The Alberta Report - COAA Major Projects Benchmarking Summary

The Alberta Report
COAA Major Projects Benchmarking Summary
February 2009

Prepared for:
Alberta Finance and Enterprise, Alberta Energy
Construction Owners Association of Alberta (COAA)
Sponsored by:
Alberta Finance and Enterprise, Alberta Energy
Construction Owners Association of Alberta (COAA)
Under Research Contract: UTA05-782

Table of Contents
List of Tables ....................................................................................................................................... i
List of Figures..................................................................................................................................... ii
Executive Summary .......................................................................................................................... iv
1 Introduction .................................................................................................................................. 1
1.1 Background......................................................................................................................... 1
1.2 COAA / CII Partnership....................................................................................................... 2
1.3 Research Objectives........................................................................................................... 4
1.4 Scope and Approach .......................................................................................................... 4
2 COAA Major Projects Benchmarking System and Data Collection........................................ 7
2.1 Development of Alberta Benchmarking System ................................................................. 7
2.2 Hierarchical Structure for Project Comparison ................................................................. 10
2.3 Project Key Report............................................................................................................ 11
3 COAA Project Performance and Productivity Metrics........................................................... 14
3.1 General Metrics................................................................................................................. 14
3.1.1 Project Performance Metrics.................................................................................. 14
3.1.2 Engineering and Construction Productivity Metrics ............................................... 14
3.1.3 Practices ................................................................................................................ 15
3.2 COAA-Specific Metrics ..................................................................................................... 15
3.3 Understanding Benchmarking Reports and Analyses ...................................................... 17
3.3.1 Metrics.................................................................................................................... 17
3.3.2 Explanation of Statistics......................................................................................... 18
4 Data Analysis ............................................................................................................................. 20
4.1 Description of Alberta Dataset .......................................................................................... 20
4.2 Selected Descriptive Analyses ......................................................................................... 22
4.3 Selected Inferential Analyses ........................................................................................... 25
4.4 Comparison of Alberta and U.S. Project Performance ..................................................... 32
4.5 Engineering Productivity ................................................................................................... 35
4.6 Construction Productivity .................................................................................................. 38
4.7 Analysis of Impact Factors................................................................................................ 44
5 Major Findings ........................................................................................................................... 49
5.1 Project Performance ......................................................................................................... 49
5.2 Productivity ....................................................................................................................... 51
5.3 Impact Factors .................................................................................................................. 51
5.4 Project Management......................................................................................................... 52
6 Conclusions and Recommendations ...................................................................................... 53
Appendices ....................................................................................................................................... 55
Appendix A: Summary of Correlation between Project Performance and Related Factors of
Alberta Based Projects ............................................................................................... 56
Appendix B: Performance Metric Formulas and Definitions......................................................... 60
Appendix C: Glossary ...................................................................................................................... 69
References ........................................................................................................................................ 74
i

List of Tables
Table 2-1 Comparison Algorithm of Alberta Project Performance Metrics ........................................ 10
Table 2-2 Comparison Algorithm of Alberta Engineering and Construction Productivity Metrics ......... 10
Table 2-3 Hierarchical Structure of Alberta Project Types.................................................................. 11
Table 3-1 Additional Study-Specific Performance Metrics.................................................................. 16
Table 4-1 Submitted Projects by Owners and Contractors at Project Completion and Sanction ....... 21
Table 5-1 The Top 5 Factors Affecting Cost, Schedule or Productivity .............................................. 52
Table A-1 Correlations of Project Characteristics with Project Performance...................................... 53
Table A-2 Correlations of Project Characteristics with Project Performance (Cont’d)........................ 54
Table A-3 Correlations of Project Characteristics with Project Performance (Cont’d)........................ 55
Table A-4 Correlations of Project Characteristics with Project Performance (Cont’d)........................ 56
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List of Figures
Figure 2-1 Development Process of Alberta Benchmarking System.................................................... 7
Figure 2-2 Number of Submitted Project Data...................................................................................... 9
Figure 2-3 Sample of Project Cost and Schedule Performance Metrics ............................................ 12
Figure 2-4 Sample of Project Engineering Productivity Metrics.......................................................... 13
Figure 2-5 Sample of Project Construction Productivity Metrics......................................................... 13
Figure 3-1 Example of Performance Metrics....................................................................................... 17
Figure 3-2 Example of Practice Metrics .............................................................................................. 17
Figure 3-3 Box and Whisker Diagram ................................................................................................. 18
Figure 4-1 Number of Submitted Projects by Project Nature and Delivery System............................ 21
Figure 4-2 Number of Projects Submitted at Sanction and Completion by Total Project Cost Category
($CDN in 2007) .................................................................................................................. 22
Figure 4-3 Construction Indirect / Direct Work hours (%) ................................................................... 23
Figure 4-4 Construction Indirect Cost / Total Project Cost (%)........................................................... 23
Figure 4-5 Modularization by Project Nature....................................................................................... 24
Figure 4-6 Project Cost Growth by Project Delivery System .............................................................. 25
Figure 4-7 Project Schedule Growth by Project Delivery System....................................................... 25
Figure 4-8 Effect of % Engineering Completed before Construction Started ..................................... 26
Figure 4-9 Actual / Estimated Number of Peak Construction Workforce............................................ 27
Figure 4-10 Construction Indirect Work-hours/ Direct Work hours (%) .............................................. 28
Figure 4-11 Construction Indirect Cost Growth by Project Size ($) .................................................... 28
Figure 4-12 Project Risk Assessment vs. Project Cost Growth .......................................................... 29
Figure 4-13 Constructability vs. Project Schedule Growth.................................................................. 30
Figure 4-14 Planning for Startup vs. Startup Phase Cost Growth ...................................................... 31
Figure 4-15 Workface Planning vs. Construction Schedule Growth................................................... 31
Figure 4-16 Project Size ($M CDN, in 2007)....................................................................................... 32
Figure 4-17 Contingency Budget (%).................................................................................................. 33
Figure 4-18 Project Cost Growth......................................................................................................... 34
Figure 4-19 Project Schedule Growth ................................................................................................. 34
Figure 4-20 Development and Scope Change Cost Factor ................................................................ 35
Figure 4-21 Comparison of Project Size ($M CDN, in 2007) for Engineering Productivity Dataset ... 36
Figure 4-22 Comparison of Concrete Engineering Productivity (WH/ Cubic Meter)........................... 37
Figure 4-23 Comparison of Structural Steel Engineering Productivity (WH/ Metric Ton) ................... 37
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Figure 4-24 Comparison of Piping Engineering Productivity (WH/ Linear Meter)............................... 38
Figure 4-25 Comparison of Project Size ($M CDN, in 2007) for Construction Productivity................ 39
Figure 4-26 Comparison of Total Concrete Construction Productivity (WH/ m 3 )................................ 40
Figure 4-27 Comparison of Total Structural Steel Construction Productivity (WH/ Metric Ton) ......... 40
Figure 4-28 Comparison of Instrumentation- Devices Construction Productivity (WH/ Count) .......... 41
Figure 4-29 Comparison of Insulation- Piping Construction Productivity (WH/ Linear Meter)............ 42
Figure 4-30 Construction Productivity Project Level Index vs. Project Size ($M CDN, in 2007) ........ 42
Figure 4-31 Actual / Estimated Construction Productivity Rate by Work Discipline ........................... 43
Figure 4-32 Actual / Estimated Total Installed Unit Cost (TIUC) by Work Discipline.......................... 44
Figure 4-33 Factors Impacting Project Cost........................................................................................ 46
Figure 4-34 Impact of Factors vs. Cost Growth.................................................................................... 46
Figure 4-35 Factors Impacting Project Schedule................................................................................ 47
Figure 4-36 Impact of Factors vs. Schedule Growth ............................................................................ 47
Figure 4-37 Factors Impacting Construction Productivity (Field Productivity) .................................... 48
Figure 4-38 Impact of Factors vs. Project Construction Productivity (CPM)......................................... 49
Figure 5-1 Project Change Cost Factor vs. Project Cost Growth ......................................................... 51
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Executive Summary
The Construction Owners Association of Alberta (COAA) as the principal industry association for
heavy industrial construction in Alberta provides leadership to enable our owner members to be
successful in their drive for safe, effective and productive project execution. The heavy industrial
construction sector represents a significant portion of the GDP in Alberta with announced major
projects in excess of $100B. In 2008, the Oil Sands sector in particular was forecasting significant
capital expenditures of $10B per year in 2009, rising to $15B by 2015. This level of construction
activity has strained the industries’ ability to execute the work effectively and has led to significant
concerns about low productivity along with cost and schedule overruns. With these concerns in mind
the COAA embarked on a benchmarking initiative in 2003, engaging the Construction Industry
Institute (CII) at the University of Texas in Austin to develop a benchmarking system which would
assess project performance considering the unique characteristics of Alberta major projects. The CII
has extensive experience benchmarking projects in the United States and globally, permitting
comparisons between Alberta projects and similar projects in the CII database. This report
summarizes the results of the first series of project assessments completed in October 2008.
A total of 78 Alberta projects were initiated in the benchmarking system since December 2005 and of
these 37 completed the data input required for benchmarking analysis at the cut off for this report in
August 2008. Twenty-seven of the 37 projects in this study were related to the Oil Sands sector with
only natural gas processing (4) and pipeline (2) sectors submitting more than one project. About half
of the projects were grass roots with additions (38%) and modernizations (11%) making up the
balance. Execution strategies varied with almost half of the projects using parallel primes; designbuild
was the second most frequent strategy at 32% of the projects. Project sizes varied from

The average cost growth for Alberta projects was 19% and average schedule growth was 17%. Cost
growth was generally lower as the % engineering completed increased and more effective Project
Risk Assessment also reduced project cost growth. Constructability assessments lead to reduced
schedule growth but had no impact on cost growth. Indirect costs averaged about 21% of total
project costs and indirect cost growth increased as the project size increased. As for other project
best practices, Planning for Start-up reduced the cost growth in start-up but there was no detectable
correlation between Workface Planning and construction schedule growth, although there were only
7 data points in this assessment.
A number of comparisons were made between Alberta and comparable U.S. projects. Although the
comparison is for similar industrial projects, no adjustment has been made to account for the
differences in project size, the prevailing economy while the projects were built and other potentially
significant project drivers. The median project size in the Alberta dataset is $186M vs. $40M for the
U.S. dataset. Project cost growth was much higher in Alberta (19%) vs. the U.S. (3%) and Alberta
project cost growth had much wider range (-27% to 69%). Development and scope changes were
similar between Alberta and the U.S..
Engineering productivity is measured as the ratio of direct engineering hours per installed quantity in
the field (e.g., for structural steel, hours per ton of steel; lower is better). In a similar way,
construction productivity is measured as the ratio of field direct work hours per installed quantity (e.g.,
for structural steel, hours per ton of steel; again, lower is better). All comparisons noted below
between the Alberta and U.S. data sets are based on weighted averages (i.e., larger projects count
more in the average productivity than smaller projects).
Engineering productivity for concrete was better in Alberta vs. the U.S. (3.5 vs. 6.3); structural steel
engineering productivity was worse in Alberta vs. the U.S. (12.6 vs. 5.9) while piping engineering
productivity is comparable (1.28 vs. 1.23).
Construction productivity for concrete is worse in Alberta vs. the U.S. (13.1 vs. 9.8) and
instrumentation devices productivity is much worse in Alberta (21.4 vs. 8.3), although the nonweighted
average between the two was comparable, so further research into this comparison is
warranted. Construction productivity for structural steel was comparable between the two datasets
(about 38) while insulation productivity was better (1.4 vs. 2.2) in Alberta.
To sum up the results to date, productivity is better in Alberta for some disciplines but worse (or
much worse) for others, so the productivity picture is mixed. Average wage rates in Alberta are
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higher than the U.S., particularly when compared to the U.S. Gulf Coast where many of the heavy
industrial projects occur, so improved productivity in Alberta will enhance competitive advantage.
Furthermore, indirect costs are higher in Alberta. All this helps explain the significantly higher cost
growth in Alberta vs. the U.S. data. The COAA and its members have developed and are
implementing a number of initiatives such as Work Face Planning and Re-Work Reduction that will
help improve project productivity. The reduced pace of project development in Alberta in the near
term should also contribute to improved project execution.
Companies that submitted data are given customized reports which show the comparison of their
projects against the Alberta and U.S. datasets. This will help determine strengths and weaknesses
and should lead to better project execution in future, which in the end is the goal of all benchmarking
work. The COAA is considering extending the benchmarking initiative for another 2 years to allow
submission of additional projects which will strengthen the data analysis and improve our insight into
causes of and potential solutions to poor project performance.
The COAA would like to thank the Benchmarking Committee and in particular the current and past
committee co-chairs, Steve Revay of Revay and Associates, Larry Sondrol of Suncor and Donald
Mousseau of Husky Energy Inc. for their outstanding efforts. The COAA is also indebted to CII for
their expertise and efforts and to the Government of Alberta who helped fund this study.
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1 Introduction
The oil sands industry plays a crucial role in Canada’s global economic position and the delivery of
energy to the world. In fact, Canada’s oil reserves are second in the world behind Saudi Arabia
(OSDG, 2008). Of these reserves, 97 percent are oil sands. Commercial production of the oil sands
began over 40 years ago and current output is expected to triple by 2020 (ibid.). The advances that
have been made in surface mining and in-situ production technologies have been driving the rapid
pace of development of the oil sands reserves. Over the past 11 years, a total of $102 Billion (CDN)
was spent on construction and operation capital necessary to develop these resources. Some have
projected that through 2012, an additional $205 Billion (CDN) could be invested given favorable
economic conditions.
The realities of the oil sands resource and the Canadian energy industry place tremendous demands
on companies engaged in the efficient and effective execution of capital projects. This report
chronicles the efforts made by owners, contractors, and other stakeholders in their delivery of capital
projects in the heavy industry sector in Alberta. Using estimated and completed capital projects as
its basis, the report examines project performance from cost, schedule, change, rework, safety, and
productivity standpoints. It recognizes the uniqueness of heavy industrial projects in Alberta,
projects often characterized by their remote locations and challenges posed by severe weather. The
story of their development is a compelling one.
The Construction Industry Institute (CII) was selected by COAA to explore the performance and
productivity concerning the execution of capital projects in Alberta. This selection was premised on
the extensive experience of CII in researching and benchmarking industrial facilities in the United
States and around the world. Extending CII’s reach into Alberta permitted tremendous
understanding of the performance of these projects, especially when compared with similar projects
in the United States. The results obtained through this study are both surprising and expected.
Using quantitative methods, the report dispels common myths regarding project execution in Alberta
while establishing a solid footing for the future study of additional projects.
1.1 Background
Benchmarking has long been used to improve the process of manufacturing. It is the continuous and
systematic process of measuring one’s own performance against the results of recognized leaders
for the purpose of finding best practices that lead to superior performance when implemented. In the
capital projects industry, benchmarking is primarily used at the project level to help participants
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identify gaps in their work processes which lead to compromised performance. For a given company,
benchmarking provides sets of external comparisons to its peer group that can be used to establish
improvement goals and objectively understand what “best in class” performance means.
The execution of capital projects in Alberta is truly unique. It is one of few geographic areas that has
such a great prevalence of capital projects. At last estimate, over 240,000 people were engaged in
the development of the oil sands resources in Alberta (OSDG, 2008). In fact, construction comprised
9.0% of Alberta’s gross domestic product (GDP) in 2007 (AFE, 2008). Spending on the Athabasca
Oil Sands resource in particular rose to $37.7 Billion (CDN) in 2007 (ibid.). However, this dramatic
amount of growth has also brought its challenges. Increasing pressures on capital projects have
been created due to significant worldwide cost escalations and labour shortages. This has led to the
creation of many perceptions regarding the potential loss of productivity or excessive indirect costs,
for example.
The purpose of this study was to quantitatively assess the performance of capital projects in Alberta.
The combined resources of COAA, CII, and Alberta Finance and Enterprise were directed to
objectively measure the performance of actual projects planned and executed in Alberta within the
past seven or eight years. While it was not possible to obtain measures of every aspect of project
performance, this study does provide data necessary to gain new insights to the results of Alberta’s
heavy industry sector projects. It directly addresses many common perceptions regarding
engineering and construction productivity and it provides a baseline of project data that can be used
to help improve the work processes used by companies developing projects in Alberta.
1.2 COAA / CII Partnership
As the principal industry association for capital projects in Alberta, the Construction Owners
Association of Alberta (COAA) strives to provide leadership to enable owner members to be
successful in their drive for safe, effective and productive project execution. Principal members of
COAA include the users of construction services in capital expansion plans. Indeed, COAA
represents a broad cross-section of owners' interests which are associated with many sectors of the
Alberta construction community. COAA also includes Associate Members which provide
construction services and other activities. COAA’s mission is to assist its members in achieving
excellence in the execution of capital projects by:
• Creating and promoting Best Practices in the construction industry
• Serving as a voice for owners to stakeholders that can make a difference
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• Providing a forum for dialogue and debate among owners, contractors, labour providers and
government
• Bringing new ideas to the construction industry and to government leaders
Headquartered at the University of Texas at Austin, CII is a consortium of leading owners,
engineering and construction contractors, and suppliers that have come together to improve the cost
effectiveness of capital projects. As the major public benchmarking resource in the capital projects
industry, CII has over 15 years experience in benchmarking capital project delivery and best
practices. CII was formed in 1983 by 28 organizations based on recommendations from an intensive
five year study of the engineering and construction industry, known as the Construction Industry Cost
Effectiveness (CICE) project. Today, there are 117 members around the world engaged in capital
projects. Over the past 25 years, CII has partnered industry practitioners with academia to study the
capital projects industry to create a vast array of knowledge. In fact, CII research products have
been widely disseminated throughout the industry through publications, conferences and workshops
and have led to the creation of a number of best practices.
CII started its Benchmarking and Metrics (BM&M) program in 1993 with an initial purpose to validate
the benefit of best practices and to support CII research. Today, CII’s BM&M program employs 10
staff members to advance project performance through benchmarking research. Over the years, an
online benchmarking system known as Project Central has been developed to allow benchmarking
participants known as Benchmarking Associates (BA’s) to enter project data and get real-time
feedback 24 hours per day. BA training is provided three times a year to ensure understanding of
CII metrics and compliance with standard data definitions. As of 2008, over 800 BAs have been
trained and a total of 1,738 projects representing over $81 Billion (USD) have been collected from
leading construction owners and contractors around the world.
Building on the collective expertise of COAA and CII, a research contract was established in 2005
between the two organizations for the purpose of benchmarking capital projects in Alberta. It was
funded by COAA with assistance from Alberta Finance and Enterprise, a component of the provincial
government of Alberta. Besides this research report, the contract established a comprehensive
benchmarking system comprised of a customized questionnaire, a dedicated database, and a suite
of individualized reports for each company submitting project data. The relationship between COAA
and CII has been very productive and has yielded many discoveries regarding Alberta’s heavy
industry sector capital projects, many of which are presented here.
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1.3 Research Objectives
The purpose of the research was to develop a benchmarking system to assess the performance of
Alberta major projects considering factors unique to their execution, to permit analysis of this
performance over time, and to include measures of engineering and field productivity. In particular,
specific research objectives included:
1) Identification of Alberta metric requirements
2) Development of a customized benchmarking questionnaire based upon the CII questionnaire,
but tailored to the characteristics and environment of Alberta projects
3) Establishment of a set of benchmarks for Alberta projects using the customized
questionnaire
4) Documentation of Alberta project performance against the Alberta benchmarks
5) Identification and documentation of factors and practices impacting project performance
As the research evolved from 2005 to 2008, COAA’s benchmarking committee worked directly with
CII benchmarking and metrics staff members to continually refine the research program, its
questionnaire and its information technology (IT) tools. For example, besides including additional
data definitions for Alberta projects, specific COAA best practices such as Workface Planning were
added to the research’s customized questionnaire. Taken together, these efforts have produced a
premier benchmarking research program for Alberta projects.
1.4 Scope and Approach
This research program used the principal components of CII’s benchmarking research program as its
foundation. CII’s existing large project questionnaire for heavy industry sector projects was used as
a basis for the COAA questionnaire. A series of development meetings was held in 2005, 2006, and
2007 between COAA’s benchmarking committee and CII benchmarking and metrics staff to create
and prioritize new metrics specific to Alberta capital projects. This led to the programming of a
customized web-based data collection instrument and key report.
Throughout the study period, and into 2008, CII conducted seven training sessions for COAA
participants in this study. These individuals, known as COAA Benchmarking Associates (BA’s), were
given access to the online system and key reports. Using the knowledge gained in training, these
BA’s collected project-specific data and entered them into the online system. Subsequently, they
worked with CII staff to validate their data to ensure conformance to accepted definitions. Finally,
4

