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Förvaltning av produktportföljen, Förvaltning av projektportföljer, Strategisk planering

Driving Portfolio Value: Advanced Portfolio Management (4 of 5) – Enrich Consulting

Publicerad By Dr. Richard Sonnenblick

This is the fourth post in our series on doing more portfolio management with less project data. You’ll find the whole series here.

Data needed at each level of portfolio management. The only required data are the three items in the orange Beginner box. How much further you go depends on your people, your portfolio, and your goals. See the first post in this series for more information.

Driving Portfolio Value: Advanced Portfolio Management

At this point, you’ve managed to collect key data about all of your projects and created some visualizations that show how those projects measure up against strategic goals and budgets. In other words, you have a good idea of what you are doing. But how do you figure out what you should be doing? That’s advanced portfolio management, and it begins with a clear picture of the value of your projects.

Not everything you work on will be of highest value, and therefore not everything you work on will be highest priority. Valuing your projects will help you prioritize projects and (perhaps more importantly) provide teams with a common understanding of the value each initiative is expected to deliver. The key word here is expected—at least initially, you don’t know if every project will live up to your expectations. For this post, we’ll put aside methods for estimating risk and focus on a few methods for calculating a best guess at likely value. There are a large number of approaches for estimating project value, so many that they represent a continuum of approaches, from simplest and broadest to most sophisticated and detailed, rather than a list. Here, I’ve highlighted a few points on that continuum.


For early-stage projects, or for teams not accustomed to building financial models to estimate project value, the most straightforward method of assessing value is scoring. Value-based scoring is useful because the questions can be used to highlight the types and magnitude of value in your portfolio. New product opportunities have a very different value proposition than manufacturing improvements, for example.

A scoring model asks project teams to assess the project on one or more dimensions by rating it on a five- or ten-point scale. The questions for which teams provide ratings are designed to capture various kinds of project value:

  • Strategic relevance—How many of our strategic thrusts does this project align with?
  • Strategic importance—How essential is this project to specific strategic goals?
  • Technology maturity/risk—Is this technology a small, incremental improvement or a risky but potentially revolutionary step change?
  • Market impact—Is the resulting product likely to spur a dramatic change in customer behavior for a large proportion of the market?
  • Process relevance—Does the process proposed in the project have wide applicability across our manufacturing infrastructure?
  • Yield impact—Does the process proposed dramatically affect yield?

That list should make it clear that the wording of the scoring questions is critical to the success of the scoring model—the questions you ask are the key to differentiating projects. We talked about how to write clear scoring questions here.

Market-Based Metrics

Business case metrics that estimate market attributes provide a more nuanced view of a product’s likely value based on its likely impact in the market. These metrics build on one another, from a simple estimate of market size to a forecast for peak revenue. The forecasting tools you use for these metrics will depend on your industry and level of product innovation; new innovations in nascent markets are the most uncertain, while product-line extensions in mature markets can be based upon historical sales data for similar launches. For early-stage new product development, market characteristics can stand in for potential product value. For example:

  • Market size—Is this a new, growing market or a mature but already large market?
  • Competitive intensity—Are there many competitors already launched in the market?
  • Market share—Can you plausibly achieve a large market share, or will your offering be relegated to a small niche?

Market-based metrics are a great middle ground between more general scoring models and financial models. We know billion-dollar companies that rely on such metrics to drive decisions on which markets to enter and exit and which product lines to expand or discontinue. Other firms that rely on financial metrics for decision making still collect market-based metrics because they underpin the financial analyses that support financial metrics.

Financial Metrics

Market-based metrics are useful for describing the market environment for a project, and hence its potential value. Metrics derived from profit & loss and cash-flow statements can provide another perspective on value. These metrics can include:

  • Peak revenue—Will the product command a price premium and achieve a large peak revenue?
  • Operating profit—After deducting project and ongoing expenses, what remains as project profit?
  • EBITDA—After also deducting a portion of capital investments, what remains as project profit?
  • Income—After taxes and all expenses are considered, what is the net profit?
  • Net present value—On a cash-flow basis, what is the present value of this project’s investment?

Financial metrics are often derided as too blunt or simplistic to be useful in portfolio decision making. We consider them like a book cover: we can’t judge the book by it, but we can decide whether we should read the book. Metrics help guide portfolio conversations to those projects that deserve further scrutiny before funding or deferring.

Choosing the Right Metrics

The calculations involved in financial metrics can be quite involved; not every company will be able to justify their use. Conversely, a simple scoring model will not provide enough detail for some companies. For our clients, we suggest the following considerations in choosing the right value metrics:

  • The preferences of the executive team for market or financial metrics, or a scoring model
  • The ability of project teams to create business cases that can drive a complete financial statement (or some portion of a financial statement)
  • The resources at risk for each project (small projects might not merit the expense and time of a deep financial analysis)

Extensive market and technology uncertainty, in our view, are not valid reasons to avoid a financial model. Business case uncertainties are, on the other hand, excellent reasons to use ranges for inputs rather than point estimates.

If you’ve read this far, or even implemented this far, congratulations. You have a solid foundation in portfolio management practices that you can leverage to make better portfolio decisions today. As your strategic goals evolve and your portfolio grows in complexity, you’ll be ready to expand your portfolio process and the data you collect to inform portfolio decisions, the final post in our series.

At Enrich, we have deep experience helping companies benefit from portfolio management at every level of maturity. From startups with developing patent portfolios to the largest life science companies on the planet, we’ve deployed processes and tools that help them all make better investment decisions. If you’re interested in learning how our tools (Viewport and the Enrich Analytics Platform) can help you level-up on your portfolio management process, drop us a line.

Relaterade inlägg

Skrivet av Dr. Richard Sonnenblick Chief Data Scientist

Sonnenblick, Planviews Chief Data Scientist, har många års erfarenhet av att arbeta med några av världens största läkemedels- och life science-företag. Genom denna djupgående studie och tillämpning har han framgångsrikt formulerat insiktsfulla prioriterings- och portföljgranskningsprocesser, poängsystem samt finansiella värderings- och prognosmetoder för att förbättra både produktprognoser och portföljanalys. Sonnenblick har en Ph.D. och MS från Carnegie Mellon University i teknik och offentlig politik och en BA i fysik från University of California, Santa Cruz.