1. © 2005. All rights reserved.1 Strategic R&D Value Lifecycle Management Doug Bodner, Bill Rouse and Mike Pennock Tennenbaum Institute Georgia Tech

2. 2 Outline R&D value and value lifecycle Value levers Simulation-based approach –Process model and results –Product model and integration Future work

3. 3 R&D Value Creation Value is derived from financial returns of deployed products or systems. Value is realized downstream. Upstream estimates of value are uncertain/dynamic. Multi-stage investment structure mitigates risk via flexibility. Stage 1 Technical failure Not Funded Stage 2 Stage 3 Stage 4 $ $ $ $ Value realized

4. 4 R&D Value Lifecycle Idea ConceptProposalProjectResult $? Archive Deploy Retire Patent Maintenance Obsolescence Production Licensing Maintenance Revisions Disposal Option Value Cash Flow Value

5. 5 R&D in a PLM/SLM Context Adapted from: IBM PLM definition slide at PDES Inc. Board Mtg. 2003-11 Portfolio Planning Concept Development Design Portfolio Planning Product/System Design Process R&D R&D Process Results Production & Test Sales & Distribution Market and technology feedback and forecasting Addressing current and future needs

6. 6 Goals Identify value levers that can be manipulated to improve value creation Understand quantitative effects in relation to enterprise parameters Provide what-if analysis capability for strategic decision-making

7. 7 Value Levers Enterprise operation –How to valuate R&D products –How to allocate budget among stages/programs –Portfolio management Enterprise design –How many R&D stages –Outsourcing R&D –Organizational structure

8. 8 Valuation Method Traditional project valuation approaches use discounted cash flow (DCF) analysis Real options analysis captures flexibility –Discontinue, defer, expand, contract Analytic, recursive and simulation- based computations Do nothing – i.e., no exercise Cost of future stage(s) – i.e., exercise price Cash flow – i.e., asset price Pay next stage – i.e., exercise Pay this stage Decision point DCF Real Options

9. 9 Budget Allocation Can be conceptualized as line-balancing –Allocate funds to meet expected budget requests weighted by cumulative failure rates Is it better to –“Balance” the allocation –Shift funds upstream –Shift funds downstream Stage 1 Technical failure Not Funded Stage 2 Stage 3 Stage 4 R&D Budget $ $ $ $

10. 10 Organizational Simulation Modeling and data gathering –Understand your system Quantitative insight via controlled experiments –Attach numbers to effects with statistics What-if analysis –Experiment and see effect of changes without using the real enterprise R&D Processes Resources Value Computer Model Dynamic behavior Resources Workflow Output People Decisions Uncertainty

11. 11 R&D World

12. 12 Model Description R&D process model (four stages) R&D modeled as lines that traverse through each stage Budget requests per stage increase by factor of 2 Technical failure rates decrease as stages are traversed Estimated deployed value varies lognormally over time Value realized after successful deployment

13. 13 Experimental Design Factors – Valuation method (DCF vs. options) – Budget allocation (LB vs. UF) – Probability of initial NPV negativity (33% vs. 50%) – Volatility (20% vs. 60%) Experiment - 10 replications - 25 years each - 5 year warm-up Dependent Variables –Total value created (TVC) –Yield (Y) = TVC/Expenditures Two 2 4 factorial experiments (one for each dependent variable).

14. 14 Results – Valuation Valuation using options outperforms DCF for total value created. –Especially when initial NPV negativity is likely, and also when volatility is high. DCF outperforms for yield. Options emphasize total value, while DCF emphasizes ROI. DCF is more conservative than options Under DCF, lower percentages of R&D budget are expended.

15. 15 Results – Allocation Shifting funds upstream outperforms line-balancing for total value created. –Especially when volatility is high and when initial NPV negativity is likely. Line-balancing outperforms for yield. –Especially with low volatility. This effect appears due to the upside potential of market risk. Line-balancing is more conservative.

16. 16 Thinking of R&D “Products” Different R&D product (RDP) types –Technologies, technical reports, prototype systems or consumer products, patents Different status possibilities –Planned, proposed for funding, in progress, available, retired Different generations Precedence relationships R&D value network –Structure and dynamics

17. 17 RDP Value Network Deployable (Market/ application risk) Technology generations Available Proposed for funding Future (planned/possible) Increasing R&D stage Precedence relationship RDP Failure possibility

18. 18 Determining Value $ $ 1 2 3 4 V 1 and V 2 are functions of C A and C B V 3 and V 4 are functions of C B A B Both 1 and 2 are required for A and B 1, 2 and 4 are required for B 3 may not be required V = value C = cash flow

19. 19 Relational Model RDP_Types Type Description TechArea_Types Type Description Gate_Types Type Description Gates ID Type RDPs ID Type TechArea Generation Status CostPerYear Duration CurrentValue DeployedValue Volatility Stage Program Parent_Child ID Gate Parent Child Status_Types Type Description

20. 20 Model Integration Update RDP status R&D Process Model Initialize process model R&D Product Model Query parameters for value computation Update current value Query parameters for stage/program submission ARENA® simulation Microsoft® Access (Read/write via ADO) Generate new RDPs

21. 21 Pull Dynamics Available Proposed for funding (could start work now) Future (prerequisites not fulfilled) Desired RDP

22. 22 Future Research Model more complex decision logic in process model Specify computationally efficient valuation methods for RDPs in complex value networks Enhance knowledge management in product model Enhance and validate via case studies Explore integration with strategic design

23. 23 Questions? ? ? ? ? ? ? ? ?

24. 24 Further Reading Bodner, Rouse and Pennock, 2005, Using simulation to analyze R&D value creation, Winter Simulation Conference, submitted. Hansen, Weiss and Kwak, 1999, Allocating R&D resources: a quantitative aid to management insight. Research Technology Management 42: 44-50. Rouse and Boff, 2003, Value streams in science & technology: a case study of value creation and intelligent tutoring systems, Systems Engineering 6: 76-91. Rouse and Boff, 2005, Organizational simulation. New York: Wiley-Interscience. Trigeorgis, 1996, Real options: managerial flexibility and strategy in resource allocation. Cambridge, MA: The MIT Press.