the BA’s used the information contained within the key reports to communicate knowledge gained
about their projects to their individual firms in order to improve key work processes.
The final aspect of this research program was the creation of this report, entitled the “Alberta Report”.
This report is intended to examine all the projects collected through this research to identify common
factors or new findings concerning the execution of capital projects in Alberta. It is also the means of
communication regarding the entirety of this research. Consequently, this report describes not only
the interesting findings of this research, but also the system used to collect, analyze, and
disseminate this information. The contents of this report have been distilled to provide commentary
only on the most critical aspects and results of this research effort. Certainly, other queries
regarding the collected project data were investigated, but only the most statistically significant are
presented here.
This research was intended to provide the first step in the dedicated study of heavy industry sector
capital projects in Alberta. Future steps are planned. Principally, this report provides quantitative
assessments of Alberta oil sands projects. It can be used to:
1) Aid understanding of generalized, current perspectives of project performance in Alberta
2) Aid understanding of the benefits obtained through best practice use in the management of
capital projects
3) Aid understanding of the drivers for improved capital project performance, especially in the
areas of planning, estimating, and productivity
4) Plan for improvements to work processes to execute capital projects more effectively
Importantly, this report should not be used to estimate any current or future projects. Results should
not be extrapolated to projects beyond those studied as, by definition, every project is both
temporary and unique. Results contained herein pertain only to those projects submitted for analysis;
projects which were executed by particular individuals in particular periods of time. Continued
benchmarking is recommended to maximize the benefits received.
Acknowledgement
Funding for this research was provided by both the Government of Alberta and COAA. In addition,
this study would not have been possible without the endless support from the COAA Alberta Major
Projects Benchmarking Committee. The individuals who collected and submitted their project data
through this study are greatly appreciated, though their names are not listed due to confidentiality
5

policies which are in effect. Members of the COAA Alberta Major Projects Benchmarking Committee
are listed next.
The COAA Alberta Major Projects Benchmarking Committee
Steve Revay*, Revay and Associates Limited
Larry Sondrol*, Suncor Energy Inc.
Donald Mousseau**, Husky Energy Inc.
Patricia Armitage, Alberta Finance and Enterprise
Aamer Ahmed, Shell Canada Limited
Billy Bai, Ledcor
Bob Montgomery, Colt Engineering
Dale Elmer, Flint Energy
Dave Williams – Bantrel
Douglas Shako, Flint Energy
Ed Catolico, WorleyParsons Ltd.
Mahendra Bhatia – Suncor Energy Inc.
Mel Otteson, Imperial Oil Resources Ltd.
Greg Sillak, BA Energy
Greg Taylor, Nexen Inc.
Hans Raj, Colt Engineering
Jared Wharton, EPCOR
Johnnas Jagonos, Flint Energy
Jennifer Koivuneva, Jacobs
Korey Jackson, Stantec
Lea Chambers, Golder Associates Ltd.
Lubo Iliev, Petro-Canada
Renee Roberge, Flint Energy
Rheal Guenette, Shell Canada Limited
Richard Haack, Shell Canada Limited
Stephan Chudleigh, Flint Energy
Tim Silbernagel, Bantrel
Umesh Krishnappa, Suncor Energy Inc.
Vladimir Deriabine, Petro-Canada
Warren Rogers, Flint Energy
Include past members and denote them as such
*current chairs
**past chairs
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2 COAA Major Projects Benchmarking System and Data Collection
Benchmarking has been recognized as a core component of continuous improvement programs in
the capital projects industry. Implementing specific benchmarking approaches on Alberta-based
projects will provide the participating companies with a systematic process to measure project
performance, enable external comparisons with peers’ projects, and establish project objectives.
Moreover, a comprehensive benchmarking system can identify areas for work process improvement.
This was the basis for the development of the Alberta Benchmarking System.
2.1 Development of Alberta Benchmarking System
This research comprises the first round of benchmarking heavy industry sector projects in Alberta.
Accordingly, a significant amount of time and effort was spent by COAA’s benchmarking committee
and CII benchmarking program staff to develop the Alberta benchmarking system. The development
process of this system can be seen in Figure 2-1.
c
Figure 2-1 Development Process of Alberta Benchmarking System
Principally, the development of a benchmarking system includes the following aspects: development
of metrics and a survey instrument, development of a data collection and reporting system, and
validation of submitted data. Each is discussed next.
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a) Development of Metrics
This study applies most of the project performance, best practice, and engineering and construction
productivity metrics developed by CII’s Benchmarking and Metrics (BM&M) program. Definition of
these metrics can be seen in Appendix B. This study also incorporates many additional metrics
focused on specific areas of interest concerning projects executed in Alberta. These additional
metrics were developed through meetings with COAA’s benchmarking committee, industry experts,
and CII’s BM&M staff. Development activities for all additional metrics are described in section 3.4.
b) Development of Survey Instrument
Once the metrics were defined, CII’s existing Large Project Questionnaire was modified to include
additional metrics for projects executed in Alberta. Primarily, this was accomplished through input
obtained from the COAA benchmarking committee. In addition, the questionnaire was refined using
the feedback and input from 180 industry representatives who attended COAA benchmarking
training over three years. To ensure the reliability and consistency of questionnaire responses, all
questions were reviewed and validated by an expert in survey design at the University of Texas at
Austin. It should be mentioned that the questionnaire was developed with both owner and contractor
data in mind. The final questionnaire was subsequently programmed by CII staff from 2005 to 2007
and can be downloaded through COAA’s website. Notably, the questionnaire requires each project
to report data concerning general project information, budget, schedule, change orders, rework,
safety, practice use, productivity, and factors known to impact project performance.
c) Data Collection System
CII has developed a robust web-based data collection system over the last eight years. This
development activity has resulted in a mature, online system that is recognized as a cost-effective
tool that companies can use to benchmark a large number of projects. This system also supports
the collaboration of data entry among multiple project participants and allows benchmarking at two
milestones: at project sanction (i.e., Approval for Expenditure (AFE)) and after project completion
(see definitions of terms in Appendix B). Benchmarking at AFE uses project estimates, while
benchmarking after completion relies upon both estimates and actual data. In addition, the Alberta
benchmarking system supports both imperial and metric systems of measurement. Here, the system
is capable of converting concrete quantities between cubic yards and cubic meters, and wire and
cable quantities between linear feet and linear meters, for example. This feature supports projects
using a hybrid quantity unit system which is advantageous to large projects managed by multiple
companies working in different unit environments.
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d) Data Collection and Validation
Figure 2-2 contains a history of COAA project data collection beginning in November 2005. Data
collection was planned to complete in October 2008 for the first round. As can be seen in the Figure,
a total of 78 projects were created by 19 COAA member companies through the end of 2008. These
19 firms include ten owner companies and nine contractors. However, only 37 projects containing
complete project data were submitted before the deadline in October 2008. For this reason, only
these projects were validated for inclusion in this study.
100
Number of Project Data in COAA DB by Month (last updated Oct. 24th, 08)
Number of Project Data
90
80
70
60
50
40
30
20
10
0
Nov-05
3
9
Dec-05
Jan-06
Training # 1
Nov. 05
Feb-06
Mar-06
Apr-06
May-06
55
53
51
47
36
38
37
30
23
13 15 18 21 25 2931 11
12
9
13
10
7
2 2 3 6 1 2
0
3 0 2 0 1 1
2 1
Jun-06
Jul-06
Aug-06
Sep-06
Oct-06
Nov-06
Dec-06
Training #2
Oct. 06
Jan-07
Feb-07
Number of projects created in database
Total number of submitted projects
Number of projects submitted by months
Mar-07
Apr-07
May-07
Jun-07
Jul-07
Aug-07
Sep-07
Oct-07
Nov-07
Dec-07
Training #3
Nov. 07
Jan-08
Feb-08
Mar-08
Apr-08
May-08
Training #4
May 08
75
Jun-08
Jul-08
Aug-08
Sep-08
37
7
78
Oct-08
Nov-08
Dec-08
Round 1
Data Cut Off :
Aug. 1st, 08
Figure 2-2 Number of Submitted Project Data
To ensure the quality and integrity of data included in the Alberta database, a comprehensive data
validation process was established by the research team. This process consists of two phases.
First, COAA benchmarking associates (BA) validate their project data through internal comparisons
and submit these data only once they have been verified. Secondly, the COAA Account Manager at
CII examined the submitted data using comparisons with additional Alberta projects, primarily to
identify outliers, thereby generating a series of questions to the responsible BA.
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2.2 Hierarchical Structure for Project Comparison
To provide meaningful benchmarking results, comparisons are made amongst projects that are as
similar as possible using five different project characteristics. These characteristics are used in a
hierarchical structure and programmed as a comparison algorithm, the logic of which can be seen in
Tables 2-1 and 2-2. The COAA and CII development teams collaboratively created these algorithms
in order to mine the database based upon: 1) project cost category, 2) project nature, 3) project type
level 2, 4) project type level 1, and 5) respondent types. In order to achieve reasonable
benchmarking by project size ($CDN), time adjustments of project costs are required. The year
during the middle of the project was used to normalize project cost dollar values to July 2007.
Table 2-1 Comparison Algorithm of Alberta Project Performance Metrics
Loop #
# 1 – no slices found,
go to 2
#2 – no slices found,
go to 3
#3– no slices found,
go to 4
4: Stop! Data Slice
found with n=10!!
Respondent
Type
Owner
Owner
Owner
Level 1 Level 2 Nature
Upstream
Upstream
Upstream
Oil Sands
SAGD
Oil Sands
SAGD
Oil Sands
SAGD
Grassroots
Cost
Category
$100-250MM
Grassroots ALL
ALL ALL
Owner Upstream ALL ALL ALL
#5 Owner ALL ALL ALL ALL
#6 ALL ALL ALL ALL ALL
Table 2-2 Comparison Algorithm of Alberta Engineering and Construction Productivity Metrics
Loop #
# 1 – no slices found,
go to 2
#2 – no slices found,
go to 3
#3– no slices found,
go to 4
4: Stop! Data Slice
found with n=10!!
Respondent
Type
Owner
Owner
Owner
Level 1 Level 2 Nature
Upstream
Upstream
Upstream
Oil Sands
SAGD
Oil Sands
SAGD
Oil Sands
SAGD
Grassroots
Cost
Category
$100-250MM
Grassroots ALL
ALL ALL
Owner Upstream ALL ALL ALL
#5 Owner ALL ALL ALL ALL
#6- Second Round with
All Response Type
ALL
Upstream
#7 ALL Upstream
#8 ALL Upstream
Oil Sands
SAGD
Oil Sands
SAGD
Oil Sands
SAGD
Grassroots $100-250MM
Grassroots ALL
ALL ALL
#9 ALL Upstream ALL ALL ALL
#10 ALL ALL ALL ALL ALL
10

