2. 2
Table of Contents
Table of Contents.......................................................................................................................... 2
1 Statement of the Research Topic............................................................................................ 3
2 Background........................................................................................................................... 3
3 Literature Review .................................................................................................................. 5
3.1 Project management approaches.................................................................................... 5
3.1.1 Developed Economies project management Models................................................. 6
3.1.2 International development project management models..........................................6
3.1.3 Systems Engineering model..................................................................................... 6
3.2 Project outcome prediction/optimisation approaches..................................................... 7
3.2.1 Methods................................................................................................................ 7
3.2.2 Critical Success Factors............................................................................................ 8
4 Problem Statement............................................................................................................. 10
4.1 Research Aims............................................................................................................. 12
4.2 Objectives ................................................................................................................... 12
4.2.1 Establish clear understanding of Chaos Theory....................................................... 12
4.2.2 Understand Chaos Theory Elements ...................................................................... 13
4.2.3 Application of Chaos Theory to Project Management.............................................. 14
4.2.4 Develop knowledge and understanding of Personality types ................................... 15
4.2.5 Develop knowledge and understanding of team dynamic types............................... 15
4.2.6 Model & simulate project nonlinear system to determine project outcome basins ... 15
5 Research Theoretical Framework.......................................................................................... 17
5.1 Logistic Model (example).............................................................................................. 17
6 Proposed ResearchApproach............................................................................................... 21
6.1 Project Non Linear Dynamic Model............................................................................... 24
6.2 Project model simulation.............................................................................................. 26
6.3 Empirical Data collection.............................................................................................. 26
7 Trial Table of Contents/Proposed Papers............................................................................... 28
8 Research Outcomes & Limitations ........................................................................................ 29
9 Research Timeline ............................................................................................................... 30
10 Bibliography.................................................................................................................... 31
3. 1 Statement of the Research Topic
This research proposes to use Chaos Theory (theory of nonlinear dynamics systems
behaviour) to predict project outcome areas (basins). The researcher is of the opinion that
project processes, whilst predictable in terms of the activities that must be performed,
always do lead to different outcomes.
Project activities occur at the interfaces of human, nature and machine. The analogy may be
made that for a given car (machine), the type or nature of the driver (human), the weather
(nature) determine the driving outcome; safe arrival or otherwise. For a given driving
outcome, the car, the driver and the weather must all be in specific states to achieve the
desired outcome. Ignoring the weather and all other external factors, for now, the research
proposals asserts that a specific driving outcome is predictable by a specific combination of
driver and car types. E.g. If the goal is safe arrival; a safe car and a safety conscious driver are
key requirements for achieving the goal. In project management speak; the research seeks
to demonstrate that successful project outcomes can be achieved through sustained
alignment of project goals with the personal goals/orientation of project managers and
teams.
The researcher considers the Chaos Theory concept of Self-Similarity as valid tool to deploy
in demonstrating the need for this alignment of goals. The research hence proposes the
topic as “Optimisation of project cost outcomes through alignment of project and project
team objectives: An application of Self similarity in Fractals”. To the best of the researcher’s
knowledge, this will be the first time that Chaos Theory is to be applied to determine project
outcome basins as a direct function of project manager and project team personal goals or
orientation. The ability to predict project outcomes basins will lead to reduction in
uncertainty in project delivery outcomes.
This theme of management impact on project outcome, to be developed further, has been
discussed by Sauser, Reilly & Shenhar (2009) when investigating the causes of the failure of
National Aeronautics and Space Administration’s (NASA) Mars Climate Orbiter project.
2 Background
Throughout the course of human existence, projects have been used to change from one set
of conditions to another set of conditions. An intuitive definition of project may be
suggested hence as an activity done within a specific period to achieve a specific outcome.
This intuitive definition implies commitment of time, resources, environment, culture,
technology, interested parties etc to a specific course of action, the work or the scope. The
variety of inputs required for a project leads to the issue of what is considered as project
success, granting the diversity of interested parties.
Project success may mean different things to different stakeholders. There is an emerging
trend to hence clarify what is meant by project success and assign wider project success
criteria. De Wit (1998) and other writers distinguish between “project success (measured
4. against the overall objectives of the project) and project management success (measured
against the widespread and traditional measures of performance against cost, time and
quality)”. Project success relating to overall project objectives, is hence determined from the
owner or dominant shareholder(s)’s point of view, while project management success is
primarily seen from the project team’s point of view. Coke-Davies (2002) defined 11 success
factors that attempted to bridge the gap between project success, and project management
success factors. Turner & Zolin (2012) also expanded the definition of project success factors
to include many more factors and covering many other stakeholders. Zwikael & Smyrk (2012)
continued in the same vein but redefined project success from funder’s perspective to
include “project delivery efficiency, project outcome realization and assigning accountability
for outcome”. “Project delivery efficiency” deals with the factors that contribute to
delivering the project on budget, time and quality. “Project outcome realization” deals with
the project outputs being put to use and used in the intended environment.
For the current purposes, project success is to be considered as “project management
process efficiency or project delivery efficiency” success and is defined along similar lines to
the Standish group (2014) definition of “projects are on time, on budget, and have a
satisfactory implementation”, from the major funder’s point of view.
Limiting project success to only project delivery efficiency, it can be shown that project
failure rates are very high Demetrios (2009). Caravel (2013) estimates that infrastructure
project failure rate in Australia is 48% and the cost of this high project failure is $30 billion
per annum. Infrastructure projects are listed to be made up of Automation & Control,
Manufacture & Construction, Infrastructure Operations and Others. Heeks (2003), reported
as many as 85% of governments’ (in both developed and transitional economies) IT projects
are only partially completed (unattained goals) or are totally abandoned. (Collins and
Bicknell 1997; Palmer and Felsing 2002; Corner and Hinton 2002; Heeks 2006; Lacovou 1999;
James 1997; Standish Group 2004) whilst not certain of the actual numbers, and considering
differing project success criteria, concluded that “only a minority of government
transformation projects are successes and majority are considered implementation failures”.
