R&D Portfolio Management Framework for Sustained Competitive Advantage

William R. Bitman

Johns Hopkins University Applied Physics Laboratory Laurel, MD 20723 USA

Abstract-Sustained competitive advantage requires a portfolio of R&D

(research and development) projects which is balanced in terms of certain qualitative factors especially competitiveness category (i.e., capacity, competency), phase (i.e., basic research, applied research, development), and novelty category (i.e., evolutionary, revolutionary) because they help ensure that a firm has a continuous stream of both short and long range sources of profitability. Another factor by which to balance a set of projects is the category of the source of the R&D idea (e.g., internal, external) because customer generated ideas can be powerful in the marketplace and should be guaranteed inclusion in a portfolio. The proposed decision making framework for R&D portfolio project selection guides decision makers through the processes of identifying qualitative attributes of projects, determining which attributes to include in the analysis, assessing projects in terms of those attributes, dividing candidate projects into balancing groups, ranking projects within those groups, and selecting the optimum projects within each group. Strategic analysis that includes multiple dimensions of technology (i.e., technoware, humanware, inforware, orgaware) and multiple perspectives (i.e., technical, organizational, political, and environmental) helps analysts identify the full value and weaknesses of projects. The knowledge gained from this analysis can reduce false negative and false positive errors in project selection. Validation of the framework through a Delphi process as well as a case study are planned.

I. BACKGROUND

U.S. companies spend about $100 billion a year on R&D (research and development). IBM annually spends almost $5 billion on its R&D program [1]. R&D formalizes and stimulates innovation, which in turn fuels competitiveness. Innovation is the successful commercialization of improved products, services, processes, and systems (herein, products).

Industry analysts estimate that only about 15 percent of R&D projects are successfully commercialized [2]. Yet many practitioners feel that the success rate is even lower than that. A low success rate is understandable given the exploratory and experimental nature of R&D. However, there are other factors which can be controlled and improved that contribute to the rate of success.

One such factor is the decision process that determines which of the many candidate projects will be funded. Twenty years ago most firms did not even have a formal R&D portfolio management process because managers felt that available formal processes did not improve decision making [3]. Almost fifteen years after that the R&D project portfolios of most firms still had not reflected business strategy, were of poor quality, lacked focus, and contained too many trivial projects [4, 5]. Unfortunately, there is no indication that things have improved much since these studies were reported in the 1980s and 1990s.

It is still common today for many once-innovative technologically-based firms, despite their formalized R&D

programs to see their customer base erode. Today, innovation is at an all time high, yet these firms seem to run out of ideas and let others commercialize new or improved products. Two behaviors that lie at the root of the problem are (a) not understanding the true value of a project and (b) having only a short term stance.

Failing to recognize the true value of good projects causes false negative selection errors. Similarly, when fatal project weaknesses are overlooked, the resulting false positive errors can cause costly failures. As a result, decision makers tend to hesitate to fund promising projects simply because those projects take longer or require a larger commitment than other projects.

Studies have shown that 30% of a firm’s profits are derived from innovations made five years earlier. These studies also show, perhaps surprisingly, that firms that focus on financial factors to evaluate R&D projects do not perform as well as those that emphasize qualitative attributes [6]. Lack of long-term and qualitative focus is known to interfere with success [7].

This paper describes a framework that addresses these issues and is geared toward producing a balanced R&D portfolio for sustained competitive advantage. The goal is to keep innovative firms innovative.

II. LITERATURE REVIEW

A review of the relevant literature reveals that there are different approaches, principles, and project attributes that are needed for effective R&D project selection. A. R&D Project Selection Approaches

The fundamental approach to R&D project selection is the go/no-go method in which decision makers evaluate each R&D project individually based on expected profitability and cost [8, 9, 10]. This is important for firms that must quickly generate profits with a minimum of expense. However, as shown above [7], sustained competitiveness requires a long range outlook.

Using a portfolio approach, decision makers create a set of R&D projects as a coherent collection. There are many systems based on this approach [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]. However, the temptation is to rank the projects based on favorable cost/benefit ratio and reject all projects above a certain value. Such a set of projects is more highly optimized as a group for short-term profitability than the project set created using a

0-7803-9139-X/05/$20.00 ©2005 IEEE. 775

go/no-go approach. As stated above [6], qualitative attributes are needed to achieve a long range solution.

