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PREDICTION OF PROJECT PERFORMANCE
DEVELOPMENT OF A CONCEPTUAL MODEL FOR PREDICTING FUTURE
PERFORMANCE OF AN OG&CPROJECT IN EPCENV IRONMENT.
Naresh K. Kaushik
Delft Unive rsity o f tec hno log y
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PREDICTION OF
PROJECT
PERFORMANCE
Developmentofapredictionmodelfor
predictingfutureperformanceofanO&C
projectinEPCenvironment
Thesis report
Public version
Naresh Kaushik
Student Number 4141555
Master of Science thesis
System Engineering Policy Analysis and ManagementFaculty of Technology, Policy and Management
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PROJECTDETAILS
Author: Naresh K. KaushikStudent Number: 4141555Email: naresh_kasuhik@hotmail.com
This report is for thesis graduation project for:
Study program: System, engineering policy analysis and management (SEPAM)Graduation section: System EngineeringFaculty of Technology, Policy and managementDelft University of technology
Graduation Date:
5th of April, 2013
This research is performed in collaboration with
FlUOR B.V, HaarlemDepartment: Project controls
Graduation committee:
Chair: Prof. dr. ir. Alexander VerbraeckSection: Systems Engineering, Faculty of Technology, Policy, and Management
First supervisor: Dr. Mamadou D. SeckSection: Systems Engineering, Faculty of Technology, Policy, and Management
Second supervisor: Dr. W.W. VeenemanSection: Policy, Organization, Law and Gaming Faculty of Technology, Policy, and Management
External supervisor: Robert V. VelzenE&C Global Leader for Project Controls/Estimating Fluor Corporation
External supervisor: Erik J. GroenewegProject controls manager Fluor Corporation
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PREFACE
This thesis report on development of performance prediction model is the result of my
graduation thesis for master program System engineering policy analysis and managementat Delft University of technology. I performed this thesis research as graduation intern atFluor Corporation at their Haarlem Office.
The past 7 months of my master thesis has been a great learning experience academically,professionally and personally. The research topic turned to be quite complex and resultedinto lot a large scope for research. However, I enjoyed every bit of this research.
At the conclusion of my research, I convey my warm thanks to my supervisors at TUdelft: Mamadou Seck, Wijnand veeneman and Alexander Verbraeck for their continuoussupport and encouragement. My special thanks to Mamadou seck for extra support in formof frequent meetings and discussion that help my research.
I would like to thank Robert V. Velzen for providing me this opportunity to conduct thisresearch at Fluor and his invaluable role as my supervisor. In addition, I would like tothank Erik for his continuous guidance and feedback on my research. Furthermore, Iwould like to thank everybody at Fluor Haarlem that contributed to my research in form ofsemi-structured interviews and informal discussions.
Finally, I would like to thank Kees Berends, Professor Hans Bakker from shell and TedOng from Exxon for providing their useful insights during interviews.
I hope you all enjoy reading the results
Naresh Kaushik
Delft, March 2013
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EXECUTIVESUMMARY
The projects in oil and chemical (O&C) industry often experience problems during theirexecution, because of those problems, some of the project ends with large cost and
schedule overruns. The poor performance of projects not only affects the strategic objective
of projects owner but also poses a dual threat to engineering and construction (E&C)
companies. They negatively affect their profit margins and their business objectives. Given
the strict budget constraints imposed by the present global economic situation, owners and
stakeholders expect their projects to be delivered cost effectively and efficiently. Therefore,
it is important for E&C companies to strive for improvement in their project management
practices.
The current thesis research is a step in direction to introduce a new concept for
improvement in performance management practices. For that purpose, the research
introduces early detection of project problems as the main instrument and uses thequantitative information from past project to develop a body of knowledge and first
conceptual model to predict the future performance of projects at their early stages.
The research is conducted in five phases, the first phase of the research explores O&C
project and their performance management practices. Based on the gathered knowledge via
literature study and available information, the main research question is formulated as
How can future problems and performance of a current O&C project be predicted at early
stages using knowledge and experience from past projects in an EPC environment?
Thereafter, a series of sub questions were formulated aimed to answer the above-mentioned
research question. The later part of the first phase developed a structured research approachand research methods.
In the second part of the research, efforts were directed to find the so-called early
warnings of problems. To identify the early warnings, two main sources were explored,
literature and experts from O&C project industry. Each investigation into respective
sources resulted into number of early warnings. Each identified early warning was
evaluated on selection criteria with three selection parameters. After the careful evaluation,
the following ten early warnings were selected.
ID Early warning indicator
LES Lack of understanding of project execution strategy among project team
PTE Project team lacks experience required for the project
COC Conflicts between owner and E&C contractorNCO Numbers of change orders
CCO Cost impact of changes
FED Percentage of missing information in FEED package
PH Growth in process man-hours
PS Delay in process engineering
CE Change in concurrency level between process and piping engineering
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DPO Delay in issuance of purchase orders
The selected early warnings were carried to the third phase of the research, in which four
detailed case studies were performed to have observatory evidence. The case studies in thisphase consisted of four project with different performance levels. The difference in
performance levels of case projects set the contrast in which the predictive capability of
early warnings could be observed. The case study investigation found that there is a
relationship between early warnings, project problems and project performance.
After obtaining the observatory evidence, the fourth phase of the research adopted a purely
quantitative approach and studied the behavior of early warnings in a relatively larger set of
past projects. Subsequently correlation analysis was performed to find correlations between
early warnings and final project outcomes (which collectively asses the project
performance). The quantitative analysis did present interesting and encouraging results.
The main results are mentioned as follows:
I. Early warnings do behave differently in case of poor and good performance
projects, few in terms of their absolute value and few in their incremental changes.
II. Correlations do exist between EWI and project outcomes, however not all the EWI
found to be correlated with all project outcomes.
III. The EWI indicators does show a dynamic quantitative relationship with project
outcomes over engineering duration of the project
Using the results from quantitative analysis, an attempt is made in the last phase of this
research for the development of prediction model, which can predict the future
performance of projects. The results of pilot prediction model were analyzed and comparedwith forecasts made via traditional forecasting methods. The comparison of forecasts found
that prediction model does make prediction that is more accurate. However, there are errors
with-in prediction models. In addition, the external validation of model suggested limited
reliability and accuracy of pilot model.
The dataset used for quantitative analysis and building of prediction model is relatively
small and limit the generalization of findings. Therefore, to have a more accurate prediction
in good projects, a dataset is required which contains a balance of Successful and less than
successful performance projects. Despite the smaller dataset, the findings and approaches
presented in this research can be used to build a useful model and subsequently applied in
O&C project industry. A set of insights and recommendations (short term and long term)
has been made for Fluor to implement the findings of this research to develop anoperational performance prediction system.
The research possibly has following main contributions to scientific and industry.
Contribution to scientific community
I. A shift from reactive project management to proactive project management
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II. A new and constructive role of past projects
Contribution to O&C project industry
I. An approach, which facilitate the early detection of future potential problems
II. An approach to capitalize on past projects to improve project performance
management
Note: The confidentially apply to the part of attachments, therefore are not attached with
this report.