In the USA Defence Acquisition projects, Peck & Scherer (1962) showed that in the course of
12 major development projects, costs increased by an average 3.2 times and schedules
lengthened by 36 percent.
The diagram in Figure 1 below, from a Standish Group 2014 report “BigBangBoom”, stated
“The chart shows the resolution of very large software projects from 2003 to 2012 within
the CHAOS database. Successful projects are on time, on budget, and have a satisfactory
implementation. Challenged projects are over budget, late, and/or have an unsatisfactory
implementation. Failed projects are projects that were either cancelled prior to completion
or not used after implementation”.
5. The opportunity cost of project
failures appear to very high. The
$30 billion cost of project failure
in Australia could have been used
in other areas of the society e.g.
infrastructure, education, aged
care services, youth
unemployment services etc. There
appears to be strong case to
ensure projects are successfully
delivered.
The research proposal argues that
despite the abundance of project management methodologies, project success rates are low
due to the continued application of linear systems dynamic models to project management.
It will assert that project management is indeed a nonlinear dynamic system process that
requires the application of principles from chaos theory. In particular, the self-similarity in
fractals is considered to be applicable to projects. A self-similar object or pattern can be
repeated infinitum whilst maintaining its basic internal properties. The research seeks to
establish that matching or aligning project manager and project team characteristics to
project basic success outcome factors (basins) creates a self-similar system that when
iterated over different times (project phases) has the potential to deliver in the basic project
basin (in our current case, the quality, cost and time).
The research will identify the self-similarity properties of non-linear dynamic systems that
allow for the possible prediction of project cost outcome basin. Chaos Theory does not seek
to predict specific outcomes but foreshadows the areas of possible outcomes. In this way
program/project managers can make informed decisions on preferred outcome basins.
Key terms: project management, linear and nonlinear dynamic systems, chaos theory,
fractals, self- similarity, project success.
3 LiteratureReview
3.1 Project management approaches
The Project Management Institute (PMI) defines a project as “a temporary endeavour
undertaken to create a unique product, service, or result” PMBOK (2013, pg 3). Project
Management is defined as “as application of knowledge, skills tools and techniques to
project activities to meet project requirements” PMBOK (2013, pg 3). From the definition of
a project, project management is applicable to all areas of human endeavour.
Figure 1Resolution Of Large Software Projects, Standish
(2014)
6. 3.1.1 Developed Economies project management Models
In general terms, project management approaches may be divided into two main groups;
namely the approaches used in the developed economies private /public and business to
business and the approaches used in the developing economies, generally termed
“international development projects” Golini (2013, Pg 13).
The former of the two approaches is covered by such standards and guidelines as the
PMBOK of the PMI, the PRINCE of the UK's Office of Government Commerce and the P2M
Guide book (Project Management Association of Japan), ISO 10006:2003 & - ISO 21500:2012
standards Catlin (2013). For the purposes of this research, for lack of better description we
shall call these Developed Economies project management Models (DEPMM).
“The stated goal of these guidelines, standards and methodologies (methods) is to provide
an optimum framework, the best practice for manage project to success. Applying these
guidelines, standards and methodologies may be prerequisite and necessity for project
success, but not always sufficient Catlin (2013).
3.1.2 International development project management models
On the other hand the European Commission has developed the Project Management Cycle
(PCM) model based on the Logical Framework Approach (LFA) Fuster (2006).
The logical framework Approach (LFA) and its derivative, the project cycle management
(PCM) are used by the World Bank and many Non-Governmental Organisations (NGO) in
delivering projects across the globe. LFA was described by its developers as “a set of
interlocking concepts which must be used together in a dynamic fashion to permit the
elaboration of a well- designed, objectively described and evaluable project” (PCI, 1979). It
should be noted that the LFA is not an integrated set of procedures, and nor is it a set of
guidelines for the evaluation of a particular type of project. Colemam (1987, pages 251-259)
Project Cycle Management is used extensively by the European Commission (EC) to ensure
that: (i) projects are supportive of overarching policy objectives of the EC and of
development partners, (ii) projects are relevant to an agreed strategy and to the real
problems of target groups/beneficiaries, (iii) projects are feasible, meaning that objectives
can be realistically achieved within the constraints of the operating environment and
capabilities of the implementing agencies and (iv) benefits generated by projects are likely to
be sustainable Ognjenovic (2004, p.69).
3.1.3 Systems Engineering model
System Engineering (SE) is a managerial and technical methodology developed in the last 60
years to improve the governance (and hence the performance) of projects designed and
delivered in complex environments. SE achieves these results, transforming the governance
from the project and pure “project management” to a more holistic system view of “system
management”. The literature review reveals that despite project governance (PG) being one
of the key factors influencing project performance, its optimal form has not been identified
7. yet. Nevertheless SE has emerged as an important technique to transform the governance in
complex project environments. SE transforms and improves the PG with several tools and
techniques centred on the Systems Thinking (ST) approach and the Integrated Product Team
(IPT) technique. ST takes into account the environment, and its interactions, in which the
project is accomplished. The IPT, involving the key stakeholders influencing the project
success, enables the definition of a complete and accurate plan with a multidisciplinary and
systemic approach. The communication among the involved organizations is supported by
requirements management tools. Systems Engineering Management Plan supports the best
definition of roles, responsibilities, requirements, interfaces and objectives. The strategic
tools that support the IPT governance are modelling, simulation and trade off analysis,
which guarantee the delivery of the project with a focus on the benefits over the
subsequent life-cycles, Locatelli, Mancini & Romano (2013). The authors did not provide any
empirical data or theoretical studies data to back up the use of SE as an approach in
predicting project outcomes.
