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

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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

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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/ m3)................................ 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; design-

build was the second most frequent strategy at 32% of the projects. Project sizes varied from <$5M

(1) although 75% were over $100M and 8 of these exceeded $1B; the average project size was

$368M.

Project performance metrics included cost, schedule, safety, change and re-work. Productivity

metrics assessed both engineering and construction productivity, overall and in specific disciplines.

The effectiveness of 14 project Best Practices were assessed for their impact on performance

metrics. Eighteen COAA-specific metrics were included, such as comparison for direct and indirect

costs, use of modularization, overtime, peak workforce and overtime. Finally a comparison was

made between Alberta projects and comparable projects in the CII database for the United States.

The reader should note that this is still a relatively small dataset, so comparisons and conclusions

should be drawn with caution.

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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 non-

weighted 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,

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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

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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.

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.

c

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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.

Number of Project Data in COAA DB by Month (last updated Oct. 24th, 08)

1

7

1 020

23 2 0 1 1

10

37

121197

2 2 36

13

23

30

7875

5147

38

39

13 1518

2125

293136 37

5355

0

10

20

30

40

50

60

70

80

90

100

Nov

-05

Dec

-05

Jan-

06

Feb-0

6

Mar

-06

Apr-0

6

May

-06

Jun-

06

Jul-0

6

Aug-0

6

Sep-0

6

Oct-0

6

Nov

-06

Dec

-06

Jan-

07

Feb-0

7

Mar

-07

Apr-0

7

May

-07

Jun-

07

Jul-0

7

Aug-0

7

Sep-0

7

Oct-0

7

Nov

-07

Dec

-07

Jan-

08

Feb-0

8

Mar

-08

Apr-0

8

May

-08

Jun-

08

Jul-0

8

Aug-0

8

Sep-0

8

Oct-0

8

Nov

-08

Dec

-08

Training #2

Oct. 06

Training #3

Nov. 07

Training # 1

Nov. 05

Training #4

May 08

Nu

mb

er

of

Pro

jec

t D

ata

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.

Number of projects created in database Total number of submitted projects Number of projects submitted by months

10

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 #Respondent

Type Level 1 Level 2 Nature

Cost

Category

# 1 – no slices found,

go to 2Owner Upstream

Oil Sands

SAGDGrassroots $100-250MM

#2 – no slices found,

go to 3Owner Upstream

Oil Sands

SAGDGrassroots ALL

#3– no slices found,

go to 4Owner Upstream

Oil Sands

SAGD ALL ALL

4: Stop! Data Slice

found with n=10!!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 #Respondent

Type Level 1 Level 2 Nature

Cost Category

# 1 – no slices found,

go to 2Owner Upstream

Oil Sands

SAGDGrassroots $100-250MM

#2 – no slices found,

go to 3Owner Upstream

Oil Sands

SAGDGrassroots ALL

#3– no slices found,

go to 4Owner Upstream

Oil Sands

SAGD ALL ALL

4: Stop! Data Slice

found with n=10!!Owner Upstream ALL ALL ALL

#5 Owner ALL ALL ALL ALL

#6- Second Round with

All Response Type ALL Upstream

Oil Sands

SAGDGrassroots $100-250MM

#7 ALL UpstreamOil Sands

SAGDGrassroots ALL

#8 ALL UpstreamOil Sands

SAGD ALL ALL

#9 ALL Upstream ALL ALL ALL

#10 ALL ALL ALL ALL ALL

11

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 Level 2 Level 3

Cogeneration

Central Plant Processing Facilities Oil Sands SAGD

Pad and Gathering

Oil Sands Mining

Upstream

(Oil Exploration/

Production) Oil Sands Mining/ Extraction

Central Plant Processing Facilities

Naptha Hydrotreater Unit Oil Sands Upgrading

Hydrogen Plant Downstream

Oil Refining Utilities and Offsite

Natural Gas Natural Gas Processing

Process Pipeline

Pipeline SAGD Pipeline

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

12

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

13

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) n

: Total Estimated Structural Steel Productivity Rate 29,000 1,000 29.00

30.80

14

Actual ($/MT)

Estimated ($/MT)

Actual DB Mean ($/MT)

n : Total Installed Unit Cost

3,200 3,000 3,100

14

Figure 2-5 Sample of Project Construction Productivity Metrics

14

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.