The hierarchical structure of Alberta project type (level 1 and level 2) can be seen in Table 2-3.
Alberta projects were divided to four types (level 1) which includes upstream and downstream oil and
gas, natural gas, and pipeline projects. This was done for data comparison and analysis purposes.
Level 1 projects are also further broken down to a second level (level 2). For example, upstream oil
and gas is divided into oil sands Steam Assisted Gravity Drainage (SAGD) and oil sands mining.
After metric values are calculated for each project, metrics are compared with the closest specific
data slice according to the previously-developed algorithms (e.g., $100M- $250M project size,
grassroots, oil sands SAGD, upstream, heavy industry, and contractor). This can be seen in ‘loop 1’
of Table 2-1. Additionally, if the comparable dataset has less than 10 projects or data from less than
3 companies, the comparison was moved to the next loop (and so on) until enough data are
available.
Table 2-3 Hierarchical Structure of Alberta Project Types
Level 1
Upstream
Level 2
Oil Sands SAGD
Level 3
Cogeneration
Central Plant Processing Facilities
(Oil Exploration/
Pad and Gathering
Production)
Oil Sands Mining
Oil Sands Mining/ Extraction
Central Plant Processing Facilities
Naptha Hydrotreater Unit
Oil Sands Upgrading
Downstream
Hydrogen Plant
Oil Refining
Utilities and Offsite
Natural Gas Natural Gas Processing
Process Pipeline
Pipeline Pipeline SAGD
Pipeline (Gas Distribution)
2.3 Project Key Report
For each participating company in this research, a standardized report was created that contained all
metric values that could be calculated based on questionnaire responses for a given project. This
standardized report is known as the ‘project key report’ which was generated to provide comparisons
of selected project performance with other similar projects in the Alberta benchmarking database
following the procedures discussed previously. The key report presents metric scores, database
means, performance quartiles, and sample size of the comparable dataset. The key report was
customized for Alberta based projects based on a series of discussions with the COAA
Benchmarking committee. A sample of the report can be seen in Figure 2-3, while a complete
sample report can be downloaded at the COAA website. Generally, metrics scores are presented in
11

quartiles with the first quartile (1Q) being preferred. However, some metrics scores are not
presented using quartiles, but rather, are presented as a continuum of observed project performance.
In the case of Figure 2-3, schedule metrics are qualified as the percent of projects spending more
time.
Figure 2-3 Sample of Project Cost and Schedule Performance Metrics
The key report also contains comparisons for both engineering and construction productivity. A
sample of engineering productivity metrics can be seen in Figure 2-4. In this figure, a calculation of
unit rate is provided that divides the total design work hours by its corresponding issued for
construction (IFC) quantity. Again, comparisons for each metric are provided with the database
mean and all comparable projects are organized into quartiles (e.g., with n=12, three projects would
reside in each quartile). While the quartiles appear to be a uniform width from a unit rate perspective,
this rarely happens in practice. Rather, project metrics may be clustered or spread out for each
observed quartile.
Figure 2-5 provides a sample of the key report for construction productivity. In this figure,
calculations and comparisons for each metric are provided in much the same way as previously
discussed. In general, additional detail is provided for construction productivity when compared with
engineering productivity for the same disciplines. In addition, the construction productivity section
12

also provides for the ability to compare estimates of construction productivity generated at sanction
with actual data from completed projects.
Figure 2-4 Sample of Project Engineering Productivity Metrics
Structural Steel
Metric
Wk-Hrs
Installed
Quantity
(MT)
Unit Rate
(Wk-Hrs/MT)
Database
Mean
n
Structural Steel 15,304 637 24.03 23.74 12*
Pipe Racks & Utility Bridge 5,391 289 18.64 28.58 19*
Miscellaneous Steel 11,882 173 68.57 51.14 11*
: Total Structural Steel
Productivity Rate
32,577 1,099 29.64 14
Est.
Wk-Hrs
Est.
Quantity
(MT)
Est.
Unit Rate
(Wk-Hrs/ MT)
: Total Estimated Structural
Steel Productivity Rate 29,000 1,000 29.00
30.80
n
14
: Total Installed Unit Cost
Actual
($/MT)
Estimated
($/MT)
Actual DB Mean
($/MT)
3,200 3,000 3,100 14
n
Figure 2-5 Sample of Project Construction Productivity Metrics
13

3 COAA Project Performance and Productivity Metrics
This section introduces the metrics used in this research. It provides an explanation of interpretation of
the project key report. In addition, this section also provides an overview of statistical terms used in
conducting the analyses for this report. As discussed previously, the COAA benchmarking system
adopted most of the proven CII project performance and practice metrics, plus some specific metrics
created for the unique factors found on projects in Alberta.
3.1 General Metrics
The category of general metrics pertains to those metrics used by CII’s benchmarking and metrics
(BM&M) program for many years. The use of these metrics was necessary to ensure the
compatibility of comparisons between project data collected in Alberta and by CII in the U.S. and in
other countries around the world. There are three sub-categories of general metrics. These are
discussed next.
3.1.1 Project Performance Metrics
The CII BM&M program measures five aspects of project performance, notably: 1) cost, 2) schedule, 3)
safety, 4) change, and 5) field rework. Project cost and schedule performance metrics evaluate the
amount of variation from planned cost and schedule estimates at sanction. These performance metrics
are further decomposed to address five primary phases of capital project execution. Known as phase
cost and schedule factors, these metrics portray the proportion of total project time and money
expended during each phase of the project. Safety, change, and rework are measured in terms of
overall project performance at project completion. The definitions of these metrics are described in
detail in Appendix B. The only aspect of project performance metrics that differs between the CII and
COAA system concerns safety metrics. For this research, the safety metrics commonly used in
Canada were included.
3.1.2 Engineering and Construction Productivity Metrics
The productivity metrics used in this research are based on the engineering and construction
productivity measurements used by CII’s BM&M program. Metrics are defined as ratios of work
hours (WH) to quantities. For most, these metrics are easy to understand and are consistent with
most estimating and cost accounting systems. For these metrics, a lower productivity rate is
generally preferred.
14

Engineering productivity metrics are defined as actual engineering work hours per Issued for
Construction (IFC) quantity, which is the number of actual direct work hours required to design a
particular unit of work. This calculation can be seen in Equation 1. Engineering productivity metrics
were captured for significant work activities for the following design disciplines: 1) concrete, 2)
structural steel, 3) equipment, 4) piping, 5) electrical, and 6) instrumentation. A definition of direct
labour for all engineering productivity metrics can be seen in the questionnaire and in Appendix B.
Engineering Productivity
Input Actual Design Work Hours
= =
[Equation 1]
Output IFC Quantity
Construction productivity metrics are defined as actual direct work hours required to install a unit
quantity. This calculation can be seen in Equation 2. In this research, construction productivity rates
were captured for significant work activities for the following disciplines: 1) concrete, 2) structural steel,
3) equipment, 4) piping, 5) electrical, 6) instrumentation, 7) insulation and 8) scaffolding. Additionally,
in this research, both estimated and actual quantity and work hours are captured for construction
activities.
Construction Productivity
Input Actual Installed Direct Work Hours
= =
[Equation 2]
Output Installed Quantity
3.1.3 Practices
This study also assessed the use of 14 project best practices during the execution of a capital project
including: Front End Planning, Project Risk Assessment, Team Building, Alignment during Front End
Planning, Constructability, Design for Maintainability, Material Management, Project Change
Management, Zero Accident Techniques, Quality Management, Automation/Integration (AI)
Technology, Planning for Startup, Prefabrication/ Preassembly/ Modularization and Workface
Planning. Excluding Workface Planning, all these best practices were adopted from the original CII
benchmarking questionnaire. A complete list of these 14 Best Practices and definitions for each are
provided in Appendix B.
3.2 COAA-Specific Metrics
This research included additional COAA-specific metrics to quantify Alberta project performance and
productivity. These metrics are listed in Table 3-1. Many of these additional metrics relate to indirect
15

and direct construction costs, mechanical and equipment costs, scaffolding work hours, the use of
offsite modules, as well as various workforce metrics. The additional metrics were developed to
evaluate suspected major causes of cost overruns and schedule delays common to large projects
(Flyvberg 2003; COAA 2005). In addition, for projects in Alberta, metrics regarding estimated
construction productivity, estimated total installed unit cost (TIUC), and metrics related to actual
versus estimated productivity and TIUC were captured in the construction productivity section. A list
of all metrics developed specifically for projects in Alberta is provided in Appendix B.
Table 3-1 Additional Study-Specific Performance Metrics
Metrics
Related to
Project Cost
Metrics
Related to
Workforce
Metrics
Related to
Construction
Productivity
Direct Construction Cost
= Direct Construction Costs
Total Construction Cost
Indirect Construction Cost
= Indirect Construction Costs
Total Construction Cost
Indirect/ Direct
= Indirect Construction Costs
Direct Construction Costs
Major Equipment
= Major Equipment Cost
Total Project Cost
Mechanical & Process Equipment = Mech. and Process Equipment Costs
Total Project Cost
Direct-Indirect Workhours
= Total Construction Indirect Work-Hours
Total Construction Direct Work-hours
%Offsite Construction WH = Offsite Construction WH of Modules x 100
Total Construction Hours
%Overtime Work-hours = Overtime Craft Work-hours x 100
Total Construction Field Work-hours
Peak Construction Workforce = Actual Peak Workforce
Planned Peak Workforce
Mode of Travel to Worksite
% Workers Living in Camps and Living Out Allowance
Scaffolding WH Factor
= Scaffolding WH
Total Direct WH
Scaffolding Cost Factor
= Total Scaffolding Cost
Total Direct Cost
Modules Installation : Pipe Rack, Process Equipment, and Building Modules
Total Installed Unit Cost ($/ Unit Quantity)
Productivity Estimate Accuracy = Estimated Productivity Rate
Actual Productivity Rate
Cost Estimate Accuracy
= Estimated Total Installed Unit Cost
Actual Total Installed Unit Cost
Practices
Workface Planning
16

3.3 Understanding Benchmarking Reports and Analyses
The project key report provides the feedback to a company regarding how their selected project(s)
performed. It compares the project against the most comparable set of projects available for each
individual metric. Importantly, each participating company can use their key report(s) to identify
performance gaps in order to set objectives on future projects and to initiate improvements to key
work processes.
3.3.1 Metrics
COAA Project performance metrics consist of cost, schedule, safety, change, field rework,
engineering and construction productivity, and estimating performance (actual / estimated
productivity rate and total installed unit cost). A lower score generally indicates better performance.
For each individual metric, a typical comparison is provided in Figure 3-1. This figure shows that the
sample project overran the budget by 3.6%, while the comparable dataset has 35 projects with an
average cost growth of -4.0% (i.e., actual cost was 4% less than initially predicted). Overall, this
project ranks in the third quartile on cost growth when compared with its peer projects. First quartile
metrics are considered ‘best in class’.
Figure 3-1 Example of Performance Metrics
Practice metrics were scored using a ten point scale, with a higher number (i.e., 10) indicating better
implementation of the selected practice. As can be seen in Figure 3-2, this sample project received
a score of 8.929 for the use of Front End Planning (FEP). In comparison with 36 similar projects, this
project ranks in the second quartile, which indicates that the project implemented FEP relatively well.
Figure 3-2 Example of Practice Metrics
17

3.3.2 Explanation of Statistics
In addition to descriptive analyses previously presented and available in project key reports, this
research also employed various statistical techniques to analyze projects residing in both COAA and
CII databases. Primarily, box and whisker plots, pie charts and tabular descriptions were used to
portray descriptive statistics for both databases. Where inferential statistics were used, methods of
correlation including regression with trend lines and statistical tests of significance were incorporated
in this research. Where used, box and whisker plots also incorporate a variety of test statistics
including the standard T-test or Analysis of Variance (ANOVA) techniques, depending on the number
of comparison groups and distribution of sample variances (Agresti and Finlay 1999). Figure 3-3
provides an example of a Box and Whisker plot and associated terminology.
Mean refers to the arithmetic average of a set of values, which is the sum of the variable value
divided by the number of samples.
Median is the number separating the higher half of a sample from the lower half.
equivalent to the second quartile (Q2).
Median is
Sample Box and W hisker Diagram
OutlierSymbol
Third Quartile
(Q 3)
Last Obs ervation below
(Q3 + 1.5IQR)
Median
Mean
First Quartile
(Q1)
Las t Observation above
(Q1 - 1.5IQR)
Figure 3-3 Box and Whisker Diagram
First Quartile (Q1) is also called as the 25 th percentile or lower quartile which refers to the threshold
below which 25% of the sample have observed value(s).
18

Third Quartile (Q3) indicates the 75 th percentile and delineates the highest 25% of data.
Interquartile Range (IQR) refers to the range between the first quartile and the third quartile.
Correlation (r) measures the strength of the linear relationship between variables (metrics) ranging
from -1 to 1. However, a strong correlation does not prove that a causal relationship exists between
observed variables. The magnitude close to -1 and to +1 merely indicates that a strong negative or
positive relationship is observed between the two variables (i.e., when the relationship between them
follows a straight line on a scatter plot). Notably, a correlation close to 0 indicates virtually no linear
relationship. In this study, r0.5 is considered to have a high degree of
correlation.
Trend Line is based on the subjective evaluation of the best fit for the data and should not be used
for the purpose of extrapolation. In the case where no evident trends exist, trend lines were omitted.
The Coefficient of Determination (R 2 ) is the most frequently quoted measure representing the
goodness of linear fit of the least square regression line. R 2 can be interpreted as the percentage of
variation of the response variable explained by the regression line with the independent variable as
the only explanatory variable. The better fit the line possesses, the closer R 2 should be to 1.
Significant Value (p) is defined as the probability of making a decision to reject the null hypothesis
when the null hypothesis is actually true. Usually, social science research accepts any probability
value below 0.05 (or alpha level = 0.05) as being statistically meaningful. Consequently, any
probability value below 0.05 is regarded as indicative of genuine effect (Field, 2005).
19

4 Data Analysis
This chapter presents selected results of significant analyses discovered by the research team.
Instead of examining each project, this chapter describes how the databases of CII and COAA
projects were used to evaluate different hypotheses regarding the performance of projects in Alberta.
The first three sub-sections provide a perspective of the database through the use of descriptive
statistics. Subsequent sub-sections provide selected inferential analyses surrounding factors known
and suspected to affect project performance. Notably, the fourth sub-section compares the project
performance of Alberta and U.S. projects. The fifth and sixth sub-sections present analysis results
for the observed engineering and construction productivity (respectively) in Alberta in comparison
with U.S. projects. Finally, the results of factors impacting project cost, schedule and construction
productivity are presented in the last sub-section.
4.1 Description of Alberta Dataset
The first round of data collection was completed over a period of 36 months. During this time, 78
Alberta-based projects were established in the Alberta benchmarking system, though not all were
finalized and submitted. By the end of October 2008, a total of 37 projects were submitted, validated
and analyzed in this research. 28 projects were submitted by owners and 9 projects were submitted
by contractors at either project sanction or completion. Table 4-1 and Figure 4-1 contain further
descriptions of the dataset by type, nature, and delivery system. As can be seen in these exhibits,
the majority of submitted projects are oil sands SAGD and upgrading facilities. Most submitted
projects are grassroots facilities using parallel prime and design-bid-build (DBB) delivery systems.
Importantly, all submitted projects used cost reimbursable contracts for their construction phases.
These terms, along with others used in this study, are presented in a glossary in Appendix C. It
should be noted that due to the limited number of projects in the first round of this research, the
lowest level of analysis that can be presented contains 10 or more projects in accordance with
guidelines established by CII and COAA. These provisions assure statistical significance and
confidentiality and conform to published policies of the COAA Benchmarking Committee.
20

Table 4-1 Submitted Projects by Owners and Contractors at Project Completion and Sanction
Submitted at
Project Types # Projects Completion
Sanction
Owner Contractor Owner Contractor
Oil Sands Upgrading 12 3 1 6 2
Oil Sands SAGD 12 8 3 1 -
Natural Gas Processing 4 1 3 - -
Oil Sands Mining/Extraction 3 1 - 2 -
Pipeline 2 2 - - -
Cogeneration 1 1 - - -
Oil Refining 1 1 - - -
Electrical (Generating) 1 - - 1 -
Gas Distribution 1 1 - - -
Total 37 18 7 10 2
Modernization
4 (11%)
Traditional
D-B-B
3 (8%)
CM at Risk
5 (14%)
Grass Roots
19 (51%)
Addition
14 (38%)
Parallel Primes
17 (46%)
Design-Build
12 (32%)
# of Projects (%of Total)
# of Projects (%of Total)
Figure 4-1 Number of Submitted Projects by Project Nature and Delivery System
In this research, the total project cost is defined as the total installed cost for owners, whereas
contractors reported total cost of their work scope. The distribution of all submitted projects at either
sanction or completion and total project cost ($CDN) in 2007 can be seen in Figure 4-2. While all
projects were normalized to July 2007, all submitted projects were also completed after 2003. Time
adjustments were accomplished by using the historical index values contained in RS Means in order
to produce valid comparison bases. The total number of projects shown in Figure 4-2 is 35,
reflecting the fact that 2 projects did not provide project cost information. As a common practice in
Alberta, most mega projects were split into several smaller projects (sub projects) and managed as a
portfolio. Among the 37 submitted projects, half of these are considered as sub projects.
Consequently, all are treated as individual projects for purposes of data analysis and comparison in
the following sections.
21