Such an enterprise-sustaining portfolio is better achieved through a balanced approach [30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40]. These systems and the R&D portfolios created using them display a number of characteristics that make them effective for long term competitiveness, as will be explained in the following sections on principles and attributes. B. R&D Project Selection Principles

There are three principles seen throughout the best R&D portfolio management processes: involvement, evaluation, and balance.

First, most successful systems are actively guided by people throughout the process. Table 1 compares the sample of balanced portfolio systems cited above. The overwhelming majority utilize an integrated framework (column B) composed of various analysis methods, such as strategy tables, decision trees, matrix models, and analytic hierarchy process (AHP) [41, 42]. They actively engage domain experts and managers in the selection process.

Second, effective systems emphasize proper evaluation of projects. Technology has the following four embodiments or dimensions: skills (humanware), knowledge (inforware), and process factors (orgaware) (column C) [43]. For example, many projects have value and commercial viability because they improve the utilization of technology by people even if there are no technical improvements per se. Decision makers who perceive these important dimensions capitalize on them and succeed in the marketplace. In addition, there are four perspectives through which to view projects and situations. They are technical, organizational, political, and environmental perspectives (column D) [44]. Very often executives make the mistake of evaluating a project purely on its technical merit. The reality is that most technology, in any and all its embodiments, can be viewed in terms of their organizational context and their effect on the environment within which a firm operates. Public opinion of a firm’s product line can make or break a company.

There are numerous attributes identified in the management literature that contribute to competitive differentiation which are qualitative and sometimes intangible (e.g., learning, knowledge, intellectual property, process, people, skills, network, culture, drive) [45, 46]. Viewing projects through the dimension/perspective framework enables professionals to better identify value in all its forms. In addition, most important project attributes are qualitative in nature.

Third, it is essential to support projects that will provide technology for major new products in the future. Although it is important to have near-term profitable projects, they should not crowd out sources of long range profitability. C. Balancing Attributes

There are four project attributes that surface repeatedly as being important for a portfolio that is balanced for both short and long range innovation. These are competitiveness category, R&D phase, extent of novelty, and idea source.

There are two ways to improve a firm’s ability to compete: capability and competency [43]. Capability is the efficiency and capacity of a firm’s operations. A firm can improve its capability through operations cost reduction, efficiency increase, and capacity increase. Improved capability creates comparative advantage but it is usually short lived. Competency, on the other hand, is the level of value that a firm provides to its customers. It is effectiveness. A firm with increased competency is more likely to have long range competitive advantage. It is important to have projects that will increase each type of competitive ability.

R&D phases are basic research, applied research, and development. Applied research is less certain but has the greatest chance of creating a vehicle for future profitability. A firm should invest in projects of all three types in order to maintain a steady stream of competitiveness [6, 39, 47].

An innovation can be evolutionary (incremental) in nature or its can be revolutionary (radical) [48]. Without new revolutionary innovation, there may not be a source of profitability when currently successful products lose popularity. Both are important for competitiveness.

TABLE I

BALANCED PORTFOLIO SYSTEMS

A Citation

B Techniques

C Dimensions (Technoware, Humanware, Inforware, Orgaware)

D Perspectives (Technical, Organizational, Personal, Environmental)

[37] Menke (1994)

Integrated: Strategy tables and decision trees

T H O T O P

[30] Brenner (1994) Integrated: AHP T H O T O P

[38] Mikkola (2001)

Integrated: matrix model T H O T O E

[39] Qingrui, Gang, Jingjang, & Jin (2001)

Integrated: AHP T T O

[31] Chien (2002) Integrated: MAU T T O

[35] Liang (2003) Integrated: AHP T H I T O P

[34] Janney & Dess (2004)

Financial: Real options T H O T O

[36] Liu & Chen (2004) Integrated: AHP T O T

[33] Enea & Piazza (2004) Integrated: AHP T T E

[32] Eilat, Golany, Shtub (2005)