3.2 Project outcomeprediction /optimisation approaches
3.2.1 Methods
Catlin (2013) and Fuster (2006) indicated that there are multiple project management
methods aiming to provide an optimum framework, the best practice to manage project
successfully. A number of methods or approaches exist to predict project outcomes. These
are summarised with their strengths and weaknesses in Table 1 below. The goal of all these
methods of delivering projects is aimed at achieving the desired goals of the specific
projects.
Table 1: Project Outcome prediction Methods
Method Strength Weakness
EarnedValue Management
(EVM)( EIA 748-A)
Able to predict both cost and
time outcomes from the
same set of baseline plan
If the plan is unrealistic or
changes frequently, the
EVMS is not a reliable
indicator of project or
program outcomes
Inputs into the EVMS can
often be based on the
project manager or project
team’s perception and hence
subjective
EvolutionarySupportVector
Machine Inference Model
(ESIM) (Min-Yuan & Yu-Wei,
2009)
Able to predict the degree of
success likely to be attained
in a new project given the
current state of critical
success factors
Uses to in pattern
Model assumes the
complete list of current
known critical success factors
are available to new project
Model uses highly developed
technological and software
8. Method Strength Weakness
recognition and
categorisation and training
inputs
tools that may not available
to many new projects
Model does not appear
predict an integrated project
outcome (i.e focuses on
singular project outcomes,
say cost, time etc)
Costsignificantitems&Chaos
Theory (Duan &, Liu, 2009).
Short-term prediction of
engineering cost estimation
Short term is undefined
Application of chaos
prediction model requires a
large amount of time-series
data.
The forecast error of chaos
prediction model increase
along with the growth of
time.
Uses time series and not self-
similarity application of
Chaos theory
Logisticregression(Narciso et.
Al, 2010)
Estimates the probability of
an event occurring
Accepts human attitudes in
the model
Accepts only one input
element.
Uses regression models and
not self-similarity application
of Chaos theory.
Artificial neuralnetwork&
Project Definition Rating
Index (PDRI) (Yu-Ren & Chun-
Yin 2011)
Shows that early planning is
an important factor to final
project success (cost and
schedule)”
Validatesimportance of
appointingaprojectmanager
and a core projectteamas key
elementsof the PDRIchecklist
Silent on the characteristics
of the project manager and
the core team.
Systems/Integratedapproach
(Small, 2011) (Doloi, 2007)
Treats projects as dynamic
organic systems
Assumes that complex social
human interactions required
for project success can be
modelled.
No empirical data provided
3.2.2 Critical Success Factors
Many papers have also been written about the factors or criteria of success and failure of
projects, generally termed Critical Success Factors, CSF. In attempt to explain the reasons for
9. unsuccessful project outcomes despite the application of various methods, Belassi & Tukel
(1996), Cooke-Davis (2002) and Turner & Zolin (2012) proposed grouping critical project
success factors. Belassi & Tukel (1996), analysed many research works that sought to
produce CSF and made the observation that “our purpose here is not to come up with all
possible critical factors that might affect project outcome, which is impossible because of
the diversity of projects, but to show that the identification of the groups to which the
critical factors belong would be sufficient for better evaluation of projects. The assumption
being that a critical set of unique factors in group of factors are more likely to repeat on
another project, rather than the unique factors themselves. This is a valid assumption. This
grouping of factors is an essential step in identifying patterns in predicting project outcome.
Projects are unique endeavours and no two projects are ever the same in terms of their
outputs. In light of this, identifying unique success or failure attributes or factors may not be
a helpful pursuit. “Furthermore, many of these factors do not, in practice, directly affect
project success or failure. Usually a combination of many factors, at different stages of
project life cycle, does result in project success or failure” Belassi & Tukel (1996). Patterns or
groups of factors are more likely to predict project outcome. Turner & Zolin (2012) stated
“We expect that there may be combinations of the leading performance indicators that
should set the alarm bells ringing”.
The use of groups of factors and their occurrence or patterns does appear to provide a more
informed indication of project progress rather than individual factors.
10. 4 Problem Statement
A number of project outcome prediction/optimisation models have been listed (Table 1)
alongside their strengths and weaknesses. The most popular of the models for predicting
project outcome is the EVM. Earned Value Analysis (EVA) is powerful in tracking project
performance if the project is well set up. That is, all main work packages are fully resourced
and accurate/correct cost and effort estimates are provided. EVA, however, has some basic
or inherent limitations. If the plan is unrealistic or changes frequently, the EVM is unable to
provide a reliable indicator of project or program outcomes. EVA uses baselined values and
estimates of progress. Baselined values are best estimates of planned work based on
resourcing levels and resource cost. Assuming all initial planning assumptions are valid,
estimating what portion of work to claim as progress is not an exact science, especially in
the changing technology, software and research & development or other creative projects.
Estimating progress is hence often qualitative. Whilst there are some quantitative options,
these are mostly applicable to situations where there are fairly well defined and non-
changing physical outputs.
For long duration projects, resourcing levels and types change; cost of plants and materials
also change. The project may hence be expending more than planned (i.e higher Actual Cost,
AC), whilst the Earned Value (normally claimed as a portion of the Planned Value, PV) will be
constrained by the PV (even though the project may have earned more due to increased
resources, plants & materials costs). In this scenario, the Cost Performance Index, CPI,
defined as EV/AC will be very low. The project scope has not changed and the project team
has not being irresponsible. The primary reason here is inability to accurately estimate
project costs so early in project planning stages.
In another scenario, a Schedule Performance Index (SPI) greater than 1.0 implies the project
is accomplishing more work than planned, and a SPI less than 1.0 indicates the project is
accomplishing less work than planned. However, the project may be doing all tasks that are
not on the critical path. The project may then fail to deliver on time. Lukas (2008) lists other
problems with EVA.
A number of authors have also presented the use of CSF in project outcome determination.
