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Project Valuation Metrics

July 15, 2010 | Author: PM Hut | Filed under: Project Portfolio Management

Project Valuation Metrics
By Miley W. Merkhofer

Once you have a project-selection decision model, it is easy to specify metrics. The desired metrics are “observables” that influence the model’s value drivers; that is, those project characteristics and impacts (i.e., model parameters) that have the greatest influence on value. These typically include forward-looking financial metrics, like NPV, but also factors and considerations on value paths that don’t directly impact cash flows. Building a decision model leads to metrics that capture the variety of ways that projects contribute value.

In my experience, a well-designed decision model will identify metrics for measuring some or all of the following types of value:

  • Financial value. Metrics are needed to account for any increases in revenues or reductions in costs that may result from conducting a project. Typically, the appropriate financial metrics are the incremental cash flows attributable to conducting the project, or standard financial metrics that are derived from such cash flows (see the next subsection for more discussion). Revenue generated from projects to develop new products is often estimated with the aid of sub-models that simulate the various development and commercialization stages, in which case metrics indicating the likelihood of success at each stage are also included.

  • Health, safety, and environmental (HS&E) value. If some projects impact the health and safety of workers or the public, or the natural environment, metrics may be needed to account for such impacts. The field of risk assessment provides many well-established metrics for this purpose. Typically, the desired metrics indicate the scope, nature, likelihood, and seriousness of the health, safety, or environmental impacts.

  • Customer value. For organizations that sell or otherwise provide products and services, metrics for estimating the impacts of projects on customers are needed. The field of economics provides various metrics for measuring customer value, including the concept of consumer surplus. The field of market analysis has developed numerous customer-satisfaction metrics. Another common approach is to adopt metrics that describe specific product or service characteristics that customers care about (e.g., attributes of product and service quality and price). such models may include custoemr choice and market penetration models for predicting the dynamics of new sales.

  • Stakeholder value. In additional to customers, organizations typically have other stakeholders whose attitudes and perceptions are important and should be managed. Examples include organized labor, regulators, business partners, etc. The relevant metrics typically depend on the type of stakeholder and ways in which the attitudes of those stakeholders are typically expressed or impact the interests of the organization. Oftentimes, metrics for shareholder value consist those metrics needed to identify and characterize the importance of the concerned stakeholder group and metrics that indicate the anticipated reaction of those groups.

  • Mission value. Public sector organizations aren’t the only ones that have missions. Many private sector organizations have adopted mission statements that identify goals beyond maximizing shareholder value. The appropriate metrics in this case measure the contribution of proposed projects to the achievement of these goals.

  • Community socio-economic quality. Some organizations conduct projects that significantly impact local communities. For example, a project to build a new manufacturing plant might have a significant impact on community jobs. The fields of sociology and economics provide numerous metrics potentially useful for such situations.

  • Option value. Consistent with the theory of real options, projects that contribute to the organization’s platform for future success provide a source of value. For example, an IT (information technology) project may give the organization new capability. An R&D project may provide new knowledge or understanding important to the business. Also, a project may have a distinct, strategic value. Metrics may be defined that capture these additional sources of value. The key is not to double count.

As suggested by the above, numerous metrics may be needed to capture all of the potential components of project value. If metrics representing some sources of value are omitted, the value of projects will be underestimated. Furthermore, there will be a bias against doing those projects that provide the types of value that are not captured due to the omitted metrics. Note that metrics need to represent timing, that is, when the project benefits are likely to occur and how long they will persist, and, oftentimes, risks (e.g., the likelihood that the project will actually produce its anticipated benefits). When all such metrics are specified, the decision model defines the algorithm that allows the value of a project to be expressed in dollar terms.

Obviously, there is a limit to how many project evaluation metrics should be used. The 80/20 rule applies. The goal should be to include the minimum number of metrics necessary to roughly capture every significant source of project value, not numerous metrics that more completely capture just a subset of components of value. In other words, don’t make the mistake of defining multiple, potentially redundant metrics for capturing things that are relatively easy to address (like financial value), while omitting metrics for something that may be important but hard to address (like impact on learning and capability). Since few if any projects will provide significant contributions under each type of value, having lots of metrics doesn’t necessarily create a significant burden for evaluating proposed projects. Estimates need only be provided for the subset of metrics that are relevant to capturing the specific motivations for doing that project.

Miley W. (Lee) Merkhofer, Ph.D., is an author and practitioner in the field of decision analysis who specializes in assisting organizations in implementing project portfolio management. He has served on advisory panels for several government agencies and has received grants and research awards for work in the area. Lee is an editor of the journal Decision Analysis.

Prior to becoming an independent consultant, Lee was a Partner of PriceWaterhouseCoopers, where he founded that organization’s capital allocation and project prioritization business practice. Lee is a founding partner of Folio Technologies LLC, a provider of web-based, project portfolio management software.

Lee received his Ph.D. in engineering economic systems from Stanford University. He is the author of the book Decision Science and Social Risk Management and co-author of the book Risk Assessment Methods..

Additional papers on project portfolio management can be found on Lee’s website, www.prioritysystem.com. E-mail: lmerkhofer@prioritysystem.com.

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