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TABLE OF CONTENTS
1 Introduction ...................................... ........................................ .......................................12
2 Research description .......................................... ........................................ .....................162.1 Summary ........................................ ............................................ ............................16
2.2 Overview of oil & chemical project execution.......................................................16
2.3 Research problem......................................... ........................................... ..............21
2.4 Research questions .......................................... ........................................ ..............25
2.5 Research goals and deliverables ........................................ ...................................26
2.6 Relevance........... ........................................... ......................................... ................26
3 Research design........................................... ........................................... .........................28
3.1 Summary ........................................ ............................................ ............................28
3.2 Research scope ...................................... ........................................... .....................28
3.3 Fundamental approach........................... ............................................ ...................28
3.4 Research methods..................................... ............................................ .................32
4 Literature study.......................... ........................................... ........................................... 34
4.1 Summary ........................................ ............................................ ............................34
4.2 Author affiliations..................................... ............................................ .................34
4.3 Concept of project success.....................................................................................35
4.4 Concept of early warnings.....................................................................................40
4.5 Conclusions and discussions ...................................... ........................................... 45
5 Early warnings in projects.................................. ........................................ .....................47
5.1 Summary ........................................ ............................................ ............................47
5.2 identification of early warnings.............................................................................47
5.3 Selection criteria of early warnings ...................................... ................................48
5.4 Selection of Early warnings...................................................................................49
5.5 Early warnings from literature.......................................... ....................................50
5.6 Early warnings from experts ...................................... ........................................... 53
5.7 Early warning indicators..................................... ........................................... .......58
5.8 Discussion and conclusion ......................................... ........................................... 62
6 Case Studies.....................................................................................................................63
6.1 Summary ........................................ ............................................ ............................63
6.2 Case study design .................................. ........................................... .....................63
6.3 Case study selection...............................................................................................64
6.4 Case 1 (Less than successful project)....................................................................656.5 Case 2 (successful project) ...................................... ........................................... ...68
6.6 Case 3 (successful project) ...................................... ........................................... ...71
6.7 Case 4 (Less than successful project)....................................................................73
6.8 Cross case analysis................................................................................................77
6.9 Discussion and conclusion ......................................... ........................................... 79
7 Quantitative analysis .......................................... ........................................... ..................81
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Summary 81
7.1 Analysis approach ........................................ ........................................... ..............81
7.2 Exploratory data analysis....... ............................................ ...................................82
7.3 Quantitative analysis ........................................ ....................................... ..............85
7.4 Correlations over engineering duration................................................................89
7.5 Discussion and conclusions........................................ ........................................... 93
8 Development of prediction model ......................................... ...........................................97
8.1 Requirements and guidelines for performance prediction model................ ..........97
8.2 Selection of prediction Methodology.....................................................................98
8.3 Prediction model development approach .......................................... ....................99
8.4 Development of pilot Prediction model ................................. ..............................102
8.5 Model evaluation methods..................................... ......................................... .....104
8.6 Analysis of predictions........................... ........................................... ...................105
8.7 External validation ....................................... ........................................... ............110
8.8 Final evaluation........ ........................................... ........................................... .....1148.9 Integration of project problems with prediction model .................................. .....115
8.10 Discussion and conclusion ......................................... .........................................117
9 Insights and recommendations for implementation ................................ ......................119
9.1 Insights and recommendations .......................................... ..................................119
9.2 Recommendations for implementation ......................................... .......................120
10 Conclusions and reflections...........................................................................................125
10.1 Revisiting research questions .............................. ...................................... ..........125
10.2 Answer to the main RESEARCH question.......................................... .................129
10.3 Discussion on research goals and deliverables.................................... ...............130
10.4 Contribution to scientific community...................................................................13010.5 Contribution to O&C project industry ......................................... .......................131
10.6 Final reflections............... ........................................... .........................................132
10.7 Future research opportunities .......................................... ...................................134
11 References........................ ........................................... ........................................... ........135
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LISTOFFIGURES
Figure 1 Success and failure of O&C projects........................................................................................... 13
Figure 2 Phases of OG&C projects............................................................................................................ 17Figure 3: The generic control cycle............................................................................................................ 19
Figure 4: Conceptual procedure for controlling of projects ..................................................................... 21
Figure 5: Cost of reactive approach........................................................................................................... 23
Figure 6: Existing knowledge gaps............................................................................................................. 24
Figure 7: The wheel of science (Wallace, 1971)........................................................................................ 29
Figure 8: Fundamental research approach................................................................................................ 31
Figure 9: Iron triangle of projects.............................................................................................................. 36
Figure 10: Project success criteria............................................................................................................. 37
Figure 11: 95 % engineering completion milestone .................................................................................. 39
Figure 12 Potential benefits of EWI ........................................................................................................... 45
Figure 13: Early warning selection criteria............................................................................................... 48
Figure 14 Classification of early warnings from literature by source ...................................................... 51
Figure 15 Early warnings from literature by sub-category....................................................................... 52
Figure 16: Early warning from experts by sub-category........................................................................... 56
Figure 17 Early warning mentioned by numbers of experts...................................................................... 57
Figure 18 Framework for mapping the relationship between early warnings, project problems, and
project outcomes.......................................................................................................................................... 64
19-27 Confidential
Figure 28: Quantitative analysis approach................................................................................................ 82
28-37 Confidential
Figure 38 Significant correlations between EWI and project outcomes at each prediction moment...... 91
Figure 39 Significant correlations of project outcomes with EWI over engineering duration................. 92
Figure 40 : Step approach for development of prediction model ............................................................ 101
Figure 41 Predictive capability comparison of traditional method and developed prediction tool. ...... 105
Figure 42 : Errors in prediction of final TIC ........................................................................................... 108
Figure 43 Errors in prediction of ESI....................................................................................................... 108
Figure 44 Errors in prediction of MHI..................................................................................................... 109
Figure 45 Errors in prediction of MCI..................................................................................................... 110
Figure 46: Model validation: prediction of TIC....................................................................................... 111
Figure 47 Model validation: prediction of MCI....................................................................................... 112
Figure 48: Model validation- prediction of ESI....................................................................................... 113
Figure 49: Usability of prediction Model................................................................................................. 121
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Figure 50 future project problems and project outcomes associated with NCO .................................... 128
Figure 51: synthesis of answer to main research question...................................................................... 129
Figure 52: Data collection moments ........................................................................................................ 153
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KEYABBREVIATIONS
O&C Oil and Chemicals
EPC Engineering, Procurement and Construction
E&C Engineering and Construction
E&P Energy and petroleum
BOD Basis of Design
BDP Basic Design Package
CII Construction Industry Institute
FEED Front End Engineering Design
IPA Independent Project Analysis
PEP Project Execution plan
EVM Earned Value Management
EWI Early Warning indicator
ESI Engineering Schedule Index
TIC Total Installed Cost
MCI Mechanical Completion index
COP Cost of Problems
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1 INTRODUCTION
The oil and chemical owner companies rely heavily on engineering and construction (E&C)
companies to meet their strategic objectives such as building of a new assets, expansionand performance improvement of existing assets. Moreover, given the strict budget
constraints imposed by the present global economic situation, owners and stakeholders
expect their projects to be delivered cost effectively and efficiently. E&C companies are
working hard to match these expectations by changing their project management methods,
tools and the way they execute projects.