Apart from the Systems approach all the other models rely on different aspects or factors of
the project itself to predict the project outcome. This approach is a feedback system
approach. A feedback loop allows the output (or part of it) of a system to be fed as input
into the same system. A feedback method assumes that the system will continue to behave
as previously observed. That is inputs at time t = t1 will produce the same outputs at time t =
t0 or an output that is linearly related to the output at t= t0. Feedback flow may however,
result in large amplification, delay, and dampening effects, which in turn would lead to
system being emergent or unstable. In project management, as an example, schedule delay,
when used as feedback to improve the schedule by deploying additional resources, could
lead to cost budget overruns. Furthermore, such deployment of additional resources could
also lead to safety and/or risk implications if the additional resources are not properly
11. trained due to the limited time available. Safety incidents and/or accidents become threats
that could lead to the failure of the project. CSF practitioners have not demonstrated the net
effect of the changing values or considerations on project outcomes.
Another CSF in project delivery is budgeted cost. A project approaching over budget may
seek to reduce cost by a number of ways. Scope reduction is one such approach. However, a
reduced scope may lead to unsatisfied stakeholder or indeed rejection of the final output.
The use of CSF or a grouping of success factors makes the assumption that these factors can
explain project outcomes, independent of the project type or industry. Belassi & Tukel
(1996) showed that even with the use of “group of critical success factors”, the elements in
each group are different for different industries. Different industries have different drivers
and therefore CSF may not be linearly translated from one industry to another.
All projects will have different initial inputs or conditions. CSFs are, by definition, static and
will have different values or considerations for each new project.
Another limitation of the CSF approach is the inability to fully explore the collective impact
of all the CSF acting together. There appears to be no system that models or determine the
collective impact of CSF on project outcomes. The point is hence that even if the most
critical set of success factors are assembled, the values or considerations assigned to the
factors at the different stages of the project will vary from project to project. The net effect
of these variations on project outcome is hence undetermined. In addition, the summative
effect on project outcome of the varying critical factors has not been explored.
There appears to be an inherent problem in project outcome determination. Project
management literature to date with the exception of Daňa, (2014) has assumed the linearity
of project implementation and hence made the implicit deduction that success factors or
methods may be applicable across different project types and environment. This author
believes this assumption does not take into account the non-linear behaviour of project
implementation.
The author is of the opinion that project management has, since its formal inception in the
early fifties Garel (2013) has followed the approach and methods of its progenitors, i.e.
linear dynamic systems “Traditionally, project management is based on linear thinking, while
project execution is inherently dynamic and nonlinear” (Daňa, 2014).
The path taken by each project to reach the end, granting even the same starting point is
usually different. Despite the observed differences, project implementation is often,
assumed to be an Euclidean-linear dynamic system. However the path taken by each project,
when subjected to different scales of measurement can be shown to be different. In effect
no two projects with identical initial conditions will lead to the same outcomes. Each project
implementation is hence unique and does not produce the same outcomes granting even
the same initial inputs. This behaviour is unique to non-linear dynamic systems.
12. 4.1 Research Aims
There is therefore a problem with use of linear dynamic systems to predict project
outcomes. A number of questions therefore arise.
1. What theoretical systems exist to deal with nonlinear dynamic systems?
2. Can the human elements, project management models and CSF be modelled using
non-linear systemtools?
3. Can these models be used to predict project outcome basins?
The author agrees that project behaviour and outcomes are better understood and analysed
using Chaos theory (the study of non-linear dynamic systems) rather than by linear dynamics
behaviour Daňa (2014). Daňa provided “(a) an overview of concepts originating from the
Chaos theory which could be utilised as a basic theoretical framework for understanding
how the Chaos theory could be applied in project management, (b) further explain how
these concepts compare and contrast with traditional project management approaches, and
(c) propose a new project management approach based on the Chaos theory”.
Daňa did not attempt to apply Chaos Theory concepts to project outcome prediction. This
current research hence establishes the following aims:
1. Determine elements of Chaos Theory that could be used to model project
implementation non linearity
2. Establish specific non-linear models that link human characteristics to CSF (cost, time
and quality)
3. Use model to predict project cost outcome basins
4.2 Objectives
The following six objectives relating to the aims are defined as:
1. Establish clear understanding of Chaos Theory
2. Understand Chaos Theory Elements
3. Apply Chaos Theory to project Management
4. Develop knowledge and understanding of Personality types
5. Develop knowledge and understanding of team dynamic types
6. Model & simulate project nonlinear systemto determine project outcome basins
4.2.1 Establish clear understanding of Chaos Theory
Chaos theory, which is the study of nonlinear dynamic systems, is used to study systems
which are such that small changes in initial conditions lead to different outcomes. That is the
system is sensitive to initial condition. The differing outcomes also means that long term
prediction of is not possible although the system’s future behaviour is always determined by
the initial input (i.e. deterministic). Systems that behave like this are named “chaos” systems
and were first described by Edward Lorenz as “when the present determines the future, but
the approximate present does not approximately determine the future”, Rosario,Pedro (2006,
13. pg 68). Lorenz stated “Chaotic behaviour can be observed in many natural systems, such as
weather and climate”, Lorenz Edward (1963, pg 130-141). A number of authors have since
applied Chaos Theory to general and project management Cartwright (1991), Young & Kiel
(1994), Levy (1994), Singh & Singh (2002), Dana (2014). Furthermore, Radzicki (1990) and Butler
(1990) amongst others have noted that social, ecological, and economic systems also tend to
be characterized by nonlinear relationships and complex interactions that evolve
dynamically over time. Project delivery is one such system which is characterized by
nonlinear relationships and complex interactions that evolve dynamically over time.
There is the debate that Chaos theory may not be applicable to social and managerial
systems that are subject to human interventions, unlike physical systems that are shaped by
unchanging natural laws. Human agency can alter the parameters and vary structures of
social systems, and it is perhaps unrealistically ambitious to think that the effects of such
intervention can be endogenized (developed something internally) in chaotic models.
Nevertheless, chaotic models can be used to suggest ways that people might intervene to
achieve certain goals Levy (1994).
