The Optsee Prioritizer™

While Sensitivity testing allows you to visualize and study the behavior of a portfolio when one attribute is varied over a specified range, the Optsee Prioritizer™ allows you to study the behavior of a portfolio when all of the attribute weights are changed simultaneously, when the project attributes are varied, or when the weights and the projects are changed simultaneously. It performs these analyses by generating up to 100,000 models using a Monte Carlo simulation, and then displays the statistical results in specialized charts and tables. These simulations provide a powerful methodology to determine the impact of the attribute rankings and project uncertainty in a portfolio, as well as the overall sensitivity of the project ranking to changes in the portfolio attribute rankings.

The 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 portfolios and/or portfolios within a set of defined parameters. The application then calculates the average rank, standard deviation, and cumulative percentage ranking for each project in your portfolio.

Three primary types of prioritizations can be performed:

1. Variable Weights: Two types of Variable Weights prioritizations can be executed. In the first type, the attribute rank order of preference is kept constant, i.e. highest-to-lowest weight, but the relative weights of the attributes are randomly varied. This is called a Fixed Sequential Order prioritization. In the second type, both the attribute rank order and the weights of the attributes are varied. This is called a Random Order prioritization. A Ranked Attribute Order prioritization will provide a statistical ranking of the projects, based on how the projects were ranked in up to 100,000 portfolios that maintained the same attribute rank order as the original portfolio. This allows you to see which projects are statistically most attractive with a particular rank order. A Random Attribute Order prioritization will provide a statistical ranking of the projects in up to 100,000 portfolios that have randomly ranked the order of attribute weights. This allows you to see which projects are statistically most attractive when the attributes are randomly ranked.
2. Variable Projects: A Variable Projects prioritization involves generating multiple portfolios using the portfolio where the project attribute values are modified within the % uncertainty of each individual project. For example, if a project has an attribute value of "500" and a % uncertainty of "5" for that attribute, the Variable Portfolio prioritization would create portfolios where that project attribute would randomly vary between "475" and "525" or between 95% and 105% of the attribute value. The random variation can be distributed over a normal Gaussian distribution (bell curve) or a uniform distribution (equally distributed between the maximum and minimum values). Maximum and minimum values outside of the portfolio best and worst attribute outcome constraints are not used in the prioritization. Also note that when a project attribute value is "0" and the % uncertainty for that value is not "0," Optsee® uses a value of 0.001 in the prioritization.
3. Combined: A Combined prioritization combines the Variable Decision Model prioritization with the Variable Projects prioritization such that the portfolios' weights and the portfolio project attribute values are varied simultaneously within the constraints described above.

For more information on prioritization models, see the Prioritization Engine, SMART Prioritization Using Simulations, and the Optsee Prioritizer™ Form.