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It’s Going to Cost You: Intermediate Portfolio Management (3 of 5) – Enrich Consulting

Veröffentlicht By Dr. Richard Sonnenblick

This is the third 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.

It’s Going to Cost You: Intermediate Portfolio Management

Beginning portfolio management is all about finding out what you have. Cost is the next frontier; after all, portfolio management is tricky (and necessary) because you have more to do than your people, facilities, and funds allow. Determining cost is not just about collecting receipts and purchase orders; it’s a complex set of questions about what’s currently being spent and what’s likely to be spent based on the forecasted needs of projects in progress and projects the company would like to take on. At this stage, portfolio management can help answer a range of questions about cost:

  • How much are you spending on each project in the current budget cycle?
  • What is the spending forecast by budget cycle through the life of each project, up to and including launch/completion?
  • What are the skills (or specific people) needed for each project? When will they be needed and for how long?

Answering these questions requires a variety of data sets, from relatively easy-to-acquire data about current spending to complex forecasts about project needs. All of it is valuable. Even the first level—a simple estimate of current spending for each project—will help you assess the alignment of resources with strategic goals and better understand where your people, funds, and attention are focused.

As with basic project information, if you’ve never collected cost data across your portfolio, it’s likely that project teams don’t have a single view of project cost or how it should be defined. To arrive at a comprehensive view, you’ll have to define and communicate data guidelines for a range of factors:

  • Loading of costs (Are G&A included? Staff costs? What is not included?)
  • Cost categories or types of costs being tracked
  • Timeframe for costs to include (fiscal/calendar, month/year)
  • Currency for cost reporting
  • Inflation and discounting adjustments being tracked

Before they embark on portfolio management, many companies don’t require project teams to track costs and staff time in a consistent manner, so it may take some time to get everyone reporting their costs consistently, in a way that allows them to be summed and compared. To get things going, you might need to meet with each project team separately to understand how to interpret the numbers they’ve provided, and to demonstrate how their numbers should be reported under the new scheme. It might be slow going at first, but it’ll pay dividends for the entire company over time.

The innovation landscape, which we described in the last post, can convey current spending on projects via the size of the bubble representing the project. This representation gives a rough impression of relative spending, highlighting projects with much higher than average project spending. A Pareto diagram (see the figure below) can show relative spending even more clearly. In the example, the projects are sorted in decreasing order of cost; a Pareto line cumulates the cost from left to right. In other words, read on the right-hand axis scale, the Pareto line shows just how much of total spending is consumed by the most costly projects. The dotted marker line at 80% of total spending graphically demonstrates that just about 25% of the projects in this portfolio are eating up 80% of the total spending.

Pareto ranking of a portfolio, illustrating both relative and absolute project cost

Another valuable view shows the costs incurred by project type, to help illuminate where the company is investing its research and development dollars—by product area, by geographic market, or by any other classification you’ve implemented in your project inventory. In the table view below, we’ve rolled up project costs by product area, to show both current year costs and total cost to launch, as well as the number of projects involved. This data can be overlaid on the innovation landscape, below, to quantify not just the number of projects, but also the total spending in the current budget for each priority and product area.

Table showing total costs, both current and projected, by product area

Innovation landscape with cost data. This visualization gathers up number of projects and costs into one image, to show how spending tracks with priorities.

 

All of these examples show how adding cost elements to your portfolio dataset allows you to answer critical questions about where money is being spent and how (or whether) spending aligns with strategic goals. These are important questions; even if you stopped here, your portfolio management process would yield healthy dividends. If you’re ready to get even more sophisticated and consider project value, move on to the next post, on advanced methods.

At Enrich, we have deep experience helping companies benefit from portfolio management at every level of maturity. From startups with early-stage research 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.

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Geschrieben von Dr. Richard Sonnenblick Chief Data Scientist

Dr. Sonnenblick, Chief Data Scientist bei Planview, verfügt über langjährige Erfahrung in der Zusammenarbeit mit einigen der größten Pharma- und Biowissenschaftsunternehmen der Welt. Dank des im Rahmen seiner Arbeit gewonnenen Wissens hat er erfolgreich aufschlussreiche Priorisierungs- und Portfoliobewertungsprozesse, Scoring-Systeme sowie finanzielle Bewertungs- und Prognosemethoden zur Verbesserung von Produktprognosen und Portfolioanalysen entwickelt. Dr. Sonnenblick hat einen Ph.D. und einen Master in Engineering and Public Policy von der Carnegie Mellon University sowie einen Bachelor in Physik von der University of California Santa Cruz.