However, there are sufficient examples of projects, where E&C companies face problems
in meeting their as sold cost estimates, agreed upon schedules and desired quality
requirements. The number of project, that fail to meet their stated objectives vary
significantly per industry, mainly due to the difference in complexity, the industrys market
dynamics, the type of stakeholders and their influence levels. Many researchers
investigated the reasons for poor performance of projects (Flyvbjerg & Bruzelius, 2003;Morris & Hough, 1987; Turner, 1999; Thamhain & Wilemon, 1986).
For example, handbook of project-based management by Turner mentions several reasons
for projects poor performance such as poor project establishment in terms of priorities, bad
initial planning, inefficient control procedures and many more (Turner, 1999). Flyvbjer and
Bruzelius (2003) suggested that in projects decision-making, planning and management are
typically multi-actor processes with conflicting interests and therefore, projects are often
faced with mistrust, violation of good project governance practices, ambiguity and poor
collective decision-making (Flyvbjerg & Bruzelius, 2003). The above-mentioned behaviors
of stakeholders penetrate through the permeable boundaries of project plans and can lead a
project to high cost and schedule overruns.
In this respect, projects in the oil and chemical (O&C) industry are no exception. Although
the performance of O&C projects seems to be better than that of civil or mining projects,
there are still ample examples of poor performing projects. Mckenna, Wilczynski and
Vandersee (2006) estimated that about 30-40 % of capital project in O&C industry suffer
from a budget and/or schedule overrun larger than 10%.
Figure 1 shows the result of a study conducted by Independent Project Analysis (IPA). The
study includes 318 projects across the O&C industry. Out of those projects, only 50% can
be categorized as successful. The other 50% incurred either 33% cost overrun and/or
schedule overrun of more than 30% (Merrow, 2012). Two third of the projects even failed
to meet the production schedule or targets, thus affecting the profitability of its investors.
The above results definitely are of serious concern for both the E&C and the ownercompanies.
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Figure 1 Success and failure of O&C projects
(Source: IPA, 2012)From the above discussion, it can be concluded that O&C projects had experienced
problems in the past and might encounter problems and challenges in future. There have
been multiple attempts by the academic as well as industry experts to explore potential
areas of improvement such as risk management, stakeholder management, benchmarking
practices, and project control practices to improve the situation. Accordingly, there have
been achievements such as development of value improvement practices (VIP), industrys
best practices, and front end loading (FEL) to mention a few. However, the majority of the
research has been focused in the frond end phase of the projects.
Prominently, the importance of the FEED phase for improving project performance is
suggested over the years (Artto, Lehtonen, & Saranen, 2000; Thamhain & Wilemon, 1986)
and little focus has been given to the execution phase of the project, where the problemsactually surface and affect the project performance. The control mechanism of project
execution phase (EPC) has seen little advancement and is still relying on the principles
defined in 50-60s such as principle of deviation management (Vanhoucke, 2011;
Nikander, 2002).
Why the deviation based traditional control mechanisms would not be suitable for
successful control of project execution? There are two major problems with traditional
deviation based control methods. First, the deviations are reported on aggregated level
therefore, the poor performance in one part of the project is masked by good performance
in other part of the project (Vanhoucke, 2011; Nikander, 2002). Secondly, even if the
localized deviations are observed, they are seen in limited manner. The cascading effect of
localized deviations on other activities is neither reported nor anticipated by these methods.Therefore, the accuracy of future forecasts of project performance based on the deviations
is somewhat debatable.
As a consequence to above mentioned fallacies in deviation management principles, often
problems in a project are not visible until they are already manifested and degraded the
project performance. The corrective strategy to manifested problems can be termed as
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reactive approach, as the mangers act to correct what has already gone wrong. This reactive
approach brings additional schedule requirements and incurs substantial cost, thus add into
the cost and schedule overrun of the projects.
In addition, forecasting of future project performance based on traditional methods isvulnerable to optimism bias. The mangers are often seen as very optimistic against the
localized deviations and do not consider them as potential risk to future activities.
Interestingly, forecasters never mention optimism bias as a main cause of inaccurate
forecasts.
Then the question arises, where to look then for the improvement in the project control
management? A guided investigation of poor and good performance of past O&C project
could provide us an answer to this question. If problems could not be eliminated from
projects, can we predict problems, allowing longer correction time at lesser cost? This
capability will allow for their pro-active management with considerably less cost and
schedule impact.
The aim of this thesis is to take a first step in creating a scientific understanding of
prediction of problems via early warnings. Using this understanding, an attempt is made
within this research to build a quantitative model to predict the future performance of
project based on selected identified early warnings.
Looking at the different chapters that build this thesis, chapter 2 provides the background
for conducting this research by defining important concepts and delineating the main
research problem. The problem delineation guides the formulation of research questions.
Furthermore, research goals are introduced, the relevance of these goals is explained and
the main deliverables are defined.
In chapter 3, the design of this research is presented. The fundamental approach isdescribed with logical sequence of research phases. Subsequently, the employed research
methods and tools are explained and coupled with the goals set in chapter 2.
A literature study regarding project performance of O&C projects is provided in chapter 4.
The adopted measures of O&C project performance are presented. In this literature study,
the concept of early warnings is explored and relevant literature is reviewed. The chapter
also highlights the potential benefits of operationalizing early warnings in projects. Chapter
5 describes the identification of the early warnings from literature and from experts from
O&C project industry. Furthermore, the selection criteria for selecting key early warnings
are formulated, by focusing on the main objective of research. Each early warning is
evaluated on selection criteria and few were selected for further analysis.
In chapter 6, in-depth case studies are performed with an objective to have the preliminary
evidence of relation between early warning, problems and their relation with project
performance. In addition, Individual case conclusion and cross case analysis is performed
and presented.
In chapter 7, quantitative analysis is performed to 1) analyze the dynamic behavior of EWI
over engineering duration of the project to map the behavior with successful or less than
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successful projects. 2) Correlation analysis of EWI with specific project outcomes and 3)
Longitudinal correlation analysis to find suitable EWIs for development of prediction
model at each prediction moment
In chapter 8, based on the past project data, early warnings are assigned quantitativeindicators and an effort is been made to build a performance prediction model. The results
of developed pilot model are analyzed and external validation is performed.
Insights and general recommendations are provided in chapter 9. In addition, a short term
and long term implementation strategy is presented in chapter 9.
Finally, chapter 10 concludes the research by revisiting the research questions and their
answers and evaluating the contribution to scientific and O&C project industry.
Reflections have been made towards research approach, adopted methods, and results of
the research.
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2 RESEARCH DESCRIPTION
2.1 SUMMARY
The main objective of the current chapter is to understand the research problem, its context
and the research questions, which needs to be answered in order to find a solution to the
main research problem. In addition, the scientific and social relevance of research had been
provided.
This objective has been achieved sequentially by understanding the 1) execution of O&C
projects 2) their controlling mechanisms.