An objective of the research is hence to establish clear understanding of Chaos Theory and
confirm its application to project management.
4.2.2 Understand Chaos Theory Elements
Although there is no universally accepted mathematical definition of chaos, a commonly
used definition says that, for a dynamical system to be classified as chaotic, it must have the
following properties Hasselblatt & Katok (2003):
1. It must be sensitive to initial conditions(“Butterfly Effect”);
2. It must be topologically mixing; and
3. It must have dense periodic orbits.
Briefly, (1) means small change or disturbance of the current trajectory may lead to
significant differences in the future. (2) Means system will evolve over time so that any given
region will overlap with any other given region, and (3) means every point in the space of
the system will be close to the periodic orbits of the system. Project management appears to
have these properties. It has been stated earlier that even given the same initial conditions,
the outcomes of the any two projects may be dissimilar;-butterfly effect. For any given
project, the decision or action taken at any stage of the project does have an effect or
impact on another stage of the project.-i.e. topological mixing. If the same project actions
were to be repeated or iterated, the decisions, actions and outcomes at similar stages of the
project will be close or identical to those of the previous project. As an example, all projects
undergo planning. The planning activities may be different for identical projects, but many of
these actions must be completed- i.e. dense periodic orbit.
Chaos theory also introduces new paradigms, Young & Kiel (1994) that include
1. Fractals, the nonlinearity but self-similarity of systems dynamics
2. Qualitative transformations to new dynamical states
14. 3. Attractors
4. Dissipative structures
5. Principle of Universality ( Feigenbaum’s Constant)
6. Self-organization
Chaos theory increasingly drew the attention of scientists around the world mainly because
its principles, laws and conceptualizations are thought to be universally applicable to the
behaviour of almost any natural system. Dana (2014).
With chaos theory, it is possible to
a. generate and display data to reveal the hidden patterns of dynamics in phase-space
b. identify the key parameters which drive a non-linear system from one dynamical
state to another,
c. reflect on the implications for a project that is close to change points at which
entirely new modalities of behaviour may emerge and,
d. adopt flexible strategies of management as causality opens and closes, Young & Kiel
(1994)
An objective of the research is hence to fully understand Chaos Theory elements.
4.2.3 Application of Chaos Theory to Project Management
Singh & Singh (2002) presented a valid case for the application of Chaos Theory to project
management. They concluded “Chaos is the theory that explains the observable
phenomenon in project management. Contrary to standard science where researchers
conceive of a theory or model and then test it for its application to prove that their theory
explains observable phenomenon, chaos theory has emerged as the science that provides a
suitable theory to explain the events in project management”.
Chaos theory is a set of ideas about the transformation from order to disorder as the
generation of new forms of order from turbulent, nonlinear dynamics. A system exhibiting
nonlinear behaviour may appear quite random over time, yet studies of chaotic regimes in
phase-space reveal underlying patterns. Such patterns are called "attractors" since a system
appears to be "pulled" toward a region in an outcome field during its cycles or periods
Mandelbrot (1977). Some attractors are called 'strange' attractors since a system behaves in
ways not expected by Newtonian physics, propositional logic, rational numbering systems or
Euclidean geometry Young & Kiel (1994).
Chaos research maps the geometry of system dynamics in phase-space (Cartesian map of
system dynamic values over time). There are five such patterns. They include two linear and
very stable equilibrium called a) the point attractor and b) the limit attractor. There are also
three generic nonlinear regimes called 1) the torus with one outcome basin, 2) the butterfly
attractor which can bifurcate into a 16n outcome field, and 3) full chaos with infinity of
outcomes to which persons, groups and firms/projects might move. Young & Kiel (1994).
A basic objective of the research is to determine which of the Chaos System elements and
paradigms most suit project delivery.
15. 4.2.4 Develop knowledge and understanding of Personality types
There exist a number of personality and team dynamic profiling methods. Chapman, A
(1999). Motivation, management, communications, relationships - focused on oneself or
others - are a lot more effective when one understands oneself, and the people one seeks to
motivate or manage or develop or help. Understanding personality is also a key to unlocking
elusive human qualities, for example leadership, motivation, and empathy, whether the
purpose is self-development, helping others, or any other field relating to people and how
we behave.
There are personality theories that underpin personality types. Developing understanding of
personality typology, personality traits, thinking styles and learning styles theories is also a
very useful way to improve knowledge of motivation and behaviour of self and others, in the
workplace and beyond. Understanding personality types is helpful for appreciating that
while people are different, everyone has a value, and special strengths and qualities, and
that everyone should be treated with care and respect. Personality theory and tests are
useful also for management, recruitment, selection, training and teaching. The research will
review and select appropriate personality determination tool. Specific personality types are
to be assigned specific values or constants- personality constants (rho).
4.2.5 Develop knowledge and understanding of team dynamic types
As with the personality types, there are many team dynamic or cohesion models in industry,
Donsbach (2009). Research and practice suggest team effectiveness is driven considerably by
the mix of team member attributes. Given the impact a team’s composition has on its
objectives, private industry and military leaders place a premium on making optimal team
staffing decisions. Nonetheless, the challenges associated with achieving optimal team
composition are significant. Donsbach (2009), in collaboration with the USA Army designed a
framework/methodology for a Team Optimal Profiling System (TOPS) and demonstrated its
use for making an optimal team composition decision. This current research will examine
the TOPS framework in addition to other team dynamics models and select one that will aid
in attaining the goals of the research. The research will review and select appropriate team
dynamic determination tool and use the outputs of the tool as team dynamic constants
(tau).
4.2.6 Model & simulate project nonlinear system to determine project
outcome basins
This research proposal aims to develop or make use of an appropriate non liner system
function (equation), generate and display the data of project systems dynamics in such a
way as to reveal the geometry of the deep structures hidden in those data. The non-linear
equation is to be iterated over different values of rho and tau, given changing duration and
quality values. The research seeks to apply specific elements of chaos theory (Self-similarity
of Fractals) in optimising project outcomes.
