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2022 | OriginalPaper | Buchkapitel

16. Research and Development Project Definition and Portfolio Management

verfasst von : Olivier L. de Weck

Erschienen in: Technology Roadmapping and Development

Verlag: Springer International Publishing

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Abstract

Ultimately, technology progresses through individual steps which are the results of specific research and development (R&D) projects. In this chapter, we first describe what kinds of R&D projects exist, and how to plan and successfully execute them. We then consider how multiple projects together – as a set – constitute an R&D portfolio. Portfolios can be defined with the help of targets set by technology roadmaps. Given a fixed total R&D budget, it is also possible to optimize the composition of an R&D portfolio by balancing expected return and risk. We give an example of what an R&D portfolio might look like, by considering the portfolio of a major technology firm.

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Vorheriges Kapitel Knowledge Management and Technology Transfer

Nächstes Kapitel Technology Valuation and Finance

The distinction between R&D and R&T is unique to some countries in Europe such as France and Germany, whereas in the United States the term R&D is used throughout. One of the subtleties is that government funding for R&T (projects at TRL 6 or earlier) is generally acceptable, whereas government funding for product and service development (R&D after TRL 6) is generally considered a government subsidy and potentially subject to adverse WTO rulings.

We focus on the “value” generated by technology in Ch. 17. In simple terms, we can think of investing some amount of money in order to improve one (or more) FOM’s by some amount, ∆FOM/∆$, and this improvement in FOM should then later return a positive multiple in terms of enhanced revenues or cost savings, ∆$/∆FOM. The product of these two terms can be interpreted as a ROI of the technology investment.

This happened to the Mars Science Laboratory (MSL) mission which carried the Curiosity rover to the surface of Mars and whose original launch date slipped from 2009 to 2011, in part due to technical challenges with cryogenic actuators.

Source: https://www.airbus.com/innovation/future-technology/autonomy.html

For example, it is usually much more expensive to raise the TRL level of a technology from TRL 5 to 6, compared to raising it from TRL 3 to 4. This is because as technology maturity progresses, the fidelity and complexity of equipment, test procedures, and (simulated or actual) use cases becomes much higher, requiring more time, effort, and money.

The scaled agile framework (SAFe) claims to be able to integrate several projects into a coherent whole at the enterprise level, see: https://www.scaledagileframework.com/

One subtlety of the basic EVM calculations is that it does not capture the interdependencies shown on the critical path diagram (e.g., Fig. 16.5), and therefore, the schedule performance in terms of SPI can be different than the schedule tracked in terms of the critical path.

This assumes that the remainder of the project will be executed at the same level of cost efficiency as the project exhibited up until “Time Now.”

A more Machiavellian perspective on overoptimism is that project proponents deliberately low ball project estimates in terms of cost and schedule such that the project is more likely to gain approval and get started. This assumes that, once underway, project leaders will be able to secure additional resources and time as project sponsors will want to see the project succeed, rather than face its cancellation.

An example of such a type of project is the Airbus E-Fan X project wherein the goal was to develop and demonstrate in flight a 2 [MW] class electric propulsion system. The project was set up as an allied partnership between Airbus, Siemens, and Rolls Royce. Note that the project was prematurely stopped due to budget cuts related to the COVID-19 pandemic in 2020.

This is a disguised name to protect the confidentiality of the actual company.

The work in this section is credited to Dr. Kaushik Sinha, mainly done during 2017–2018.

A fundamental assumption for φ_min is that even a small investment in a technology may yield value, for example, partnering on an R&D project with external organizations, doing in-depth technology scouting (Ch. 14), modeling and simulation, etc. R&D investments in a technology are usually not “all or nothing” propositions. However, there may be a minimum level of investment needed to “unlock” any value at all.

The details of the individual technologies are not important here, we simply want to illustrate the overall principle of R&D portfolio optimization.

Most technology-based companies, including financial departments led by CFOs, use deterministic planning to allocate resources and are uncomfortable using probabilities or statistical analysis of any sort. This is somewhat surprising, since statistical-based risk analysis is the very basis of financial markets.

Garvey P.R., “Probability Methods for Cost Uncertainty Analysis: A Systems Engineering Perspective”, CRC Press (2000), ISBN-10: 0824789660.

Georgiopoulos P., Fellini R., Sasena M. and Papalambros P., “Optimal design decisions in product portfolio valuation”, DETC2002/DAC-34097, Montreal, 2002

Legge Jr RS, Lozano PC. Electrospray propulsion based on emitters microfabricated in porous metals. Journal of Propulsion and Power. 2011 Mar;27(2):485-95.

Markowitz, Harry. "Portfolio selection." The Journal of Finance, 7, no. 1 (1952): 77-91.

Pennings E. and Sereno L., “Evaluating pharmaceutical R&D under technical and economic uncertainty”, Volume 212, Issue 2, Pages 374-385, European Journal of Operational Research, 2011

Sega R., de Weck O.L, et al., “Controlling Cost Growth of NASA Earth and Space Science Missions” By Committee on Cost Growth in NASA Earth and Space Science Missions, National Research Council (NRC) of the National Academy of Sciences,ISBN-13: 978-0-309-15737, Washington D.C., July 2010

Shishko R. , Ebbeler D. H. , and Fox G., “NASA Technology Assessment Using Real Options Valuation”, Systems Engineering, Vol. 7, No. 1, 2004

Sinha K., de Weck O., “Empirical Validation of Structural Complexity Metric and Complexity Management for Engineering Systems”, Systems Engineering, 19(3), pp. 193-206, May 2016

Wheelwright, S.C. and Clark, K. B., 1992, “Creating Project Plans to Focus Product Development,” Harvard Business Review, 70(2), pp. 70-82.

Titel: Research and Development Project Definition and Portfolio Management
verfasst von: Olivier L. de Weck
Verlag: Springer International Publishing
Buch: Technology Roadmapping and Development
Print ISBN: 978-3-030-88345-4

Electronic ISBN: 978-3-030-88346-1

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-88346-1_16

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