Alicia Hong
(Advisor: Dr. Dimitri Mavris)

will propose a doctoral thesis entitled,

A Methodology for R&D Decision Making Under Uncertainty – Applied Towards the Elimination of Stalling Projects and Improved Ideation Quality

On

Monday, December 13 at 2:00 p.m.

Webex: https://boeing.webex.com/boeing/j.php?MTID=m617e77a27392f3e90f80f0a2429e1ab0

Meeting number (access code): 2682 361 1452

Meeting password: bGW3jFwQh43 

Abstract
Portfolio management is a strategic practice applied to R&D investments with the objective to optimize returns, balance investments and maintain a competitive advantage.  Best practices such as defining value and uncertainty of projects have been well established to enable portfolio selection using various tools and analyses such as risk balancing and Pareto frontiers.  However, challenges with exist practices continue to be problematic for portfolio managers, partially driven by the uncertainties often associated with R&D projects.  This proposal will highlight some of the drivers that contribute to the continued investment in stalling or failing R&D projects and the negative effects on an R&D portfolio. 

The method as proposed addresses a different way to view productivity in the maturity of R&D projects. Using a measure of uncertainty reduction and a historical data derived threshold, project performance is proposed to be assessed using a different lens by which the initial stalling of a project can be anticipated.  Supplemental insights from the progress in uncertainty reduction measured using data typically collected through the Technology Readiness process minimizes the burden of collecting additional data and provides leverage for portfolio managers to reallocate resources to projects with higher potential value.    It is anticipated that in addition to the improvement in resource allocations, a collateral impact in motivating higher quality ideas will ensue.   Ideas that populate an opportunity space are key enablers to optimizing the value from R&D investments.  This method which utilizes existing data to enable better R&D project exit strategies is anticipated to be tactically efficient to further enrich best practices in portfolio management.

Committee

• Dr. Dimitri Mavris – School of Aerospace Engineering (advisor)
• Dr. Daniel Schrage– School of Aerospace Engineering
• Dr. Elena Garcia – School of Aerospace Engineering
• Larry Schneider – Retired VP Chief Program Engineer