INNOVATION PORTFOLIO ARCHITECTURE—PART 2: ATTRIBUTE SELECTION AND VALUATION
A “sufficiently simple” valuation philosophy quickly identifies the most valuable concepts in an innovation portfolio while minimizing analytical time and cost.
Scott Mathews is a Boeing Technical Fellow and technical lead for the Business Engineering initiative within the Chief Engineer’s office of the Boeing research and development division. At Boeing, he provides technical consulting to business units for investment and risk models for new products, strategically significant projects, and innovation portfolio management. He is an internationally recognized expert in complex financial and investment decision modeling that features real option valuation, holding more than 20 patents and patents pending in the field. For the past 20 years, Scott has been engaged in stochastic modeling, capital markets investment, and financial and strategic analysis. Scott chaired the session on portfolio management at the 2010 IRI Annual Meeting; this article is adapted from his forthcoming book, Business Engineering: A Technologist’s Handbook on Integrating Financial and Systems Engineer, to be released in 2012 as part of the Springer International Series in Operations Research and Management Science. firstname.lastname@example.org
OVERVIEW: This article presents a “sufficiently simple” valuation philosophy that quickly elicits the most valuable concepts in an innovation portfolio while minimizing analytical time and labor cost. Extending from the initial article, published in the November/December 2010 issue, this article focuses on three areas: 1) accommodating the nonlinear progression of early-stage concepts within an innovation portfolio through the use of phases rather than staged gates, 2) selecting a minimum set of uniform and broadly comparable attributes, and 3) calculating value metrics to assess the relative value of individual concepts in the portfolio. Real option methods provide the overall philosophy for capturing the relationship of concept value, investment, and risk.
KEY CONCEPTS: Innovation portfolio, Strategic portfolio, Portfolio management, Real options, Datar-Mathews Method
As opposed to a project portfolio, which is focused on managing and delivering projects in development (Cooper, Edgett, and Kleinschmidt 2000, 2002b), an innovation portfolio is used to analyze and manage early-stage concepts (O’Connor and Ayers 2005; Paulson, O’Connor, and Robeson 2007; Rosenø 2008). Unlike a project portfolio, which is managed primarily as a linear process and which typically has a low attrition rate, an innovation portfolio is a complex and emergent process, in part because of the high uncertainty regarding the concepts included in the portfolio and consequent high attrition rates. The innovation portfolio architecture must help shape an evolving strategy leveraged by small, incremental investments in the most promising concepts and concomitant abandonment of other, less promising or less relevant concepts at each phase of analysis (MacMillan 2002; Cooper and Edgett 2007).1
Why is an innovation portfolio necessary? Couldn’t a knowledgeable manager simply select the best concepts and dispense with the portfolio process, saving time and money? In fact, there is significant value for early-stage, unproven concepts in having a structured decision-making process coupled with transparent investment justification (Terwiesch and Ulrich 2008). An innovation portfolio process can increase the number and quality of innovative concepts entering the downstream project portfolio. And the process has another important benefit: the generation and substantiation of a matured strategy that emerges from the innovation portfolio’s internal decision-making processes, significantly improving the likelihood that the project portfolio will produce successful outcomes (Say, Fusfeld, and Parish 2003).
To achieve this goal at minimal cost, the innovation portfolio process must provide a rapid, unbiased decision-making structure that quickly identifies the most valuable concepts among the hundreds that may originate in ideation events or through other mechanisms. The innovation portfolio model we have developed at Boeing accomplishes this through the generation of simple metrics based on a minimal set of relevant, measurable attributes that allow widely divergent concepts to be effectively compared while being funneled through three phases of analysis. Real-option methods provide the overall philosophy for selecting attributes and a guide to the metrics of valuation. These same attributes simultaneously provide for other valuation approaches, such as net present value (NPV).