15

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 QuantityIFC

Hours WorkDesignActual

Output

Input== [Equation 1]

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 QuantityInstalled

Hours WorkDirectInstalledActual

Output

Input== [Equation 2]

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

16

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

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

Metrics

Related to

Project Cost

Metrics

Related to

Workforce

Metrics

Related to

Construction

Productivity

17

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

18

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. Median is

equivalent to the second quartile (Q2).

Sample Box andWhisker Diagram

OutlierSymbol

Last Obs erva tion be low

(Q3 + 1.5IQR)Third Quartile

(Q 3)

Median

First Quartile

(Q1)

Las t Obse rvation above

(Q1 - 1.5IQR)

Mean

Sample Box andWhisker Diagram

OutlierSymbol

Last Obs erva tion be low

(Q3 + 1.5IQR)Third Quartile

(Q 3)

Median

First Quartile

(Q1)

Las t Obse rvation above

(Q1 - 1.5I

Sample Box andWhisker Diagram

OutlierSymbol

Last Obs erva tion be low

(Q3 + 1.5IQR)Third Quartile

(Q 3)

Median

First Quartile

(Q1)

Las t Obse rvation above

(Q1 - 1.5IQR)

Mean

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).

19

Third Quartile (Q3) indicates the 75th 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, r<0.3 is defined as low correlation, between 0.3 to 0.5 is considered to

have a moderate amount of correlation, while r>0.5 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 (R2) is the most frequently quoted measure representing the

goodness of linear fit of the least square regression line. R2 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 R2 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).

20

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.

21

Table 4-1 Submitted Projects by Owners and Contractors at Project Completion and Sanction

Submitted at

Completion Sanction Project Types # Projects

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

Addition

14 (38%)

Grass Roots

19 (51%)

Modernization

4 (11%)

# of Projects (%of Total)

CM at Risk

5 (14%)

Design-Build

12 (32%)Parallel Primes

17 (46%)

Traditional

D-B-B

3 (8%)

# 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.

22

Number of Submitted Projects at Sanction& Completion

($M CDN, in 2007)

1

34

1

34 4 4

2

32

4

0

1

2

3

4

5

6

7

8

9

10

Nu

mb

er

of

Pro

jects

Sanction 2 3 2 4

Completion 1 3 4 1 3 4 4 4

<$5MM$5MM -

$15MM

$15MM -

$50MM

$50MM -

$100MM

$100MM -

$250MM

$250MM -

$500MM

$500MM -

$1B>$1B

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.

Total

11

24

23

Construction Indirect/ Direct Work-Hours (%) 70

60

50

40

30

20

10

0

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)

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 (%)

• % Modularization1

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.

(N=20)

24

Modularization/Total Proejct Cost(%)

AdditionGrass Roots

50

40

30

20

10

0

12.31

19.41

(N=17) (N=13)

Modularization/Total Proejct Cost(%)

AdditionGrass Roots

50

40

30

20

10

0

12.31

19.41

(N=17) (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.

25

Project Cost Growth

OtherParallel Primes

1.2

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

0.13

0.23

(N=14) (N=10)

Project Cost Growth

OtherParallel Primes

1.2

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

0.13

0.23

(N=14) (N=10)

Figure 4-6 Project Cost Growth by Project Delivery System

Project Schedule Growth

OtherParallel Primes

1.00

0.75

0.50

0.25

0.00

0.190.15

(N=14) (N=10)

Project Schedule Growth

OtherParallel Primes

1.00

0.75

0.50

0.25

0.00

0.190.15

(N=14) (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

26

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 (R2

= 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.

1009080706050403020100

1.00

0.75

0.50

0.25

0.00

-0.25

-0.50

-0.75

% Design completed before construction started

Construction Phase Cost Growth

Figure 4-8 Effect of % Engineering Completed before Construction Started

27

• 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 (R2 = 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).

Actual/ Estimated Peak Construction Workforce

Project Cost Growth

3.02.52.01.51.00.50.0

1.2

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

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 (R2= 0.20, p= 0.05).

28

Construction Indirect WH/Direct WH (%)

Project Schedule Factor

1009080706050403020100

2.00

1.75

1.50

1.25

1.00

0.75

0.50

0.25

0.00

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 (R2= 0.36, p= 0.011)

value.

