Help Contents


Quick Start Guide

Portfolio Lists





Bubble Charts

Bar Charts

Ranking Charts

Efficient Frontier Charts

Sensitivity Charts


SMART Prioritization Using Simulations

As you have learned, uncertainty tracking in Optsee® allows you to specify the highest, lowest, and likely values for individual attributes, and automatically calculate the high, low, and likely SMART Scores for each project based on those assigned values and the assigned attribute weights.

In the real world, the actual project attribute values are likely to fall somewhere in-between the highest and lowest values, so the individual project and total portfolio values can be more accurately represented by a distribution of possible outcomes, rather than a single SMART score or a combination of 3 SMART Scores.

The example histograms below illustrate the outcome of a simulation prioritization for an entire portfolio. The chart on the left displays where the median and 90% and 10% limits are located. The chart on the right shows the mean and standard deviations for the portfolio. These histograms are generated automatically.

The SMART Simulation Prioritizer uses a methodology called “Monte Carlo simulation.” Monte Carlo simulation methodology was named for Monte Carlo, Monaco; a city that is famous for its casinos and games of chance such as roulette wheels, dice, cards, and slot machines. Games of chance exhibit random behavior within the context of the game equipment and rules. For example, a shuffled deck of cards will contain 52 cards, but the card order is random.

An Optsee® Monte Carlo simulation involves creating thousands of random attribute weights and/or portfolios within a set of defined parameters. Optsee® then calculates a complete statistical ranking for all the projects in the portfolio and a probability curve of potential outcomes for the entire portfolio.

By combining the SMART Simulation-Prioritization with optimizations, “Portfolio Views,” and Efficient Frontier analyses, you have the capability to compare and select portfolios based on predicted outcomes of optimized portfolio models.

When running a Prioritization-Simulation using variable attributes, the random value assigned to an attribute is determined by the assigned probability distribution curve for that attribute for that particular project and the “Plus” and “Minus” and “Likely” values.

The probability distribution curves for the individual project attributes are set in the project form. There are 3 types of distribution curves to choose from:

The probability distribution curves for the individual project attributes are set in the project form using the pop-up menu in the “Distribution” column.

Distribution curves can not be assigned to Date and Category attributes, therefore, they are labeled “Not Applicable” in the “Distribution” column.

1. Click on “Prioritizer” button on the Portfolio form to open the Prioritizer form.

2. Choose whether to apply the prioritization simulation to the current portfolio or create a new portfolio. Creating a new portfolio duplicates the entire “parent” portfolio including charts and views before prioritizing it. Choosing [Overwrite] will overwrite the current SMART Scores in the current portfolio, and it cannot be changed back into a non-simulated portfolio.

3. Select the simulation parameters in the Prioritizer form.

A progress monitoring form is displayed as the Prioritization Simulation is executed:

Each time a simulation test is performed, the SMART Score and Rank by Score is collected. At the end of the Prioritization Simulation a statistical analysis of the results is performed. These results are summarized in the Simulation Summary Results Form under the “Simulation Results” tab in the new portfolio form:

The columns contain the following statistical results:

You can view the distribution histogram charts in the Distribution List form for each project by clicking on the [Distrib. Chart] button at the top of the form:

You can scroll through the projects using the Distribution List form and open individual projectsin the Distribution Detail form to view the median and mean result details.

You can view and analyze all the data from the statistical summary report using Ranking Charts and Bubble Charts. For example, the ranking chart below displays the median SMART scores, the 90% and 10% limits, the maximum and minimum scores achieved in the simulation, and the “Profit” as bubble area.

You can view the Cumulative Percentage Bar chart for the portfolio by clicking on the [Cum. %] button at the top of the form:

The cumulative percentage is calculated based on the number of times a project was ranked at a specific rank. Projects that were ranked higher more often have higher cumulative percentage rankings than projects not ranked higher often. Cumulative percentage thus represents the strength of a ranking. The Cumulative Percentage Bar chart (above) represents the normalized data from the Cumulative Percentage Line chart.

You can view the Cumulative Percentage Line chart for the portfolio by clicking on the [Line Chart] button at the bottom of the Cumulative Percentage Bar chart form:

These lines can be interpreted as follows: Using Project Pegasus as an example, we can see that it was ranked 9th or higher in 100% of the simulations (Point A). It was ranked 4th or higher in approximately 85% of the simulations (Point B). It was ranked 3d or higher in 60% of the simulations (Point C)., and it was never ranked higher than 3d (Point D at 0%). Because these line charts are complex, the bar chart is the preferred way of interpreting the Cumulative Percentage results.

You can also view the simulation prioritization results in bubble charts.

You can choose what uncertainty data you wish to display in the first tab of the Bubble Chart Preferences form. The display options available depend on the data selected for the X or Y axes.

Re-prioritizing after a Portfolio Change:

Whenever a project or a portfolio attribute is changed, the portfolio’s SMART values and projects rankings should be re-calculated. This is done automatically in portfolios not prioritized using Monte Carlo simulations. In a Simulation-Prioritized portfolio, it requires running the simulation again on the modified portfolio. This can take several minutes for large portfolios, portfolios with many active attributes, and portfolios with many saved Portfolio Views.

Optsee® provides several options: You can have the re-prioritization run automatically each time a change is made or you can run them manually.

Automatic Re-prioritization: A Prioritization Simulation will run after any saved change if the [Automatically Re-prioritize Projects] checkbox in the Portfolio Preferences form is checked. The prioritizer will use the last used prioritization parameters. Note that the portfolio and projects cannot be modified while a simulation is running.

Manual Re-prioritization: A portfolio can be re-prioritized at any time simply by clicking the [Prioritizer] button at the top of the Portfolio form to open the prioritizer form.

Simulation Current Indicators: If a change has been made that effects the prioritization and the “automatically re-prioritize projects” option is off, a red blinking light will appear above the at the top of the Portfolio form and an “X” will appear in the “Simulation Is Current” column in the Portfolio List form:

Portfolio Data Display: You can choose what data you want displayed in the SMART Score columns in the Portfolio form in the Portfolio Preferences form:

Project Data Display: You can choose what data you want displayed in the SMART Score boxes in the Project form in the Project Preferences form:

Next: Optimizing Your Portfolios