Designing the Qualitative and Textual Input User Interface
A few software companies offer strategic valuation portfolio systems, which differ from the many project portfolio offerings. These strategic valuation systems focus on strategic decisions, and their usage is in the hands of a relatively small number of managers and skilled analysts who provide arm’s-length assessments coupled with business modeling savvy that assures that the portfolio data is independent, unbiased, and reasonably perspicacious. These systems must manage very early-stage concepts that have scant quantitative data. Most strategic valuation portfolio systems solve this challenge by using scored qualitative (quasi-quantitative) assessments augmented by weighting mechanisms. In many respects, these methods are similar to well-known AHP techniques, much like the IRI anchor scales (Scriven 2001). This approach works well for managers who understand the concepts and the strategic field. However, when the strategic portfolio is integrated with ideation events and there are scores or hundreds of concepts to evaluate, time constraints prevent most managers from making informed assessments.
The portfolio system developed for Boeing retains this quasi-quantitative phase for those managers who are comfortable with this approach. However, an additional utility of this initial phase, which is termed Phase 0, is that it provides a very low threshold for the initial attribute data capture while allowing an analyst to rapidly assess the many raw concepts arriving from ideation events and other sources. Phase 0 is a coarse screening process in which qualitative attributes are assigned to candidate concepts. The qualitative attributes are scored and occasionally weighted to yield a preliminary concept ranking. The ranking serves to bound the field of concepts considered acceptable for further analyses. Because qualitative attributes rapidly exhaust their ability to differentiate among concepts, qualitative assessments generally have a very limited shelf life within the portfolio and are quickly subordinated by quantitative approaches as the concept moves into Phase 1. Regardless, the portfolio is able to mix qualitative (Phase 0) and quantitative (Phases 1–3) data as part of its analytic capabilities.
The user interface also provides text fields to capture concept contextual data, including
- Concept description,
- Value proposition,
- Industry/market/competitor trends,
- Related technology/product trends, and
- Major uncertainties.
Sometimes this information is captured during the ideation process, and it can be transferred automatically to the portfolio. Otherwise the analyst can enter and alter it at any time in the portfolio process.
Another important group of textual inputs captures the key assumptions that align with and substantiate every scenario value input. For example, documented assumptions about the range of market growth rates should support the optimistic, most likely, and pessimistic revenue estimates. In this way, the key assumptions provide insight into the risks and opportunities presented by a particular concept.
The analyst is also able to attach categories and tags to the concept by selecting from several lists under headings such as Business Segment, Technology Area, or Customer, for instance. The categories are closely tied to the various enterprise alignments of the business unit. These categories are used later to help the portfolio analyst sort and arrange the concepts within the portfolio.
Each of these fields is completed briefly as the concept enters into the innovation portfolio. These text descriptions are revisited and revised frequently in later phases as the concept is matured and promoted and sometimes morphed and merged with other concepts. These descriptions provide the innovation portfolio manager the story of the concept’s evolution from a simple idea to a mature concept ready for promotion into the project portfolio.
The innovation portfolio design incorporates some of the latest technology in the fields of complex adaptive systems and real option models. Boeing is still experimenting with this portfolio model, which has been evaluated as an operational prototype.
Structuring the Innovation Portfolio
The innovation portfolio architecture is aligned with and designed to accommodate the nonlinear innovation process. A structure of phased information transitions accommodates the complex and emergent process of maturing concepts within an innovation portfolio. The phase structure emphasizes that collecting and organizing information about an innovative concept is as important as tracking its maturation. The information threshold for each phase is set as low as possible so as to minimize the resources required to gather the data necessary to derive the value metrics needed for decision making. The type of information to be gathered is defined by the set of attributes that form the basis for strategically differentiating among the concepts in the portfolio.
Phases differ from “gates,” the stop/go locks of project management oversight (Cooper, Edgett, and Kleinschmidt 2002a), in that, although phases are numbered, concept transitions from one phase to another may not adhere to a linear progression, and the transitions do not require a go/no-go decision. Staged gate reviews are unwieldy in the context of an innovation portfolio that includes small investments in scores of constantly evolving concepts. Rather than an alignment of gates to be hurdled, phases can be understood as information-gathering steps. Sequential phases elicit increasing concept information detail and, in general, a concept progresses through the phases as it is subjected to increasing scrutiny. Unlike the linear, sometimes plodding, progression of the gated management process common for project portfolios, concept detail can be added in the innovation portfolio at any time in any order. The phased structure helps ensure efficient use of the analyst’s time, partitioning the information as it becomes available. Analysts provide independent assessments of the concepts, assuring that the information in the portfolio is current and unbiased.