Adjusted Total Project Cost ($M CDN, in 2007)

Construction Indirect Cost Growth

120010008006004002000

1.50

1.25

1.00

0.75

0.50

0.25

0.00

-0.25

-0.50

Figure 4-11 Construction Indirect Cost Growth by Project Size ($)

29

• 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 (R2= 0.19, p= 0.048).

Project Risk Assessment

Project Cost Growth

109876543210

1.2

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

-0.6

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), (R2 = 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.

30

Constructability

Project Schedule Growth

109876543210

1.25

1.00

0.75

0.50

0.25

0.00

-0.25

-0.50

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

31

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.

Planning for Startup

Startup Phase Cost Growth

1098765

3

2

1

0

-1

-2

Figure 4-14 Planning for Startup vs. Startup Phase Cost Growth

Workface Planning Index

Construction Schedule Growth

109876543210

0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

Figure 4-15 Workface Planning vs. Construction Schedule Growth

32

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

U.S.Alberta

1200

1000

800

600

400

200

0

Adjusted Project Cost ($M, in 2007)

40.43

185.82

84.83

367.83

(N=23) (N=353)

U.S.Alberta

1200

1000

800

600

400

200

0

Adjusted Project Cost ($M, in 2007)

40.43

185.82

84.83

367.83

(N=23) (N=353)

Figure 4-16 Project Size ($M CDN, in 2007)

33

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 (%)

U.S.Alberta

20

18

16

14

12

10

8

6

4

2

0

7.77%8.04%

(N=17) (N=52)

Total Contingency Budget/ Total Project Cost (%)

U.S.Alberta

20

18

16

14

12

10

8

6

4

2

0

7.77%8.04%

(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

34

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.

Project Cost Growth

U.S.Alberta

1.2

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

0.03

0.19

(N=24) (N=352)

Project Cost Growth

U.S.Alberta

1.2

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

0.03

0.19

(N=24) (N=352)

Figure 4-18 Project Cost Growth

U.S.Alberta

1.25

1.00

0.75

0.50

0.25

0.00

-0.25

-0.50

Project Schedule Growth

0.06

0.17

0.06

0.17

(N=24) (N=338)

U.S.Alberta

1.25

1.00

0.75

0.50

0.25

0.00

-0.25

-0.50

Project Schedule Growth

0.06

0.17

0.06

0.17

(N=24) (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

35

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.

Change Cost Factor

Scope_ChangeDevelopment_Change

U.S.AlbertaU.S.Alberta

0.4

0.3

0.2

0.1

0.0

-0.1

-0.2

0.040.06

0.040.02

(N=11) (N=14) (N=13) (N=14)

Change Cost Factor

Scope_ChangeDevelopment_Change

U.S.AlbertaU.S.Alberta

0.4

0.3

0.2

0.1

0.0

-0.1

-0.2

0.040.06

0.040.02

(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.

36

U.S.Alberta

1200

1000

800

600

400

200

0

Adjusted Project Cost ($M, in 2007)

90.40

367.83

30.88

185.82

(N=23) (N=57)

U.S.Alberta

1200

1000

800

600

400

200

0

Adjusted Project Cost ($M, in 2007)

90.40

367.83

30.88

185.82

(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 Productivity2

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.

37

Figure 4-22 Comparison of Concrete Engineering Productivity (WH/ Cubic Meter)

• Total Structural Steel Engineering Productivity3

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.

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.

Total Concrete- Eng. Prod. Rate (WH/CM)

U.S.Alberta

20

15

10

5

0

4.52

5.92

⊗⊗⊗⊗⊗⊗⊗⊗ 33..5533

⊗⊗⊗⊗⊗⊗⊗⊗ 66..2266

(N=17) (N=31)

Steel- Eng. Prod. Rate (WH/Metric Ton)

U.S.Alberta

70

60

50

40

30

20

10

0

10.96

23.08

(N=19) (N=56)

⊗⊗⊗⊗⊗⊗⊗⊗ 55..8866

⊗⊗⊗⊗⊗⊗⊗⊗ 1122..6644

38

• Piping Engineering Productivity4

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.

Figure 4-24 Comparison of Piping Engineering Productivity (WH/ Linear Meter)

4.6 Construction Productivity

Thirty-three Alberta-based projects provided data concerning construction productivity for this

research study. Many of these reported both estimated and actual work hours and quantities.