With the understanding of context, a critical review of current practices enabled the
delineation of research problem and existed knowledge gaps. The problem delineation
helped in forming the main research question. Furthermore, the main research question has
been broken into sub questions that need to be answered to obtain the solution to mainresearch problem.
The section 2.3.1 provides the overview of oil and chemical projects. Section 2.2.2
provides information on the subject of controlling mechanism of projects. Section 2.2.3,
integrates the above two sections and shift the attention specifically on current project
controlling mechanism employed in O&C projects.
Section 2.3 provides a critical overview of the current controlling mechanism and describes
the research problem. Based on the defined research problem, research questions are
formulated in section 2.4. Section 2.5 describes the research goals and main research
deliverables followed by relevance of research in scientific, social and business domains
(section 2.6).
2.2 OVERVIEW OF OIL & CHEMICAL PROJECT EXECUTION
2.2.1 Oil & Chemical projects
O&C plants are also addressed as process plants, mainly due the fact that they have
chemical processes at their heart. The chemical process convert the input (Crude oil,
chemicals) into other chemicals with higher economic value (Fuels, industrial chemicals)
by means of mechanical equipments, auxiliary facilities and the infrastructure to support
the whole plant.
The process plants are strategic assets of major petrochemical companies and are
fundamental to their business. Furthermore, the O&C chemical projects should not be seen
just as economical assets, they do contribute significantly in meeting the rising demands for
energy of society as a whole. Although an increase in production of renewable energy is
expected, experts still believe that the O&C industry will play an important role as energy
producer, at least in near future.
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O&C projects are capital intensive and do require systematic economic and project
planning to deliver their intended results. Therefore, almost every (O&C) project is
executed in systematic phases and its project life cycle encompasses the total time between
identification of the project need to its completion.
The different phases in project life cycle are (sub) projects in themselves and are separated
by gates or decision points. The gated project lifecycle means that at certain points in the
life cycle of project, the evolving design or plant concept and associated parameters (e.g.
cost, schedule, and environmental impact) must pass through certain decision/review gates.
The gated process allows for the evaluation of options based on the intended objectives of
its stakeholders and consequently the selection of optimal option. In this sense, the gated
process for a project allows for the structured way of decision-making. In addition, due to
the comprehensive reviews, the project stakeholders are more informed about the
deficiencies and/or risks in the project at a certain gate.
The figure 2 shows the stage gated project life cycle of a typical O&C project from scopedefinition phase to its completion. Harpum, in his article in book titled: The Wiley guide
to managing projects defined the basic rules for a project to pass through these gates
(Harpum, 2004). However, the specific rules and passing requirements differ according to
the individual companys procedures and criticality of a project.
Figure 2 Phases of OG&C projects
(Adapted from: The Wiley guide to project management, 2004)
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Furthermore, in practice, the strictness of these gates also depends upon many other
factors such as capital expenditure, urgency of project, contracting philosophy of owner
to name a few. The following paragraphs describe each phase of an O&C project in brief
manner.
At scope definition level, the requirements of owner are identified and what has to bedone is defined on a broader level. At the conceptual phase, basic functional
characteristics of a project are described as a system in terms of input(s), throughput(s),
outputs and major equipments required to achieve the desired production. In addition, the
major interconnections between subsystems of a project are determined based on the
process philosophy of the project (CII, 2004).
Subsequently, the preliminary design is performed to provide basic design information i.e.
process flow sheets, general design specifications, preliminary equipment specifications
and their arrangements, preliminary plot plans, preliminary estimates and preliminary
project execution strategy. In oil and chemical industry, conceptual and preliminary
engineering phase together are called front-end engineering design (FEED) and a key
deliverable at the end of FEED phase is the basic design package (BDP) (CII, 2004).
In the detailed engineering design phase, the BDP is detailed further as engineering
disciplines initiates detailed engineering in their respective domains. The main deliverables
of this phase are technical, procurement and construction documents. Table 1 shows the
main engineering disciplines typically involved in typical O&C project and their associated
main deliverables.
Table 1 Main engineering deliverables of detailed engineering phase
Engineering disciplines Key deliverables in detailed design
Process engineering Process and instrument diagrams (P&ID), equipmentand Instrument requirement list, control and relief valve
specs
Mechanical Equipment data sheets and equipment bid evaluation
Piping engineering Plot plan, Piping design, stress calculations, Iso metrics
and plant 3D model
Civil, structural and
Architectural
Foundations drawings, Structural steel drawings
Electrical and control systems Power system design, instrument data sheets, DCS
specification
In procurement phase, the buying process is initiated based on the design specifications of
equipment, instruments and materials. Later the contracts for civil works and installation of
mechanical, electrical, instruments and piping materials are awarded.
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During the construction phase, the facility is constructed according to the drawings and
specifications prepared during detailed design phase using material and equipment obtained
via procurement. In the start-up phase equipment are subjected to testing and inspection,
both individually and in combination to validate the proper functioning of the facility. The
phases detailed engineering, procurement and construction phase together are commonlyknown as EPC phase of project (CII, 2004).
2.2.2 Controlling of projects
Controlling is the measurement and correction of performance in order to make sure that enterprise
objectives and the plans devised to attain them are accomplished.
- Harold Koontz (1909-1984)
By definition, the control in project execution is exercised by measurement and comparing
of what was planned with what is being done i.e. finding the deviation between the
planned (known as baseline) and the actual. Figure 3:The generic control cycleShows thegeneric control cycle employed in a project.
The deviations could be caused by internal sub optimal performance and/or by influences
from external environment penetrating the permeable boundaries of project.
Fundamentally, control tries to make sure that the project stays on course to meet its pre-
defined objectives and goals. By definition, good monitoring and control mechanism
provides a better performance management over a project.
Figure 3: The generic control cycle
(Source: Brandon, 2004)
In the control cycle, What to measure varies with the type of project and the perspective
of the organization managing the project. The same is true for how to measure.
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Corrective actions are management prerogatives that are available to project manager based
on the type of organization and authorities of the project manager. Taking action to
improve the performance refers to the corrective action necessary to bring deviation to a
minimum level. Various examples of corrective action employed in project are fast
tracking, adding additional resources, scope reduction, trade-offs, increasing risks anddisciplinary actions and so on. Moreover, a specific corrective action is depending on the
type of problem causing the deviation.
2.2.3 Controlling of O&C projects:
Having defined the control mechanisms, the project execution control of O&C projects
could be seen in similar manner except the variables to be measured and tools could vary in
accordance with O&C projects.
The section 2.2.2 implies that for controlling, the first requirement is to establish a baseline
against which we could measure the deviation and actual performance of project. Toestablish a project baseline for an O&C project, the following project information should be
in available.
I. Overall cost estimates (-10%/+20% variation)
II. Work scope (refers to activities need to be accomplished to achieve the project
objectives)
III. Cost breakdown structure (Cost associated with activities i.e. services, equipments,
overheads, contingency)
IV. Project approved schedule (Milestones dates, activity durations)
V. Comprehensive risk analysis along with accepted risks, planned mitigation
strategies and actions
VI. Commercial baseline: As sold pricing, time bound revenue and margins.
The above documents act as basis for baseline developments. The final baselines for scope,
schedule and cost are established along with control strategies and parameters to identify
the deviations from the baseline.
