Project Cost Models
AIPM IRC Bibliography
15th July, 2005
Introduction: While the literature on Project Cost Models in the engineering and construction industries is reasonably extensive, the
same cannot be said for other industries, such as the IT and Financial Services sectors. This small collection of articles looks at Cost
Models currently used across a range of non-engineering projects, including Monte Carlo, Earned Value, and CAIV. See also the
related bibliographies Finance and Costs for projects; initiating and defining projects Successfully; Beyond the Time Cost
Quality Triangle; and Banking and Finance PM.
1. Wang W. C. Supporting project cost threshold decisions via a mathematical cost model. International Journal of
Project Management 22(2), 99-108(10). 2004.
Keywords: cost models; cost model threshold.
Abstract: A project cost threshold must be determined in a relatively short time by a project owner as a reference for
evaluating competitive bids. In practice, such a decision is mainly based on subjective experience. This work presents a
novel systematic procedure for assessing a reasonable project cost threshold. The proposed procedure involves a utility-
based multi-criteria evaluation model and a cost model. The multi-criteria evaluation model is applied to accurately reflect
an owner’s preferences in relation to the decision criteria, as the cost model is derived to generate a cumulative cost
distribution to set boundaries on the cost threshold. The proposed cost model potentially saves the computer time and
coding effort that is required by a simulation-based model.
2. Anbari, F. T. Earned Value Project Management Method and Extensions. Project Management Journal 34(4), 12-23.
Keywords: Earned Value Management; EVM; Cost Variance; Schedule Variance.
Abstract: The earned value project management method integrates three critical elements of project management: scope
management, cost management, and time management. It requires the periodic monitoring of actual expenditures and
physical scope accomplishments, and allows calculation of cost and schedule variances, along with performance indices. It
allows forecasting of project cost and schedule at completion and highlights the possible need for corrective action. This
paper shows the major aspects of the earned value method and presents graphical tools for assessing project performance
trends. It provides logical extensions and useful simplifications to enhance the effective application of this important method
in project management. Includes illustrative matter; includes bibliographic references.
3. Bauer, N. Calculate With Confidence: Estimating software eliminates guesswork in project planning and frees
project managers to focus on the deliverables. PM Network 17(7), 42-47. 2003.
Keywords: cost estimating; project management software.
Abstract: Cost-estimating software can be very useful, but is not the answer to all front-end problems. This article
overviews some of the cost-estimating software products available on the market, and discusses the process by which an
organization can choose between them. This process starts with an internal study to ensure that well-configured procedures
are in place before the software is purchased. An appropriate product is one that captures your organization's estimating
methods. The software provider should have a good track record, and offer adequate support and training. Once a cost-
estimating product has been purchased, it must be diligently used and maintained in order to produce its expected value.
4. Leach, L. Schedule and cost buffer sizing: how to account for the bias between project performance and your
model. Project Management Journal 34(2), 34-47. 2003.
Keywords: buffer; contingency; management reserve; PERT; Monte Carlo; Critical Chain; CCPM.
Abstract: Bias in project performance causes schedule and cost to overrun baseline estimates (your model). Bias is the
one-sided tendency of actual schedule or cost to overrun the model. A Guide to the Project Management Body of Knowledge
(PMBOK® Guide) and supporting literature recommend estimating the variability for all project time and cost estimates and
sizing appropriate schedule or cost buffers (also known as contingency or management reserve) using Monte-Carlo analysis
or PERT. This paper describes a number of sources of bias in performance of projects to schedule and cost estimates and
provides recommendations to size buffers that ensure your projects come in under your baseline schedule and budget.
5. Kinsella, S. M. Activity-Based Costing: Does it Warrant Inclusion in A Guide to the Project Management Body of
Knowledge (PMBOK Guide)? Project Management Journal 33(2), 49-56. 2002.
Keywords: PMBOK; activity based costing; cost determination; accounting; profitability.
Abstract: Profit is a critical consideration in project selection. Using traditional cost-accounting techniques standardized
during the Industrial Age, estimators and accountants determine project costs. Those techniques result in costs that are
substantially different from costs calculated using activity-based costing (ABC).