Compared to engineering productivity on Alberta-based projects, construction productivity is

considered to be more susceptible to variance due to environmental factors such as weather. As

can be seen in Figure 4-25, the average project cost of the 33 projects submitted in this study is

$460 Million (CDN) after time adjustment to July 2007. By contrast, the 29 projects used from the CII

Benchmarking Database for comparison have an average project cost of $122 Million (CDN) after

4 Total piping includes small bore (diameter 2-1/2” and Smaller) and Large Bore diameter 3” and Larger.

Piping- Eng. Prod. Rate (WH/LM)

Total_PipingLargeBore(>3")SmallBore(<2.5")

U.S.AlbertaU.S.AlbertaU.S.Alberta

8

7

6

5

4

3

2

1

0

2.08

1.00

2.58

0.88

1.971.60⊗⊗⊗⊗⊗⊗⊗⊗ 11..7700

⊗⊗⊗⊗⊗⊗⊗⊗ 22..0033

⊗⊗⊗⊗⊗⊗⊗⊗ 11..2233 ⊗⊗⊗⊗⊗⊗⊗⊗ 00..7788 ⊗⊗⊗⊗⊗⊗⊗⊗ 00..4477

⊗⊗⊗⊗⊗⊗⊗⊗ 11..2288

(N=10) (N=23) (N=12) (N=26) (N=22) (N=61)

39

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.

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 Productivity5

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/m3, 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/m3, 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.

U.S.Alberta

1800

1600

1400

1200

1000

800

600

400

200

0

Adjusted Project Cost ($M, in 2007)

122.08

459.68

81.13

285.49

(N=33) (N=29)

40

Figure 4-26 Comparison of Total Concrete Construction Productivity (WH/ m3)

• 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.

Figure 4-27 Comparison of Total Structural Steel Construction Productivity (WH/ Metric Ton)

U.S.Alberta

60

50

40

30

20

10

0

Total Concrete Productivity Rate (WH/ CM)

14.44

19.39

(N=12) (N=32)

⊗⊗⊗⊗ 1133..1100

⊗⊗⊗⊗ 99..7722

Total Steel Prod. Rate (WH/Metric Ton)

U.S.Alberta

180

160

140

120

100

80

60

40

20

0

42.4153.95

(N=21) (N=32)

⊗⊗⊗⊗ 3377..9966

⊗⊗⊗⊗ 3388..3377

41

• 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.

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.

Instrumentation- Devices Con. Prod. Rate (WH/ Count)

U.S.Alberta

50

40

30

20

10

0

13.53

13.37

(N=9) (N=22)

⊗⊗⊗⊗

⊗⊗⊗⊗

2211..4400

88..2288

42

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 macro-

view 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.

Adjusted Total Project Cost ($M CDN, in 2007)

Construction Prod. Project Level Index

120010008006004002000

3

2

1

0

-1

-2

-3

Figure 4-30 Construction Productivity Project Level Index vs. Project Size ($M CDN, in 2007)

Insulation- Piping Con. Prod. Rate (WH/LM)

U.S.Alberta

8

7

6

5

4

3

2

1

0

1.93

1.90

(N=16) (N=15)

⊗⊗⊗⊗ 11..4411

⊗⊗⊗⊗

22..1199

43

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 (R2

= 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.

Total_PipingTotal_SteelTotal_Concrete

3.0

2.5

2.0

1.5

1.0

0.5

0.0

Actual/ Estimated Productivity Rate

1.45

1.221.04

(N=8) (N=17) (N=10)

Total_PipingTotal_SteelTotal_Concrete

3.0

2.5

2.0

1.5

1.0

0.5

0.0

Actual/ Estimated Productivity Rate

1.45

1.221.04

(N=8) (N=17) (N=10)

Figure 4-31 Actual / Estimated Construction Productivity Rate by Work Discipline

44

• 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.

Total_PipingTotal_SteelTotal_Concrete

2.5

2.0

1.5

1.0

0.5

0.0

Actual/ Est. Total Installed Unit Cost

1.10 1.111.02

(N=9) (N=15) (N=8)

Total_PipingTotal_SteelTotal_Concrete

2.5

2.0

1.5

1.0

0.5

0.0

Actual/ Est. Total Installed Unit Cost

1.10 1.111.02

(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.

45

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., R2 = 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.

46

Figure 4-33 Factors Impacting Project Cost

-10

-8

-6

-4

-2

0

2

4

6

8

10

-1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2

Project

Cost Growth

Sum Degree of

Impact

Sum Degree Impact of Top 16 Factors vs. Cost Growth

Hi Negative

Impact

Hi Positive

Impact

Poor Cost

Performance

Figure 4-34 Impact of Factors vs. 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.