The innovation portfolio architecture is aligned with and designed to accommodate the nonlinear innovation process.
The innovation portfolio has four phases, each characterized by increasing detail in concept information (Figure 1). An ideation event produces scores of ideas. Subject matter experts cluster these ideas into loosely coherent business or product propositions, called concepts, which are then passed along to the innovation portfolio for analysis. Phase 0 provides an optional initial coarse screening in which qualitative attributes are assigned to candidate concepts. Contextual and category information about the concept may be gathered at this or any later phase (see “Designing the Qualitative and Textual Input User Interface,” p. 40). Phase 1 gathers the initial quantitative information at the level of rough order-of-magnitude (ROM) estimates for each of the attributes. Phase 2 requires more detail, in the form of range values in which three scenarios are explicitly delineated. Finally, Phase 3 provides a format for estimating annualized cash flows for all scenarios; these estimates then become the initial elements for the concept’s business case.
Figure 1.—An innovation portfolio provides a four-phase structure to help analysts manage the nonlinear behavior of new concepts as they mature through an emergent strategy.
New concepts entering the portfolio have very little definition and are accompanied by only the scantiest of information, almost none of it quantitative. Recognizing this, Phase 1 provides the lowest information threshold for an initial quantitative assessment, requiring only a ROM estimate from a list of values arrayed along an exponential scale. The exponential scale spans the wide range of possible attribute values at this early stage while also providing a natural segmentation by scope of opportunities. This “bucket,” or approximate, valuation allows Phase 1 data to be gathered and assessed quickly and inexpensively. As an example, the selection for revenues might be “>$600M” with the next value on the scale at “>$1.3B,” followed by “>$2.5B.” The portfolio records as the revenue estimate the mean value of the range; for the range between $600 million and $1.3 billion, that would be $900 million. Paired with a rough operating profit margin of, say, 10 percent, this calculates the base case, or most likely value, for Phase 1 operating profits, yielding an estimate of $90 million (Figure 2).
Figure 2.—Data types of the three phases illustrating the scenario architecture. Example is for TRL 3 maturity setting (Multipliers: P–0.48, ML–1.0, O–3.5).
While the ROM estimate can serve as a base case for calculating NPV, variance information is required for real-option assessments. NPV is calculated only from the most likely scenario values, whereas the real-option valuation is calculated from the full set of variances, or scenario values. Referenced from the base case or most likely scenario, the other two scenario values come from high and low estimates (or optimistic and pessimistic scenarios) based on differences in the key assumptions underlying the concept value drivers. These key assumptions reveal the risks and opportunities facing a particular concept, a significant information goal of the innovation portfolio.
In Phase 1, where concept information is scant, a NASA technology readiness level (TRL) serves as a proxy for variance around the base ROM estimate (Mankin 1995). Engineers can easily assign a TRL for the technology of the concept without having knowledge about variance; an algorithm then applies a set of multipliers, low and high, to calculate values for pessimistic and optimistic scenarios. This technique is grounded on a Boeing-patented process that relates multipliers to TRL levels, with an immature technology having a wide spread between the multipliers and a mature technology having a narrower spread. For example, a TRL 3 assignment—TRL 3 indicates that the readiness state of the concept is an analytical or experimental proof of concept—yields pessimistic and optimistic multipliers of 0.48 and 3.5, respectively. For our example of a base ROM estimate of $90 million operating profit, this imputes a pessimistic scenario value of $43 million, and an optimistic scenario value of $315 million.
Phase 2 analysis builds on the information gathered for Phase 1 by requiring explicit values for each of three scenarios (pessimistic, most likely, and optimistic), essentially three ROM estimates representing a range of values. Phase 2 is an opportunity for the analyst to update the TRL-based values with better estimates, if additional data are available. The values generated by the Phase 1 process may be accepted unchanged, or they may be modified based on additional analyses conducted outside the portfolio process.
