Project Risk Management Tool

Free Webcast This Thursday: “Integrated Project Risk Analysis - Structuring the Model Effectively”

Monday, November 30, 2009 by DMUU Training Team

On Thursday, December 3, 11am-Noon ET, Jay O’Connor will present a free Live Webcast about project risk management.

A project risk analysis is only as good as the model that was used to prepare it. It is critical that the model be constructed to reflect the risks specifically associated with the project. The model must be able to accurately reflect the risks associated with schedule, quantities, cost and the residual unmitigated risk items from the qualitative risk analysis. The model should also take into account the interrelationships and dependencies of these items.
This webcast will address these issues and present examples of how results can vary based on the level of detail used in preparing the risk analysis, and will include the use of @RISK, and @RISK for Project.

Palisade is pleased to host Jay O’Connor’s presentation. With over 25 years of experience in the areas of estimating, planning and quantitative risk analysis for international projects, Jay understands the complexities that are associated with identifying and assessing project risks. His experience includes both the owner’s and contractor’s side of engineering and construction projects. He has worked in the upstream and downstream oil and gas industry sectors and the pulp and paper sector. His career has taken him to the United Kingdom, Japan, Indonesia, Malaysia, Singapore and Australia.

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Allocating Contingencies to Risk Events that were identified in a Risk Register

Friday, October 30, 2009 by DMUU Training Team

In a previous blog, I presented a very simple way to allocate contingencies to uncertain cost elements in the project risk management process. However, that methodology works well when there are not risk events that affect a cost element or a group of cost elements.
A risk event is described by two elements: the probability of occurrence and the conditional impact to the project given its occurrence. For example, we have a risk that describes the possibility of a new regulation. If it occurs, it will increment the cost of group of cost elements by a minimum of 10%, most likely 15%, and a maximum 20%. If the risk does not occur, no impact will be observed. Using a Discrete and a PERT distribution, we can model such risk such as:

When sampling from this distribution approximately only 20% of the time will generate a multiplier with a minimum of 1.1, most likely 1.5 and a maximum of 1.2; in 80% of instances the multiplier will be 1. That means that only 20% of the time the risk will increment the cost of selected cost elements by the multiplier previously described as show in the figure below:

In addition to risk events in our cost risk analysis models, we often use distributions that describe cost uncertainties. These distributions model ranges are mostly in a different order of magnitude. Therefore, the variance will also be in a even greater order of magnitude. For example, the cost of Item 3 modeled using a 3-point estimate (i.e., min 100,000, ML 120,000, and max 150,000) has a variance of 87,698,412.70), while the variance of the risk event is 0.0036.

If we are to distribute the contingency using the % of contribution of the variance method, the risk event that we just modeled will be ignored even though we know that such risk event has an impact that we cannot dismiss. Given this practical scenario, the method of variance contribution will not work appropriately.

As an alternative, we can use a tornado diagram that results from @RISK’s sensitivity analysis. Here we can use the regression coefficients to understand what risk events or uncertainties are affecting the total cost in a more drastic way. In the case that you also incorporated events that represent an opportunity to reduce cost, you will observe that the coefficient is negative; in your allocation calculations you should not consider negative coefficients.

In the figure below you can observe the Regression Tornado. Here risk events and uncertainties are represented in a scale that goes from 0 to +/-1:

Knowing the regression coefficient of each input that affects the total cost in a negative way, we can construct a table and obtain a normalized percent that can be used to distribute contingency. If for example, we have a contingency of $100,000, it can be distributed to each input proportionally to the regression coefficient as shown below.

Some risk management experts do not distribute the entire amount of the calculated contingency. It is common practice to distribute only a percentage of it (i.e., 70%). The remaining amount will be used as a reserve that will handle unidentified risks.

Javier Ordóñez, Ph.D
Director of Custom Solutions

Allocating Contingencies to Uncertain Cost Elements in a Cost Risk Analysis Model

Tuesday, October 20, 2009 by DMUU Training Team

In a previous entry to this blog I discussed how to assess the contingency required in a cost risk analysis study. The next step is to allocate the calculated contingency to uncertain cost elements that drive the variation in the total cost of the project. In this way, the contingency can be better managed and controlled throughout the life of a project.