Number of Submitted Projects at Sanction& Completion
Number of Projects
10
9
8
7
6
5
4
3
2
1
0
1
$1B
Total
11
24
Figure 4-2 Number of Projects Submitted at Sanction and Completion by Total Project Cost
Category ($CDN in 2007)
4.2 Selected Descriptive Analyses
The analyses in this sub-section were conducted to provide an appreciation of the baseline metric
values for the projects submitted by both owners and contractors. For these analyses, the
distribution of metrics mostly related to project characteristics and performance and is presented with
mean, median, range and quartile statistics using box and whisker diagrams.
• Construction Indirect Cost and Work-Hours
Figure 4-3 contains the distribution of construction indirect work hours for 20 projects as a proportion
of direct work hours (i.e., the n value below the graph indicates the number of projects reporting this
particular data). On average, the amount of indirect construction work hours for Alberta-based
projects is about 34% of direct construction work hours. Moreover, the average indirect construction
cost is 20.71% of total project cost. This can be seen in Figure 4-4.
22

Construction Indirect/ Direct Work-Hours (%)
70
60
50
40
30
20
10
0
(N=20)
34.09%
Figure 4-3 Construction Indirect / Direct Work hours (%)
Construction Indirect Cost/ Total Project Cost (%)
50
40
30
20
10
0
20.71%
(N=20)
Figure 4-4 Construction Indirect Cost / Total Project Cost (%)
• % Modularization 1
Figure 4-5 contains a comparison of the percentage of project cost spent using modularization by
project nature. Here, the dataset includes 17 grass roots projects and 13 addition projects with
modularization data (there was insufficient data to report for modernization projects). On average,
grassroots projects spent 19.4% of their total project cost on modularization, compared to 12.3%
spent on addition projects. This difference, however, is not statistically significant (t = 1.517, p>0.05).
1 % Modularization is a percentage value that describes the level of modularization (offsite construction), and
defined as a ratio of the cost of all modules divided by total installed cost.
23

Modularization/Total Proejct Cost(%)
50
40
30
20
10
0
19.41
Grass Roots
(N=17)
12.31
Addition
(N=13)
Figure 4-5 Modularization by Project Nature
• Project Delivery Systems
Figures 4-6 and 4-7 compare the effectiveness of parallel primes and all other project delivery
methods combined by cost and schedule growth, respectively. The research found that 46% of the
projects in the Alberta benchmarking database used a parallel primes project delivery method. Other
project delivery methods included traditional design/bid/build (D/B/B), design/build (D/B), multiple
design-build and construction management (CM). These methods were combined due to insufficient
numbers of projects in each of these categories. Nonetheless, these results show a slight advantage
to the use of parallel primes over other delivery methods with respect to schedule, but not with
respect to project cost. In addition, parallel primes projects had slightly lower average project
schedule growth (0.15) in comparison with all other project delivery methods (0.19) and,
simultaneously, higher project cost growth (0.23 vs. 0.13). These differences are not statistically
significant (t = -0.378, p>0.05) for project schedule growth, nor are they significant for project cost
growth (t = 0.756, p>0.05). These results are presented here merely for the reader’s enhanced
understanding of the project data used for this report.
24

1.2
1.0
Project Cost Growth
0.8
0.6
0.4
0.2
0.0
0.23
0.13
-0.2
-0.4
Parallel Primes
(N=14)
Other
(N=10)
Figure 4-6 Project Cost Growth by Project Delivery System
1.00
Project Schedule Growth
0.75
0.50
0.25
0.15
0.19
0.00
Parallel Primes
(N=14)
Other
(N=10)
Figure 4-7 Project Schedule Growth by Project Delivery System
4.3 Selected Inferential Analyses
Inferential analyses were used to explore the relationships amongst project characteristics and the
implementation of best practices on project performance. This sub-section provides samples of
analyses used to determine if any trends or relationships exist between variables for the purpose of
identifying the potential root causes that may help explain the performance of Alberta-based projects.
Importantly, it should be noted that the trends or relationships presented in this sub-section should
25

not be used to predict, or forecast the performance of any current or future project. Regression and
trend lines shown here are considered explanatory only for the projects being analyzed.
• Percent Engineering Completed before Construction Started
The relationship between percent engineering completed before construction started and
construction phase cost growth can be seen in Figure 4-8. Note that a complete list of project
metrics is included in Appendix B. Given the expert opinions of members of the COAA
Benchmarking Committee, the relationship displayed in Figure 4-8 uses a cubic polynomial pattern
due to the fact that as more design is completed before construction begins, the project tends to
have less construction phase cost growth. This trend holds true until, at a certain point, the cost
growth curve flattens and subsequently increases. Thus, an optimum value is found at approximately
60% engineering complete. This result is consistent with other studies completed by CII and other
industry forums. The results are also statistically significant, meaning that a strong relationship
exists between the percentage of engineering completed prior to construction start and construction
phase cost growth (R 2 = 0.63, p = 0.016). Likewise, the results also demonstrate a statistically
significant correlation with r = -0.723, p = 0.003. Due to limited number of data, predictability is not
inferred, nor concluded, in this research.
1.00
Construction Phase Cost Growth
0.75
0.50
0.25
0.00
-0.25
-0.50
-0.75
0
10 20 30 40 50 60 70 80 90
% Design completed before construction started
100
Figure 4-8 Effect of % Engineering Completed before Construction Started
26

• Peak Construction Workforce
Analyses were conducted to examine how a change in construction workforce peak numbers affects
project performance. This can be seen in Figure 4-9. The results indicate that the projects which
estimated their actual peak workforce numbers with greater precision experienced higher levels of
project cost and schedule performance. In fact, the relationship between the ratio of actual to
estimated peak workforce and project cost growth is statistically strong (r = 0.787, p =0.001). The
regression model also indicates a statistically strong high R-square (R 2 = 0.62, p< 0.001). A medium
to strong relationship was also identified between project schedule growth and the ratio of actual to
estimated peak construction workforce (r = 0.486, p = 0.011).
1.2
1.0
Project Cost Growth
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Actual/ Estimated Peak Construction Workforce
Figure 4-9 Actual / Estimated Number of Peak Construction Workforce
• Construction Indirect Work-Hours/ Direct Work hours (%)
The relationship between construction indirect work hours and project schedule factor can be seen in
Figure 4-10. This figure indicates that projects with high ratios of construction indirect work hours tend
to have better project schedule performance. Here, the lower schedule factor may mean that better
schedule performance may be obtained by isolating the schedule impact of change orders. The
analysis shows that the relationship between these metrics can be characterized as medium to strong
and statistically significant (r= -0.446, p=0.05). The linear regression model is also statistically
significant, yet presents a low R-square value (R 2 = 0.20, p= 0.05).
27

2.00
1.75
Project Schedule Factor
1.50
1.25
1.00
0.75
0.50
0.25
0.00
0
10
20
30
40
50
60
70
80
90
100
Construction Indirect WH/Direct WH (%)
Figure 4-10 Construction Indirect Work-hours/ Direct Work hours (%)
• Project Size ($)
Figure 4-11 contains the analysis of how project size ($ CDN, in 2007) affects construction indirect
cost growth. Construction indirect cost growth is calculated as actual indirect cost divided by
estimated indirect cost. The results indicate that larger projects tend to have higher levels of
construction indirect cost growth, based on their own estimates. These results are statistically strong
(r= 0.599, p=0.011) and indicate that a relationship does exist between project size and construction
indirect cost growth. The regression model also possesses a medium R-square (R 2 = 0.36, p= 0.011)
value.
1.50
Construction Indirect Cost Growth
1.25
1.00
0.75
0.50
0.25
0.00
-0.25
-0.50
0
200 400 600 800 1000
Adjusted Total Project Cost ($M CDN, in 2007)
1200
Figure 4-11 Construction Indirect Cost Growth by Project Size ($)
28

• Project Risk Assessment (PRA)
The effects of Project Risk Assessment (PRA) on project performance are well known and were
investigated for this study. As defined by CII, PRA is the process needed to identify, assess and
manage risk. In PRA, the project team evaluates risk exposure for potential project impacts in order
to provide focus for mitigation strategies. The analysis of COAA data indicate that high levels of
implementation success of PRA are accompanied by better project cost performance as can be seen
in Figure 4-12.
For this practice, the statistical relationship is medium-strong and statistically
significant (r = -0.436, p = 0.048). However, for PRA as implemented on Alberta projects, the
regression model exhibits low R-square values (R 2 = 0.19, p= 0.048).
1.2
1.0
Project Cost Growth
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
0
1
2
3
4
5
6
7
8
9
10
Project Risk Assessment
Figure 4-12 Project Risk Assessment vs. Project Cost Growth
• Constructability
Constructability is defined as the effective and timely integration of construction knowledge and
experience into the conceptual planning, design, construction, and field operations of a project to
achieve the overall project objectives.
As can be seen in Figure 4-13, increased use of
constructability on Alberta project leads to better project schedule performance (i.e., lower project
schedule growth). This relationship is statistically significant (r = -0.528, p = 0.008), (R 2 = 0.28, p =
0.008). Interestingly, the effect of constructability on cost performance is not significant for the
COAA dataset.
These results are consistent with previous analyses developed using CII’s
benchmarking and metrics database.
29

1.25
Project Schedule Growth
1.00
0.75
0.50
0.25
0.00
-0.25
-0.50
0
1
2
3
4 5 6
Constructability
7
8
9
10
Figure 4-13 Constructability vs. Project Schedule Growth
• Analysis of Other Best Practices
Analyses of other best practices were also conducted. Two are included here: planning for startup
and workface planning. Other evaluations of additional surveyed best practices are not presented
here due to limited space and the fact that they are summarized in a table which can be found in
Appendix A.
Planning for startup is defined as the effective facilitation of the activities that occur between
mechanical completion (i.e., plant construction completion) and the commencement of commercial
operations. As can be seen in Figure 4-14, the analysis conducted by this research indicates that
improved use of planning for startup methods tends to improve the cost performance of the startup
phase. However, this conclusion is made with caution due to the small sample size involved.
Finally, the COAA best practice of workface planning was also assessed. Workface planning is
defined by COAA as the process of organizing and delivering all elements necessary, before work is
started, to enable craft persons to perform quality work in a safe, effective and efficient manner. The
relationship between workface planning and construction phase schedule performance can be seen
in Figure 4-15. However, no regression line is plotted due to the limited number of projects reported
in this first round. Nonetheless, the figure is shown here as it is believed that workface planning
does help improve jobsite productivity by assuring that the required resources, tools, equipment and
material are made available to craft workers in a timely fashion. More data will be needed for a more
30

comprehensive evaluation of this specific best practice. However, even then, as discussed by
Kellogg et al (1981), the optimization of jobsite performance may be limited in comparison with the
planning and engineering.
3
Startup Phase Cost Growth
2
1
0
-1
-2
5
6
7
8
Planning for Startup
9
10
Figure 4-14 Planning for Startup vs. Startup Phase Cost Growth
0.8
Construction Schedule Growth
0.6
0.4
0.2
0.0
-0.2
-0.4
0
1
2
3
4
5
6
7
8
9
10
Workface Planning Index
Figure 4-15 Workface Planning vs. Construction Schedule Growth
31

4.4 Comparison of Alberta and U.S. Project Performance
A primary focus for this research effort was to obtain comparisons of project performance in Alberta with
projects executed in the United States using the CII Benchmarking and Metrics (BM&M) database. To
accomplish this objective, U.S. projects are limited to projects with an adjusted total installed cost
greater than $5 million (CDN), normalized to 2007. In this study, we consider U.S. EMR projects to
include oil exploration / production, oil upgrading / refining, natural gas, pipeline, chemical manufacturing,
power generation, and mining. In the following analyses, the symbol ♦ is used to indicate arithmetic
mean and the symbol is used to indicate the median of a particular group.
Alberta-based projects are unique projects. There are significant differences when comparing them
to U.S.-based projects. Principally, most of the COAA projects are larger than their U.S.
counterparts in terms of cost. They are also located in remote locations and are subjected to
extreme (northern climate) weather conditions. Often, work camps are built and transportation for
large numbers of workers becomes necessary. However, the analyses presented in this sub-section
are not intended to quantify these differences, but rather, examine differences in project size,
contingency, and cost, schedule, and change performance.
• Comparison of Project Size ($) for two Datasets
Figure 4-16 provides the distribution of Alberta-based and U.S.-based projects included in this study
in terms of project cost. Notably, the average size and range of the 353 included U.S. projects are
Adjusted Project Cost ($M, in 2007)
1200
1000
800
600
400
200
0
367.83
185.82
Alberta
(N=23)
84.83
40.43
U.S.
(N=353)
Figure 4-16 Project Size ($M CDN, in 2007)
32

notably smaller when compared with Alberta-based projects.
Based on past research and
benchmarking experience, this difference is a significant factor in quantifying performance and
should be considered in understanding the analyses presented here.
• Comparison of Contingency Budget (%)
As can be seen in Figure 4-17, the amount of contingency for Alberta and U.S.-based projects are
quite comparable.
Project data shows a slightly higher average contingency rate (8.04%) for
Alberta-based projects when compared to U.S.-based projects (7.77%). However, the difference is
not statistically different (t= 0.33, p= 0.742).
Total Contingency Budget/ Total Project Cost (%)
20
18
16
14
12
10
8
6
4
2
0
8.04%
Alberta
7.77%
U.S.
(N=17)
(N=52)
Figure 4-17 Contingency Budget (%)
• Comparison of Project Cost and Schedule Performance
Figures 4-18 and 4-19 compare the project cost growth and project schedule growth of projects
executed in Alberta and in the U.S. Results show significantly higher average cost growth and
schedule growth for the Alberta projects. These projects also demonstrate that a much wider range
of performance exists as well. On average, Alberta-based projects experienced 19% project cost
growth and 17% project schedule growth, while U.S. projects experienced 3% and 6% cost and
schedule growth, respectively. The test of mean difference between these two groups also indicates
that the average cost and schedule growth of Alberta project is statistically significant (t = 3.89, p =
0.000 for cost and t = 3.838, p = 0.000 for schedule). Additionally, these figures show that the
33

Alberta-based projects possess a much wider range of performance (i.e., -27% to 69% for cost
growth and -15% to 35% for schedule growth) when compared to projects executed in the U.S.
1.2
1.0
Project Cost Growth
0.8
0.6
0.4
0.2
0.0
0.19
0.03
-0.2
-0.4
Alberta
(N=24)
U.S.
(N=352)
Figure 4-18 Project Cost Growth
1.25
1.00
Project Schedule Growth
0.75
0.50
0.25
0.00
0.17
0.06
-0.25
-0.50
Alberta
(N=24)
U.S.
(N=338)
Figure 4-19 Project Schedule Growth
• Comparison of Change Cost Factor
Figure 4-20 contains an analysis of the comparison of development change cost factor and scope
change cost factor for the selected Alberta and U.S. projects. In this analysis, the Alberta-based
projects have a slightly higher average development change cost factor (0.06) when compared to
U.S.-based projects (0.04). In contrast, the average scope change cost factor for projects in Alberta
34

is slightly lower than that of U.S. projects. However, these differences are not considered to be
statistically significant (p > 0.05). Finally, an analysis of total field rework cost (defined as a ratio of
total direct cost of field rework to actual construction phase cost) indicated that Alberta projects are
also in line with U.S.-based projects.
0.4
0.3
Change Cost Factor
0.2
0.1
0.0
0.06
0.04
0.02
0.04
-0.1
-0.2
Alberta
U.S.
Alberta
U.S.
Development_Change
Scope_Change
(N=11) (N=14) (N=13) (N=14)
Figure 4-20 Development and Scope Change Cost Factor
4.5 Engineering Productivity
Twenty-three of the 37 Alberta-based projects submitted for this research provided measures of
engineering productivity. Of these 23 projects, their average project cost was $367 Million (CDN),
normalized to July 2007. As can be seen in Figure 4-21, the CII BM&M database was able to return
57 EMR projects that also contained engineering productivity data. These projects reported an
average cost of $90 Million (CDN), also normalized to July 2007. As previously described, this
differential in average project cost may impact the direct measures of engineering productivity
reported here. Also, in order to ensure appropriate comparisons, the unit of measure of both U.S.
and Alberta projects used to calculate engineering productivity is the metric system (e.g., linear
meter, metric ton) and data have been converted to meet this standard. In general, engineering
productivity metrics use direct engineering work hours in metrics comparing them with specific
issued for construction (IFC) quantities for specific disciplines. These are discussed next.
35