47

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

-10

-8

-6

-4

-2

0

2

4

6

8

10

-1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2

Project

Schedule

Growth

Sum Degree of

Impact

Sum Degree Impact of Top 18 Factors vs. Project Schedule Growth

Hi

Negative

Impact

Hi Positive

Impact

Poor Cost

Performance

Figure 4-36 Impact of Factors vs. Schedule Growth

48

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., R2 = 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)

49

-10

-8

-6

-4

-2

0

2

4

6

8

10

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3

CPM

Sum Degree of

Impact

Sum Degree Impact of Top 17 Factors vs. Project Construction

Productivity (CPM)

Hi Negative

Impact

Hi Positive

Impact

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

50

beyond 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.

51

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.

1.41.21.00.80.60.40.20.0-0.2-0.4

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

Project Change Cost Factor

Project Cost Growth

Alberta

U.S.

Location

52

Table 5-1 The Top 5 Factors Affecting Cost, Schedule or Productivity

Rank Cost Schedule Productivity

1 Amount of Unplanned Overtime

% Engineering completion prior to Construction Start

% Engineering completion prior to Construction Start

2 % Engineering completion prior to Construction Start

Business Market Conditions

Amount of Unplanned Overtime

3 Business Market Conditions Craft Labour Skill Business Market Conditions

4 Craft Labour Skill Quality of Field Level Supervision

Quality of Field Level Supervision

5 Coordination with Plant Shutdown

Weather Conditions 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.

53

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%.

54

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.

55

Appendices

56

Ap

pen

dix

A:

Su

mm

ary

of

Co

rrela

tio

n b

etw

een

Pro

ject

Perf

orm

an

ce a

nd

Rela

ted

Facto

rs o

f A

lbert

a B

ased

Pro

jects

Ta

ble

A-

1 C

orr

ela

tio

ns

of

Pro

jec

t C

ha

rac

teri

sti

cs

wit

h P

roje

ct

Pe

rfo

rma

nce

Pro

ject C

hara

cte

ristic

s

Tota

l Pro

ject C

ost

($M

CD

N)

Tota

l Pro

ject

Dura

tion (

we

eks)

Constr

uction

Dura

tion (

we

eks)

% C

ontin

gency

Bud

get

% E

ng. co

mple

ted

befo

re c

on.

start

ed

Perf

orm

ance M

etr

ic1

N

r N

r

N

r N

r

N

r

CO

ST

P

roje

ct C

ost G

row

th

23

0.4

68

22

0.0

17

21

0.7

14

17

-0

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19

-0

.551

Pro

ject B

udg

et F

acto

r 23

0.4

99

22

0.3

62

21

0.5

22

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56

19

-0.2

48

Constr

uction P

hase C

ost G

row

th

18

-0.0

04

17

0.2

80

16

0.4

96

16

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70

14

-0.7

23

* C

onstr

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ndirect

Cost

Gro

wth

17

0.5

99*

16

0.4

87

15

0.7

51

15

-0.4

29

14

-0.6

79

Sta

rtu

p C

ost G

row

th

9

-0.1

62

10

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11

10

0.0

70

10

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8

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90

SC

HE

DU

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row

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21

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31

23

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96

17

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21

-0

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Pro

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che

du

le F

acto

r 21

-0.0

56

23

-0.1

69

22

-0.2

33

17

0.2

85

21

-0.1

05

Constr

uction P

hase S

che

dule

Gro

wth

20

-0.0

59

22

0.2

01

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54

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Sta

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17

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18

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18

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14

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05

CH

AN

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S

Tota

l Cha

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Cost F

acto

r

14

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15

0.4

29

15

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99

12

-0

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15

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58

Develo

pm

ent C

han

ge C

ost

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r 10

0.1

42

11

0.4

71

11

0.6

77

11

-0.5

56

10

-0.6

55

Scope C

ha

nge

Cost F

acto

r 12

0.1

48

13

0.4

97

13

0.4

60

10

-0.0

81

12

-0.4

69

RE

WO

RK

F

ield

Re

work

Cost F

acto

r 8

-0

.174

8

0.4

11

8

0.0

83

8

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94

8

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18

SA

FE

TY

Lost tim

e F

reque

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(LT

F)

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(LT

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15

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PR

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E

ng

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roductivity

(EP

M I

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20

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22

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03

20

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15

-0

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19

-0

.401

Constr

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roductivity

(CP

M I

nd

ex)

20

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64

* 19

-0.1

61

19

-0.1

28

15

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43

17

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26

1 M

etri

c an

d p

has

e d

efin

itio

ns

are

pro

vid

ed i

n A

pp

end

ix A

.

