Managing Research as an Investment Portfolio: Lessons from PARC
As part of its transformation from an internal research center to a commercial business, PARC has needed to innovate its business practices, as well as its research and technology. How do we balance the seemingly conflicting goals of long-term research vs. short-term profits, of creating breakthrough innovations vs. providing client services, of diversifying research into many markets vs. developing critical mass in just a few?
While PARC has been successful financially, it hasn’t been easy balancing multiple goals in the innovation landscape today—one in which we create business opportunities through technology and services for multiple clients, ranging from global enterprises and government agencies to startups.
One way to handle this balance is by managing research as a portfolio of investments. Together with PARC’s VP of Global Business Development, Tamara St. Claire, I researched, planned, and executed an approach that would work for PARC—and that I believe other technology organizations can adapt to suit their business needs.
The Goals: Holistic View, Sustainable Growth
Unlike portfolio management approaches used in many technology companies, PARC’s Portfolio Management Tool is not based on creating a balanced mix of technologies, products, or markets. Instead, we based our approach on financial portfolio principles such as risk/return and asset allocation. Why? Because it’s difficult to compare relative merits when you have work in so many diverse technical areas.
Furthermore, our portfolio management approach was intended to: 1) provide a holistic view and clear criteria for evaluating PARC’s investments, elevating them above traditional organizational divisions; 2) increase visibility, accountability, and alignment of group and individual decision-making given overall organizational goals; and 3) enable a sustainable, growth-oriented business.
Here’s how we did it.
Step 1: Define the Asset Classes
In financial portfolio asset allocation, you want the right mix of assets to suit your goals. For PARC, our assets are our research staff, and our investments are projects and research programs (a cluster of related projects).
We created four different “asset classes,” or portfolio segments, for categorizing research programs and projects into similar risk/return profiles. We represented this as a 2×2 matrix that distinguishes stages of maturity for technology and market understanding:
Core segment - Programs and projects where we are well known for having deep expertise, and that generate current year revenue with a healthy profit margin.
- RISK PROFILE: Relatively little technical and market risk, some execution risk.
- IMPLICATIONS: Focus program management in this segment on increasing sales, margin, and efficiencies in execution.
Scouting segment - Programs and projects in which we are probing adjacent markets to see if we can adapt our existing technologies.
- RISK PROFILE: High execution risk, some market risk, some technical risk.
- IMPLICATIONS: Conduct market probes faster (“Get to no quickly”)—involving both business development and research investment—to understand whether there are positive opportunities on a risk-adjusted basis.
Next-Gen segment – programs and projects in which we attempt to meet the future breakthrough technology needs of markets based on deep technical understanding and interactions with our clients.
- RISK PROFILE: More technical risk, less market and execution risk.
- IMPLICATIONS: Since it’s often difficult to introduce disruptive technologies to market incumbents, try to introduce next-gen technologies in adjacent markets first to demonstrate their value.
Options segment – programs and projects where we are inventing and developing new technologies to address new, high-potential market opportunities.
- RISK PROFILE: High technical and execution risk.
- IMPLICATIONS: Since risks are high, PARC manages investments in this segment using a phased options approach (much like Real Options), in which we gradually increase investment as we learn new information about the return scenarios and the different types of risks.
Step 2: Propose a Target Allocation Across Portfolio Segments
After you define your asset classes, you have to decide the relative weighting among them based on your financial goals. We didn’t find much public information to benchmark our targets, and most other innovation portfolio management approaches are more descriptive than prescriptive. But if you don’t give yourself a target (even if based on incomplete information), then it’s difficult to know whether you’re making any progress.
Make sure to re-balance regularly as your financial goals change over time. In our case, as Core programs become more efficient and generate more profits that we can reinvest in other segments, we increase the weights in other segments—enabling us to develop a pipeline of future Core programs.
Step 3: Categorize Programs and Projects into Segments
Categorizing programs and projects into the appropriate segment is the most difficult part of the process because the boundaries can be fuzzy. For example, there’s a gray area when adapting an existing technology for a new market—is it new or existing technology? While it’s important to have clear definitions, you also have to make judgment calls based on the intent behind the return/risk categorizations.
Since the asset classes or segments correspond to varying degrees of technical, market, and execution risk, you want to define “existing” relative to your company. PARC defined:
- Existing technology as one where we have a prototype suitable for engagement with commercial customers—otherwise, it’s still considered new technology;
- Existing market as one where we have at least one commercial reference client (which greatly increases the probability of future commercial success)—otherwise, it’s a new market for us, even if the market is already out there.
Step 4: Evaluate Programs and Projects Within Each Segment
The goal of evaluating and ranking programs and projects is to provide insightful data to foster productive management discussion—NOT to do management by spreadsheet.
To promote the intended behavior and avoid unintended consequences from gaming the system, each company will need to think carefully about the specific factors, weights, and definitions used for scoring. You also don’t want to make the process of scoring programs and projects too cumbersome if you don’t have teams of analysts and instead require your line managers to do the scoring.