Adjusted Project Cost ($M, in 2007)
1200
1000
800
600
400
200
0
367.83
185.82
Alberta
U.S.
90.40
30.88
(N=23)
(N=57)
Figure 4-21 Comparison of Project Size ($M CDN, in 2007) for Engineering Productivity
Dataset
• Comparison between Alberta versus U.S. Projects by Engineering Disciplines
Selected disciplines of engineering productivity are presented in this section. Comparisons between
Alberta-based and U.S.-based projects are presented by using both arithmetic mean value (indicated
by the symbol ♦), and weighted mean value (represented by the symbol ⊗). Here, the weighted
mean is calculated as an aggregated productivity rate and is weighted by project size. Essentially,
this mean creates one large, imaginary project where total work hours and total quantities are
assimilated. Previous analysis by CII indicates that this approach is valid and that large projects
typically experience better productivity rates (due to larger quantities) when compared with smaller
projects possessing smaller quantities and lower levels of repetitive work and economies of scale.
• Concrete Engineering Productivity 2
As can be seen in Figure 4-22, the results of engineering productivity metrics for Alberta-based
projects and U.S.-based projects are mixed. Alberta-based projects have comparable concrete
engineering productivity in line with U.S.-based projects when considering mean values. In fact, after
considering project size, the weighted average concrete engineering productivity rate of Alberta
projects is actually better than that of U.S. projects.
2 Total Concrete include slabs, foundations, and concrete structures.
36

Total Concrete- Eng. Prod. Rate (WH/CM)
20
15
10
5
0
5.92
⊗ 3.53
Alberta
(N=17)
⊗ 6.26
4.52
U.S.
(N=31)
Figure 4-22 Comparison of Concrete Engineering Productivity (WH/ Cubic Meter)
• Total Structural Steel Engineering Productivity 3
Figure 4-23 indicates that U.S.-based EMR projects perform the engineering of structural steel with
higher levels of productivity when compared with Alberta-based projects (10.96 WH / ton versus
23.08 WH / ton). The weighted average productivity rate of U.S. projects is also better (5.86 WH /
ton versus 12.64 WH / ton). This difference of average structural steel engineering productivity
rates is statistically different (t = 2.501, p = 0.02). No cause of this difference is indicated.
Steel- Eng. Prod. Rate (WH/Metric Ton)
70
60
50
40
30
20
10
0
23.08
⊗ 12.64
Alberta
(N=19)
10.96
⊗ 5.86
U.S.
(N=56)
Figure 4-23 Comparison of Structural Steel Engineering Productivity (WH/ Metric Ton)
3 Total of structural steel include structural steel, pipe racks & utility bridges, and miscellaneous steel.
37

• Piping Engineering Productivity 4
As can be seen in Figure 4-24, Alberta-based projects demonstrate higher levels of piping
engineering productivity when compared with U.S.-based projects. This holds true for comparisions
of small bore pipe, large bore pipe, and all pipe sizes combined. These results are consistent using
both measures of average and weighted productivity as can be seen in the figure. It should be noted
that the difference portrayed here in the average piping engineering productivity rates is statistically
significant only for large bore pipe (t = -2.663, p = 0.012), yet this difference is negligible when
examining only the projects which reported only total piping data (i.e., no reporting of bore size).
Again, no cause of the differential reported here is indicated.
8
Piping- Eng. Prod. Rate (WH/LM)
7
6
5
4
3
2
1
0
2.58
2.08
⊗ 1.97
⊗ 1.70
2.03 1.60
⊗
1.00
0.88
1.28 ⊗ 1.23
⊗ 0.78 ⊗ 0.47
Alberta U.S. Alberta U.S. Alberta U.S.
SmallBore(

time adjustment to July 2007. While the project sizes differ, the comparisons which follow are
considered to be valid because of how this study defined construction productivity as the ratio of field
direct work hours (WH) per applicable installed quantity.
1800
Adjusted Project Cost ($M, in 2007)
1600
1400
1200
1000
800
600
400
200
0
459.68
285.49
122.08
81.13
Alberta
(N=33)
U.S.
(N=29)
Figure 4-25 Comparison of Project Size ($M CDN, in 2007) for Construction Productivity
• Comparison Between Alberta and U.S. Projects by Work Disciplines
Construction productivity for selected disciplines are presented in this sub-section. As was done with
measures of engineering productivity, the arithmetic mean is represented by the symbol (♦), and the
weighted mean (i.e., a hypothetical single large project) is represented by the symbol (⊗) for
purposes of comparisons of construction productivity rates between Alberta and U.S.-based projects.
• Concrete Construction Productivity 5
Figure 4-26 provides an assessment of concrete construction productivity. As can be seen in the
figure, U.S.-based projects place concrete more efficiently than do Alberta projects (U.S. average
total concrete productivity rate is 14.44 WH/m 3 , compared to 19.39 WH/m 3 for Alberta projects). The
results are also considered to be consistent even given the differences in the size of the projects
used for this analysis. Notably, the weighted average productivity rates of U.S.-based projects is
9.72 WH/m 3 , compared to 13.10 WH/m 3 for Alberta-based projects, although this difference is not
considered to be statistically significant.
5 Total Concrete includes slabs, foundations, and concrete structures.
39

Total Concrete Productivity Rate (WH/ CM)
60
50
40
30
20
10
0
19.39
⊗ 13.10
Alberta
(N=12)
14.44
⊗ 9.72
U.S.
(N=32)
Figure 4-26 Comparison of Total Concrete Construction Productivity (WH/ m 3 )
• Total Structural Steel Construction Productivity
As can be seen in Figure 4-27, U.S.-based projects are more productive in erecting structural steel
than Alberta projects are (53.95 WH/MT versus 42.41 WH/MT). This difference is not statistically
significant. However, the weighted average productivity rate of Alberta-based projects is slightly
better than that of U.S.-based projects by 1.06% when considering project size.
Total Steel Prod. Rate (WH/Metric Ton)
180
160
140
120
100
80
60
40
20
0
53.95
⊗ 37.96
Alberta
(N=21)
42.41
⊗ 38.37
U.S.
(N=32)
Figure 4-27 Comparison of Total Structural Steel Construction Productivity (WH/ Metric Ton)
40

• Instrumentation – Devices Construction Productivity
Figure 4-28 provides an assessment of the productivity of installation of instrumentation – devices.
Project data reported for this study revealed that the arithmetic mean of instrumentation – devices
productivity rate of Alberta-based projects is comparable to that of U.S.-based projects (13.37
WH/Count versus 13.53 WH/Count, respectively). However, the weighted average productivity rate
of U.S.-based projects is significantly better (i.e., 158% better) than that of their Alberta-based
counterparts. This is primarily due to the discrepancies that exist for instrumentation – devices
productivity rates between small projects and large projects.
Instrumentation- Devices Con. Prod. Rate (WH/ Count)
50
40
30
20
10
0
⊗
21.40
13.37
Alberta
(N=9)
13.53
⊗ 8.28
U.S.
(N=22)
Figure 4-28 Comparison of Instrumentation- Devices Construction Productivity (WH/ Count)
• Insulation – Piping Construction Productivity
As can be seen in Figure 4-29, the average (i.e., arithmetic mean) piping insulation productivity rate
of Alberta-based projects is comparable to that of U.S.-based projects (i.e., 1.90 WH/LM versus 1.93
WH/LM, respectively). However, when the weighted mean calculation is used, the Alberta-based
projects outperformed their U.S.-based counterparts by 35.6%. One hypothesis for this observed
difference is that Alberta-based projects likely have much more piping insulation, on average, and
that the metrics used for this study are possibly indicating the benefits of repetition for this particular
construction activity.
41

Insulation- Piping Con. Prod. Rate (WH/LM)
8
7
6
5
4
3
2
1
0
1.90
⊗
1.41
Alberta
(N=16)
2.19
⊗ 1.93
U.S.
(N=15)
Figure 4-29 Comparison of Insulation- Piping Construction Productivity (WH/ Linear Meter)
• Construction Productivity Project Level Index (CPM Index)
Recently, CII has produced a method for examining construction productivity at the project level by
weighting and combining the productivity rates for various disciplines for each observed project. This
method, known currently as the Construction Productivity Method (CPM) Index, provides a macroview
of project performance. As a relative productivity performance measure, the CPM Index ranges
from -3 to 3, with -3 indicating the poorest observed productivity performance. Here, one unit
difference in the CPM index is equivalent to a 100% observed difference in productivity.
Construction Prod. Project Level Index
3
2
1
0
-1
-2
-3
0
200 400 600 800 1000 1200
Adjusted Total Project Cost ($M CDN, in 2007)
Figure 4-30 Construction Productivity Project Level Index vs. Project Size ($M CDN, in 2007)
42

The project-level CPM index is critical to the examination of factors affecting construction productivity
because, most of the time, these factors affect all disciplines and not individual disciplines. As can
be seen in Figure 4-30, the project data for Alberta-based projects indicates that large projects had
better overall construction productivity than small projects did. The analysis provides for a
statistically significant, medium strength correlation (r = -0.374, p = 0.008), although the linear
regression line is not statistically significant (R 2 = 0.13, p = 0.115) and should not be used for
estimating or forecasting purposes. However, the idea that larger projects have better construction
productivity is made with caution since the CPM index includes only measures direct construction
productivity. Indeed, larger projects tend to have larger installed quantities and higher amounts of
repetitive work and these factors may impact overall construction productivity figures. Notably, these
results are consistent with previous analyses conducted by CII.
This research explored other aspects of construction productivity as well. One example involves the
analysis of the effect work schedule (days on and off) has on construction productivity. This study
concluded that a work schedule of 10 days on and 4 days off was more productive than a work
schedule of 5 days on and 2 days off. In fact, an 11% difference was observed using the CPM Index,
although strong statistical significance was not present due to low numbers of project observations.
• Actual / Estimated Construction Productivity Rate by Work Discipline
Figure 4-31 provides an assessment of the accuracy of field productivity estimates for three crafts.
This research discovered that Alberta-based projects significantly underestimate construction
productivity. Observed data demonstrate that piping, structural steel, and concrete actual rates
exceeded their estimated rates by 4%, 22% and 45% (respectively), on average.
3.0
Actual/ Estimated Productivity Rate
2.5
2.0
1.5
1.0
0.5
0.0
1.45
1.22
1.04
Total_Concrete
Total_Steel
Total_Piping
(N=8) (N=17) (N=10)
Figure 4-31 Actual / Estimated Construction Productivity Rate by Work Discipline
43

• Actual / Estimated Total Installed Unit Cost (TIUC) 6 by Work Discipline
To give some context to Figure 4-31, Figure 4-32 provides an assessment of the accuracy of unit
cost estimates by craft. Due to the fact that labour accounts for about 30% of total installed unit cost
(TIUC), a moderating effect exists on field productivity when other factors are considered. In fact,
underestimates of 2%, 11%, and 10% were observed for piping, structural steel, and concrete,
respectively. Consequently, construction labour productivity rates must be estimated with caution.
Actual/ Est. Total Installed Unit Cost
2.5
2.0
1.5
1.0
0.5
0.0
1.10 1.11
1.02
Total_Concrete
Total_Steel
Total_Piping
(N=9) (N=15) (N=8)
Figure 4-32 Actual / Estimated Total Installed Unit Cost (TIUC) by Work Discipline
4.7 Analysis of Impact Factors
Working with CII, the COAA Benchmarking Committee developed a list of 18 potential factors known
to impact project cost, schedule, and engineering and construction productivity. This sub-section
contains analyses of these impact factors and their relationship to cost, schedule, and overall
construction productivity performance. Importantly, these analyses rely upon the subjective
knowledge of industry professionals working on the Alberta-based projects contributing data for this
study. Beyond the 12 impact factors routinely used by CII, the COAA Benchmarking Committee
added an additional 6 factors, specifically for Alberta-based projects:
6 Total installed unit cost (TIUC) is defined as the burdened cost of direct labour, bulk material, final asset equipment, and
civil and sitework equipment by pro rata share including overhead and profit from both direct hire and subcontract. Burden
cost of direct labour includes insurance, welfare and other fund and charges associated to labour by regulations.
44

1) Quality of Field-Level Supervision
2) Amount of Scheduled Overtime
3) Amount of Unplanned Overtime
4) Engineering Labour Skill
5) Percentage of Engineering Completed Prior to Project Sanction
6) Percentage of Engineering Completed Prior to Construction Start
For this study, the industry professionals contributing data for each project were requested to assess
whether each of these factors adversely or positively affected project performance (beyond which
was planned for) using a scale ranging from “highly negative”, to “highly positive”.
The results in this sub-section show the combined, relative impact of factors affecting project cost,
schedule and construction productivity. In Figures 4-33, 4-35, and 4-37, each factor is ranked using
the average degree of impact as reported on all projects as indicated by the symbol ( ). In these
figures, the whisker line spreading out from the average indicates the dispersion of impact rating by
using one standard deviation (S.D.) in both directions (i.e., -1 to +1). Notably, the factors with less
than 10 responses were not reported. Consequently, some figures contain more comparisons of
impact factors than others.
As can be seen in Figure 4-33, the factor having the most impact on project cost (when compared to
that which was planned) was the amount of unplanned overtime. This was followed closely by the
percent of engineering completed prior to construction, business market conditions, craft labour skill,
and coordination of plant shut down. These are the top four impact factors on project cost and
demonstrate an average impact of -0.889, -0.722, -0.565, and -0.500, respectively.
Figure 4-34 provides a perspective of the impact of 16 factors on project cost growth. The
cumulative impact is significant and a very strong correlation exists (i.e., R 2 = 0.56). As a result, this
research found that the 16 factors seen in Figure 4-33 had a large, negative impact on the cost
performance of the Alberta-based projects. In all cases, these impacts (on average) were worse
than the projects had planned for.
45

Figure 4-33 Factors Impacting Project Cost
Hi Positive
Impact
Hi Negative
Impact
Sum Degree Impact of Top 16 Factors vs. Cost Growth
Sum Degree of
Impact
10
8
6
4
2
0
-1.2 -1 -0.8 -0.6 -0.4 -0.2-2
0 0.2 0.4 0.6 0.8 1 1.2
-4
-6
-8
-10
Poor Cost
Performance
Figure 4-34 Impact of Factors vs. Cost Growth
Project
Cost Growth
As can be seen in Figure 4-35, the factor having the most impact on project schedule (when
compared to that which was planned) was the percentage of engineering completed prior to
construction start. This was followed closely by business market conditions, craft labour skill, quality
of field-level supervision and weather conditions. The top five impact factors on project schedule
demonstrate an average impact of -0.667, -0.611, -0.565, -0.525, and -0.524, respectively.
46

Figure 4-36 provides a perspective of the impact of the top 18 factors on project schedule growth. In
this case, no regression line has been plotted as no significant correlation exists. Nonetheless, the
figure provides some evidence that the surveyed project factors do provide an impact to the
schedule performance of the project. In all but one factor (on average) the impact was worse than
the projects had planned for.
Figure 4-35 Factors Impacting Project Schedule
Sum Degree Impact of Top 18 Factors vs. Project Schedule Growth
Hi Positive
Impact
Hi
Negative
Impact
Sum Degree of
Impact
10
8
6
4
2
0
-1.2 -1 -0.8 -0.6 -0.4 -0.2-2
0 0.2 0.4 0.6 0.8 1 1.2
-4
-6
-8
-10
Poor Cost
Performance
Project
Schedule
Growth
Figure 4-36 Impact of Factors vs. Schedule Growth
47

