Prediction of Project Performance-Fluor

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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: [email protected]

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

Prediction of Project Performance-Fluor

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Transcript of Prediction of Project Performance-Fluor