Phase 3 further extends the Phase 2 information detail by requiring annual cash flow estimates for each of the three scenarios. The cash-flow data play an important role should the concept be promoted out of the innovation portfolio and into the downstream project portfolio; in the project portfolio, the cash-flow data captured in Phase 3 of the innovation portfolio process will serve as the elements of a full business-case assessment.
As innovation, and therefore the process of information gathering, may not be linear, the resulting phase number of the concept is not necessarily indicative of its forward progress. This necessitates another metric to indicate the analyst’s degree of confidence in the quality of the analysis. The analysis fidelity rating, which is given as none, low, medium, high, or project portfolio ready, may correspond with the phase level of the concept, but it may not. For example, a low fidelity indicator may be appropriate for a concept that has only Phase 1 data attributed to it, but the analyst may also apply a low fidelity rating to Phase 3 data if he or she has low confidence in the cash-flow estimates.
The additional information required to mature the concept through each of the phases will most likely come from several sources—R&D investment estimates from subject-matter experts, launch cost estimates from engineering, revenue estimates from business development—and may be made available at different levels of detail. R&D investments might be provided as a ROM estimate, aligned to Phase 1 inputs, while launch costs might be given as a range estimate, appropriate for Phase 2 inputs, and revenue estimates as scenario-defined cash flows, required by Phase 3. This rather arbitrary combination of information is fairly typical of innovation concepts in the portfolio. Consequently, the innovation portfolio process is agnostic to the type of data provided; a combination of information from any of the phases is sufficient to calculate a concept’s value metrics.
The attributes used for concept valuation are the cornerstone of the innovation portfolio architecture.
Selecting Appropriate Attributes
The attributes used for concept valuation are the cornerstone of the innovation portfolio architecture, and identifying appropriate attributes is a central step in the portfolio design process. Like the statistics used to measure and compare the performance levels of professional athletes, attributes help answer the most relevant performance questions about the prospects of a concept. The goal is to gather just enough attribute data to make a decision about a concept, but not so much that the analyst is overburdened.
An innovation portfolio may contain concepts as varied as a product produced from a novel composite to a new type of service contract. How can these be compared? Selecting attributes that are uniform and broadly comparable enables a more transparent decision process and supports a coherent investment strategy across a wide range of concepts. Besides being uniform and broadly comparable, hard-working attributes should also be:
- Objective and verifiable,
- Quantitative to allow calculation of additional value metrics,
- Independent and mutually exclusive,
- Applicable at all stages of concept maturity, and
- Necessary and sufficient to guide effective decision making.
Choosing attributes that are independent and mutually exclusive gives the few selected attributes full import and eliminates the confusion that might arise from using correlated attributes. For example, the attribute “revenues” is closely related to and can in large part be derived from the attribute “market size”; including both as attributes adds unnecessary complexity without contributing additional insight. Attributes that can be applied equally to early- and late-stage concepts provide the ability to track the progression of value as the concept advances through the portfolio process.
The right set of attributes maximizes the efficiency of the portfolio process by defining the minimum amount of information required for decision making. Option valuation in general, and real option valuation specifically, offers one set of guidelines for selecting attributes; option techniques are the sine qua non of concise investment analytics for uncertain propositions (Mathews 2010b). Options, which are used to value trades of risky assets from interest rate futures to hog bellies, are valued based on just a handful of attributes, all of which conform to the essential qualities listed above.
The attributes that Boeing selected for its innovation portfolio, which are designed to provide answers to key questions governing decision making, have strong parallels to these factors (Table 1). For example, in evaluating the worth of a concept, the most frequently asked question is “What are the likely benefits?” That attribute in an option’s formulation is the payoff, and the parallel for a business or product opportunity is, succinctly, the forecasted operating profits. Yet another option attribute is the strike price, the price at which the option will be exercised, and the equivalent business or product opportunity is the one-time cost (final design, manufacturing installation) to launch the product into the market place.
Table 1.—The innovation portfolio evaluation process uses a small set of attributes that closely parallel option valuation parameters.