As can be seen in Figure 4-37, the greatest impact factor on construction productivity (when
compared to plan) was the percentage of engineering completed prior to construction start. This was
followed by the amount of unplanned overtime, business market conditions, the quality of field-level
supervision, and craft labour skill. The top five impact factors on construction productivity
demonstrate an average of -0.833, -0.778, -0.765, -0.476 and -0.455, respectively.
Figure 4-38 provides a perspective of the impact of the top 17 factors on construction productivity.
Notably, a medium-strength correlation exists (i.e., R 2 = 0.27) between the CPM Index of
construction productivity and the cumulative impact factors observed for each project. Indeed, this
research found that the 17 factors seen in Figure 4-37 had an overall, negative impact on the
construction productivity performance of the Alberta-based projects. In all cases, these impacts (on
average) were worse than that which the projects had planned for.
Figure 4-37 Factors Impacting Construction Productivity (Field Productivity)
48

Sum Degree Impact of Top 17 Factors vs. Project Construction
Productivity (CPM)
Hi Positive
Impact
Hi Negative
Impact
Sum Degree of
Impact
10
8
6
4
2
0
-3 -2.5 -2 -1.5 -1 -0.5-2
0 0.5 1 1.5 2 2.5 3
-4
-6
-8
-10
CPM
Unproductive Direct
Construction Productivity
Figure 4-38 Impact of Factors vs. Project Construction Productivity (CPM)
5 Major Findings
Through the participation of COAA members in this research, a number of major findings were made
concerning the performance of major capital projects in Alberta’s heavy industry sector. However,
this is only seen as a ‘first step’ on the journey of continuous improvement for many COAA members.
Because the COAA benchmarking system enables each member to identify gaps in their project
execution performance relative to that of their peers, the system may empower them to modify work
processes or implement best practices. This type of introspection, development, and deployment is
critical. Without it, industry-wide improvement is not possible.
This report contains selected perspectives of observed data across multiple COAA member
companies and their unique projects. Taken collectively, there are a number of significant findings
amongst the projects analyzed for this research. These are discussed next.
5.1 Project Performance
Alberta-based projects demonstrated significant issues with cost and schedule performance as
evidenced by cost growth and schedule growth metrics used in this study. Compared to similar U.S.-
based projects, these metrics were 533% and 183% higher, respectively. These overruns are
49

eyond unpredictable – they are alarming. Due to the fact that these two metrics compare actual
and planned costs and durations, the estimation of anticipated costs and schedules is seemingly an
issue and potentially at the heart of the capital project performance which was observed in Alberta.
Several factors may be at work:
1) Alberta-based projects have comparatively high proportions of indirect (to direct) cost. This
study found, on average, that indirects account for 20.71% of total project cost. These costs
may be due to factors such as large projects in remote jobsites and executed in harsh
climates. However, the estimation and management of indirects deserves close attention.
2) This research revealed that the actual peak construction workforce was highly correlated
with project cost growth. Yet, the research also discovered that, in many cases, construction
productivity was not highly differential to that experienced on U.S.-based projects. Again,
the proportion of indirect labor is an issue. While this study did note that higher amounts of
construction indirect labor hours results in better schedule performance, a renewed effort to
accurately estimate peak workforce, indirect cost, construction productivity, and unit cost is
suggested. Additional benchmarking data can be useful for this purpose.
3) A number of Alberta-based projects submitted for this study began construction with less
than 30% engineering complete. CII has conducted many previous studies that show that
the most appropriate time to start construction is when more than 60% of engineering is
complete. This dichotomy reveals itself in metrics related to construction phase growth and
construction productivity, amongst others. Mobilizing to the field too soon is often
accompanied by negative cost growth and construction productivity rates.
4) Alberta-based project management teams frequently fail to recognize that project cost
growth is driven by, and managed through, scope and project development changes. As
can be seen in Figure 5-1, U.S.-based projects report virtually all variance in actual project
costs as change. This is visually represented as the near-overlay of the U.S. trend line with
a line of unity between cost growth and change cost factor. Such is not the case with the
Alberta project data set, which consistently experience cost growths which far exceed their
associated change cost factors.
50

1.4
1.2
Location
Alberta
U.S.
Project Cost Growth
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Project Change Cost Factor
Figure 5-1 Project Change Cost Factor vs. Project Cost Growth
5.2 Productivity
Despite the wide range of observed capital project performance in Alberta, this research found that
engineering productivity as well as construction rework and productivity were comparable to similar
U.S.-based projects in some disciplines. However, construction productivity in some disciplines was
much worse than U.S. projects. These points are especially applicable when considered and
measured at the discipline level for both engineering and construction labour productivity. However,
it should be noted that this research measured productivity as a ratio of direct work hours to issued
for construction (IFC) quantities for engineering and to installed quantities for construction.
5.3 Impact Factors
An array of factors exist that impact the performance of capital projects. Using the subjective
evaluation of experienced project professionals, this research was able to categorize the primary
factors impacting the cost, schedule, and construction productivity of Alberta-based projects.
Surprisingly, there was a fair amount of consistency with the objective findings of this research. The
top five ranked factors are listed in Table 5-1 according to their impact on cost, schedule, and
construction productivity performance. In many cases, the top five factors were contrary to popular
opinion (as surveyed) as they dealt with both managerial and site-related issues.
51

Table 5-1 The Top 5 Factors Affecting Cost, Schedule or Productivity
Rank Cost Schedule Productivity
1 Amount of Unplanned
Overtime
2 % Engineering completion
prior to Construction Start
% Engineering completion
prior to Construction Start
Business Market
Conditions
% Engineering completion
prior to Construction Start
Amount of Unplanned
Overtime
3 Business Market Conditions Craft Labour Skill Business Market Conditions
4 Craft Labour Skill Quality of Field Level
Supervision
5 Coordination with Plant
Shutdown
Weather Conditions
Quality of Field Level
Supervision
Craft Labour Skill
5.4 Project Management
Project management best practices make a huge difference in the performance of capital projects.
Better implementation of Project Risk Assessment is shown to significantly reduce project cost
growth. Constructability reviews can reduce project schedule growth as much as 50%. Not
surprisingly, planning for startup improves startup phase cost performance. While insufficient data
were available to examine the effects of workface planning on project performance, it is believed that
its influence may be limited in an environment where effective project management is lacking. The
dedicated implementation of proven project management practices such as front-end planning,
constructability reviews, and project risk assessment during a project’s earliest phases will maximize
the project’s potential to achieve high levels of performance.
52

6 Conclusions and Recommendations
In recent years, numerous global forces have been at work dramatically altering the worldwide
marketplace for energy. These forces have also led to a significant increase in the amount of
investment and project activity in Alberta surrounding its oil sands resources. Owner companies
holding leases in the oil sands accelerated their development of capital projects needed for
increased production during the study period. This acceleration of the pace of development may be
explanatory to the findings and results of this study. To be sure, the increased pace and amount of
capital projects in Alberta resulted in many effects observed in this research. These effects were
further compounded by extremes experienced in Alberta related to such things as labour availability,
harsh weather conditions, and remote project locations, amongst others. Yet, the benefit of this
research is that it was able to objectively quantify the performance of observed projects and the
impact of certain factors submitted for this study.
The research began with an overarching focus on engineering and construction productivity. The
implied belief was that productivity in Alberta was a prime component in the observed performance
of its capital projects. The definitions of productivity adopted by COAA’s benchmarking committee
were those used for many years by CII. Notably, these primarily measured direct productivity, that is,
the ratio of work hours to installed quantities. By definition, they did not include indirect labour or
costs. Because the COAA productivity definitions were common to CII, comparisons to U.S.
productivity data were possible. For the projects included in this study, actual direct productivity for
both engineering and construction was very similar to that observed in the United States. However,
the observed project performance between Alberta and U.S. projects was differential in favor of the
latter.
Further investigation revealed that Alberta projects typically overestimate their direct productivity.
For example, on average, actual construction productivity was 45% and 22% worse than estimated
for concrete and steel craft workers, respectively. This overestimation is compounded by much
higher amounts of indirect labour and cost required for Alberta projects when compared to U.S.
projects. On average, 25% of hours worked were for indirect labour and almost half of the projects
submitted experienced construction indirect costs more than 25% higher than estimated. Taken
together, the underestimation of total labour required yielded significant resource peaks much higher
than estimated which showed strong correlations with upward project cost growth. The analysis of
this trend indicates that projects experiencing labour (direct and indirect) peaks of 50% more than
estimated experienced project cost growth approaching 40%.
53

It is worth mentioning that all the projects reported for this research that were executed in Alberta
used cost reimbursable contracts for their construction phase. This may have an impact on some of
the project performance metrics reported here. Importantly, the Alberta-based projects presented
here consistently demonstrate that large projects often experience much higher amounts of
construction indirect cost growth when compared with their smaller counterparts. Given the
anticipated number of planned projects exceeding $1 Billion (CDN) of total installed cost, the proper
estimation and control of both direct and indirect costs is paramount. Future projects should not
experience a range of project cost growth from -25% to 69% if the learnings from this report are
applied effectively. Predictability in estimating and project management is needed. Here,
management best practices such as project risk assessment, planning for startup, and
constructability reviews are already showing significant abilities to impact project performance.
Proper application of best practices would alleviate situations where 19% average cost growth was
addressed using only 8% contingency (on average), for example. Indeed, improvement is needed in
management-related aspects of planning, estimating, and controlling work. This is not just a
contractor issue; owners routinely gave full funding authorization to projects with as little as 10%
engineering complete. A thorough evaluation of management policies and procedures is
recommended.
The path forward is bright. While a focus on improved engineering and construction productivity is
always warranted, quicker improvement is possible through increased focus on application of better
project management practices. For owners, adherence to effective planning through asset
development processes (ADP’s) tailored to Alberta projects may be helpful. For contractors, revised
emphasis on effective project execution plans (PEP’s) may be needed. Intensive implementation of
industry best practices and stern adherence to basic project management practices is recommended.
Fortunately, the lessons of the past few years have created an improved awareness and added
experience to the abilities of Alberta-based companies and personnel to manage the unique projects
found in Alberta. Regardless, the only way to truly and objectively know whether or not project
execution is improving is through continued measurement. Continued use of benchmarking products
in current and additional aspects will generate improved intelligence concerning Alberta-based
projects. There is ample reason to suspect that tomorrow’s projects will be much better than those
executed today.
54

Appendices
55

Appendix A: Summary of Correlation between Project Performance and Related Factors of Alberta Based Projects
Table A- 1 Correlations of Project Characteristics with Project Performance
Project Characteristics
Total Project Cost
($M CDN)
Total Project
Duration (weeks)
Construction
Duration (weeks)
% Contingency
Budget
% Eng. completed
before con.
started
Performance Metric 1 N r N r N r N r N r
COST
Project Cost Growth 23 0.468 22 0.017 21 0.714 17 -0.591 19 -0.551
Project Budget Factor 23 0.499 22 0.362 21 0.522 17 -0.456 19 -0.248
Construction Phase Cost Growth 18 -0.004 17 0.280 16 0.496 16 -0.470 14 -0.723*
Construction Indirect Cost Growth 17 0.599* 16 0.487 15 0.751 15 -0.429 14 -0.679
Startup Cost Growth 9 -0.162 10 -0.211 10 0.070 10 -0.177 8 -0.190
SCHEDULE
Project Schedule Growth 21 0.031 23 0.122 22 0.096 17 0.029 21 -0.117
Project Schedule Factor 21 -0.056 23 -0.169 22 -0.233 17 0.285 21 -0.105
Construction Phase Schedule Growth 20 -0.059 22 0.201 22 0.195 17 -0.254 20 0.016
Startup Schedule Growth 17 -0.166 18 -0.129 18 -0.067 14 -0.120 16 -0.005
CHANGES
Total Change Cost Factor 14 0.055 15 0.429 15 0.499 12 -0.360 15 0.358
Development Change Cost Factor 10 0.142 11 0.471 11 0.677 11 -0.556 10 -0.655
Scope Change Cost Factor 12 0.148 13 0.497 13 0.460 10 -0.081 12 -0.469
REWORK
Field Rework Cost Factor 8 -0.174 8 0.411 8 0.083 8 0.394 8 0.418
SAFETY
Lost time Frequency (LTF) © © 8 -0.260 8 -0.482 © © © ©
Lost Time Severity (LTS) 17 0.733 17 0.481 17 0.481 14 -0.447 15 -0.227
PRODUCTIVITY
Engineering Productivity (EPM Index) 20 0.329 22 0.203 20 0.518 15 -0.652 19 -0.401
Construction Productivity (CPM Index) 20 -0.364* 19 -0.161 19 -0.128 15 0.143 17 -0.226
1 Metric and phase definitions are provided in Appendix A.
© indicate small sample size (N

Table A- 2 Correlations of Project Characteristics with Project Performance (cont’d)
Performance Metrics
%
Modularization
*
% Offsite
Work-hours
Project Characteristics
% Construction
Indirect/Direct
WH*
%
Construction
Ind./ Dir. Cost*
Actual/ Est.
Peak
Workforce
% Scaffolding/
Direct WH
N r N r N r N r N r N r
COST
Project Cost Growth 22 -0.236 22 -0.119 19 -0.379 17 -0.033 20 0.787* 18 0.362
Project Budget Factor 22 -0.114 22 -0.003 19 -0.353 17 -0.203 20 0.598 18 0.252
Construction Phase Cost Growth 1 17 -0.211 17 -0.244 15 -0.307 17 -0.013 15 0.773 15 0.188
Construction Indirect Cost Growth 16 -0.282 16 -0.413 14 -0.010 17 0.095 14 0.596 15 0.705
Startup Cost Growth 10 -0.542 10 -0.271 10 0.537 8 -0.201 10 0.347 9 0.348
SCHEDULE
Project Schedule Growth 22 -0.362 22 -0.583 19 -0.194 15 0.055 21 0.543 17 -0.097
Project Schedule Factor 22 0.081 22 -0.004 19 -0.446* 15 -0.109 21 0.314 17 -0.030
Construction Phase Schedule Growth 21 -0.647 21 -0.337 19 0.067 14 0.144 21 0.486 17 0.020
Startup Schedule Growth 18 -0.210 18 -0.425 18 -0.110 11 -0.206 19 0.402 16 -0.050
CHANGES
Total Change Cost Factor 16 -0.326 16 -0.386 16 0.511 10 0.356 16 -0.200 15 -0.261
Development Change Cost Factor 11 -0.343 11 -0.530 11 -0.244 10 0.044 11 0.809 10 0.326
Scope Change Cost Factor 13 -0.279 13 -0.184 13 0.063 8 0.588 13 -0.003 12 0.445
REWORK
Field Rework Cost Factor 11 0.242 11 0.406 10 -0.013 9 -0.121 10 -0.158 11 -0.347
SAFETY
Lost time Frequency (LTF) 8 0.629 8 0.672 8 -0.382 © © 8 -0.387 © ©
Lost Time Severity (LTS) 18 -0.112 18 0.091 18 -0.327 13 0.512 19 0.520 17 0.171
PRODUCTIVITY
Engineering Productivity (EPM Index) 22 -0.201 22 -0.168 17 0.029 13 0.345 19 0.282 15 0.643
Construction Productivity (CPM Index) 20 -0.387 20 -0.119 19 0.178 14 -0.093 20 -0.078 18 0.059
57

