Determining portfolio health

According to Cooper et al. (2001) state that portfolio management has the following four objectives:

  • Maximize the value of your portfolio
  • Seek balance in your portfolio
  • Your portfolio must be strategically aligned
  • Pick the right number of projects

DQFunnelThese portfolio management objectives need to be translated into Key Performance Indicators (KPI-s) so they can be efficiently assessed and compared across alternatives. Common KPI-s for value maximization include Net Present Value and Return on Investment indicators. For strategic alignment these include the forecasted coverage of growth targets and geographic or market spread. Current research by Eindhoven University of Technology and Bicore is underway to identify further best practices in KPI-s for portfolio balance. This balance aspect is linked to the risk profile in the portfolio (not all eggs should be in one basket or bucket) and to the funding mechanisms in a business (how much can we afford to fund at each time, and can we fund longer-term innovation from quick wins).

Objective Criteria KPI Advantages Dis(advantages)
Maximize portfolio value Absolute value creation NPV Easy to useInclude the discount rate Less reliable in FFE; Ignores probability and risk; Disfavor long, new-product projects
  ECV Deals with risk and probability of success Less reliable in FFE; Difficult to use (especially under uncertainty)
  Relative value creation PI Creates higher portfolio value than just NPV Takes resource constraint into account Less reliable in FFE; Ignores probability and risk
  IRR Indicate the efficiency, quality of an investment Less reliable in FFE; Ignores probability, risk and cost of capital
  ROI Easy to use; Demonstrate the ratio of money gained or lost Less reliable in FFE; Ignores risk, discount rate, break-even time
  Non-economic value creation Score from models Can be used during whole NPD process; can include more detail in later stages Scaling the criteria is subjective and difficult; Projects should be independent
Balance long-term vs. short projects Reduce risk
  high risk vs. low risk Reduce risk
  across various markets Reduce risk
  across various technologies Reduce risk
  across various product categories Reduce risk
  across various project types (e.g. [..] research) Reduce risk
  Pipeline balance Reduce risk
Alignment with strategy Strategic fit % of projects that fit the strategy Make strategy explicit Qualitative
  Score from models (tech. and market fit) Can be used during whole NPD process; Can include more detail in later stages Scaling the criteria is subjective and difficult; Projects should be independent
  Growth from new products Sales rate from new products Easy to use Measurement after launch
  Turnover from new products Turnover rate from new products Easy to use Measurement after launch
  Sufficiency Growth targets Make strategy explicit Qualitative
Portfolio gridlock Sufficient resources 80% occupation Reduce overload
  Sufficient nr. of projects % projects on hold Reduce overload

Looking at the above objectives reveals the KPI-s should cover value, cost, and risk; in other words they should cover the dimensions of a good business plan or business case. Key to understanding these KPI-s is that they are forecasts: they describe the impact on future costs, value and risk from the portfolio decisions. Since all forecasts are based on assumptions, the quality of the underlying assumptions drives the usefulness of the predictions and hence of the KPI-s. In addition to having the cost, value, and risk values, their drivers need to be identified. Based on the quality of the assumptions about these drivers, the so-called data quality, the reliability of the KPI-s can be included explicitly in the decision-making process.

Data quality will always vary across initiatives, knowing the data quality level is more relevant than striving for all data quality to be high (and not deciding before that).

Decision analysis [ or] as a competence offers practical tools for dealing with the concept of value of information (when does it makes sense to gather more information before deciding) and with sensitivity analysis (how big is the impact if a certain assumption changes?).

The importance of forecasts

The focus on forecasts (and not on actuals) requires a joint agreement on the right forecasting models to be used. The inherent uncertainty of forecasts needs to be included in the decision-making process as well. This may make the collection of detailed operational data less relevant where it does not directly support a better forecast. Top-down or outside-in data also come into play here, such as macro trends in market growth, spending patterns, and cost curves. This makes the portfolio management environment not just about consolidating and choosing between bottom-up proposals.

DashboardIn this context of portfolio forecasts, the role of data about actuals (describing the past and present in terms of spend, resource allocation, progress, market positions, sales volumes, etc.) is mainly to validate assumptions. Checking if the actual spend is in line with the planned spend up to now provides an indication about the reliability of the plan. Significant deviations must lead to an update of the planning and trigger a new portfolio decision cycle. In terms of data quality, one of the indicators of data quality is how well the actual progress aligns with the plan (at project and program level). This is an important contribution, but does not necessarily require a high level of detail in the actuals master data.

In a metaphor comparing portfolio management to driving a car, the main KPI-s are the dashboard indicators (including how long until we need to refuel, estimated time of arrival according to our satnav, and current speed to see if we are driving safely). The forecasts are based on our view through the wind screen, and the actuals are what we see in the rear view mirror.

Personally, I prefer to spend most of my time looking ahead, and periodically looking in the mirror. How about you?