Integrated: DEA and BSC T O T P

Key: AHP: Analytic Hierarchy Process BSC: Balanced Score Card DEA: Data Envelopment Analysis MAU: Multiple Attribute Utility

0-7803-9139-X/05/$20.00 ©2005 IEEE. 776

The source of an idea (e.g., external (e.g., customer, lead customer, potential customer, supplier), internal (e.g., R&D group, business unit, firm executive and forecasting/foresight method)) can have a great influence on success of commercialization [29, 49, 50, 51, 52, 53, 54]. A firm should make sure that projects stemming from both internal and external sources of innovative ideas are funded. Customer-generated ideas can be powerful in the marketplace and the selection process should ensure their representation and inclusion in the portfolio. D. Selection Attributes

Within the distribution created by the balancing factors, there are different project attributes that should be used to select projects. For example, the degree of uncertainty [55] is a powerful discriminator. In addition there are project attributes that may be important discriminators for certain firms. Some examples are procedural mode (e.g., single team, cross-functional team, collaboration with complementary organization, alliance with competitors) [56]; extent of project inter-dependencies [57]; number of objectives [57]; and output type (i.e., product, service) [56, 58, 59]. In practice, a firm should utilize the Delphi method to develop a list of those attributes that are important for the selection of projects.

III. CONCEPTUAL FRAMEWORK

The above findings, concepts, and principles point to a

conceptual framework (Figure 1) which guides decision makers through the processes of analyzing R&D projects, balancing them into groups, and selecting projects from each group in order to support sustained competitiveness. It consists of an integrated assessment framework and a process for performing effective R&D project selection. The framework can apply to focused projects, such as improving an automobile tire for wet road traction, as well as to large scope system projects, such as a new integrated medical imaging system. Each firm can customize and tailor the process to fit its own operations, environment, objectives, work flow, and culture. A. Example Process

For this framework, the process has been formulated into an example set of steps diagrammed on the right side of the conceptual framework (Figure 1) for simplicity as a sequential flow. In actual practice there is constant feedback among steps to create iterative interactions. There are numerous points in the process when the assessment framework is utilized. The process steps are:

1. Collect the R&D projects to be evaluated. The submissions describe the projects, an initial description of their potential benefits, and their costs.

2. Determine the attributes that will be used to balance the portfolio. A Delphi process which gets input from firm and industry experts is advantageous for this. A full assessment can be performed utilizing the Assessment Framework. This framework will be explained in a subsequent section. As

stated above, attributes such as competitiveness category, phase, novelty category and category of the source of the R&D idea are good candidates. The final number of balancing attributes may partly be determined by the number of candidate projects, the R&D budget, and the weights given to each balancing group. Weights will be determined in the next step. Ideally, there should be numerous projects in each category from which to select. For example, if all four balancing attributes are used, there are 24 groups (i.e., 3 (phase) x 2 (competitiveness category) x 2 (novelty) x 2 (idea source)). This is fine for firms with large R&D budgets that fund at least one hundred R&D projects. Smaller firms can opt to balance across two or three attributes instead.

3. Determine the weights for the balancing groups. This can be determined by the relative comparison correlation matrix method which is a part of AHP.

4. Determine the value of each balancing attribute for each project. This determines which group each project resides in. The set of balancing attributes can be used to create a matrix for visual evaluation of the portfolio. Traditional business strategy matrices such as the growth-share matrix label products as stars or dogs, for example, in order to steer firms away from so-called dogs. The objective of the balancing matrix, however, is to have projects in each grid area. There are no inherently negative areas to avoid, although a firm may decide to downplay some groups. If there are certain groups that have few or no projects, it will be readily apparent from the matrix. When confronted with such a situation, a firm should try to develop or solicit ideas in the sparse categories in order to better balance its portfolio.

5. Determine the attributes that will be used to select projects by. This correlates to evaluation criteria found in problem solving and decision making models. A Delphi process by domain experts is helpful here. Again the Assessment Framework is of central importance in making sure all aspects, dimensions, and perspectives are considered.

6. Determine the weights for each selection attribute using the relative comparison correlation matrix technique.

7. Determine the values of each selection attribute for each project. Here again the Assessment Framework guides analysts in identifying and integrating all factors.