16. The research topic is hence proposed as “Optimisation of project cost outcome basins
through alignment of project and project team objectives: An application of Chaos Theory
Self Similarity in Fractals”.
17. 5 Research Theoretical Framework
Chaos research tracks the transformations of dynamical systems from one behavioural
regime (attractor state) to another. As key parameters of systems are changed, the system
displays an orderly procession from one dynamical state to another. The procession ceases
to be orderly and becomes very chaotic at different input values. As a system becomes more
chaotic, i.e., it transforms from a simple outcome basin to a much more complex causal field
Young & Kiel, (1994).
5.1 Logistic Model (example)
A simple system that is useful to illustrate some properties of chaotic systems is the logistic
map. This is a non-linear recurrence relation with a single control parameter, μ Singh &
Singh, (2002)
Xn+1 = μ Xn (1− Xn ). (1) [37]
The recurrence relation is started with X0 between 0 and 1. For μ < 3, the recurrence relation
rapidly converges to a limit i.e. after convergence; each iteration gives the same value for X.
For 3 < μ <3.45, the limiting behaviour is an oscillation between two values. Hence two
iterations are required before the same x value is obtained. Hence at μ = 3 a period doubling
has occurred. A second period doubling occurs near μ = 3.45 and another near μ = 3.545. A
careful inspection of the figure below (called a bifurcation diagram) shows that there are
more period doublings and that the values of μ at which the period doublings occur get
closer together Singh & Singh, (2002).
Figure 2: Shows how a population may grow for certain values of the u, oscillate for a range
of u values and then become chaotic for certain values of u. Singh & Singh, (2002):
18. By zooming in on regions of the bifurcation diagram for the logistic map of Figure 1, we find
that the pattern of the bifurcation diagram re-appears at all scales. This is an example of self-
similarity.
Zoom into the range 3.4 < μ < 3.65, we find
Figure 3: Image in zoom Range 3.4 < μ < 3.65. Singh & Singh, (2002)
Zooming in again to 3.53 < μ < 3.585, we find
Figure 4: Image at zoom range 3.53 < μ < 3.585. Singh & Singh, (2002)
19. And zooming in once more 3.562 < μ < 3.573
Figure 5: Image at Zoom range 3.53 < μ < 3.585 Singh & Singh, (2002)
Fractal, is a term first coined in 1975 by 20th century mathematician Benoit Mandelbrot,
from the Latin word fractus to mean irregular or fragmented. In their simpler forms, fractals
are images of a repeating pattern or formula that starts out simple and gets progressively
more complex. Fractals abound in nature. The English mathematician Lewis Fry Richardson
in attempt to study the length of the English coastline realised that the length of the
coastline depends on the measurement tool. Different measuring scales produced different
results as the different scales take in more detailed accounts of the irregularity of the
coastline Peitgen, Jurgens & Saupe(pg 192, 1992). The differently sized measuring scale
results point to a property of fractals termed self-similarity.
All fractals show a degree of self-similarity. This means that as one looks closer and closer
into the details of a fractal, one can see a replica of the whole. The English coastline is an
example of self-similarity in nature. There are many others. E.g, the leaves on a tree, clouds,
human faces etc. each instant looks similar to the entire lot. They are self-similar to the
original, just at a different scale. Fractals are also self-repeating or recursive, regardless of
scale. In traditional geometry, length, width and height are the three dimensions. In fractal
geometry irregular shapes are created using fractal dimension; the fractal dimension of a
shape is a way of measuring that shape's complexity. In general, if the dimension is delta ɗ,
and k pieces of size 1/n are to be fitted together to reassemble the original shape, then k =
nd.. Taking the log, we have
ɗ = log k/log n (2)
20. The self-similar patterns shown in figures 2-6 are the result of a simple equation, or
mathematical statement. Fractals can hence be created by repeating non linear equations
with known fractal dimension ɗ.
21. 6 Proposed Research Approach
1. Conduct further literature review
2. Show that current project management prediction methods are linear by using
information contained in earlier published papers
3. Discuss chaos theory, fractal and self-similarity intuitively and using diagrams
4. Demonstrate that project management is fractal and self-similar
5. Postulate that projects by their behaviours are better understood and analysed using
chaos theory (fractals- non-linear dynamics), rather than linear dynamics. Therefore
fractal theory, particularly self-similarity can be used to better predict project
outcomes (i.e mimic initial objectives) if
i. Project goal/objective(s) are clearly specified
ii. PM has personal attributes the skill set that matches the project
goal/objective (self to (1)
iii. Team members are also aligned to all or at least one goal each (similar to i &
ii)
6. Provide evidence for the postulation—
i. model and simulate the postulates
a. define one project expected project outcome; e.g. cost
b. define other project factors that affect cost, duration and quality
c. identify human preferences, behaviours and skills that may be considered
as closely related to cost
d. identify team preferences, behaviours and skills that may be considered
as closely related to cost
e. define a nonlinear function that links all of the above
f. iterate the function as necessary so as to show various patterns (different
stages of non-linear dynamic systems behaviour)
g. use these patterns to illustrate acceptable and non-acceptable regions of
project performance
ii. Interpret empirical data to confirm approach:
a. Collect project related data estimated cost, actual cost, duration, quality
outcome, project manager personality and team dynamic type for several
projects.
b. Normalise the data.
c. Plot the normalised data hoping to show self-similarity (scale of project
does not appear to affect statistics)
22. 7. Apply the postulates- List and discuss possible uses of the postulates in predicting
project successful outcomes
8. Thesis write up
The above approach is illustrated diagrammatically below:
23. Figure 6: Research Approach
Familiarise with University and School Environment &
Proceed with Further Literature Review
Review project
outcome prediction
methods
Review Project
Management
Methods
Link PM methods to
prediction methods
Demonstrate Non
linearity of PM
Link PM to
prediction to
Identify FLAW in
prediction
assumptions
Are prediction
models too linear
for a Non linear
process?