© i

nd

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e sm

all

sam

ple

siz

e (N

<8

).

r =

Pea

rso

n c

orr

elat

ion

;

Sh

adin

g i

ndic

ates

sta

tist

ical

ly s

ign

ific

ant

corr

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bet

wee

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acto

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per

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rics

at

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level

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scat

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plo

ts w

ith

sta

tist

ical

res

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s p

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hap

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4.

57

Ta

ble

A-

2 C

orr

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of

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(c

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hara

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ristic

s

%

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lari

zation

*

% O

ffsite

W

ork

-hours

%

Constr

uction

Indirect/

Direct

WH

*

%

Constr

uction

Ind./ D

ir. C

ost*

Actu

al/

Est.

Peak

Work

forc

e

% S

caff

old

ing/

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H

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etr

ics

N

r N

r

N

r N

r

N

r N

r

CO

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P

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ct C

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row

th

22

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19

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17

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.033

20

0.7

87*

18

0.3

62

Pro

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udg

et F

acto

r 22

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14

22

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03

19

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53

17

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20

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52

Constr

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44

15

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07

17

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15

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wth

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AN

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ield

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work

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acto

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SA

FE

TY

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e F

reque

ncy

(LT

F)

8

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65

8

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8

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8

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Lost T

ime S

eve

rity

(LT

S)

18

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00

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54

PR

OD

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ITY

E

ng

ineerin

g P

roductivity

(EP

M I

ndex)

22

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69

18

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22

-0

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22

0.2

21

21

0.2

92

16

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22

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22

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Constr

uction P

roductivity

(CP

M I

nd

ex)

19

0.3

16

16

0.1

32

19

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21

19

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40

18

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59

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ble

A-

4 C

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of

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cti

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wit

h P

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rfo

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(c

on

t’d

)

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ctices

Chan

ge M

gm

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Zero

Accid

ent

Tech.

Qualit

y M

gm

t.

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ing

for

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PP

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F

Work

face

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94

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91

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uction P

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dule

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wth

23

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53

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tart

up S

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wth

19

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AN

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16

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pm

ent C

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ost

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r*

11

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ha

nge

Cost F

acto

r*

13

0.1

77

13

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62

13

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62

13

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70

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76

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RE

WO

RK

F

ield

Re

work

Cost F

acto

r 9

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88

9

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9

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25

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17

9

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FE

TY

Lost tim

e F

reque

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(LT

F)

8

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47

8

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59

8

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34

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©

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Lost T

ime S

eve

rity

(LT

S)

18

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50

18

0.0

45

18

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12

17

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37

18

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48

17

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07

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OD

UC

TIV

ITY

E

ng

ineerin

g P

roductivity

(EP

M I

ndex)

22

-0

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18

0.5

25

21

-0

.458

21

-0

.543

20

-0

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19

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©

©

Constr

uction P

roductivity

(CP

M I

nd

ex)

20

-0.1

70

19

0.0

77

19

-0.2

01

18

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68

19

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23

18

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66

©

©

60

Appendix B: Performance Metric Formulas and Definitions

Performance Metric Category: COST

Metric: Project Cost Growth

Formula:

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

Formula: Actual Phase Cost

Actual Total Project Cost

Metric: Phase Cost Growth

Formula:

Actual Phase Cost – Initial Predicted Phase Cost Initial Predicted Phase Cost

Definition of Terms

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.

Initial Predicted Project Cost:

• Owners – Budget at the time of Project Sanction.

• Contractors – Cost estimate used as the basis of contract award.

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.

61

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.

62

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)

Formula:

Total Number of Medical Aid Cases x 200,000 Total Site Work-Hours

Metric: First Aid Frequency (FAF)

Formula:

Total Number of First Aid Cases x 200,000 Total Site Work-Hours

Metric: Total Recordable Injury Frequency (TRIF)

Formula:

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

63

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.

