MANAGEMENT

RESEARCH PAPER (ATTACHED)

2 pages, but not more than 3 pages, in the narrative and it should be typed in APA formatting (title page, reference page, no abstract page,  double-spacing, Times New Roman 12 font, 1 inch margins, in-text citations, etc

T
he word contingency has been given a completely
new meaning since it was introduced to heavy oil and
gas energy construction industry. Perhaps it is one of
the most confusing concepts in project cost and

schedule management systems. More often than not, the
contingencies are simply hidden in the base estimate, causing
chaotic results [7]. Now, with the historical cost data becoming
illusory, the efforts of benchmarking will be misleading and
meaningless. Lack of structured transparency has led to the
misconception that the contingency fund is a phenomenon of
wide utility as a catch-all to cover project extra costs which
otherwise can not be legitimately accounted for.

J.P. Morgan has been quoted as saying that “the market will
fluctuate” [2]. This claim also applies to project cost forecasts.
Contingency forecasts fluctuate as well, following the project
progress and overall project risk shifts. One of the most effective
ways to avoid the catch-all syndrome is to break down contingency
into risk affected components. The level of detail shall be driven
by a well established work breakdown structure (WBS), which
shall be inherently built into a contingency risk model. When the
contingency amount is derived, it will be automatically dispersed
into appropriate accounts based on the risk levels and cost weigh-
ing factors.

Allocating contingency instead of a single liner provides a
great advantage to the management of the contingency. By using
moderate cost breakdown details, the cost engineer knows exactly
how the contingency model is put together and the amount each
account deserves, and the timing when the amount is exhausted.
Hence, the cost engineer can easily generate a contingency draw-
down plan, and manage the depletion of contingency in a
controlled manner.

PROJECT COST OVERRUN PHENOMENON

Fifteen years after the economic recession of the 1990s—a
result of a world-wide oil price drop, the Oil Sands in northern
Alberta of Canada became a hot spot, attracting numerous
investors from the United States and other parts of the world.
Scarcity of oil and its current high price were the catalysts for a
booming oil and gas construction industry in the province of
Alberta. Between 2005 and 2013, a total amount of 63.5 Billion

Canadian dollars [6] will be invested in the exploration, mining,
crude refining and transportation of bituminous oil sands.
However, the heavy construction in the energy industry has been
plagued with a history of poor performance, budget overrun and
significant schedule delays, according to the Construction
Owner’s Association of Alberta (COAA).

Various attempts have been made by the COAA and other
interest groups to improve the reputation of the industry with
little success to date. Perhaps there is no easy way to save the
industry’s notoriety for cost overruns. However, one method to
avoid further cost slippage—the implementation of a stringent
risk management process, including adequate cost and schedule
contingency—is being advocated by the author. According to Dr.
J. Murther , a Houston-based risk management consultant, the
reason that risk-based contingency management has not yet been
overwhelmingly successful in the energy construction sector is
because the project risk management is still in its infant cradle
and not yet fully embraced by the owners’ senior management.

Although contingency has become a buzz word, it is
unfortunately not being truly understood in the heavy oil and gas
construction industry. In fact, the term contingency fund has
been widely misused—for project scope creep or to cover extra
project costs which have otherwise been either omitted from the
estimate or have arisen as a surprise.

It is also not uncommon for the contingency fund to be kept
as a secret in many projects. Hartman [7] highlighted this
problem in his assertion that “hidden contingencies are hard to
manage” and that “[getting] them in the open without having
them taken away” poses a significant challenge, not only to the
project management team but also – and more importantly—to
the risk analyst.

CONFUSION AND MISCONCEPTIONS
ABOUT CONTINGENCY

Risk and uncertainty are inherent to projects during their life
cycles. Stralser [16] noted that “not many project managers
realize that managing risk is their primary responsibility.” Project
managers make decisions, give directions, select optimal options
and spend large amounts of money, often without realizing that
they are managing various risks; they are going through a rigorous

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2006 AACE International Transactions

RISK.05

Significance of WBS in Contingency Modeling

Mr. John Gengxin Zhao

risk management and decision-making process. However, not all
risks can be effectively managed to an acceptable level, and
sometimes “risk taking is a major aspect of managerial work” [15],
with contingency being one of the most popular forms of project
risk management [7].

Capital cost estimate is to some extent a perfect example of
human error, estimate preparation timing, quality of input data
and completeness of design, maturity of engineering deliverables
and project contracting strategy, as well as the execution plan – all
predominate their risky states and uncertain conditions. While
some still refuse to acknowledge the factual characteristics of the
cost estimate, many are prepared to take the risks, but have
applied for an adequate contingency as a safeguard; “contingency
has been added to enable us to expect the unexpected” [21].
Clark and Lorenzoni [5] suggest that a “technology of
contingency” be used to cover these risks and uncertainties since
the perfection and 100 percent accuracy of a capital cost estimate
will never be achievable due to its inherent uncertainties and the
nature of the construction industry.