Knowledge of ABC principles is important for project managers because it offers an alternative costing methodology that
allocates costs through a cause-and-effect relationship that is more appropriate for today's operating methods. This paper
reviews cost determination methods included in A Guide to the Project Management Body of Knowledge (PMBOK® Guide)
and offers an argument for enhancing the PMBOK® Guide by including ABC as a cost-determination methodology.
6. Graves, R. Open and Closed: The Monte Carlo Model. While some late-finishing and over-budget projects may be
attributable to poor project management, a more likely reason is that the original budgets and schedules were
unrealistic. PM Network 15(12), 48-52. 2001.
Keywords: Monte Carlo Simulation; Project Success; Uncertainty.
Abstract: Projects that overrun their budgets and deliver late are common enough. Whatever the causes, one thing is
certain - the budget and schedule allowed were insufficient for the work performed. While better results may be obtained in
some instances by improved project management techniques, in many cases overruns occur simply because the original
budget and schedule were inadequate. More often than not, this inadequacy is the result of uncertainty surrounding the
scope and extent of the work. When faced with uncertainty in estimating time or cost, many project managers simply assign
a more or less arbitrary value and hope for the best. However, there is a better way of doing things, based on an
understanding of the nature of uncertainty. Monte Carlo simulation can be used to predict overall project costs or
completion dates in much the same way that election results can be predicted by polling.
7. Kile RL, Rolley DC. Cost as independent variable (CAIV) - A CMM compatible process. PMI Seminars and Symposium
Proceedings 2001; 2001 Nov. 1-2001 Nov. 10; AIPM (CDRom). USA: PMI; 2001.
Keywords: application software - development - costs; capability maturity model - (computer software).
Abstract: Cost As an Independent Variable (CAIV) is a focus area within the development community arising from the need
to balance delivered functionality with the real-world realities of fiscal volatility and cost constraints. The fundamentals of
CAIV are a method to accurately determine the amount of functionality that can be delivered for a given funding level and
the ability to perform function/cost tradeoffs. Basic to both of these are cost models with current calibrations and enough
sensitivity to see the incremental costs of functionality. Not so obvious is the peripheral need to reduce variability in cost
models to increase their accuracy and reduce risk in the estimate. Variability in the data used to calibrate cost models
comes from both common cause and special cause variations in the project team's performance. Both types of variation can
be reduced through the process discipline inherent in achieving a Software Capability Maturity Model (SW-CMM) rating of
Level 2 and 3. A process will be described that fully supports the goals and implements CAIV in a manner compatible with
the CMM Level 2 and 3 requirements.
The Eight Phase Estimating Process (EP2) was developed in the early eighties in conjunction with the REVIC model and used
as the basis for training many DOD and civilian personnel in cost estimating. Many of the ideas from the process were used
by the Software Engineering Institute as the basis for key practices within the Project Planning and Project Tracking &
Oversight Key Process Areas (KPAs) in the SW-CMM Level 2. The traceability and linkage between the phased products
within EP2 allow an easy and repeatable method for determining the amount of functionality that can be delivered for a
given funding level and a disciplined method for performing the functionality/cost trade-offs. The process also provides a
document trail for the trade-offs and risks to support detailed analysis for management review. Thus EP2 provides a
disciplined, repeatable, and SW-CMM compatible method for performing true CAIV analysis.
The process uses an affinity grouping of activities that separates them into clearly defined phases with specific objectives
and outputs. The outputs of each phase are directly linked to the inputs of the subsequent phases with an appropriate level
of management review at each phase completion. Each phase's is part of a linked cause and effect chain. A desired change
of the output of any given phase can only be accomplished by a change to its inputs. Thus at any point in the linked
process, a determination that an applied constraint is being violated will kick off a disciplined process of backing up the
chain until a suitable remedy is found that will effect the desired change.
The eight phases are; 1) Design Baseline, 2) Size Baseline, 3) Environment Baseline, 4) Baseline Estimate, 5) Project
Estimate, 6) Risk Analysis, 7) Determine 'Best Estimate', 8) Collect & Use Historical Data.
8. Kandaswamy S. The basics of Monte Carlo simulation: A tutorial. PMI Seminars and Symposium Proceedings 2001;
2001 Nov. 1-2001 Nov. 10; AIPM (CDRom). USA: PMI; 2001.
Keywords: cost estimating; scheduling; simulation.