Table A- 3 Correlations of Practices with Project Performance
Practices
Performance Metrics
FEP PDRI PRA
Team
Building
Alignment
Design for
Maintainability
Constructability
Material
Mgmt.
N r N r N r N r N r N r N r N r
COST
Project Cost Growth 21 0.560 17 0.119 21 -0.436* 22 0.197 21 0.308 15 -0.314 22 -0.036 22 -0.394
Project Budget Factor 21 0.510 17 -0.165 5 0.252 22 0.151 21 0.265 15 0.075 22 0.066 22 -0.376
Construction Phase Cost Growth 16 0.532 12 0.281 5 0.170 17 0.098 17 0.118 14 -0.437 17 -0.284 17 -0.240
Construction Indirect Cost Growth 15 0.731 11 -0.444 15 -0.205 16 0.192 16 0.431 13 -0.078 16 -0.091 16 0.116
Startup Cost Growth 10 0.544 © © © © 10 -0.517 10 -0.411 9 -0.625 10 -0.375 10 -0.351
SCHEDULE
Project Schedule Growth 23 0.224 19 -0.134 7 0.083 23 -0.200 22 -0.321 17 -0.230 24 -0.528* 24 -0.635
Project Schedule Factor 23 -0.097 19 -0.229 7 0.431 23 -0.195 22 -0.368 17 -0.079 24 -0.296 24 -0.534
Construction Phase Schedule Growth 22 0.333 18 0.062 7 -0.166 22 -0.215 21 -0.268 16 -0.487 23 -0.474 23 -0.523
Startup Schedule Growth 19 0.142 15 0.152 6 0.179 18 -0.207 18 -0.411 14 -0.204 19 -0.715 19 -0.299
CHANGES
Total Change Cost Factor 16 0.126 12 0.249 16 0.158 15 0.025 15 0.202 11 0.284 16 0.091 16 0.338
Development Change Cost Factor 11 0.285 © © 11 -0.775 11 0.002 11 0.163 9 -0.670 11 -0.379 11 -0.477
Scope Change Cost Factor 13 0.432 9 -0.441 13 0.032 13 -0.077 13 0.365 8 -0.402 13 -0.108 13 0.050
REWORK
Field Rework Cost Factor 9 -0.390 © © 9 0.415 8 0.636 8 0.282 © © 9 0.351 9 0.181
SAFETY
Lost time Frequency (LTF) 8 -0.815 © © 8 0.610 8 0.565 8 0.316 © © 8 0.461 8 -0.077
Lost Time Severity (LTS) 18 0.300 14 -0.505 18 -0.322 17 0.111 17 0.247 13 -0.203 18 0.217 18 -0.654
PRODUCTIVITY
Engineering Productivity (EPM Index) 22 0.569 18 0.022 22 -0.221 22 0.221 21 0.292 16 -0.049 22 -0.197 22 0.046
Construction Productivity (CPM Index) 19 0.316 16 0.132 19 -0.221 19 -0.540 18 -0.397 13 -0.768 20 -0.312 20 0.000
58

Table A- 4 Correlations of Practices with Project Performance (cont’d)
Performance Metrics
Change Mgmt.
Zero Accident
Tech.
Practices
Quality Mgmt. A/I Technology
Planning for
Startup
PPMOF
Workface
Planning
N r N r N r N r N r N r N r
COST
Project Cost Growth 22 -0.050 19 0.460 21 0.001 20 -0.617 20 0.808 19 0.278
© ©
Project Budget Factor 22 -0.165 19 0.282 21 0.050 20 -0.481 20 -0.731 19 0.364 © ©
Construction Phase Cost Growth 17 -0.033 15 0.427 17 -0.386 15 -0.584 15 -0.750 14 0.086 © ©
Construction Indirect Cost Growth 16 0.364 14 0.559 16 -0.288 14 -0.619 14 -0.515 13 0.345 © ©
Startup Cost Growth 10 -0.559 10 0.533 10 -0.094 10 -0.588 10 -0.450* 9 -0.253 © ©
SCHEDULE
Project Schedule Growth 24 -0.182 21 0.107 23 -0.171 21 -0.358 22 -0.431 21 0.331
© ©
Project Schedule Factor 24 -0.291 21 -0.202 23 -0.171 21 -0.012 22 -0.365 21 0.055 © ©
Construction Phase Schedule Growth 23 -0.340 21 0.288 22 -0.209 20 -0.508 22 -0.429 21 0.053 © ©*
Startup Schedule Growth 19 -0.060 19 0.206 19 -0.408 18 -0.311 19 -0.087 18 0.213 © ©
CHANGES
Total Change Cost Factor 16 0.305 16 0.213 16 0.174 15 -0.399 16 0.356 15 0.266
© ©
Development Change Cost Factor* 11 0.075 11 0.325 11 0.161 11 -0.437 11 -0.544 10 -0.322 © ©
Scope Change Cost Factor* 13 0.177 13 0.462 13 -0.227 13 -0.362 13 -0.270 12 0.376 © ©
REWORK
Field Rework Cost Factor 9 0.088 9 -0.594 9 0.425 8 0.717 9 0.518 9 0.368
SAFETY
Lost time Frequency (LTF) 8 -0.204 8 -0.003 8 0.247 8 0.759 8 0.034
© ©
© © © ©
Lost Time Severity (LTS) 18 -0.350 18 0.045 18 0.412 17 -0.237 18 -0.748 17 0.407 © ©
PRODUCTIVITY
Engineering Productivity (EPM Index) 22 -0.058 18 0.525 21 -0.458 21 -0.543 20 -0.178 19 -0.062
© ©
Construction Productivity (CPM Index) 20 -0.170 19 0.077 19 -0.201 18 -0.368 19 -0.023 18 -0.166 © ©
59

Appendix B: Performance Metric Formulas and Definitions
Performance Metric Category: COST
Formula:
Metric: Project Cost Growth
Actual Total Project Cost - Initial Predicted Project Cost
Initial Predicted Project Cost
Metric: Delta Cost Growth
Formula:
| Cost Growth |
Metric: Project Budget Factor
Formula:
Actual Total Project Cost
Initial Predicted Project Cost +Approved Changes
Metric: Delta Budget Factor
Formula:
| 1- Budget Factor |
Metric: Phase Cost Factor
Metric: Phase Cost Growth
Definition of Terms
Actual Total Project Cost:
• Owners –
o
o
All actual project cost from front end
planning through startup
Exclude land costs but include in-house
salaries, overhead, travel, etc.
• Contractors – Total cost of the final scope of work.
Initial Predicted Project Cost:
• Owners – Budget at the time of Project Sanction.
• Contractors – Cost estimate used as the basis of
contract award.
Formula:
Formula:
Actual Phase Cost
Actual Total Project Cost
Actual Phase Cost – Initial Predicted Phase Cost
Initial Predicted Phase Cost
Actual Phase Cost:
• All costs associated with the project phase in
question.
• See the Project Phase Table in Appendix C for phase
definitions.
Initial Predicted Phase Cost:
• Owners – Budget at the time of Project Sanction.
• Contractors – Budget at the time of contract award.
• See the Project Phase Table in Appendix C for phase
definitions.
Approved Changes:
• Estimated cost of owner-authorized changes.
60

Performance Metric Category: SCHEDULE
Metric: Project Schedule Growth
Formula:
Actual Total Proj. Duration - Initial Predicted Proj. Duration
Initial Predicted Proj. Duration
Metric: Delta Schedule Growth
Formula:
| Schedule Growth |
Metric: Project Schedule Factor
Formula:
Actual Total Project Duration
Initial Predicted Project Duration + Approved Changes
Metric: Delta Schedule Factor
Formula:
| 1- Schedule Factor |
Metric: Phase Duration Factor
Formula:
Actual Phase Duration
Actual Overall Project Duration
Metric: Total Project Duration
Actual Total Project Duration (weeks)
Metric: Phase Schedule Growth
Formula:
Actual Phase Duration – Initial Predicted Phase Duration
Initial Predicted Phase Duration
Definition of Terms
Actual Total Project Duration:
(Detailed Engineering through Start-up)
• Owners – Duration from beginning of detailed
engineering to turnover to user.
• Contractors - Total duration for the final scope
of work from mobilization to completion.
Actual Overall Project Duration:
(Front End Planning through Start-up)
• Unlike Actual Total Duration, Actual Overall
Duration also includes time consumed for the
Front End Planning Phase.
Actual Phase Duration:
• Actual total duration of the project phase in question.
See the Project Phase Table in Appendix C for phase
definitions.
Initial Predicted Project Duration:
• Owners – Predicted duration at the time of Project
Sanction.
• Contractors - The contractor's duration estimate at
the time of contract award.
Approved Changes
• Estimated duration of owner-authorized changes.
61

Performance Metric Category: SAFETY
Metric: Lost Time Frequency (LTF)
Formula:
Total Number of Lost Time cases x 200,000
Total Site Work-Hours
Metric: Medical Aid Frequency (MAF)
Metric: First Aid Frequency (FAF)
Metric: Total Recordable Injury Frequency
(TRIF)
Formula:
Formula:
Formula:
Total Number of Medical Aid Cases x 200,000
Total Site Work-Hours
Total Number of First Aid Cases x 200,000
Total Site Work-Hours
Total Number of Recordable Cases x 200,000
Total Site Work-Hours
Metric: Total Injury Frequency (TIF)
Formula:
Total number of all injury or illness cases x 200,000
Total Site Work-Hours
Metric: Restricted Work Frequency (RWF)
Formula:
Total Number of Restricted Work Cases x 200,000
Total Site Work-Hours
Metric: Lost Time Severity Rate (LTSR)
Formula:
Total Number of Lost Time Workdays x 200,000
Total Site Work-Hours
Metric: Total Severity Rate (TSR)
Formula:
Total Number of Recordable Lost Time Cases
and all Restricted Work Cases x 200,000
Total Site Work-Hours
62

Performance Metric Category: SAFETY (cont’d.)
Definition of Terms
• Lost Time Days: Equals the number of scheduled work days away from work as a result of an
occupational injury or illness, disabling injury or illness which prevents a worker from reporting to
work on next regularly scheduled.
• Medical Aid Case: Any occupational injury or illness requiring medical treatment administered by a
physician, not including first aid treatment
• First Aid Case: Any one time treatment which does not require medical care or further medical aid
e.g. minor scratches, cuts, burns, splinters.
• Recordable Case: A work event or exposure that is the discernable cause of an injury or illness or
of a significant aggravation to a pre-existing condition. A recordable case requires medical aid,
restricted work in relation to either medical aid or lost time, or fatality.
• Total number of all injury or illness cases: Equals the number of lost time (LT) cases, medical aid
(MA) cases, first aid (FA) cases and the number of restricted work cases for lost time (RWLT),
medical aid (RWMA) and first aid (RWFA).
• Total Number of Restricted Work Cases: Equals the number of restricted work lost time cases,
restricted work medical aid cases and restricted work first aid cases.
• Lost Time Case: Lost Time cases are the result of an occupational injury or illness including any
disabling injury which prevents a worker from reporting to work on his/her next regularly scheduled.
• Restricted Work Case: Includes restricted work lost time cases, restricted work medical aid cases
and restricted work first aid cases.
• Restricted Work Days: Equals the number of scheduled work days that the worker was unable to
work their regular duties as a result of an injury or illness as defined in restricted work.
• Total Number of Recordable Lost Time Cases and all Restricted Work Cases: Includes the
number of lost workdays plus the number of restricted work days for all lost time, medical aid and
first aids.
63

Performance Metric Category: CHANGES
Metric: Scope Change Cost Factor
Metric: Project Development Change Cost Factor
Formula:
Formula:
Total Cost of Scope Changes
Actual Total Project Cost
Total Cost of Project Development Changes
Actual Total Project Cost
Definition of Terms
• Total Cost of Scope Changes: Total cost impact
of scope and project development changes.
• Total Cost of Project Development Changes:
Total cost impact of project development
changes.
Actual Total Project Cost:
• Owners –
o All actual project cost from front end planning
through startup
o Exclude land costs but include in-house
salaries, overhead, travel, etc.
• Contractors – Total cost of the final scope of work.
Performance Metric Category: REWORK
Metric: Total Field Rework Factor
Formula:
Total Direct Cost of Field Rework
Actual Construction Phase Cost
Definition of Terms
• Total Direct Cost of Field Rework: Total direct
cost of field rework regardless of initiating cause.
• Actual Construction Phase Cost: All costs associated
with the construction phase. See the Project Phase
Table in Appendix C for construction phase definition.
64

Construction Productivity and Total Installed Unit Cost (TIUC)
Metrics Categories and Breakouts
Concrete
- Total Concrete
o Slabs (CM)
• On-Grade (CM)
• Elevated Slabs/On Deck (CM)
• Area Paving (CM)
o Foundations (CM)
• < 4 CM
• 4 – 15 CM
• 15 –38 CM
• ≥ 38 CM
o Concrete Structures (CM)
Structural Steel
- Total Structural Steel (MT)
o Structural Steel (MT)
o Pipe Racks & Utility Bridges (MT)
o Miscellaneous Steel (MT)
Instrumentation
- Loops (Count)
- Devices (Count)
Piping
- Small Bore (2-1/2” & Smaller) (LM)
o Carbon Steel (LM)
o Stainless Steel (LM)
o Chrome (LM)
o Other Alloys (LM)
o Non Metallic (LM)
- Inside Battery Limits (ISBL) (LM)
Large Bore (3” & Larger) (LM)
o Carbon Steel (LM)
o Stainless Steel (LM)
o Chrome (LM)
o Other Alloys (LM)
o Non Metallic (LM)
- Outside Battery Limits (OSBL) (LM)
Large Bore (3” & Larger) (LM)
o Carbon Steel (LM)
o Stainless Steel (LM)
o Chrome (LM)
o Other Alloys (LM)
o Non Metallic (LM)
- Heat Tracing Tubing (LM)
Electrical
- Total Electrical Equipment (Each)
o Panels and Small Devices (Each)
o Electrical Equipment below 1kV (Each)
o Electrical Equipment over 1kV (Each)
- Conduit (LM)
o Exposed or Above Ground Conduit (LM)
o Underground, Duct Bank or Embedded Conduit (LM)
- Cable Tray (LM)
- Wire and Cable (LM)
o Control Cable (LM)
o Power and Control Cable below 1kV (LM)
o Power Cable above 1kV (LM)
- Transmission Line (LM)
o High Voltage above 25kV (LM)
- Other Electrical Metrics
o Lighting (Each)
o Grounding (LM)
o Electrical Heat Tracing (LM)
Equipment
- Pressure Vessels (Field Fab.& Erected) (Each), (MT)
- Atmospheric Tanks (Shop Fabricated) (Each), (MT)
- Atmospheric Tanks (Field Fabricated) (Each), (MT)
- Heat Transfer Equipment (Each), (MT)
- Boiler & Fired Heaters (Each), (MT)
- Rotating Equipment (Each), (HP)
- Material Handling Equipment (Each), (MT)
- Power Generation Equipment (Each), (kW)
- Other Process Equipment (Each), (MT)
- Modules & Pre-assembled Skids (Each), (MT)
Insulation
- Equipment
o Insulation Equipment (SM)
- Piping
o Insulation Piping (ELM)
Module Installation
- Pipe Racks (MT)
- Process Equipment Modules (MT)
- Building (SM)
Scaffolding
- Scaffolding Work-Hours/ Total Direct Hours
Construction Work-Hours
- Construction Indirect/ Direct Work-Hours
Construction Productivity Unit Rate =
Productivity Estimating Performance =
Direct Work hours
Installed Quantity
Actual Productivity Rate
Estimated Productivity Rate
Cost Estimating Performance = Actual TIUC
Estimated TIUC
65

Engineering Productivity Metrics Categories and Breakouts
Concrete
- Total Concrete (CM)
o Total Slabs (CM)
• Ground and Supported Slab (CM)
• Area Paving (CM)
o Total Foundations (except Piling) (CM)
• Foundation (