While reviewing literature on this topic, I found a practical way to do this. This methodology uses the percentage contribution of each uncertain variable (usually 3 point estimate distributions) to the variance of the resulting distribution of the total cost.

To apply this method, we need to report the variance of each input distribution and the variance of the end result. In case that input distributions are independent from each other, we can just add up individual variances to estimate the variance of the total. However, this is hardly the case since correlation between input variables is expected in cost models.

@RISK allows reporting statistics from an input distribution without running a simulation as well as statistics that describe an output. These functions are from part of the @RISK functions library: Statistic Functions> Theoretical and Statistic Functions>Simulation Results, respectively. These functions can be accessed using the fx icon from the @RISK toolbar.

To report the variance of input distributions we can use the RiskTheoVariance and for the output RiskVariance. The construction of the allocation model is shown below.

In the project risk management model above, it can be observed that the % Contribution to the Variance of the Total Cost is calculated as a proportion of the input variance to the total variance. Once these percentages are determined we can use them to allocate the management contingency to each cost element. It can be also observed that the engineering allowance is also calculated, and the decision maker now has criteria to manage and control contingencies.

Javier Ordóñez, Ph.D
Director of Custom Solutions

Contingency Calculation in Cost Risk Analysis

Tuesday, October 13, 2009 by DMUU Training Team

When performing a cost risk analysis study, one of the key results is the amount of extra monetary resources that is to be added to the project cost baseline to guarantee that the budget is not exceeded at a certain confidence level. Good project risk management strategies must take this into account.

After defining the uncertain variables and risk events that affect the cost performance of the project, we can run a Monte Carlo simulation with @RISK to find out what the range of the total project cost is. Simulation results can help us to explain the risk exposure that we have in the total cost of the project. The most popular statistics are the mean (average cost), the most likely cost, and the 10th and 90th percentiles.

To determine the contingency to be allocated to the project, we need to define what confidence level we would like to achieve: The higher the contingency level, the larger amount of contingency needed. For example, in the figure above, we are reporting the total cost of the project. Here we can observe that we are showing the 85th percentile that corresponds to a total cost of $7.8M (right delimiter). We can say that there is only a 15% chance that we will exceed $7.8M, or alternatively, we have an 85% chance that the total cost will be less than or equal to $7.8M. In the same figure we can also see that the 90th percentile of the total project cost is $8.02M. We can say then that in order to increase our confidence level from 85% to 90%, we will need to add $220,000 to the total cost.

The calculation of the contingency is then accomplished by using the base cost estimate (BE) before the risk analysis was implemented, and the expected cost (EC) of the simulated results.

Some practitioners separate the contingency into two components: engineering allowance, and management contingency.

Engineering allowance (EA) is the difference between the expected cost and the base estimate:

EA = EC – BE

Management contingency (MC) is calculated using the difference between the cost at certain confidence lever (Cp) and the base estimate:

MC = Cp – EC

In our example, our BE = $6.5M; therefore, engineering allowance EA = EC – BE = 6.86M – 6.5M = $0.36M.

For the calculation of management contingency, we use a confidence level of 85% so Cp(85%) = $7.8M; therefore, MC = Cp – EC = 7.80M – 6.86M = $0.94M.

In many situations, the suggested contingency might be excessive, so the need for a mitigation study is necessary. We can use the sensitivity analysis tool in @RISK to detect the key drivers affecting our total cost. This is valuable information so that we can concentrate our efforts in reducing the impact of risk events and uncertainties to the total cost. Below, we see a tornado graph with the most important drivers. The analyst will then explore the appropriate mitigation strategies and assess their implementation cost. A second simulation can be run to assess the effectiveness of the proposed mitigation plan, and compare the pre-mitigated and post-mitigated cost distributions.

In following blog posts, I will explain how to distribute the assessed contingency to cost elements and identified risk events in project risk management models.

Javier Ordóñez, Ph.D
Director of Custom Solutions

Risk Analysis Paralysis

Friday, August 7, 2009 by Holly Bailey

About a week ago, consultant James Yoakum posted a blog asking if the enormous amount of attention business pays to risk analysis has resulted in a state of "decision paralysis." Yoakum is a leadership coach who specializes in project risk management, operations risk, and analytical thinking.