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Figure 4: Conceptual procedure for controlling of projects
(Source: Fluor Corporation)
The Figure 4 above shows the applied concept of control cycle, specific to O&C projects.
As soon as the project proceeds into detailed engineering execution, progress and
performance are measured and monitored. In addition, the risks are monitored and dealt
with during the course of execution.
The progress in engineering, procurement and construction is monitored through earnedvalue
1(EVM) concept with visualization via progress curves (cost progress and schedule
progress). The primary instruments of project control are deviations between planned value
of work (PV), earned value of the work performed (EV), actual cost (AC).
Performance ratios are calculated at project level, phase level and discipline level,
signifying the performance at respective levels. Based on the deviations and performance
ratios, the required resources and cost for the balanced scope of work is forecasted along
with incorporation of any strategy to recover the deviations (Vanhoucke, 2011). Along with
cost and schedule performance ratios, multiple key performance indicators (KPI) such as
safety performance, quality performance are monitored.
2.3 RESEARCH PROBLEM
1Earned value management is a concept, in which progress is measured via integration of scope, costand schedule. (For more information on EVM, please refer Christensen, 1998; Lipke et.al, 2009.)
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2.3.1 Problem in current controlling practices
During project execution, projects are evaluated periodically using above described
parameters such as earned value, performance ratios, KPIs and variances from baseline.
Such conventional methods are based on the principle of deviation management. At a
certain moment in time, aggregated deviations reflect two aspects of project execution 1.)
How much project is deviating from its baseline 2) given the deviations, how the project is
performing i.e. performance of project?
When aggregated deviations in the project are visible and are regarded as significant, it
implies that there is/are problem(s) that has already manifested and degrading the project
performance: the problem can no longer be avoided. After identifying deviations and non-
desirable performance ratios, backward analysis is performed to search for the problems
and strategies to manage the impacts of the problem(s).
It should be noted that the deviations in project are mostly seen on aggregated level and the
impacts of deviations within an area are often seen as limited that area, as their impact on
total project performance is not clear. These localized problems become more critical if
they have significant effect on downstream parts of the project. However, in current
practices, these localized problems are not seen as problems but overlooked by aggregated
performance of project might be still in acceptable limits. Furthermore, when localized
problems develop into project problems, their delayed identification leads to additional
cost.
Figure 5 below explains this current problem more explicitly. The additional cost due to
reactive approach called as cost of reactive approach which could be significant based on
the nature of the problem and the timing of problem detection.
Generally, this cost of reactive approach contributes significantly to cost overrun onprojects. In addition, if the reporting of deviations is delayed due to any reason the cost to
fix those problems will increase significantly, driving the project cost and schedule way off
the baseline. The key to manage a project with predictability and certainty is to manage the
problems before they affect the project outcomes. In other words, acting proactively based
on the symptoms of problems (termed as early warnings) rather than reactively to the
problems.
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Figure 5: Cost of reactive approach
If these localized problems appear in the early phase of a project, given the interdependent
nature of project activities in O&C projects it is almost certain that will have negative
effect of downstream activities. However, the cascading effect of these problems at
aggregated level performance reporting is likely delayed. Therefore, the future forecasts
and project performance based on aggregated current performance is inaccurate.
The above paragraphs clearly indicate that the current controlling and performance
management practices lack the capability to detect the problems early enough and arealways somewhat late. In addition, the forecast based on these traditional methods might
not capture the change in dynamics of project due to localized problems.
Thus, rather than minimizing the cost and schedule overrun in projects they add to it by
providing inaccurate picture of project performance. However, if the localized problems
can be measured as early warnings in projects and proactive management of these early
warnings could minimize their impact and could significantly reduce the cost and schedule
overrun in projects.
In addition, having a more focused proactive approach can predict the future performance
of project with more certainty, But how can E&C companies can achieve that is still to be
discovered. Despite the vast body of literature covering the topic of project control andproject performance, there is still no clear knowledge regarding early detection of problems
and performance prediction based on the early warnings of problems (Vanhoucke, 2011;
(Nikander & Eloranta, 2001). Most of the literature either focuses on quantifying
deviations, diagnosis of deviation cause or corrective action decision making signifying a
clear knowledge gap.
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or limited quantitative data for improving their benchmarking database (Barber, 2004;
Williams, 2004).
This fallacy in past project analysis is another identified knowledge gap, which this
proposed research intends to fill. The fulfillment of above two identified knowledge gapscan be seen as complement to each other towards the development of performance
prediction model.
2.4 RESEARCH QUESTIONS
Having provided a background of the topic and description of the problem that the
proposed research intends to tackle, the main research question is formulated as follows:
How can future problems and performance of a current O&C project be predicted at
early stages using knowledge and experience from past projects in an EPCenvironment?
In order to find the answer to this main research question, it is necessary to proceed
systematically through a series of sub questions. The first set of sub-questions will
investigate performance assessment criteria employed in O&C projects and the concept
early warnings of potential future problems.
RQ.1 What constitutes project success and what are performance assessment criteria ofO&C projects?
RQ.2 What do we understand by early warnings of project problems?
The second set of sub questions will focus on identifying the early warnings in project
execution in general followed by identifying early warnings that are specific to O&C
projects. After having a set of early warnings the efforts will be directed to search the early
warnings that can be used in an accurate performance prediction model.
RQ.3 What early warnings can be identified in project execution?
RQ.4 Which early warnings can be operationalized to build a performance prediction
model.
The third set will use the identified early warnings in RQ.4 and investigates their detection,
problem prediction capability and their relation with project performance
RQ.5 What are the dynamics between early warnings and project performance?
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The final set of sub questions investigates the development of prediction model for
predicting the probable future performance of O&C projects.
RQ.6 What early warnings are indicative of deviation in project performance?
RQ.7 How performance prediction model could predict the future performance of O&C
projects?
2.5 RESEARCH GOALS AND DELIVERABLES
Having described the research problem and main research question, this thesis ultimately
aims to achieve following goals
I. To provide a new scientific base for understanding and analyzing the early
detection of project problems in capital O&C projects
II. To present a new scientific approach which facilitates more constructive utilization
of knowledge from past projects and exploring the power of prediction modeling
for successful performance management of capital O&C projects
In order to meet the above-mentioned goals, this thesis intent to deliver
I. An overview of early warnings to predict future problems in projects derived from
both academic literature and industry leaders, with observatory and quantitative
evidence from real past projects.
II. A methodology for analyzing early warning indicators in projects, their associated
future project problems and project performance
III. A conceptual performance prediction model systematically derived from
quantitative information from past projects.
Apart from this thesis, a set of recommendations will be presented along with conceptual
performance prediction model to Fluor Corporation
2.6 RELEVANCE
The relevance of the research results presented in this thesis is both scientific and social.
2.6.1 Scientific relevance
This thesis will contribute to scientific knowledge on project management, with a specific
focus on project execution of O&C projects, by
I. Exploring and gathering industry specific knowledge regarding early detection of
future project problems
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II. Providing a methodology to utilize past project information by pointing out the
early warnings and their relations to project performance
III. Exploring usefulness of performance prediction modeling techniques in project
management
The points mentioned above can act as a starting point for future research in project
management, marking a shift from traditional methods of project control to more enhanced
performance prediction. In addition, the content of this thesis will highlight the usefulness
of past project data, beyond their current use as estimation and planning benchmarks.