Abstract: The Monte Carlo simulation method is a very valuable tool for planning project schedules and developing budget
estimates. Yet, it is not widely used by the Project Managers. This is due to a misconception that the methodology is too
complicated to use and interpret.
The objective of this presentation is to encourage the use of Monte Carlo Simulation in risk identification, quantification, and
mitigation. To illustrate the principle behind Monte Carlo simulation, the audience will be presented with a hands-on
experience. Selected three groups of audience will be given directions to generate randomly, task duration numbers for a
simple project. This will be replicated, say ten times, so there are ten runs of data. Results from each iteration will be used
to calculate the earliest completion time for the project and the audience will identify the tasks on the critical path for each
iteration. Then, a computer simulation of the same simple project will be shown, using a commercially available tool. At the
end of these exercises, the audience will be able to appreciate the insight offered by the simulation. They will learn that the
earliest project completion time, yielded by the Critical Path Method is too optimistic. They will also find out that tasks on
the critical path could vary from one simulation run to another.
9. Cook MS. Real-world Monte Carlo analysis. PMI Seminars and Symposium Proceedings 2001; 2001 Nov. 1-2001 Nov.
10; AIPM (CDRom). USA: PMI; 2001.
Keywords: cost estimating; schedule development; simulation.
Abstract: One of the most difficult project management tasks is the estimation of project costs and the development of the
project baseline and budget. This is especially true in industries where each project has a flavor of R&D to it, such as IT,
new product development and environmental remediation. One tool that is available to address the issue estimating costs
under conditions of uncertainty is Monte Carlo analysis. Formerly relegated to the realm of mainframe-based project
management software, current microcomputer-based technologies allow for the real, practical use of Monte Carlo analysis in
Real-world Monte Carlo analysis contd…
many project management applications. However, many project managers are only vaguely aware of Monte Carlo analysis
as a risk analysis and cost estimation technique, and others believe that the technology is too arcane and academic for real-
This paper will counter misconceptions about Monte Carlo analysis by showing how inexpensive and practical Monte Carlo
technologies can be applied to address three common problems faced by project managers who are planning projects with a
high degree of technical uncertainty. The three problems are: 1) How much is it going to cost to complete my project and
how long will it take? 2) What cost and schedule contingency levels should I establish? and 3) What is the benefit of
increasing my budget or extending my schedule?
Part 1 of the paper will address establishing your project baselines. Attendees will learn two techniques for developing
project estimation data (PERT analysis, and establishing bands around high-risk activities)that can be used in a Monte Carlo
analysis, how to use that data to conduct a Monte Carlo analysis, and how to interpret the subsequent output. Part 2 will
address how to use the base data and a project risk assessment to estimate the necessary size of project contingency
reserves (either cost or schedule). Part 3 will address how to address the inevitable senior management questions of
efficiency. Management typically wants to know the effects of proposed cuts to the estimated budget, or acceleration of the
estimated schedule. Practical Monte Carlo technologies provide information to help address these thorny questions, and
attendees will learn how to use their already-generated estimation data to conduct What if? analyses.
At this end of this session, attendees will be able to:
- Develop a set of initial project cost and schedule estimates.
- Use risk analysis to modify the base estimates so that Monte Carlo technologies can be applied to them.
- Establish project baselines using Monte Carlo analysis output.
- Use Monte Carlo output to develop project cost and schedule contingency reserves.
- Use Monte Carlo technologies to conduct What if? and sensitivity analyses.
- Understand what practical automation tools are available to support Monte Carlo analysis.
10. Anbari FT. Applications and extensions of the earned value analysis method. PMI Seminars and Symposium
Proceedings 2001; 2001 Nov. 1-2001 Nov. 10; AIPM (CDRom). USA: PMI; 2001.
