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5 06, 2017

New How-To Video: Portfolio Optimization

By |2017-06-05T14:54:56-08:00June 5th, 2017|Blog|0 Comments

In previous posts, I’ve discussed how to prioritize your portfolio of R&D investments in great detail. Today, I want to focus on prioritization’s flashy sibling, optimization.

Unlike simple prioritization schemes, optimization methods can handle multiple constraints to address competing portfolio objectives. So where a prioritization result may unduly favor later stage, less innovative projects, an optimization will suggest a more nuanced investment plan. In addition, optimization methods easily handle dependencies between lead and follow-on projects and can consider multiple development and go-to-market plans for key initiatives.

This video will take you through the whys and hows of portfolio optimization. We’re using the Enrich Analytics Platform to accomplish the optimization and scenario analysis in a way that lets us easily explore both portfolio-level implications as well as the project-by-project trade-offs suggested by each investment plan. (more…)

30 01, 2014

Project Prioritization is Not Enough: Why No One Uses Optimization for R&D Portfolio Management, and Why You Should

By |2017-05-23T15:42:01-08:00January 30th, 2014|Blog|0 Comments

R&D-driven organizations face the constant challenge of deciding whether to continue funding existing projects and when to start new initiatives. The overwhelming majority of firms will use project prioritization to rank the opportunities as part of that exercise, with a small minority suspecting that optimization is better suited to the task of project selection.

So if is it so well-suited to the task, why isn’t optimization used and how should it be used?

Why (Almost) No One Uses Optimization

Optimization seems like something for hard-core geeks, a method that would be hard to understand and even harder to explain to management. How could we possibly explain something that throws around terms like ‘simplex’, ‘branch and bound’, and ‘simulated annealing’?


14 04, 2012

Efficient Frontiers and Productivity Rankings in Project Prioritization

By |2017-05-23T15:42:08-08:00April 14th, 2012|Blog|0 Comments

Will the real efficient frontier please stand up?

Will the real efficient frontier please stand up?

Each year, we attend many conferences focused on the themes of new product development, innovation, and portfolio management. One of my biggest pet peeves at these conferences arises when speakers discussing project prioritization present a scatter chart with individual projects cumulatively plotted in descending order of bang-for-the-buck (e.g., NPV per dollar of development cost), and refer to it as an ‘efficient frontier’.
(Hint: The faux frontier is the upper graph in the picture)

The graph in question is actually what we prefer to call a productivity ranking. Such a chart features cumulative val

ue (e.g., NPV or eNPV) on the y-axis, and cumulative cost (e.g., cost to launch or cost to the next gate) on the x-axis. The projects are then cumulatively plotted on these axes in descending order of productivity (i.e., value over cost). The projects advance upwards from the origin at a steady pace and then levels out (or trends downwards) as each successively less productive project adds less value and more cost to the overall portfolio.

A true efficient frontier (illustrated by the lower of the two graphs above) is an entirely different beast. Efficient frontiers are built by running multiple optimizations against the set of projects, while varying available resources (development budgets, headcount, or a combination) for each optimization run. The highest value portfolio suggested by the optimizations at each level of resourcing defines the efficient frontier. The result is a curve that may look quite similar to a productivity ranking, but differs in some critical ways.

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