Non Linear Dynamic Systems & Applications
Yes
No
Study Chaos Theory
& Applications
Review Non Linear
system models
Study Elements of
Chaos Theory
Applicable to PM
Study Fractals & Self
Similarity (SS)
Apply Fractal SS to PM
prediction
Deduce Research
Postulates
Provide Evidence to support postulates
Empirical Data ProcessModelling & Simulation Process
Model Postulates Simulate model
Analyse and graph
simulation outcomes
Provide observations
on outcomes
Define survey
parameters/
questions/format
Define survey
platform or medium
Conduct Pilot
survey results
Conduct Major
survey
Assemble/ Analyse /
Graph Survey
results
Provide
observations on
outcomes
Validate/Reject Postulate
Compare/Contrast
model with
empirical data
List applications of
model in PM
Thesis Write
Validate/Reject
Model
Uncover reasons/
Indicate possible
improvement
Suggest possible
improvement
Reject
Validate
Evidence stage
ended
24. 6.1 Project Non Linear Dynamic Model
Project implementation is often stated to be constrained by the Cost, Time and Quality. The
Iron triangle or triple constraints. This assumes a static view of project activities that cost,
time and quality are fixed and meet at only certain points. In reality, these three interact
throughout project implementation and decisions made regarding which of them is most
relevant. Considering dynamics in project, the research proposes to use a three dimensional
representation of project works, representing cost, time and quality over a continuum of
changing states. That is cost; time and quality are continuums that meet in infinite places in
the boundaries set by their three continuums.
The research will propose project management non-linear dynamic system equations of the
form:
Project outcome, Po = f(c) (3)
Project Cost, C = f(d, q, ρ, ϒ) (4).
For a given scope of work, equations (3) is stating that project outcome, P is a function of
cost(c). Cost in turn is a function of duration (d), quality (q), project manager efficiency (ρ,
rho) and project team efficiency (ϒ, tau). The equation comes from intuitive knowledge of
projects. The cost to build one house is lower than the cost to build two houses. The cost to
build one house with all quality requirements fulfilled is more likely to be more than the cost
of the same house with just about half of quality requirements fulfilled. The cost to build
one house in say 6 month is more likely to be lower than to build the same house in say
twelve months. A more efficient project manager and project team are more likely to build
one house cheaper than a less efficient project manager and project team. It is hence likely
that the values that optimise equation (4) will also optimise equation (3).
Using the box model we shall make the following definitions:
Quality: all scoped works are completed as per the documented requirements of the
sponsor. When this occurs quality q is defined to have value of 1.
Cost: all scoped works are completed as per the documented requirements of the sponsor at
the initial estimated cost of the project. When this happens cost c is defined to have value of
1.
Cost, Time, Qualityinterface anywhere
in box
Quality
Cost Duration
Figure 7: Project Delivery Constraints Models
25. Duration: all scoped works are completed as per the documented requirements of the
sponsor at the initial estimated duration of the project. When this happens duration d is
defined to have value of 1.
The box representation of the project is hence a unit sized cube. The research seeks to
determine particular nonlinear dynamics systems model fitted with p and ϒ values that keep
project outcome constrained within the cube or otherwise. The research will establish
appropriate equations of the forms above (3) and (4) and iterate them over different values
of ρ and ϒ.
The values of ρ and ϒ are to be obtained as follows:
It has been stated earlier that a number of personality and team dynamic profiling methods
exist. The research work will review and select the most relevant method for project
manager and team performances. The personality and team performance or characteristic
groupings in these methods will be assigned unique values rho (ρ) and tau (ϒ). For example,
using the popular Myers Briggs MBTI classifications, unique rho values will be assigned to
each of the sixteen different Myers Briggs MBTI personality types. These are to be used in
the non-linear dynamic equation established earlier as personality constants. Similarly using
the TOPS or other appropriate team model, tau values will be assigned to each of the model
outcomes. These are to be used in the non-linear dynamic equation established earlier as
team dynamic constants. The natures and types of values used will be determined according
to the form of equation (4).
In addition to cost, duration, quality, scope, projects outcomes are also determined by
additional factors of project sponsor/owner, governance, environment, and stakeholder. In
this respect a project can be described as having eight (8) dimensions. There is however an
infinite number of ways that each of these, influences the project outcome. This means that
the project outcome determination is not just an easy case of knowing the values of the
eight factors at a specific time and then concluding the outcome of the project. Like the
classic case of the coast line of England, the outcome of a project is dependent on the scale
of measurement being used. That is how closely or loosely do these factors interact to
determine project outcome basin?
The final shape of a project is therefore a function of these eight elements (k in equation 2)
but their degrees of combination (n in equation 2) to arrive at the final project outcome or
shape is very much unique to each project and pretty very much undetermined. The degree
of combination of the eight elements (n) is very much a decision made by the project
manager and team. That is the project manager and project team become the “ɗ” in
equation 2. They make the call on the best ways to combine the eight elements to arrive at a
desired project outcome basin or shape. In this research, the number of elements is to be
reduced to cost, duration and quality (i.e. k = 3).
26. 6.2 Project model simulation
The logistic mapping diagrams in figures 2 – 5 show self-similarity of fractals. The behaviour
of a chaotic system takes on fractal geometry. There are several techniques with which to
measure fractals and thus measure the degree of order amid disorder (Holden, Chapters 13
and 14, 1986). The fractal dimension “ɗ” which is an estimate of the degree to which a
system occupies a region in an outcome basin can hence be computed for fractals. In a
similar way, equation 4, when iterated over several instances of rho and tau will produce
fractals that will exhibit self-similarity. The research proposes to determine which
combination values of rho and tau, ”ɗ” lead to desired project outcome basins or shapes.