What exactly is decision paralysis? According to Yoakum, it is a point in the decision process where the opportunity cost of further analysis is greater than its potential benefit. Here he's thinking in terms of decision evaluation involving too many variables, too much data, too many twists and turns, and--for him this is the killer--too great an emphasis on perfection. In short, unnecessarily complicated thinking. The result, he says, is too often a postponed decision, procrastination.

It's not hard to imagine a project management team swamped in just this kind of muddle. But how can that team figure out when good enough is good enough?

Doesn't this bring us back around full circle to apply risk assessment to the decision analysis process? And isn't this what a tautology is?

I'd love to hear from any of you who have experienced analysis paralysis and to have your thoughts on how to know when more analysis becomes too much analysis.

Palisade Conference Provokes Six Sigma Buzz

Friday, November 21, 2008 by Steve Hunt

Last week, Palisade Corporation held its North American User Conference; it was a very successful event that brought together @RISK Users from around the world. Presentations and discussions touched on topics such as the subprime mortgage crisis, financial risk management, modeling flu, project risk management and of course, the ways to Monte Carlo simulation in Six Sigma.

It was great to see such a high level of interest in the Six Sigma related presentations and buzz they created in both the social networking opportunities as well as the feedback forms that were submitted after the conference. This shows despite the economic difficulties and the natural tendency to eliminate all unessential spending, Six Sigma and Design for Six Sigma is rightfully viewed as part of the solution.

SigmaFlow’s president Jay Holstine, presented Process Mapping for Knowledge Transfer: Doing More with Less. A very pertinent topic in today’s economic times, which will be presented live as an ISSSP Focused Session on November 25 at 2pm EST. Please join us.

Ed Biernat from Consulting with Impact led a presentation on the use of Six Sigma in Process Industries. If you are interested in viewing his presentation, Lean Six Sigma Applicatin of @RISK Part I, it can be viewed online. Part II will be live on December 12, 2008 at 1pm EST where he will dive deeply into the use of @RISK in this case study. Please join us.

A recent article, Executives Switch to Survival Mode, in the Wall Street Journal indicates that two of the top issues in crisis management can be managed with a strong Lean Six Sigma program, these were:

Excellence in Execution – Whether on the shop floor or in administrative processes, there is no longer room for inaccuracies or waste.
Speed, flexibility and adaptability to change is another area where a strong Six Sigma program mitigates the effects of crisis.

The interest at our User Conference in exploring the use of @RISK to reduce project cycle times and costs indicates to me that smart business leaders are looking to reduce risks and strengthen their companies during this time of crisis.

Project Risk Management in Six Sigma

Thursday, October 30, 2008 by Steve Hunt

The main limitation for any business activity when it comes to improving efficiency is time. A project typically has a definite start and end date, which means any improvement initiative has to be focused within that limited time period. This translates to working in parallel with the activities of a project as they actually happen. The second limitation is budget. Besides completing the project on time, can we do it on budget?

As defined by the Project Management Institute, "Project management is the application of knowledge, skills, tools, and techniques to project activities to meet project requirements".

The DMAIC and DFSS approaches tend to focus on controls for the improvements, process and product development, not the control of the project management process. This division between Project management and project improvement/development is fine if the project has a Project Manager, preferably a PMP, and a Black Belt, DFSS or DMAIC. But the reality is we are positioning our Black Belts as project leaders, and most times both roles are performed by the Black Belt. This approach is acceptable only if the BB is apt at both. In both DMAIC and DFSS training classes the focus is typically on the tools used to complete the project. Little (if any) time is spent on project duration or cost predication and management. Critical decisions must be made based on the probability of completing on-time and on-budget. If these probabilities are too low, the project may need to be redefined or even scraped

One excellent tool the certified PMPs use is @RISK for Project, which uses Monte Carlo simulation to show you many possible outcomes in your project – and tells you how likely they are to occur. This means that you finally have, if not perfect information, the most complete picture possible. You can determine which tasks are most important, and then manage those risks appropriately. It can help you choose the best strategy based on the available information, which is why many PMPs and large companies with in-house training programs are standardizing on Palisade Corporation’s @RISK for Project for project management.

A good article to read to learn more about Integrating Project Management into a Six Sigma System is located in iSixSIgma’s Library and was authored by Daniel Zucker