When project managers make decisions, they are faced with
three situations: certainty, risk, and uncertainty [15]. Risky and
uncertain situations empower probability-based decision-making
techniques. A contingency fund, often derived from Monte Carlo
simulation, is consequently added to improve the managers’
confidence level and aid a better and quicker decision-making
process.

However, it is this very covert contingency fund that has
caused many misconceptions, ad hoc interpretations, and
confusion in the construction industry. Worse yet, some
overconfident project managers completely ignore the use of
contingency, assuming that they have a perfect cost estimate that
they can maintain until the end of the project completion:
“Unnecessary doubts may incapacitate the professional, and
create an impression of incompetence, but unjustified
complacency can be equally disastrous in the long run” [18]. On
the contrary, some project managers opt to hide the contingency
fund without any transparency to the project team, leaving the
project struggling with an inadequate budget from the onset. At
best, the contingency is calculated and managed at a higher
level—normally just an entry or a single line item in the cost
management system—leaving project team members to wonder
what this contingency actually entails.

Eventually all that is desired is to cost effectively manage
project risks by improving the project uncertainties in order to
eliminate or reduce the chances of project failure.

THE IMPORTANCE OF WORK
BREAKDOWN STRUCTURE

Beholding the current industry situation, one way to better
understand and manage the project contingency is to break the
contingency fund down and allocate it into various slots based on
the work breakdown structure (WBS). The WBS “turns one large,
unique, perhaps mystifying, piece of work, project, into many
small manageable tasks” [17]. In conjunction with the WBS, a
new taxonomy, risk breakdown structure (RBS), has also been
used for better risk and contingency management using the same

WBS principles. While the WBS helps provide visibility to
important or risky work efforts [4], the RBS can assist in
understanding the distribution of risk on a project or across a
business, aiding effective risk management [10].

In discussing the work breakdown structure, Hillson [11]
stated: “The most obvious demonstration of the value of structur-
ing within project management is the work breakdown structure
(WBS), which is recognized as a major tool for the project
manager because it provides a mean to structure the work to be
done to accomplish project objectives.”

The importance of the WBS is further emphasized by Verzuh
[17] who states that “it is the foundation of project planning and
one of the most important techniques used in project
management.” With the help of the WBS, the exorbitant cost
estimate or project schedule can be broken down into
meaningful, manageable and traceable pieces, not only for the
purpose of quantitative cost/schedule risk analyses but also for cost
management and schedule updating.

In the qualitative risk assessment process, the risk breakdown
structure (RBS) offered “a source-oriented grouping of project
risks that organizes and defines the total risk exposure of the
project” in a hierarchical structure [11]. Currently, the use of the
RBS is limited to risk identification and ranking and reporting
processes, although it has the potential to become the most
valuable single tool in assisting the project manager to understand
and manage risks to his project in a quantitative manner. Despite
the fact that many individuals, as well as corporations, have
attempted to produce an integrated RBS based WBS using
emerging technology—such as London-based Pertmaster™ and
US-based Ares Corporation’s PRISM Risk Manager™—to aid
quantitative risk assessment, the true benefits of this integrated
approach have yet to be tested, realized and ascertained.

When building a risk model to simulate cost estimate or
schedule contingency using the Monte Carlo technique, proper
breakdown of the estimate or schedule to leverage the contents of
variables in each WBS account is always a difficult task. Each
WBS account bears different types and degrees of risks; therefore
the variability of uncertainty can be a wide swing between
accounts. An unbalanced WBS could severely skew the
simulation results because the variations of risks could be
generalized instead of specific. A set of calibrated WBS with the
help of RBS inputs will significantly enhance the reliability of the
contingency model.

WBS INHERENT RISK MODEL TECHNIQUE

Statistics work only for large numbers [2], and when a large
amount of variables are assessed in a model, the Monte Carlo
simulation tends to produce a symmetrical bell-curve probability
density function (PDF) in the shape of a histogram, according to
Laplace’s (1809) Central Limit Theorem. These large volumes of
variables appear and poise in a very disorderly pattern, which
makes data management a nightmare. Further more, the task of
systematically assigning uncertainty ranges to inter-related
variables without a structured data breakdown framework not only
becomes cumbersome and time consuming, but also looses
consistency and train of thinking process.

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2006 AACE International Transactions

“Order is impossible to find unless disorder is there first” [2].
The use of the Monte Carlo simulation aims to better forecast
project future outcomes—a process which involves a tremendous
amount of quantitative data in a disorderly manner. Since the
Monte Carlo simulation is a type of parametric simulation, little
historical data or benchmark information is known [13]. Mun
[13] also stressed that “forecasting is the art and science of
predicting the future. It can be based on quantitative approach
and qualitative approach.” This qualitative approach largely
depends upon expert judgement and management assumptions.
Without a WBS aided thinking process, the human brain is
simply not capable of effectively handling more than seven pieces
of data, according to psychologists (Greenwood, 2004).