Keywords: EVM; earned value management; project cost management; project scope management; project time
Abstract: This paper presents extensions and applications of the Earned Value Analysis (EVA) method. It builds on known
basic formulation and assumptions of EVA and provides enhanced concepts for forecasting project cost and time at
completion for improved decision making. The paper presents graphical tools for improving the understanding of project
performance trends that would support improvements in planning, scheduling, estimating and control of project schedule
and cost. The comprehensive treatment of EVA in this paper would encourage software developers to provide enhanced
functionality of their software packages by applying the formulations presented in the paper in future releases of their
software. The concepts and specific tools in this paper would enhance implementation efforts of private and public
11. Swikael O, Globerson S, Raz T. Evaluation of models for forecasting the final cost of a project. Project Management
Keywords: Cost performance index - Earned value - Cost control - Forecasting.
12. Zou DX. An economic Shewhart control chart adjustment strategy for the twenty-first century PMBOK quality
management education. Connections 2000. PMI Seminars and Symposium. Proceedings; 2000 Sept. 7-2000 Sept. 16;
AIPM (CD-Rom). USA: PMI; 2000.
Keywords: PMBOK; cost control - mathematical models.
Abstract: Shewhart control charts are widely used to display sample data form a production process. They are used to
indicate whether a process is in control. They have also been found valuable in evaluating process capability, in estimating
process parameters, and in monitoring the behavior of a production process. A control chart is maintained by taking
samples from a process and plotting in time order on the chart some statistic computed form the samples.
Control limits on the chart represent the limits within which the plotted points would fall with high probability if the
operating in control. A point outside the control limits is taken as an indication that something, sometimes called a special
cause of variation, has happened to change the process. When the chart signals that a special cause is present, rectifying
action is taken to remove the special cause and bring the process back into control. In what follows, consider the situation
in which the quality of then of the quality variable.
The usual practice in maintaining a control chart is to plot the sample form the process relative to constant width control
limits, say 3-sigma limits. In this paper, a modification to standard practice in which the sampling control limits are not fixed
but instead can vary after the process has operated for a period of time is investigated. The basis of choice of control limit
width is a model for the cost of operating the chart. Cost model is developed to describe the total cost per unit of time of
monitoring the mean of a process using both the standard and the generalized Shewhart control chart. The cost model is
developed under the assumption that the quality characteristic of interest is normally distributed with known and constant
The definition of the cost model for the standard Shewhart control chart proceeds in two steps. First, the uniform lifetime
distribution is employed to describe the random variable t, the time until a process shift. It is assumed that the process is
An economic Shewhart control chart adjustment strategy contd…
subject to a shift from the in-control value of the process mean, m , to an-out-of-control value, m , at a random point in
time. Then, the cost of operating a standard Shewhart control chart is defined using four cost terms. They are, (1)
Inspection cost; (2) False alarm cost; (3) True signal cost; (4) Cost of producing additional non-conforming items when the
process is out-of-control. In addition, the expected cycle length is determined. Then the expected total cost per unit time is
constructed as the inspection cost plus the ratio of the sum of the three expected costs to the expected cycle length. The
definition of the corresponding cost model for the generalized Shewhart control chart proceeds in a similar manner.
13. Raz T, Elnathan D. Activity based costing for projects. International Journal of Project Management 1999;17(1):61-7.
Keywords: Cost estimating.
14. Diekmann, James, Featherman, David, Moody, Rhett, Molenaar, Keith, and Rodriguez-Guy, Maria. Project cost risk
analysis using influence diagrams. Project Management Journal 27(4), 23-30. 96.
Keywords: Monte Carlo; risk management; influence diagrams; external risks; internal risks.
Abstract: This paper summarises new techniques developed in ongoing research of cost risk analysis for the US
Department of Energy (DOE) environmental remediation projects. This paper is based on the working premise that risks can
be classified into two categories: internal or external. Internal risks are those that are inherent to a specific project. They
usually affect items in the project cost estimate; internal risks can be evaluated using standard Monte Carlo approaches.
External risks can influence the project cost but are not found in the cost estimate. These risks can include regulatory
changes, scope changes, and public involvement. To model external risks, variations of Monte Carlo models and Influence
Diagramming techniques were tested. This paper describes the various risk analysis formulations that were developed to
evaluate both internal and external project risks. The authors conclude that Influence Diagramming (for external risks),
used in conjunction with Monte Carlo methods (for internal risks), were best for evaluating cost risks for these projects.
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