8. Rank each project within its group. For each project multiply each selection attribute value by its attribute weight and add all the resulting values for all attributes. The higher the total, the higher the rank. Based on the cost and budget for the group, select the highest ranking projects in the group that can be funded. The result is a set of projects that cover each of the balancing groups. B. Assessment Framework

The interactions among elements in the assessment framework, which is shown on the left side of Figure 1, exhibit feedback which is indicated in the figures by the double arrows between elements. In practice the various elements are performed in various sequences and feedbacks for a variety of purposes throughout the process.

0-7803-9139-X/05/$20.00 ©2005 IEEE. 777

Figure 1. Conceptual Framework

The assessment framework is utilized throughout the selection process, including identifying balancing attributes, selection attributes, and values. The framework integrates four major activities that are essential for effective assessment and decision making. A traditional strategic assessment is included. SWOT analysis is selected for the figure as an example. Each firm can utilize the process that works best for it. In addition, analysts should perform a future assessment. The ideas and factors from these analyses reveal project attributes that are important for sustained competitiveness decision making. This analysis includes the four embodiments or dimensions of technology. In addition, it is essential to view all the factors through the four different perspectives as described above.

IV. DISCUSSION

A Delphi study is planned. The Delphi questions will be open-ended in order to allow participants to express their opinions freely about the framework. These comments will be used to improve the framework which then will be administered in a case study.

ACKNOWLEDGMENT

I greatly thank Dr. Nawaz Sharif for his constant guidance

on this project. I also thank Drs. Hasan Sayani and John Aje for their helpful and constructive suggestions.

REFERENCES

[1] K. Heidenberger, A. Schillinger, and C. Stummer, “Budgeting for research and development: A dynamic financial simulation approach,” Socio-Economic Planning Sciences, vol. 37, pp. 15-27, 2003.

[2] H.J. Thamhain, “Can innovative R&D performance be managed?” Proceedings of PICMET ’01, pp. 151-159, 2001.

[3] M.L. Liberatore, and G.J. Titus, “The practice of management science on R&D project management,” Management Science, vol. 29, pp. 962-974, 1983.

[4] R.G. Cooper, S.J. Edgett, and E.J. Kleinschmidt, “Portfolio management in new product development: Lessons from the leaders – I,” Research Technology Management, pp. 16-28, September-October 1997.

[5] R.G. Cooper, S.J. Edgett, and E.J. Kleinschmidt, “Portfolio management in new product development: Lessons from the leaders – II,” Research Technology Management, pp. 43-50, November-December 1997.

[6] R.G. Cooper, S.J. Edgett, and E.J. Kleinschmidt, “New product portfolio management: Practices and performance,” Journal of Product Innovation Management, vol. 16, pp. 333-351, 1999.

[7] R.H. Hayes, and W.J. Abernathy, “Managing our way to economic decline,” Harvard Business Review, vol. 58(4), pp. 67-77, 1980.

[8] J. Butler, D.J. Morrice, and P.W. Mullarkey, “A multiple attribute utility theory approach to ranking and selection,” Management Science, vol. 47, pp. 800-816, 2001.

[9] S. Coldrick, P. Longhurst, P. Ivey, and J. Hannis, “An R&D options selection model for investment decisions,” Technovation, vol. 25(3), pp. 185-193, March 2005

[10] J. Klapka, and P. Pinos, “Decision support system for multi-criterial R&D and information systems projects selection,” European Journal of Operational Research, vol. 140(2), pp. 434-446, July 2002.

[11] I. Bardhan, S. Bagchi and R. Sougstad, “Prioritizing a portfolio of information technology investment projects,” Journal of Management Information Systems, vol. 21(2), pp. 33-60, Fall 2004.

0-7803-9139-X/05/$20.00 ©2005 IEEE. 778

[12] G.J. Beaujon, S.P. Marin, and G.C. McDonald, “Balancing and optimizing a portfolio of R&D projects,” Naval Research Logistics, vol. 48, pp. 18-40, 2001.

[13] D.C. Bell, and A.W. Read, “The application of a research project selection method,” R&D Management, vol. 1, pp. 35-42, 1970.

