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The Optsee Optimizer™: Solving the Rubik's Cube Of Project Selection
A Rubik's cube has billions of possible configurations.
And so does any portfolio containing more than 30 projects. Optimization involves finding the most valuable set of projects to work on within a set of constraints such as limited funds, resources, and time.
Trying to optimize a portfolio of dozens of projects simultaneously can quickly become a rat's nest of conflicting priorities, deadlines, project dependencies, and budget constraints. Competing agendas and objectives, poorly defined priorities, and insufficient metrics often lead to meetings and analyses that take way too much time and end up with results that aren't even close to optimal.
For example, suppose you have 36 worthy projects that would cost a total of $50 million and would require 30 full-time employees, but you only have a budget of $20 million and 22 full-time employees. And what if you want to have a minimum ROI of 15%? There are more than 68 billion possible project combinations!
So how do you choose?
Actual Video of Optsee® Optimizing a 36 Project Portfolio (over 68 billion possible
combinations!) in just 27 seconds
The built-in Optsee Optimizer™ can get this optimization done in minutes, and give you a rigorously optimized result. At the end of the process, you'll know exactly what your top projects are and why.
The Optsee Optimizer™ has three optimizers built-in including a proprietary genetic optimizer, a "fast" optimizer, and a "brute force" optimizer (for small portfolios). These optimizers can find an optimized set of projects or choices that satisfies up to 30 different constraint limits, including minimum, maximum, and average constraints. You can also maintain dependencies between projects. The Genetic and Fast Optimizers allow you to quickly and efficiently test millions or billions of possible combinations of choices, if necessary, to determine an optimized set that meets your constraints. You can also save the results of different optimizations to compare different constraint combinations.
For example, one of our pharmaceutical clients was trying to prioritize and manage more than 60 tech services projects from different departments such as Regulatory Affairs, Manufacturing, and Research and Development. His project lists changed daily (sometimes hourly!), and the departments often thought that their latest project should immediately become his top priority. So he and his staff were constantly fire-fighting, switching from one project to the next, and never certain that they were working on right set of projects for the business as a whole.
We have shown him how to prioritize and apply the right business metrics to his project list leading to optimized capital and resource allocation, transparent prioritization, and a much more productive organization.