2.6.2 Social relevance
The insight gained from this research can be used to improve controlling practices in O&C
projects. The systematic process of early problem detection and development of prediction
model will be most important contribution, which can be applied to other industries. The
concept can be extended to other industry such as offshore facility development or civilinfrastructure.
More realistic predictions could lead to more proactive and informed decision making and
ultimately to better project performance. In a world of projects, where the capital
investments are high and efficient capital utilization is a prerequisite for development of
new projects, an improved project performance can provide strategic certainty in capital
planning.
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3 RESEARCH DESIGN
3.1 SUMMARY
As indicated in chapter 2, the main objective of this research project is to develop a model
to predict the future performance of projects at early stages by capitalizing on the
knowledge from past projects. The main instrument argued in chapter 2 for development of
such capability is the early warnings of problems in projects.
To direct the research efforts towards the achievement of the objective, a clear research
approach has been designed and is presented in this chapter. The section 3.2 illustrates the
scope of the research together with the argumentation for its selection. Subsequently, the
chapter provides a blue print of the researchs fundamental approach (section 3.3).
The section 3.4 aims to provide an overview of the research methods, tools and data
collection methodology. The chapter concludes by discussing the possible limitations of theadopted research approach.
3.2 RESEARCH SCOPE
To tackle the research problem efficiently, it is wise to limit the scope of research project
around relevancy of existed knowledge gaps. The present research focuses on EPC phase of
the project, which means phases between start of detailed engineering and mechanical
completion. More specifically, the research is focused on project within O&C industry,
which consists of either refineries or petrochemical processing plants and exclude offshore
projects.
In present research, the perspective of main engineering and construction (E&C) contractor
is adopted, mainly due the fact that throughout the EPC phase of the project, E&C
contractor is the main custodian and has primary responsibility to deliver the project as per
agreed term and conditions. In addition, the present research has been conducted with
significant support from Fluor Corporation, which is a renowned multinational E&
company and main stakeholder in this research.
The research is performed with in project controls department at Fluor Corporation at their
Haarlem office. Fluor Corporation is one of the largest multinational E&C contractors and
executed many O&C projects since its inception 100 years ago. The past projects executed
by Fluor Corporation are the primary sources of industrys project execution practices and
past project data.
3.3 FUNDAMENTAL APPROACH
In a research approach, two main methods of logic can be distinguished: deductive and
inductive reasoning. These are described in a well-known wheel of science (Wallace,
1971). The starting point of the present research is deductive in nature; Theory of weak
signals or early warnings is explored analogically in project management domain. This is
done by exploring the relevant scientific and professional literature. To compensate for the
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practical deficiencies in literature on early warnings, expert interviews are conducted to
gain in depth knowledge of early warnings and project performance in O&C project
execution. Based on the literature sources and expert interviews the main hypothesis is
formed and defined as early warning indicators have a (in) direct relationship with project
performance. Consequently, the early warnings have the ability to predict projectperformance. In the subsequent deductive phase, the hypothesis has been put to test via in-
depth case studies. Case studies based on past projects are performed to have observational
evidence of the hypothesis.
Subsequently, in the induction phase of the research, quantitative analysis is performed on
a larger set of past projects to have an empirical evidence of prediction ability of early
warning indicators. Based on the finding of quantitative analysis, a conceptual prediction
model is build and has been validated. In the last part of the research, conclusions are
formed based on results obtained from model and its validation. In the final section,
recommendations are made for implementation of conceptual model and future research
work.
Figure 7: The wheel of science (Wallace, 1971)
The fundamental approach has been illustrated in figure 8 with a detailed description in
following paragraphs along with the research processes. The research methods are
described in more detail in section 3.4.
In the first phase of the research, the concept of project performance is explored and criteria
for measurement of project performance are defined. The second part of this phase includes
exploration of early warnings concept, its application in project management and its
potential benefits during control of project execution.
In the second phase of the research, relevant literature is explored and experts areinterviewed to find out to what early warnings can be detected during execution of O&C
projects. Semi-structured interviews are held with experts in the O&C industry. The
majority of expert interviews are conducted within Fluor Corporation, along with some
experts from owner companies (to get the perspective of project owners). The second phase
is concluded by consolidating the early warnings from both literature and interviews,
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followed by the selection of those early warnings that are used in further investigation. The
employed selection criteria are based on main objective of the research project.
Third phase of the research is focused on the in-depth explanatory case studies. The cases
are selected from projects executed by Fluor in the past. Choosing different projects withinone company reduces the variations in execution procedures of projects, as all the projects
were executed with more or less same standard of project execution processes. The selected
projects include both Successful and less than successful performance projects to set the
contrast in which the differences can be visible.
In observatory sense, this phase is used as a reality check of our hypothesis and at the same
explained the relationship between early warnings, project problems and project outcomes
(performed in subsequent sections). As a result, this phase has a more explanatory
character.
The fourth phase of the research is purely quantitative in nature and investigates the
quantitative data from past projects with an objective to establish the predictive relationshipbetween early warnings and project performance. The quantitative data from eight O&C
projects is collected via available project documentation such as close out reports, project
status reports and detailed monthly progress reports.
The final phase of the research explores the methodology for building the prediction model
and presents the model itself. In this phase, several quantitative prediction methods are
presented and discussed, followed by selection of stepwise multi-regression as adopted
method. The developed model has been evaluated with a new past project (different from
projects those used to develop the model).
The research is concluded at two levels,
I. Presenting a set of recommendations and implementation strategy for Fluor
Corporation to adopt the model in their project control processes
II. Discussing the results of each phase and drawing conclusions from them and
subsequently integrating the parts of research to provide answer the main research
question.
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Figure 8: Fundamental research approach
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3.3.1 Limitations
Having provided the detailed overview of research problem and adopted fundamental researchapproach. It is necessary to realize the limitations of research approach and methods.
Quantitative analysis is highly depended on availability of data corresponding to early
warnings. The early warnings, for which the past data is not available, will be excluded from
quantitative analysis. This in turn, will affect the quality of research and subsequently,
development of prediction model.
Another identified and more critical limitation is that the past project data is very limited
therefore could limit the accuracy and reliability of prediction model. Furthermore, the data
is specific to Fluor Corporation. Thus, the data will likely be product of the standard and
practices of Fluor rather than O&C industry as whole.
3.4 RESEARCH METHODS
3.4.1 Bibliographic and desk research
The proposed research project consists of an evaluation of the existing knowledge on the
concepts of project management and primarily on early detection of problems in projects.
Relevant literature from scientific and professional domains was studied.
The main aim of this part is to understand the tools and procedures applied in management of
O&C projects. Project performance and success are defined based on the academic,
professional literature study and Fluors measurement standards. The concept of early
warnings was defined by the study of available literature by academicians, professional
organizations such as CII, IPA, and PMI along with expert interviews.