A typical $500 million cost estimate can easily contain
between 200—300 variables, of which more than 50 percent need
to be carefully assessed to derive a meaningful estimate
contingency as the result of acceptance of the risky state and
conditions of the cost estimate. For instance, the validity of a
single point cost estimate at EDS stage, when only approximate
50 percent engineering is progressed, relies on the entry of
contingency [9] to achieve the required confidence level. Future
predictability of the true estimate outcome depends on many
variables. Even with those variables achieving some degrees of
certainties, the potential changes during the course of execution
and human errors during the estimate preparation will ultimately
alter the final estimate results. Using the WBS to help break down

the estimate—figure 1— has far reaching effects for both cost
management and contingency depletion, because:

• Future changes can be attributable to the associated WBS
accounts;

• Human errors can be traceable back to appropriate WBS
home domains;

• Responsible individuals can be accountable for respective
WBS contingency;

• Potential savings can be recognizable in corresponding risk-
free WBS.

Intuitive judgement based on heuristics and biases [12] plays
an important role in deciding which variables are to be analyzed.
Past experiences have accumulated dense mindsets among
engineering and construction professionals in regards to assessing
risks. Variables that are often surfaced for evaluations have been
anchored in their minds , while hidden and invisible variables are
often neglected or ignored. Without the help of a structured
approach, risk analysis is merely an activity of the random
selection of apparent variables for ranging exercises. This
phenomenon was also discovered during the author’s experiences
of 22 risk workshops and nine major quantitative estimate and
schedule assessments in various oil and gas energy projects.

Regression correlations among those variables can drastically
skew the final statistical results. Correct and righteous variable
dependency requires that the implementation and application of

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2006 AACE International Transactions

Figure 1—WBS Based Cost Elements

Figure 2—WBS Based Labour Cost Risk Model

the WBS be built in the simulation models. The following
examples of typical correlations illustrate the criticality of the
existence of a WBS in cost risk simulation modelling.

Positive correlation
If the Quantity of Piping account Materials Take-Off [WBS:

1-3.02-Q] is increased, the labour manhours for the same Piping
account [WBS: 1-3.02-L] will be positively increased by a
percentage (say 60 percent). EHT and insulation subcontractor
[WBS: 1-6-03-S] pricing will also be increased, positively, by a
percentage (say 35 percent). Indirect and construction
management costs [WBS: 2-11.04-O] are also slightly increased
by 10 percent.

Negative correlation
The example of the negative correlation is that if the shop

modularization quantity is increased, the field labour manhours
for structural steel and piping accounts will be decreased, and
field indirect costs and camp usage costs will also be decreased.

“Analyzing a system can not be selective process, subject to a
single perspective of an analyst” [22]. A risk simulation model
without a structured hierarchy and the sorting ability to
differentiate various risk components may easily experience this
pitfall. Preferences for selective and apparent variables will not
only generate unreliable statistical results but also an incomplete
and unsuccessful model. Inconsistent simulation results, which
are based on variable preferences, will also not be beneficial for
the purpose of meaningful benchmarking. Only a preformatted
analytical simulation conclusion based on a pre-established WBS
will help to avoid this pitfall and achieve correct statistics.

CONCLUSION: PRE-FORMATTED WBS ENHANCING
MODELLING RESULTS

To avoid “applying a politically correct percentage” to the
number, a more intelligent means of adding contingency [7] is to
use the Monte Carlo simulation technique by engaging all
stakeholders in the game play. Doing so demands the colossal cost
estimate be dismantled and sorted structurally.

Heinze [9] stresses the importance that “a major project is
broken down into various cost components first.” The Monte
Carlo simulation model mimics the real project scenario,
depicting the estimate and cost control system structure.
Typically, the model is built around an organization’s WBS and
tweaked with an RBS to reflect its risk tolerability, and also for the
decision making process. In the model, each variable is
represented by a WBS/RBS code residing in its designated
domain, and the variability of its upper and lower limits,
pessimistic and optimistic views, is measured and endorsed
collectively by a group judgement, or by using Delphi Technique.

The importance of a WBS-based risk analysis approach was
strongly advocated by David Vose [20]. In his book “Risk Analysis:
A Quantitative Guide,” he says of the cost risk analysis: “A cost risk
analysis is usually developed from a work breakdown structure
(WBS) which is a document that details, from the top down, the
various work packages (WP) of which the project consists.”