Project Phase Definition Table
Project Phase Start/Stop Typical Activities & Products Typical Cost Elements
Front End Planning
Typical Participants:
• Owner Personnel
• Planning Consultants
• Constructability
Consultant
• Alliance / Partner
Start: Single project adopted
and Formal project team
established
Stop: Project Sanction
• Options Analysis
• Life-cycle Cost Analysis
• Project Execution Plan
• Appropriation Submittal Pkg
• P&IDs and Site Layout
• Project Scoping
• Procurement Plan
• Arch. Rendering
• Owner Planning Team Personnel Expenses
• Consultant Fees & Expenses
• Environmental Permitting Costs
• Project Manager / Construction Manager Fees
• Licensor Costs
Detail Engineering
Typical Participants:
• Owner Personnel
• Design Contractor
• Constructability Expert
• Alliance / Partner
Start: Contract award to
engineering firm
Stop: Release of all approved
drawings and specs for
Construction (or last
package for fast-track)
• Drawing & spec. preparation
• Bill of material preparation
• Procurement Status
• Sequence of operations
• Technical Review
• Definitive Cost Estimate
• Owner Project Management
Personnel
• Designer Fees
• Project Manager / Construction
Manager Fees
Procurement
Typical Participants:
• Owner personnel
• Design Contractor
• Alliance / Partner
Start: Procurement plan for
engineered equipment
Stop: All major equipment
has been delivered to site
• Vendor Qualification
• Vendor Inquiries
• Bid Analysis
• Purchasing
• Expediting
• Engineered Equipment
• Transportation
• Vendor QA/QC
• Owner project management personnel
• Project Manager / Construction
Manager fees
• Procurement & Expediting personnel
• Engineered Equipment
• Transportation
• Shop QA / QC
Note: The demolition / abatement phase should be reported when the demolition / abatement work is a separate schedule activity (potentially paralleling the
design and procurement phases) in preparation for new construction. Do not report the demolition / abatement phase if the work is integral with
modernization or addition activities.
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Project Phase Table (Cont.)
Project Phase Start/Stop Typical Activities & Products Typical Cost Elements
Construction
Typical Participants:
• Owner personnel
• Design Contractor
(Inspection)
• Construction Contractor and
its subcontractors
Start: Commencement of
foundations or driving
Piles
Stop: Mechanical Completion
• Set up trailers
• Procurement of bulks
• Issue Subcontracts
• Construction plan for
Methods/Sequencing
• Build Facility & Install
Engineered Equipment
• Complete Punchlist
• Demobilize construction
equipment
• Warehousing
• Owner project management personnel
• Project Manager / Construction
Manager fees
• Building permits
• Inspection QA/QC
• Construction labour, equipment &
supplies
• Bulk materials (including freight)
• Construction equipment (including
freight)
• Contractor management personnel
• Warranties
Start-up / Commissioning
Note: Does not usually apply to
infrastructure or building type
projects
Typical Participants:
• Owner personnel
• Design Contractor
• Construction Contractor
• Training Consultant
• Equipment Vendors
Start: Mechanical Completion
Stop: Custody transfer to
user/operator (steady
state operation)
• Testing Systems
• Training Operators
• Documenting Results
• Introduce Feedstocks and
obtain first Product
• Hand-off to user/operator
• Operating System
• Functional Facility
• Warranty Work
• Owner project management personnel
• Project Manager / Construction
Manager fees
• Consultant fees & expenses
• Operator training expenses
• Wasted feedstocks
• Vendor fees
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Appendix C: Glossary
General Terms
Addition (Add-on) – A new addition that ties in to an existing facility, often intended to expand capacity.
Grass Roots, Green Field – A new facility from the foundations and up. A project requiring demolition of
an existing facility before new construction begins is also classified as grass roots.
Modernization, Renovation, Upgrade– A facility for which a substantial amount of the equipment,
structure, or other components is replaced or modified, and which may expand capacity and/or improve
the process or facility.
Percent Offsite Construction Labour Hours– The level of offsite labour hours for building modules.
This value should be determined as a ratio of the offsite labour hours of all modules divided by total
construction hours.
Rework - is defined as activities in the field that have to be done more than once in the field or activities
which remove work previously installed as part of project.
Total Construction Hours – The summation of all direct and indirect hours associated with the
construction phase.
Project Delivery System
Design-Bid-Build– Serial sequence of design and construction phases; Owner contracts separately with
designer and constructor.
Design-Build (or EPC) – Overlapped sequence of design and construction phase; procurement normally
begins during design; owner contracts with Design-Build (or EPC) contractor.
CM at Risk– Overlapped sequence of design and construction phases; procurement normally begins
during design; owner contracts separately with designer and CM at Risk (constructor). CM holds the
contracts.
Multiple Design-Build– Overlapped sequence of design and construction phases; procurement normally
begins during design; owner contracts with two Design-Build (or EPC) contractors, one for process and
one for facilities.
Parallel Primes– Overlapped sequence of design and construction phases; Procurement normally
begins during design. Owner contracts separately with designer and multiple prime constructors.
Cost Definition
Construction Costs – include the costs of construction activities from commencement of foundation or
driving piles to mechanical completion. The costs include construction project management, construction
labour, and also equipment& supplies costs that are used to support construction operations and
removed after commissioning. See “Instruction for Construction Direct and Indirect Costs” for detail
of typical cost element.
Contingency –Contingency is defined as an estimated amount included in the project budget that may
be required to cover costs that result from project uncertainties. These uncertainties may result from
incomplete design, unforeseen and unpredictable conditions, escalation, or lack of project scope
definition. The amount of contingency usually depends on the status of design, procurement and
construction, and the complexity and uncertainties of the component parts of the project.
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Direct Costs – Direct costs are those which are readily or directly attributable to, or become an
identifiable part of, the final project (e.g., piping labour and material) [AACE].
Direct Cost of Field Rework– The sum of those costs associated with actual performance of tasks
involved in rework. Examples include: Labour, Materials, Equipment, Supervisory personnel, Associated
overhead cost.
Modularization– Modularization refers to the use of offsite construction. For the purposes of the
benchmarking data, modularization includes all work that represents substantial offsite construction and
assembly of components and areas of the finished project. Examples that would fall within this
categorization include:
• Skid assemblies of equipment and instrumentation that naturally ship to the site in one piece, and
require minimal on-site reassembly.
• Super-skids of assemblies of components that typically represent substantial portions of the plant,
intended to be installed in a building.
• Prefabricated modules comprising both industrial plant components and architecturally finished
enclosures.
Modularization does not include offsite fabrication of components. Examples of work that would be
excluded from the definition of modularization include:
• Fabrication of the component pieces of a structural framework
• Fabrication of piping spool-pieces
Indirect Costs – Indirect costs are all costs that cannot be attributed readily to a part of the final product
(e.g., cost of managing the project) [AACE].
Schedule Definition
Project Sanction – is defined as the milestone event at which the project scope, budget, and schedule are
authorized. Project Sanction is the start of the execution phase of the project.
Commissioning and Startup – The transitional phase between construction and commercial operations;
major steps include turnover, checkout, commissioning, and initial operations. Commissioning is the
integrated testing of equipment and facilities that are grouped together in systems prior to the introduction
of feedstocks.
Detail Engineering – Detail engineering is the project phase initiated with a contract to the firm providing
detail engineering for the project. The typical activities included in this phase are: preparation of drawings,
specifications, bill of materials, development of a definitive cost estimate, technical reviews, and
engineering procurement functions. The detail engineering phase terminates with release of all approved
drawings and specifications for construction.
Mechanical Completion - The point in time when a plant is capable of being operated although some
trim, insulation, and painting may still be needed. This occurs after completion of pre commissioning.
Changes Definition
Change - A change is any event that results in a modification of the project work, schedule or cost.
Owners and designers frequently initiate changes during design development to reflect changes in project
scope or preferences for equipment and materials other than those originally specified. Contractors often
initiate changes when interferences are encountered, when designs are found to be not constructable, or
other design errors are found.
Change Order - A contractual modification executed to document the agreement and approval of a
change (See definition of Change above).
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Project Development Changes – Project Development Changes include those changes required to
execute the original scope of work or obtain original process basis. Examples include:
1) Unforeseen site conditions that require a change in design / construction methods
2) Changes required due to errors and omissions
3) Acceleration
4) Change in owner preferences
5) Additional equipment or processes required to obtain original planned throughput
6) Operability or maintainability changes. (See Change above)
Scope Changes – Scope Changes include changes in the base scope of work or process basis.
Examples include: 1) Feedstock change, 2) Changed site location, 3) Changed throughput, 4) Addition of
unrelated scope
Practice Definition
Front End Planning– is the essential process of developing sufficient strategic information with which
owners can address risk and make decisions to commit resources in order to maximize the potential for a
successful project. Front End Planning is also known as pre-project planning, front end loading, feasibility
analysis, conceptual planning/ schematic design, and early project planning.
Project Risk Assessment –Project risk assessment is the process to identify, assess and manage risk.
The project team evaluates risk exposure for potential project impact to provide focus for mitigation
strategies.
Team Building– is a project- focused process that builds and develops shared goals, interdependence,
trust and commitment, and accountability among team members and that seeks to improve team
members’ problem- solving skills.
Alignment during Front End Planning– is the condition where appropriate project participants are
working with acceptable tolerances to develop and meet a uniform defined and understood set of project
objectives.
Constructability– is the effective and timely integration of construction knowledge into the conceptual
planning, design, construction, and filed operations of a project to achieve the overall project objectives in
the best possible time and accuracy at the most cost- effective levels.
Design for Maintainability– Design for maintainability is the optimum use of facility maintenance
knowledge and experience in the design/engineering of a facility to pertain the ease, accuracy, safety and
economy in the performance of maintenance action; a design parameter related to the ability to maintain.
Material Management – the planning, controlling, and integrating of the materials takeoff, purchasing,
economic, expediting, transportation, warehousing, and issue functions in order to achieve a smooth,
timely, efficient flow of materials to the project in the required quantity, the required time, and at an
acceptable price and quality, and the planning and controlling of these functions (CII Publication SP-4)
Project Change Management– is the process of incorporating a balanced change culture of recognition,
planning, and evaluation of project changes in an organization to effectively manage project changes.
Practices related to the management and control of both scope changes and project changes.
Zero Accident Techniques– include the site- specific safety programs and implementation, auditing,
and incentive efforts to create a project environment and a level of that embraces the mind set that all
accidents are preventable and that zero accidents is an obtainable goal.
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Quality Management– Quality management incorporates all activities conducted to improve the
efficiency, contract compliance and cost effectiveness of design, engineering, procurement, QA/QC,
construction and startup elements of construction projects.
Automation/Integration (AI) Technology– The Automation and Integration Technology practice
addresses the degree of automation/level of use and integration of automated systems for predefined
tasks/work functions common to most projects.
Planning for Startup– is the effectiveness of planning on startup activities that facilitate the
implementation of the transitional phase between plant construction completion and commercial
operations, including all of the activities bridging these two phases. Critical steps within the startup phase
include systems turnover, checkout of systems, commissioning of systems, introduction of feed stocks,
and performance testing.
Prefabrication/ Preassembly/ Modularization– Prefabrication/Preassembly/Modularization (PPMOF) is
defined as several manufacturing and installation techniques, which move many fabrication and
installation activities from the plant site into a safer and more efficient environment. For each technique,
more specific definitions are provided below.
• Prefabrication: a manufacturing process, generally taking place at a specialized facility, in which
various materials are joined to form a component part of a final installation. Prefabricated components
often involve the work of a single craft.
• Preassembly: a process by which various materials, prefabricated components, and/or equipment are
joined together at a remote location for subsequent installation as a sub-unit: generally focused on a
system.
• Module: a major section of a plant resulting from a series of remote assembly operations and may
include portions of many systems: usually the largest transportable unit or component of a facility.
• Offsite Fabrication: the practice of preassembly or fabrication of components both off the site and
onsite at a location other than at the final installation location.
This practice consists of two part, constructability at AFE phase and constructability at mechanical
completion. Please fill out one part of this practice according to your current project phase.
Workface Planning– The process of organizing and delivering all elements necessary, before work is
started, to enable craft persons to perform quality work in a safe, effective and efficient manner
Engineering Productivity
Engineering Direct Work hours - should include all detailed design hours used to produce deliverables
including site investigations, meetings, planning, constructability, RFIs, etc., and rework. Specifically
exclude work hours for operating manuals and demolition drawings.
- Engineering work hours reported should only be for the categories requested and may not equal
the total engineering work hours for the project. (See “Instructions for Computation of Work
hours and Rework-Hours” reference table)
- Exclude the following categories: architectural design, plumbing, process design, civil/site prep,
HVAC, insulation and paint, sprinkler/deluge systems, etc. Within a category, direct work hours that
cannot be specifically assigned into the provided classifications, and have not been excluded,
should be prorated based on known work hours or quantities as appropriate.
IFC Drawing– Issued for Construction drawings.
Construction Productivity
Actual Quantities and Work hours - are all quantities and work hours of actual installation and include
rework hours for these quantities and work-hours.
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Estimated Productivity – are the estimated productivity of direct labour work hours required for
installation according to the estimated quantity.
For owners:
For contractors:
Estimated Quantity, Work hours and Total Installed Unit Cost at the time of
Project Sanction (or as soon as available following sanction)
Estimated Quantity, Work hours and Total Installed Unit Cost used as the basis
of Contract Award.
Estimated Quantities and Work hours – are the estimated quantity to be installed, the estimated work
hours required for the installation and include all change orders.
Estimated Total Installed Unit Cost – including labour and material cost at the time of project sanction
(or as soon as available following sanction).
Estimated Total Installed Unit Costs (TIUC) – is the burdened direct cost of labour, material and
equipment by pro rata share which are directly attribute to, or become a part of the final product including
overhead and profit at the time of project sanction (or as soon as available following sanction).
Actual Total Installed Unit Costs (TIUC) – the burdened direct cost of labour, material and equipment
by pro rata share which are directly attribute to, or become a part of the final product including overhead
and profit from both direct hire and subcontract.
• The direct labour costs are considered as the costs of the labours listed as Direct in the
“Instructions for Computation of Actual Work-Hours, Rework-Hours, and Installed Costs” table in
Construction Productivity Section.
73

References
AACE (2004). “Estimating Lost Labour Productivity in Construction Claims.” TCM Framework: 6.4-
Forensic Performance Assessment, AACE International Recommended Practice No. 25R-03.
Agresti, A. and Finlay, B. (1999). Statistical methods for the social sciences, 3rd ed., Prentice Hall, Inc.,
Upper Saddle River, NJ, 07458, Prentice Hall, Inc., Upper Saddle River, NJ, 07458.
Alberta Finance and Enterprise (AFE) (2008). “Highlights of the Highlights of the Alberta Economy”,
http://www.albertacanada.com/statpub.
Aminah R. (2005). “Results of a Survey of Performance Deviations on Major Industrial Construction Projects in
Alberta (1990-2003).” Report to Construction Owner Association of Alberta, Spring, 2005.
COAA (2008). “Construction Owners Association of Alberta”, http://www.coaa.ab.ca.
Field, A. (2005). “Discovering Statistics Using SPSS.” Second Edition, SAGE Publications.
Flyvbjerg, B., Bruzelius, N., & Rothengatter, W. (2003). “Megaprojects and risk: An anatomy of ambition.”
Cambridge, UK: Cambridge University Press.
Kellogg, J., Taylor, D., and Howell, G. (1981). “Hierarchy Model of Construction Productivity.”
Journal of the Construction Division, 107(1), March 1981, pp. 137-152.
ASCE,
OSDG (2008). “Oil Sands – Important to Canada’s Present and Future”. Oil Sands Developers Group.
E-mail: info@oilsandsdevlopers.ca.
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Companies Participating in COAA Benchmarking Training Sessions:
• Air Products & Chemicals Inc.
• Alberta Economic Development
• Alberta Infrastructure and Transportation
• Ascension Systems Inc.
• BA Energy
• Bantrel
• BIRD Construction Company
• Canadian Natural Resources
• Canonbie Contracting Ltd.
• Cobra Group of Companies
• Colt Corporation
• ConocoPhillips
• CPI Construction
• DriverCheck
• Enbridge Pipelines Inc.
• EPCOR
• Esso Petroleum Canada
• Flint Energy
• Fluor Corporation
• Husky Energy Inc.
• IBEW Local 424
• Imperial Oil Resources Ltd.
• Intergraph Corporation
• Jacobs
• Kellogg Brown & Root
• Laird Electric Inc.
• Ledcor
• Murdoch International Inc.
• Nexen Inc.
• OPTI Canada Inc.,
• PCL
• Petro-Canada
• Revay and Associates Limited
• RSC Equipment Rental
• SafeTech Consulting Group Ltd.
• Shell Canada Limited
• Stantec
• Steeplejack Industrial Group Inc.
• Suncor Energy Inc.
• Syncrude Canada Ltd.
• Tartan Canada Corporation
• ThyssenKrupp Safway, Inc.
• TransCanada Pipelines, Ltd.
• Westwood Companies
• WorleyParsons Limited
CII Staff:
Stephen Mulva, Ph.D., Associate Director Benchmarking and Metrics
Stephen R. Thomas, Ph.D., Associate Director, Research, Academic, and Breakthrough
Jiukun Dai, Ph.D., Research Engineer
Arpamart Chanmeka, Graduate Research Assistant
Deborah DeGezelle, Senior Systems Analyst
Hong Zhao, Senior Systems Analyst
75

The Construction Industry Institute
The University of Texas at Austin
3925 West Braker Lane
Austin, TX 78759-5316
(512) 232-3000
FAX (512) 499-8101
Construction Industry Institute
http://construction-institute.org
Bureau of Engineering Research
The University of Texas at Austin