3.4.2 Expert interviews
Identifying early warnings relevant to O&C projects is an important task of the proposed
research. For that, the concept of early warning is defined upfront. Experts from O&C
industry were asked to provide potential early warnings based on their experience. The expert
interview is selected as suitable method because there is little or no literature is available
regarding early warnings, especially during the execution of O&C projects. Past project could
be seen as potential source of selecting early warnings. Nevertheless, the time required for
analysis of vast project data does not fit into the available timeframe, yet the past projects
played as role for observatory evidence and provider of past data to build the prediction
model.
Interview base include experienced project directors, project managers and project control
managers within Fluor Corporation and from some external owner companies. Interviewees
were asked to provide potential early warning along with their possible measurement criteria.
Furthermore, the interviewees were asked to provide additional information such as associated
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future problems. A measurable quantitative attribute were attached to each identified early
warning and will be termed as EWI.
3.4.3 Data collection
To obtain the understanding of relationship between early warnings and project performances,
data had to be collected and analyzed. Fluor Corporation is the primary source of past project
data. Due to the time constrain, date from past eight projects is used for quantitative analysis,
However the each project will provide 8 data collection point, collected at 0% (baseline),
15%, 30%, 45%, 60%, 75% and 95% of actual engineering duration to normalize the project
with different durations. The primary objective of collecting multiple data within one project
is to understand the dynamic relationship between early warning and project performance and
to develop a dynamic prediction model.
Apart from quantitative analysis, part of past projects are studied as case studies understand
the relationships and to differentiate between coincidences and causality of early warning andproject performance.
3.4.4 Performance prediction model development
Exploratory data analysis was performed before establishing statistical relationship between
identified early warnings and project performances. R Project for Statistical Computing will
be used to perform the statistical analysis due to its capability of customization the statistical
techniques and graphical outputs.
Relationship of EWI with project outcomes was established through collection and analysis of
past project data via stepwise multi regression. The conceptual prediction model was validatedusing past project data (which were not included in training) and current projects. The results
of the validation test will be analyzed to form recommendations and conclusions.
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4 LITERATURE STUDY
4.1 SUMMARY
First step in collecting the available literature on the topic of project performance management
and early warning is database research. Various search phrases were used to find the relevant
literature. Google scholar was used as primary internet search tool. For all the relevant
literature that could be identified, an attempt was made to get access. Further references of
many sources were searched to get the more specific literature regarding O&C industry.
The purpose of this chapter is to investigate how project management literature treats the
detection of early warnings during project execution. The chapter first defines the project
success and project performance, followed by adaption of project performance from current
research perspective. The chapter then proceeds to define the concept of early warnings from
theoretical perspective, followed by reflecting on their benefit in project execution control.
4.2 AUTHOR AFFILIATIONS
When the preliminary literature research was performed, it seems logical to describe the
affiliations of respective authors because the different affiliations are strongly related to the
mental framework from which the literature was written. The different groups of authors, their
interests, and assumption that might underlie their respective literature are presented below:
Construction Industry Institute (CII):
Established in 1983, the construction industry institute (CII) based at the University of Texas
at Austin, is a consortium of over 100 owner, engineering-construction contractors and
suppliers. Its aim is to improve the business effectiveness of its member organizations and
cost effectiveness of capital projects through research, related initiatives and alliance amongorganizations. The research by CII focus on 14 knowledge areas of engineering and
construction industry such as design optimization, project organization and planning and
project controls are to name a few. Their primary focus of research in CII is the current
practices employed by industries. For each knowledge area, CII identified best practices
(methods or processes, which lead to enhance project performance), other practices (methods
or processes that are not proven to enhance project performance) and research information
(which are neither method nor processes). (CII, 2012)
Consulting companies:
Professional consulting companies such as Independent Project Analysis (IPA), schlumberger
business consulting (SBC) have published their companys perspective and experience onproject management systems, project performance of engineering and construction projects.
Especially, IPA focuses primarily on project development and execution through its project
evaluation system (PES).
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The professional consulting companies serve their customers globally thus representing their
findings on projects all over the world, along with publishing region based reports. These
consulting companies published mainly through their official publications.
Academic researchers:
Academic research group involves authors from academic institutions like technical and
business universities, research schools and sponsored academic research by organizations. The
focus of authors is to enhance theoretical and scientific knowledge base regarding overall
project management and specific domains of project management. Their research explores the
science and engineering to delineate unknown causes and potential solutions of practical
problems faced by industry and to find their theoretical solutions. The most applicable
findings are further explored and tested by industries before adopting them as practices. The
present research investigates (but do not limit itself) academic publications relevant to early
detection of problems in projects, problems in execution and their performance management.
Limitations of the review:
Terminology in project management is not uniform for early warning indicators of problems.
Some describes them as leading indicators, symptoms, early warnings, problem causes.
Moreover, many authors see actual problems as potential indicators of future problems.
The diverse approaches and many implicit mentioning of early warning indicators could make
the literature study a time consuming activity. Therefore, it is logical and necessary to adhere
to the discussion of more relevant literature, which explicitly deals with early warnings in
context of project execution. This approach will result in only a part of literature that could
possibly be relevant and might bring the risk of leaving block of literature which might
result in extra concepts.
4.3 CONCEPT OF PROJECT SUCCESS
4.3.1 Project management view:
Having a view on what O&C project are, what are their phases and their performance
management, a natural question arises what are successful projects, in other words How do
we perceive success of a project. Before answering this question, it is necessary to
understand the concept of project success. The Figure 9 shows the best-known and most used
representation of project success i.e. iron triangle with time, cost and scope (or
performance/quality) on its corners (See e.g. Freeman & Beale, 1992, Larsen & Gobeli, 1989,
Might & Fischer, 1985) and Oisen, 1971). From perspective of cost, time and scope, the green
colored triangle is seen as a successful project, whereas the dotted red triangle can be termedas unsuccessful project, due to overrun in three dimensions of success. Although, this
approach has been seen as too narrow and often criticized. See: (Atkinson, 1999), (Raz &
Dvir, 2002) and (W.Hughes, Tippett, & Thomas, 2004)
To widen the concept of project success, Morris and Hough defined three dimensions of
project success (Moris & Hough, 1987):
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I. Project functionality: to what extent does the project perform financially and or
technically in the way expected by the project sponsors?
II. Project management: how close is the implementation of the project to budget,
schedule and technical specification?
III. Contractors commercial performance: did the contractors have a commercial benefit
in either short or long term?
Figure 9: Iron triangle of projects
The project success dimensions comprehend project success from different perspectives of;
the customer, project execution contractors and sub contractor and other stakeholders.
However, in reality the perspectives differ more than they look. A project could be delayed,but can be termed as a commercial success from client perspective given changes in his
strategic financial goals. This indicates that whether a project is success depends largely on
the perspective from which the project is viewed (Lientz & Rea, 1995).
(Lim & Mohamed, 1999) addressed the differences in perspective of stakeholders and defined
project success into two criteria: project completion criteria and satisfaction criteria. At macro
perspective the criteria involves both the project completion and satisfaction criteria. On the
other hand, the micro perspective only involves completion criteria. This is shown below in
Figure 10 below.