The first attribute used in Boeing’s innovation portfolio process is operating profits. Operating profits come in many different forms; we have devised a structure to capture the various representations of operating profits (Figure 3). In this structure, operating profits may be entered directly, or, more typically, as revenue and unit costs (either as margin or as cost of goods sold [COGS]). The portfolio input captures only the essence of the attributes. For example, operating profits may be calculated by a separate spreadsheet using methods specific to a business unit, but the analyst enters into the portfolio tool just the results of that external calculation. (If desired, the spreadsheet can be stored in the portfolio’s database for future reference). This retains the simplicity and uniformity of the portfolio user interface, while allowing each business unit to preserve its unique approach to concept assessment. Operating profits are discounted using the market date attribute, the date when the product is expected to be introduced to the market and operating profits will commence.
Figure 3.—The structure for entering operating profits attribute data is designed to capture the range of ways in which concepts may contribute value.
Launch costs are the nonrecurring expenses associated with the final design process and factory and manufacturing equipment investments. Launch costs are discounted using the launch date attribute, the date when the organization will commit to launching the product and launch costs will be incurred. The next attribute, uncertainty, measures variance and is captured through the calculation of scenario values or, as a proxy, the maturity level of the underlying technology, expressed as a TRL.
Platform synergy quantifies the anticipated future benefits contingent on the success of the initial concept that are not captured fully in the operating profits and launch costs attribute value. Such benefits may include knock-on benefits to related concepts, future derivative products, technology spinoffs, etc. This attribute succinctly captures the analyst’s perception of the second-stage optionality of the concept without elaborate computation.
The final attribute is R&D investments. R&D investments are the funds necessary to develop the concept through the project portfolio. The required R&D investment ought to be justified by the value of the concept as calculated from the other attributes. At the future launch date, the organization typically will reassess the market and the now product-ready concept and decide whether to commit the significantly larger costs necessary for final design and manufacturing. This contingency, whether the concept will ultimately be launched into the market or not, is the option that the organization essentially purchases with the original R&D investments.
Calculating Value Metrics
The attribute values combine to create the various metrics that together answer the most pertinent questions bearing on investment decisions, such as the total value of the concept, its uncertainty, and how leveraged the R&D investments are. The value metrics are calculated using several different methods—NPV and real option—to match the valuation approaches of differing business units. Other methods that may be used, but are not discussed in this context, are nondiscounted and risk-adjusted NPV calculations.
The portfolio process calculates three NPV Value Metrics (Figure 4). Concept value is the NPV value of the concept itself. Total value extends concept value by adding platform synergy and subtracting R&D investment. Return on investment (ROI) is the simple net return on the R&D investment. Note that in conforming to the standard approach for calculating NPV, only the most likely scenarios are used.
Figure 4.—NPV-based calculation of value metrics for a concept in Phase 1 of the innovation portfolio process. Only the most likely scenario is used in NPV calculations as per standard valuation practice.
Alternatively, the portfolio manager may choose to examine the concepts using real option valuation, which extends NPV valuation by capturing uncertainty in the form of variance around attribute estimates. Variance is captured using inputs that define optimistic, most likely, and pessimistic scenarios, each of which highlight the underlying risk and opportunity assumptions for each concept. The option valuation Boeing uses follows the Datar-Mathews Range Option Method (Mathews, Datar, and Johnson 2007; Mathews 2009).
The portfolio process calculates four option-value metrics (Figure 5)2 that have parallels to the NPV metrics. In the option-value context, concept value is an approximate but conservative estimate of the option value of the concept.3 Total value extends concept value by adding platform synergy and subtracting R&D investment. The uncertainty metric, which is derived from the values of the three scenarios for revenue/margin (i.e., operating profits),4 can be likened to a confidence range for the concept value metric. Leverage measures how impactful the initial R&D investments are relative to the total value of the concept. More highly leveraged investments produce more “bang for the buck.”
Figure 5.—Option-based calculation of value metrics for a concept in Phase 1 of the innovation portfolio process. Option valuation requires range values. In this Phase 1 example, TRL settings generate the optimistic and pessimistic values based on most likely ROM estimates.