[14] P.V. Chu, Y. Hsu, and M. Fehling, “A decision support system for project portfolio selection,” Computers in Industry, vol. 32, pp. 141-149, 1996.

[15] M.A. Coffin, and B.W. Taylor, “Multiple criteria R&D project selection and scheduling using fuzzy logic,” Computational Operations Research, vol. 23, pp. 207-220, 1996.

[16] T.M. Devinney, and D.W. Stewart, “Rethinking the product portfolio: A generalized investment model,” Management Science, vol. 34, pp. 1080-1095, 1988.

[17] M.W. Dickinson, A.C. Thornton, and S. Graves, “Technology portfolio management: Optimizing interdependent projects over multiple time periods,” IEEE Transactions on Engineering Management, vol. 48, pp. 518-527, 2001.

[18] K.F. Doerner, W.J. Gutjahr, R.F. Hartl, C. Strauss, and C. Stummer, “Pareto ant colony optimization with ILP preprocessing in multi-objective project portfolio selection,” European Journal of Operational Research, in press.

[19] K. Golabi, “Selecting a portfolio of non-homogeneous R&D proposals,” European Journal of Operational Research, vol. 21(3), pp. 347-357, September 1985.

[20] S.B. Graves, and J.L. Ringuest, “Evaluating competing R&D investments,” Research Technology Management, vol. 34(4), pp. 32-36, 1991.

[21] J.B. Guerard, Jr., and A. Mark, “The optimization of efficient portfolios: The case for an R&D quadratic term,” Research in Finance, vol. 20, pp. 213-247, 2003.

[22] C.P. Lawson, P.J. Longhurst, and P.C. Ivey, “The application of a new research and development project selection model in SMEs,” Technovation, vol. 24, pp. 1-9, September 2004.

[23] C.H. Loch, and S. Kavadias, “Dynamic portfolio selection of NPD programs using marginal returns,” Management Science, vol. 48, pp. 1227-1241, 2002.

[24] J.R. Moore, and N.R. Baker, “Computational analysis of scoring models for R&D project selection,” Management Science, vol. 16, pp. 212-232, 1969.

[25] P.J. Regan, and S. Holtzman, “R&D decision advisor: An interactive approach to normative decision system model construction,” European Journal of Operational Research, vol. 84(1), pp. 116-133, July 1995.

[26] J.L. Ringuest, S.B. Graves, and R.H. Case, “Mean-Gini analysis in R&D portfolio selection,” European Journal of Operational Research, vol. 154(1), pp. 157-169, April 2004.

[27] L.P. Santiago, “Value of flexibility and dynamic management of research and development projects,” Unpublished doctoral dissertation, Boston University College of Engineering, 2004.

[28] R.L. Schmidt, “A model for R&D project selection with combined benefit, outcome and resource interactions,” IEEE Transactions on Engineering Management, vol. 40, pp. 403-410, 1993.

[29] C. Stummer, and K. Heidenberger, “Interactive R&D portfolio selection considering multiple objectives, project interdependencies, and time: A three-phase approach,” Proceedings of PICMET ’01 Book of Summaries, pp. 384, 2001.

[30] M.S. Brenner, “Practical R&D project prioritization,” Research Technology Management, vol. 37(5), pp. 38-42, September-October, 1994.

[31] C. Chien, “A portfolio evaluation framework for selecting R&D projects.” R&D Management, vol. 32, pp. 359-368, 2002.

[32] H. Eilat, B. Golany, and A. Shtub, “Constructing and evaluating balanced portfolios of R&D projects with interactions: A DEA based methodology,” European Journal of Operations Research, in press.

[33] M. Enea and T. Piazza, “Project selection by constrained fuzzy AHP,” Fuzzy Optimization and Decision Making, vol. 3, pp. 39-62, 2004.

[34] J.J. Janney, and G.G. Dess, “Can real options analysis improve decision making? Promises and pitfalls,” Academy of Management Executive, vol. 18(4), pp. 60-75, November 2004.

[35] W.-Y. Liang, “The analytic hierarchy process in project evaluation: An R&D case study in Taiwan,” Benchmarking, vol. 10(5), pp. 445-456, 2003.