64

Performance Metric Category: CHANGES

Metric: Scope Change Cost Factor

Formula: Total Cost of Scope Changes

Actual Total Project Cost

Metric: Project Development Change Cost Factor

Formula: 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.

65

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 = Direct Work hours Installed Quantity

Productivity Estimating Performance = Actual Productivity Rate Estimated Productivity Rate

Cost Estimating Performance = Actual TIUC Estimated TIUC

66

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 (<4CM) (CM)

• Foundation (≥4CM) (CM)

o Concrete Structures (CM)

o Total Piling (Each)

Structural Steel

- Total Steel (MT)

o Combined Structural Steel / Pipe Racks & Utility

Bridges (MT)

• Structural Steel (MT)

• Pipe Racks & Utility Bridges (MT)

o Miscellaneous Steel (MT)

Electrical

- Total Electrical Equipment (Each)

o Electrical Equipment 600V & Below (Each)

o Electrical Equipment Over 600V (Each)

- Conduit

o Conduit (LM)

o Conduit (Number of Runs)

- Cable Tray (LM)

- Wire & Cable

o Wire & Cable (LM)

o Wire & Cable (Number of Terminations)

- Other Electric Metric

o Lighting (Each Fixtures)

o Electrical Heat Tracing (LM)

Piping

- Total Piping (LM)

o Small Bore (2-1/2” and Smaller) (LM)

o Large Bore (3” and Larger) (LM)

o Engineered Hangers and Supports (Each)

- Heat Tracing Tubing (LM)

Instrumentation

- Loops (Count)

- Tagged Devices (Each)

- I/O (Count)

Equipment (Individual Design and Total Quantity)

- Total Equipment (Each)

o Pressure Vessels (Each)

o Atmospheric Tanks (Each)

o Heat Transfer Equipment (Each)

o Boiler & Fired Heaters (Each)

o Rotating Equipment (Each)

o Material Handling Equipment (Each)

o Power Generation Equipment (Each)

o Other Process Equipment (Each)

o Vendor-Designed Modules &

Pre- Assembled Skids (Each)

Engineering Productivity = Direct Design-Hours* IFC Quantity** * Per Design Component ** IFC (Issued for Construction)

67

Pro

jec

t P

ha

se

De

fin

itio

n T

ab

le

Pro

ject P

hase

Sta

rt/S

top

T

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ties &

Pro

ducts

T

ypic

al C

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Fro

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nd P

lann

ing

Typ

ica

l P

art

icip

ants

: •

Ow

ner

Pers

onne

l •

Pla

nn

ing

Consu

ltants

•

Constr

ucta

bili

ty

Consultant

• A

llia

nce / P

art

ner

Sta

rt:

Sin

gle

pro

ject

ad

op

ted

an

d F

orm

al p

roje

ct

team

esta

blish

ed

S

top

: P

roje

ct

San

cti

on

• O

ptio

ns A

naly

sis

•

Life-c

ycle

Cost A

naly

sis

•

Pro

ject E

xecutio

n P

lan

• A

ppro

pria

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ubm

ittal P

kg

• P

&ID

s a

nd

Site

La

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t •

Pro

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cop

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• P

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lan

• A

rch. R

enderi

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• O

wn

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Pla

nnin

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eam

Pers

onne

l Exp

enses

• C

onsultant

Fees &

Exp

enses

• E

nvi

ronm

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l Perm

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•

Pro

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anag

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Deta

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Typ

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: •

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to

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sp

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Co

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(o

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spec. pre

para

tio

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f m

ate

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para

tion

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s

• S

eq

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of

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pers

on

ne

l •

Desig

n C

on

tracto

r •

Alli

ance / P

art

ner

Sta

rt:

Pro

cu

rem

en

t p

lan

fo

r

en

gin

eere

d e

qu

ipm

en

t S

top

: A

ll m

ajo

r eq

uip

men

t

has b

een

deli

vere

d t

o s

ite

• V

en

dor

Qua

lific

atio

n

• V

en

dor

Inqu

irie

s

• B

id A

naly

sis

•

Purc

hasin

g

• E

xped

iting

• E

ng

ineere

d E

quip

ment

• T

ransport

atio

n

• V

en

dor

QA

/QC

• O

wn

er

pro

ject m

anagem

ent pers

onn

el

• P

roje

ct M

anag

er

/ C

onstr

uctio

n

M

anag

er

fees

• P

rocure

ment &

Exped

iting p

ers

onn

el

•

Eng

ineere

d E

quip

ment

• T

ransport

atio

n

• S

ho

p Q

A /

QC

Note

: T

he

dem

olit

ion /

aba

tem

ent phase

sho

uld

be

re

port

ed

wh

en

th

e d

em

olit

ion

/ a

ba

tem

ent

work

is a

se

pa

rate

sche

du

le a

ctivi

ty (

pote

ntia

lly p

ara

llelin

g t

he

desig

n a

nd p

rocure

ment

phases)

in p

repara

tion f

or

ne

w c

onstr

uction.