Both NPV and real-option value metrics are calculated from the present value of attribute data using appropriate discount rates. Two discount rates are applied; the first, the market risk discount rate, applies to operating profits and is usually the hurdle rate assigned to the business unit. The second rate, the investment discount rate, applies to launch costs. And again, two date attributes apply to discounting: market date, for the start of operating profits, and launch date, for the expenditure of launch costs. Somewhat different discounting methods are reserved for the platform synergy and R&D investments attributes to simplify the internal calculations.5
Following the general practice of using a single discount rate for NPV-based calculations, the investment rate is set equal to the market risk rate. For option-based calculations, the two discount rates are unequal, following the Datar-Mathews method. The market risk rate remains high, set to the hurdle rate, as in NPV valuation. However, the investment rate is significantly lower, reflecting the fact that the cash flows for launch costs are not at market risk, but rather reflect the lower, general obligation (so called “private”) risk of the corporation. Following option thinking, the launch costs are only committed contingent on sufficient prospects of market success for the concept, for example a greater than 80 percent probability of success.
Just these three (NPV) or four (option-value) metrics, combined with the six quantitative attributes and any Phase 0 quasi-quantitative attributes, define a myriad of decision dimensions across which to analyze the relative merits of the hundreds of concepts that may be contained in an innovation portfolio. The combinations form the basis of simple but necessary and sufficient metrics for effective decision making. For example, at a scheduled portfolio review, the portfolio manager can select promising concepts by comparing concept value, R&D investments, and uncertainty (Figure 6). As a strategic choice and in alignment with corporate goals and constraints, the manager may select for promotion to the next phase those concepts that have the earlier market date, acceptable launch costs, and a relatively mature technology readiness level. A different, but equally valid, strategic choice (perhaps reflecting more time spent on preliminary research) might be a later market date, lower launch costs, and somewhat less mature technology, but substantially higher net positive concept value. The concepts not selected are then set aside, archived in a repository of ideas that can be reexamined for future initiatives. Through periodic portfolio reviews, a portfolio of viable promising concepts emerges, along with a refined overall strategy, all of which are transferred to the project portfolio.
Figure 6.—Example of data entered into the portfolio for four prototypes, with a table of value metrics, applying real-option valuation. In the upper right, a bubble chart comparing the four concepts using attribute and value metric data illustrates one of the many visualization options offered by the tool.
The innovation portfolio process provides a structured system for assessing the potential value of each of the hundreds of concepts a corporation may consider before selecting those to develop as well-funded projects. The innovation portfolio amalgamates defined metrics across groupings of related, but often highly divergent concepts; these amalgamated metrics facilitate informed decision making about strategy, concept selection, and effective investment in the underlying concept groups. The result is an emergent portfolio strategy that evolves, becoming more clearly defined with each round of analysis.
The process also creates uniform valuation structures, allowing for attribute values and metrics to be aggregated and rolled up from the business units to a portfolio of portfolios, an executive-level portfolio that helps to shape an overarching corporate strategy. Meanwhile, rejected concepts are stored in a repository, from which they may be reactivated in the context of a different portfolio motivated by a different strategy.
The innovation portfolio has been tested in a business unit as a prototype system. The concepts were dozens of air vehicle concepts designed to address one area of the aerospace market. The strategic choice was to determine which set of concepts would be selected as projects for prototype development, an expensive proposition constrained by limited R&D funds and availability of skilled engineers. The innovation portfolio provided to the project portfolio a matured strategy as well as an aligned set of qualified concepts with an expectation of relatively low attrition. The real options structure for the design added the ability to understand the range of possible outcomes for each of the concepts, thus helping the portfolio manager assess his level of risk tolerance. One of the several challenges addressed in the innovation portfolio was to provide a mechanism by which the contributing parties (business development, finance, and engineering) could agree on forecasts, including those of the scenarios, for the many concepts. I am hopeful that a version of the innovation portfolio will be implemented companywide in the near future.
The innovation portfolio process provides a structured system for assessing the potential value of each of hundreds of concepts.