Structured risk assessment of variables minimizes the risk of
omitting critical items; it systematically scrutinizes the
uncertainties of MTO quantity, material cost, wage rate,
productivity factor (PF) and estimated man-hours. See figure 2.
When an incurred cost is confirmed, it can be easily removed
from the estimate following its WBS domain, because risk
management is the ability to define what may happen in the
future [1].

T
he whole purpose of modelling risks goes to the heart
of cost management: cost effectively manage risks by
depleting contingency. Appropriate allocation of the
contingency funds to risk-affected WBS accounts also

increases the project manager’s accountability following the risk
allocation principle [3]. Further more, the contingency, derived
from the simulation process, must be properly scaled and
dispersed into WBS accounts following the approved project
execution schedule; this dispensation of contingency funds over
time is referred to as contingency drawdown management plan.

“All contingency budgets should be tied to specific risks”
[19]. The advantages of locking contingency into WBS based risk
accounts not only provides project managers with certain
flexibilities in managing their cost forecasts, but also prevents

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2006 AACE International Transactions

Figure 3—WBS Based Risk Simulation Results

borrowing contingency money from other accounts or advancing
from future timeline.

The catch-all syndrome is eliminated and details of
contingency are transparent. The visual sensation of the adequacy
of the remaining contingency against the remaining work and risk
levels, depicted in a draw-down curve , clearly illustrates the
characteristics of the project manager’s risk attitude, i.e. risk
aversion or risk taking.

“A risk analysis model, however carefully crafted, is of no
value unless its results are understandable, useful and believable”
[20]. The original estimate amount in comparison with the
simulated total amount, including contingency by WBS
accounts—figure 2—provides the project management team with
a clear and direct pictorial understanding of the contingency
modelling technique. It is of equal value to estimators, cost
analysts, project executors and contract administrators as well as
project managers. Subsequently, a WBS-based risk analysis and
contingency modelling technique add net value to the project
team.

REFERENCES

1. Aven, Terje (2003) Foundations of Risk Analysis – A
knowledge and decision oriented perspective. University of
Stavanger, Norway. John Wiley & Sons Ltd. West Susses,
England.

2. Bernstein, Peter (1996) Against The Gods – The Remarkable
Story of Risk. John Wiley & Sons, Inc. New York, USA.

3. Chapman, Chris and Ward, Stephen (2003) Project Risk
Management – Processes, techniques and Insights. University
of Southampton. John Wiley & Sons, Ltd. Sussex, England

4. Chapman, James (2005) Principle based Project
Management. Washington DC. Accessed 26 November
2005. < http://www.hyperthot.com/pm_wbs.htm>

5. Clark, Forrest D and Lorenzoni, A.B. (1997) Applied Cost
Engineering, 3rd Edition. New York, Marcel Dekker Inc.

6. Global and Mail (2005) Alberta Oil Sands Development,
Business Section, December 4, 2005, Calgary Newspaper,
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7. Hartman, Francis (1999) Don’t Park Your Brain Outside: A
practical guide to improving shareholder value with SMART
Management. Pennsylvania, USA, Published by Project
Management Institute Inc.

8. Harvard Business Essentials (2004) Crisis Management –
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9. Heinz, Kurt (1996) Cost Management of Capital Projects.
Marcel Dekker, Inc. New York, USA.

10. Hillson, David (2005) Using a Risk Breakdown Structure in
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11. Hillson, David (2002) Using a Risk Breakdown to
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12. Kahneman, Daniel, Tversky, Amos and Slovic, Paul (1982)
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13. Mun, Johnathan, (2004) Applied Risk Analysis – Moving
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15. Shapira, Zur (1995) Risk Taking: A managerial perspective.
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16. Stralser, Steven (2004) MBA in A Day. John Wiley & Sons.
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17. Verzuh, Eric (1999) The Fast Forward MBA in Project
Management. John Wiley & Sons, Inc. New York, USA.

18. Veryard Projects (2004) Contingency and Risk Models.
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19. Veryard Projects (2005) Three Notions of Contingency.
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20. Vose, David (2000) Risk Analysis – A Quantitative Guide,
John Wiley & Sons. Ltd. West Sussex, England.

21. Whitten, Neal (2005) No-nonsense advice for successful proj-
ects. Vienna, VA, Published by Management Concept Inc.

22. Yacov Y. Haimes (2004) Risk Modelling, Assessment, and
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John Wiley & Sons. Inc. New Jersey, USA.
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Yew York, USA.
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Harvard Business School Publishing, Boston, MA, USA
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Companies, Inc. USA.
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Management. Centre for Risk Management, resources for the
Future, January 1990, Washington D.C.

Mr. John Gengzin Zhao
Senior Cost Engineer

Suncor Energy Services, Inc.
PO Box 38, 112-4th Avenue, SW

Calgary, AB T2P 2V5 Canada
Phone: 403-920-8576

Email: jzhao@suncor.com

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2006 AACE International Transactions