Project strategy determination can apply fractal self-similarity principles. It will involve the
optimal arrangement of dynamical states (k elements) available to project planners. This
means that one analyses the data of performance/orientation of project manager and
project team members to map out the ideal self-similar fractal dimension (rho and tau) that
will result in optimal combination of the units (n in equation 2) of k elements to result in
desired project outcome shape or basin. With this knowledge, one can identify key
parameters which push a person, a team or project into creative or into destabilizing change.
The task of the project manager is to expedite bifurcations (branching) which produce
desirable attractors and, at the same time, to control key parameters to stabilize such
outcome states for the project.
6.3 Empirical Data collection
To validate the modelling and simulation approach, the research will collect data of finished
projects. The data to be collected is to include cost, duration, quality outcome, project
manager personality and team dynamic type. This is to be done by a two part survey. Part 1
of the survey is essentially project data (cost, duration, quality outcome). Part 2 deals with
project manager personality and team dynamic type. Survey questionnaires will be used to
generate project data from a large population of industry practitioners. (Tharenou, Donohue
& Cooper 2007; Easterbrook et al. 2008). Such large population of industry practitioners is
expected to be available from the various project management associations, two of which
the researcher belongs to. Statistical methods are to be used to determine target population
and sample sizes.
For reasons of ensuring quality and avoiding wasted resources, an initial feasibility study will
be conducted with a small group of respondents representing the target population to gauge
and evaluate the level of understanding and accuracy of interpreting questions (Forza 2002;
Easterbrook et al. 2008; Bowen, Edwards & Cattell 2009).
Considering the highly technical and perceptive nature of the Part 2 survey, a key informant
approach will be used to select the respondents of the survey (Jin & Zhang 2010). A key
requirement of the respondents is that they and/or team would have already undertaken a
personality or team dynamic profiling. Potential respondents are to be sourced from public,
private and NGOs institutions with significant project delivery activities who are preferably
27. members of Australian Institute of Project Managers or/and the Project Management
Institute. The researcher is a member of these associations.
Online survey is recognised as an effective method of quantitative data collection (Forza
2002; Bowen, Edwards & Cattell 2009). In particular, Survey Monkey (web based tool) which
has been shown to an effective and an economically viable tool, especially when considering
widespread geographical distribution of the participants (Van Selm & Jankowski 2006) will
be used. Survey Monkey can also preserve anonymity and will serve well for the Part 2 of
the survey. Potential respondents will be supplied with background information along with
the web link for the questionnaire survey via e-mails (Forza 2002). Return of completed
questionaries will be considered as provision of consents in participating in the research.
The data collection will not involve any one taking a personality test. Rather, personality type
and team dynamic options will be presented and a choice made on which options best fitted
the project manager and the team.
The data generated from the survey is to be normalised by a common factor and then
plotted. The aim is to establish that empirical data plots generate the same or identical
project outcome basins as those from the non-linear simulation.
28. 7 Trial Table of Contents/Proposed Papers
Chapter1 Research Statement & Rationale
Background
Overview of the thesis
Research justification
Summary of results
Chapter2 Literature review
Chapter3 Chaos Theory and Elements ( Fractalsand Self Similarity)
Chapter4 Project management, fractals and self-similarity
Paper1: “Application of fractals and self-similarity to predicting project
outcome basins?”
Chapter5 Project,ProjectManager/TeamGoalsdefinition
Personality ProfilingMethods
TeamDynamicProfilingMethods
Paper2: ”Defining Project, Project Manager and Team goals to enhance desired
project outcome basins”
Chapter6 Modelling and Simulation of postulates (Evolution of self-similar goals
over time)
I. Definition of project expected outcome basin
II. Identification of human preferences
III. Identification of team preferences
IV. Define linkage function
V. Function iteration
VI. Outcome basins
Paper3: “Outcome patterns (basins) for self-similar goals over project phases”
Chapter7 Empirical data to confirm approach
I. Empirical data gathering
II. Data Normalisation
III. Aligning normalised data to simulated outcome basins
Paper4: “Predicting project cost outcomes: A comparison of simulated and
empirical project data”
Chapter8 Discussion of alignment outcomes
Paper5: “Application of fractal self-similarity in predicting project outcome
basins
Chapter9 Conclusions
Introduction
Summary of results
Conclusion
Recommendation for further research
29. 8 Research Outcomes & Limitations
In achieving the six (6) objectives listed earlier, the research expects to produce the
following outcomes:
Given a specific project goal, it is possible to determine the
1. optimal combination of project manager personality and team dynamics to achieve
the project goal
2. general project cost outcome basins (i.e. possible areas costs would lie, given specific
duration and quality)
3. points or times in the project delivery when cost blow outs could start occurring
It is instructive to note that the research proposal nominated eight (8) elements that
influence project outcome. The research is making the explicit assumption that the ways
these eight elements are combined are determined by the project manager and the team.
The research hence expects that these eight factors could be managed so as to arrive at
different cost outcomes. If the research approach and the model used are validated, the
tool has the potential therefore to predict cost blow outs on all large projects. This in the
main will be a significant outcome for the research.
There are a number of limitations to the approach. The general disposition of a person may
be revealed through personality profiling. However, a person’s basic personality is subject to
daily variations due to various factors in the person’s life. There is no system yet to profile
how the person will be behave on daily basis considering all the different things that may
happen to the person. This means that whilst the general personality type may be by
constant, the daily behaviour and decisions may be influenced by events in the person’s life
that are not predictable nor conform to the general personality type. The research assumes
that personality types and associated behaviour remains constant. This is a limitation.
The same reasoning holds for team dynamic and behaviour too.
Although the research proposal nominated eight (8) elements that influence project cost
outcome, only three are considered. This is a limitation for the current research. However, if
the research approach is validated, the model can be extended to all other eight elements.
The research assumes the availability of data relating to project budgeted cost, final cost,
team and personality profiles. Whilst there may be data for all of them, linking them
together to have a significant sample size may prove problematic.
The above are significant limitations, however, the researcher is of the view that models are
often built on ideal conditions. It is hence appropriate to use idealised parameters in the
model.
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