For us, I created a two-part scoring system with a weighted return score that is then discounted by a risk score. We used ranges of values to normalize all scores between 0 and 3, instead of asking for absolute values. I suggest you do the same, because in any model, a critical mistake is to get a false sense of precision from data calculated from assumptions.
Return factors. After considering many different factors, I ended up with: 1) Projected profit with five-year outlook, 2) Time to revenue, 3) Value of IP, and 4) Business model. Additional factors could be profit margin and alignment with strategy.
Risk factors. To reduce scoring variance, I developed a questionnaire for each type of risk and based on the responses, calculated a score between 0 and 1 for each, corresponding roughly to its probability of success (so a 0.9 technical risk score means 90 percent probability of the project achieving its technical goals):
- Technical risk focused on our ability to achieve the technical breakthroughs or maturity level that was necessary for commercialization.
- Market risk focused on size and growth characteristics of the market, PARC’s degree of competitive advantage, and whether we had just one-shot or multiple opportunities.
- Execution risk focused on our understanding of the market, value chain, and partner landscape, and whether we had gotten market validation yet.
We then multiply the different risk scores to arrive at an overall risk score. In our system, a low score means a high risk. We treat each type of risk as independent, because any one of them can prevent us from achieving the potential return… even if technical risk and execution risk are not strictly independent.
Ranking. Once all the scores are calculated, rank order all programs and projects in descending order and take a look at the results. Do the top ranked and lowest ranked projects seem to make intuitive sense, or is something wrong with the scoring methodology? I guarantee that this data will spark interesting conversations among management and scientists/engineers.
The most productive discussions we’ve had at PARC have been around looking at the lowest-scoring programs and projects to understand why they were at the bottom. For the ones with high return scores and high risk, it was useful to understand which risk factors were dragging the scores down, and to discuss plans for mitigating the risks and improving the score over time. For the ones with low return scores and high risk, we had to think hard about shifting the target offering or market, or transitioning out of the program or project.
Step 5: Actively Manage Programs and Projects to Move Across Segments
A portfolio is not static. Assets evolve and programs and projects should be actively managed to move across boundaries in specific directions. A portfolio approach gives you the visibility and levers to shift your investments across segments over time to meet your target allocations.
In our case, the portfolio management tool also helped us understand the different roles that different programs play within the portfolio, so we don’t ask all programs to be all things. This understanding enabled us to be much more focused and productive in our quarterly management strategic reviews. We moved from one-size-fits-all questioning to tailoring the frequency and relevance of discussion questions for each portfolio segment.
Since we think of the Core segment as our being in “harvesting” mode and all the other segments as our being in “investment” mode:
- Core programs should spawn new Scouting and Next-Gen projects. Because programs in the Core are expected to generate profitable revenue in the current year, our reviews focus more on marketing, pricing, and operations.
- Options projects should move to the Core as they meet their technical and business objectives. Because Options are high-risk investments, we manage them more like ventures: with regular milestones aimed at reducing technical and execution risk, and requiring investment proposals that specify activities and resources needed to achieve the next milestone.
This movement is especially important if some of your segments are overweighted or underweighted. And it’s not a one-way evaluation process—the strategic reviews enable us to optimize each program using data from the portfolio. We try to understand what are the highest value applications for our technology, who are our highest value partners and customers, and what is the optimal order of risks to reduce over time.
Step 6: Measure, Refine, and Re-balance Based on Learning and State of Business
As with individual investment portfolios, it’s important to re-balance and adjust asset allocations annually based on the previous year’s performance, current financial goals, and risk appetites informed by the economic environment.
While portfolio management should be tied to execution and not just treated as a retrospective tool, nothing will be perfect out of the box. So it’s still important to take time each year to reflect on how the portfolio management tool impacts decision-making. Did it result in better decisions and better outcomes? Did it deliver insights that weren’t visible before? Are there decisions that were made that would not have been made before? Too often I see companies make decisions that are either too broad or too local. Decision-making without clear context, criteria, and data is not only difficult but also vulnerable to politics and other negative influences.
Learning and Evolving
Here’s the paradox: as a commercial business, we want predictability in meeting financial goals. As an R&D center, we want to nurture and invest in breakthrough innovation—which is by definition unpredictable in how it will shape our (and our clients’) futures.
To balance these competing interests, we created a novel portfolio management approach. But I should note that the above framework does not account for our early-stage, exploratory projects; these projects are only included when they reach a level of investment that warrants inclusion within the portfolio management framework. Where do we manage these projects then? Locally, within research divisions—because that’s where PARC’s cultural norms (for technical vetting among peers, debating impact of ideas, exploring passions and intuitions, etc.) prevail. This ensures we continually and evenly invest in exploratory ideas. We’ll share lessons learned in managing the tradeoffs between possibility and reality as we continue on our journey.
Portfolio management at PARC is new, and it will evolve as our organization and the overall innovation ecosystem does. While we have borrowed, sometimes loosely, from financial portfolio management principles and adapted them to our needs here, I hope you can draw on our approach and experiences to help you understand your R&D investments more holistically.