Do n

ot

report

the d

em

olit

ion /

aba

tem

ent

phase i

f th

e w

ork

is i

nte

gra

l w

ith

modern

ization

or

additio

n a

ctiv

ities.

68

Pro

jec

t P

ha

se

Ta

ble

(C

on

t.)

Pro

ject P

hase

Sta

rt/S

top

T

ypic

al A

ctivi

ties &

Pro

ducts

T

ypic

al C

ost E

lem

ents

Constr

uction

Typ

ica

l P

art

icip

ants

: •

Ow

ner

pers

on

ne

l •

Desig

n C

on

tracto

r (

Inspection)

• C

onstr

uction C

ontr

acto

r an

d

it

s s

ubco

ntr

acto

rs

Sta

rt:

Co

mm

en

cem

en

t o

f

fo

un

dati

on

s o

r d

riv

ing

Pil

es

Sto

p:

Mechanic

al C

om

ple

tion

• S

et

up tra

ilers

•

Pro

cure

ment of

bulk

s

• Is

sue S

ubcontr

acts

•

Constr

uction p

lan f

or

M

eth

ods/S

eque

ncin

g

• B

uild

Facili

ty &

Insta

ll

E

ngin

eere

d E

qu

ipm

ent

• C

om

ple

te P

unch

list

• D

em

obili

ze c

onstr

uctio

n

e

quip

ment

• W

are

housin

g

• O

wn

er

pro

ject m

anagem

ent pers

onn

el

• P

roje

ct M

anag

er

/ C

onstr

uctio

n

M

anag

er

fees

• B

uild

ing p

erm

its

• In

spection Q

A/Q

C

• C

onstr

uction lab

our,

equ

ipm

ent &

s

upp

lies

• B

ulk

mate

rials

(in

clu

din

g f

reig

ht)

•

Const

ruct

ion e

quip

ment (incl

udin

g

freig

ht)

•

Contr

acto

r m

anagem

ent p

ers

onnel

• W

arr

antie

s

Sta

rt-u

p / C

om

mis

sio

nin

g

Note

: D

oes n

ot

usually

ap

ply

to

infr

astr

uctu

re o

r bu

ildin

g t

ype

pro

jects

Typ

ica

l P

art

icip

ants

: •

Ow

ner

pers

on

ne

l •

Desig

n C

on

tracto

r

• C

onstr

uction C

ontr

acto

r •

Tra

inin

g C

onsu

ltant

• E

qu

ipm

ent V

end

ors

Sta

rt: M

echanic

al C

om

ple

tion

Sto

p:

Cu

sto

dy t

ran

sfe

r to

use

r/o

pera

tor

(ste

ad

y

sta

te o

pera

tio

n)

• T

estin

g S

yste

ms

• T

rain

ing O

pera

tors

•

Docum

entin

g R

esults

•

Intr

oduce

Fee

dsto

cks a

nd

o

bta

in f

irst P

roduct

• H

and-o

ff to u

ser/

op

era

tor

• O

pera

ting S

yste

m

• F

unctio

na

l Facili

ty

• W

arr

anty

Work

• O

wn

er

pro

ject m

anagem

ent pers

onn

el

• P

roje

ct M

anag

er

/ C

onstr

uctio

n

M

anag

er

fees

• C

onsultant

fees &

exp

ense

s

• O

pera

tor

tra

inin

g e

xpe

nses

• W

aste

d f

eedsto

cks

• V

en

dor

fees

69

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.

70

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.

73

Estimated Productivity – are the estimated productivity of direct labour work hours required for installation according to the estimated quantity.

For owners: Estimated Quantity, Work hours and Total Installed Unit Cost at the time of

Project Sanction (or as soon as available following sanction) For contractors: 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.

74

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.” ASCE,

Journal of the Construction Division, 107(1), March 1981, pp. 137-152.

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

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