Cooper, R. G., and Edgett, S. J. 2007. Generating Breakthrough New Product Ideas: Feeding the Innovation Funnel. Product Development Institute.
Cooper, R. G., Edgett, S. J., and Kleinschmidt, E. J. 2000. New problems, new solutions: Making portfolio management more effective. Research-Technology Management 43(2): 18–33.
Cooper, R. G., Edgett, S. J., and Kleinschmidt, E. J. 2002a. Optimizing the stage-gate process: What best-practice companies do. Research-Technology Management 45(5): 21–27.
Cooper, R. G., Edgett, S. J., and Kleinschmidt, E. J. 2002b. Portfolio Management For New Products. 2nd ed. New York: Basic Books.
MacMillan, I. C., and McGrath, R. G. 2002. Crafting R&D project portfolios. Research-Technology Management 45(5): 48–59.
Mankin, J. C. 1995. Technology Readiness Levels: A White Paper. Advanced Concepts Office, Office of Space Access and Technology, NASA. http://www.hq.nasa.gov/office/codeq/trl/trl.pdf (accessed April 18, 2011).
Mathews, S. 2009. Valuing risky projects with real options. Research-Technology Management 52(5): 32–41.
Mathews, S. 2010a. Innovation portfolio architecture. Research-Technology Management 53(6): 30–40.
Mathews, S. 2010b. Valuing high-risk high-return technology projects using real options. In The Handbook of Technology Management: Core Concepts, Financial Tools and Techniques, Operations and Innovation Management, vol. 1, ed. H. Bidgoli, 581–600. New York: Wiley.
Mathews, S. H., Datar, V. T., and Johnson, B. 2007. A practical method for valuing real options. Journal of Applied Corporate Finance 19(2): 95–104.
O’Connor, G. C., and Ayers, A. D. 2005. Building a radical innovation competency. Research-Technology Management 48(1): 23–27.
Paulson, A. S., O’Connor, G. C., and Robeson, D. 2007. Evaluating radical innovation portfolios. Research-Technology Management 50(5): 17–29.
Rosenø, A. 2008. Developing radical innovation capabilities in established firms. A paper presented at Front End of Innovation Europe, Vienna, January 31.
Say, T. E., Fusfeld, A. R., and Parish, T. D. 2003. Is your firm’s tech portfolio aligned with its business strategy? Research-Technology Management 46(1): 32–38.
Scriven, E. 2001. Determining a project’s probability of success. Research-Technology Management 44(3): 51–57.
Terwiesch, C., and Ulrich, K. 2008. Managing the opportunity portfolio. Research-Technology Management 51(5): 27–38.
1 The design philosophy of the innovation portfolio was discussed in “Innovation Portfolio Architecture,” which appeared in the November/December 2010 issue of Research-Technology Management (Mathews 2010a).
2 Real option calculations are explained in more detail in Mathews 2009. Figure 5 illustrates the range option method of option valuation.
3 Technically, in the real option-based valuation approach, the calculated concept value is the single-stage option value of the concept. This value is the ceiling amount a corporation should be willing to pay in R&D investments, called a “premium” in option terminology. In theory, R&D investments should be less than or equal to the concept value. However, a corporation is often able to purchase an option on a concept at below-market rates because its internal technical know-how allows risks to be resolved by means of targeted R&D investments. The leverage metric reflects the degree of this advantage as the ratio of the option-based total value to the R&D investment necessary to fund the option.
4 Uncertainty is calculated from the standard deviation of the three scenarios for nondiscounted operating profit. By definition, if only the most likely scenario data is available, then Uncertainty = 0, such as for NPV-based calculations.
5 The R&D investment attribute is not discounted because it is assumed to start immediately or within a short period of time. Since R&D investments tend to be small relative to other cash flows, using the nondiscounted value has little material impact on the outcome. On the other hand, the platform synergy attribute, which captures future benefits (next-stage options) contingent on the success of the initial concept, is highly discounted to account for time value and for the low probability of success for the underlying concept. These discount shortcuts reduce processing complexity while having little material impact on the decisions made within the portfolio.