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Figure 10: Project success criteria(Source: Lim and Mohamed, 1999)
As mentioned in the scope of this research project, (see section 3.2) the present research
adopted the perspective of E&C contractors as they have the key responsibility of EPC phase
of project. In other words, the research is focused upon micro level criteria as defined by Limand Mohamed (1999).
However, it would be wrong to assume that the perspective of client and subcontractors are
ignored, because to achieve the sustainable success, an engineering and construction
contractor has to work collaboratively with its customer and suppliers by integrating their
perception of success into its own to the possible extent.
The success of a project is determined by evaluating its performance against success criteria
(Wit, 1988), which implies that performance needs to be measured to determine the
successfulness of a project.
Another noteworthy point regarding project performances is that intermediate projectperformance varies with the time during project execution. A bad performing project could be
turned around by making necessary strategic changes or a good performing project could turn
into a poor performing project due to multiple reasons. However, final project performance is
static and determines the success or failures of a project.
4.3.2 Project performance measurement in O&C projects
Having adopted the micro level success, the next step is to develop performance measurement
criteria to measure the success. Menches and Hanna (2006) developed a performance
measurement index with the following six project outcomes:
I. Percentage budget overrun,
II. Percentage schedule overrun,
III. Actual percentage profit,
IV. Change in work hours
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V. Number of change orders
VI. Communication between project team
The change in work hours is more of a factor rather than an outcome and contributes to the
final cost overrun of the project. Therefore, the use of numbers of change orders and change inwork hours as project performance criteria could be debated (Atkinson, 1999), (Shenhar &
Dvir, 1996) and (Hughes, Tippet, & Thomas, 2004).
The actual percentage profit also seems to be contradictory with definition of project success,
as it is highly dependent of the perspective of the stakeholder and type of contract. For
example in reimbursable contracts, the percentage profit for E&C contractor may increase
with scope and delay, whereas on contrary the project cost and schedule performance will
decrease.
With the adopted perspective of E&C contractors, it seems logical to limit and translate the
measures of project success to following project outcomes. Knowing that this is very limited
view on project success, yet they are the most commonly used across industry, therefore theavailability of actual data for these indicators are higher than other indicators.
I) Mechanical completion schedule of plant
Mechanical completion (MC) of plant is defined as The checking and testing of equipment
and construction to confirm that the installation is in accordance with drawings and
specifications and ready for commissioning in a safe manner and incompliance with project
requirements (Norwegian Technology Standards Institution, 2009). The scope of MC
includes construction validation, testing of equipments (dynamic and static) and handover for
start-up to owner. However, the testing phase could be excluded based on prior agreed upon
scope between E&C contractor and owner (Fluor Corporation, 2012). MC can be seen as animportant milestone from E&C contractor perspective as well as owner perspective.
For the E&C contractor, incentives or liabilities are attached with MC milestone. Moreover,
for an owner, completeness of MC marks as an indicator that plant is ready for startup. Delay
in MC could negatively affect its production plans and prior agreements with buyers, in other
words its revenue generation (Choi, Anderson, & Kim, 2006).
II) 95 % engineering complete
As explained in section 2.2.1, detailed engineering phase takes BOD as input (from FEED
phase) and transform conceptual engineering into detailed engineering documents. It provides
an input to procurement and construction. Although, the average engineering cost is only 20%of the project cost (CII, 2012), but it has significant influence on the rest of 80 % cost. In
O&C project, the key deliverables of detailed engineering are as follows (Fluor Corporation,
2012):
Input to procurement: Input to construction:
- Equipment data sheets - Process and instrument diagrams
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- Instrument data sheets - Plant plot plan
- Bulk material take-offs - Civil foundation drawings
- Technical bid reviews - Structure fabrication drawings
- Pipe routing drawings (UG/AG)
- Piping isometrics
- Electrical single line diagrams
- Installation procedure and manuals
The milestone for 95 % engineering complete signifies the completion of all major
engineering activities including final issuance for key deliverables (Issue for construction). In
other words, marks the completion of E phase of EPC project. The rest 5 % of engineering
is designated to miscellaneous construction and start-up support, which could extend untilcompletion of construction or MC (Fluor Corporation, 2012). Therefore, in industry practice
95% engineering completion is seen as finish of engineering efforts. Figure 11 shows the 95 %
engineering milestone on EPC progress curves.
Figure 11: 95 % engineering completion milestone
Adapted from Fluor Corporation, 2012
III) Total installed cost of project
Total installed cost (TIC) by definition means that it is the total cost of installing a plant. TIC
includes the cost of engineering efforts, cost of all equipments, materials and construction and
other costs such as contingency, services fee, and escalation.
The most cost effective project execution is the one allowing lowest TIC consistent with as
sold estimates and owner requirements. TIC is an important project outcome for both E&C
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contractor and the owner due to the simple fact that TIC is the important determinant factor in
net present value (NPV) of a plant.
In contractual terms, the dynamics of TIC on project economics of owner and E&C contractor
can be illustrated by following model (Berends, 2007):
Where:
P = Actual E&C profit
Pt= Target E&C profit
= E&C sharing cost related profit; 0 1 (Based on contract type)
Ct= Target/as sold TIC
C = Actual TIC cost
Cc= Owner contract cost
From Equation 3, it is evident on higher level that growth in TIC (C) will shrink the profit
margin for E&C contractor and at the same time will increase the cost for owner.
The cost performance in terms of TIC as project outcome can be assessed as follows:
IV) Engineering man-hours
The amount of engineering man-hours in a project can be seen as an indicator of engineering
efforts required in a project. Although from cost perspective, the cost of engineering efforts is
quite small as compared to the cost of equipments and construction (on average varies
between 10-15 % of TIC). In addition, the maximum engineering cost could be as high as 31%
of TIC and as low as 8 % (Bakker, 2012).
However, from project execution perspective engineering is the most important activity. As
the engineering set the basis for equipment, purchase documents and construction drawings
(see section 2.2.1). Any significant variation or a change in engineering man-hours has direct
effect of procurement and construction activities. Therefore, from project performance
perspective, a project has high chances of being a good performing project and successful
project, if it consume more or less the same hours as estimated.
4.4 CONCEPT OF EARLY WARNINGS
The secret of all victories lies in the organization of non-obvious-Marcus Aurelius (Roman emperor, AD 161-180)
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provides an opportunity for the project manager to act proactively. He defined an early
warning as:
An early warning is an observation, a signal, a message or some other item that is can
be seen as an expression, an indication, a proof, or a sign of the existence of some futureor incipient positive or negative issue. It is a signal, omen, or indication of future
development(Nikander, 2002; p. 49).
The research conducted by Nikander marks a stepping-stone in the direction of early detection
of problems however, lacks the quantitative nature and ability to forecast project performance
in light of early warnings. In addition, the majority of early warnings identified within the
research composed of feeling and behavior of the project team and its stakeholders. Their
detection largely depends upon the experience and intuition of project manager.
Another relevant research titled Leading indicators to project outcomes was conducted by
CII to identify the leading indicators (beyond the conventional methods or standard practicesused to evaluate the status of projects) which may have a significant impact on project
outcomes. The research defines leading indicators as:
Leading indicators are funda
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