[36] C. Liu, and C. Chen, “A two-dimensional model for allocating resources to R&D programs,” Journal of American Academy of Business, Cambridge, vol. 5, pp. 469-473, 2004.

[37] M.M. Menke, “Improving R&D decisions and execution,” Research Technology Management, vol. 37(5), pp. 25-32, September/October 1994.

[38] J.H. Mikkola, “Portfolio management of R&D projects: Implications for innovation management,” Technovation, vol. 21, pp. 423-435, 2001.

[39] X. Qingrui, Z. Gang, L. Jingjang, and C. Jin, “Evaluating R&D effectiveness: A study on Chinese practice and trend,” Proceedings of PICMET 2001, pp. 206-214, 2001.

[40] E. Van Dyk, and D.G. Smith, “R&D portfolio selection by using qualitative pair-wise comparisons,” Omega, vol. 18, pp. 583-594, 1990.

[41] T. Saaty, The Analytical Hierarchy Process. NY: McGraw-Hill, 1988. [42] T. Saaty, “How to make a decision: The analytic hierarchy process,”

Interfaces, vol. 24(6), pp. 19-43, November-December 1994. [43] N. Sharif, “Technology and economics,” Science and Public Policy, vol.

15(4), pp. 217-229, August 1988. [44] H.A. Linstone, Decision Making for Technology Executives: Using

Multiple Perspectives to Improve Performance. Boston: Artech House, 1999.

[45] M.G. Brown, and R.A. Svenson, “Measuring R&D productivity,” Research Technology Management, vol. 41(6), pp. 30-35, 1998.

[46] S. Karani, “Building the organization’s long-term success through strategic values,” Proceedings of the International Engineering Management Conference, pp. 397-401, 2004.

[47] M.J. Lanigan, “A structure for technological innovation,” Engineering Management Journal, pp. 71-77, April 1994.

[48] F. Kodama, “Measuring emerging categories of innovation: Modularity and business model,” Technological Forecasting & Social Change, vol. 71, pp. 623-633, 2004

[49] A. Kameoka, Y. Yokoo, and T. Kuwahara, “A challenge of integrating technology foresight and assessment in industrial strategy development and policymaking,” Technological Forecasting & Social Change, vol. 71, pp. 579-598, 2004.

[50] O. Kuusi, and M. Meyer, “Technological generalizations and leitbilder – the anticipation of technological opportunities,” Technological Forecasting & Social Change, vol. 69, pp. 625-639, 2002.

[51] C. Sata, S. Kumagai, J. Tsukuda, and J. Numata, “Framework for innovation in business practice unit,” Proceedings of the International Engineering Management Conference, pp. 435-439, 2004.

[52] M. Sawhney, and E. Prandelli, “Communities of creation: Managing distributed innovation in turbulent markets,” California Management Review, vol. 42(4), pp. 24-54, Summer 2000.

[53] E. Von Hipple, The Sources of Innovation, New York: Oxford University Press, 1994.

[54] B. Yoon, and Y. Park, “A systematic approach for identifying technology opportunities: Keyword-based morphology analysis,” Technological Forecasting & Social Change, vol. 72, pp. 145-160, 2005.

[55] A.H. Rubinstein, “50 years of engineering and technology management,” IEEE Transactions on Engineering Management, vol. 51(4), pp. 407-408, 2004.

[56] M.L. Tushman, “From engineering management/R&D management, to the management of innovation, to exploiting and exploring over value nets: 50 years of research initiated by the IEEE-TEM,” IEEE Transactions on Engineering Management, vol. 51(4), pp. 409-411, 2004.

[57] R.S. Rosenbloom, “Reflections on the evolution of the field,” IEEE Transactions on Engineering Management, vol. 51(4), pp. 404-406, 2004.

[58] J.B. Quinn, Intelligent Enterprise, New York: Free Press, 1992. [59] E.B. Roberts, “A perspective on 50 years of the engineering

management field,” IEEE Transactions on Engineering Management, vol. 51(4), pp. 398-403, 2004.

0-7803-9139-X/05/$20.00 ©2005 IEEE. 779