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Valuing R&D: Investment and Risk Modeling Methods and Tools at Boeing

Valuing R&D: Investment and Risk Modeling Methods and Tools at Boeing

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Valuing R&D: Investment and Risk Modeling Methods and Tools at Boeing

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  1. Valuing R&D:Investment and Risk ModelingMethods and Tools at Boeing Scott Mathews, Technical Lead Mathematical Modeling Analyst Computational Finance and Statistical Modeling Analytic Modeling & Simulation, PW 253-773-2695

  2. Investment QuestionsAnswered by Boeing Methods • What should I be investing in today, how much and why? • Are these investments either increasing my return opportunities or decreasing risk? How? • What are the risks and how am I hedged for their eventuality? • How does this investment optimize my long term strategy-portfolio holdings/risk/return?

  3. Issues with Traditional Financial Models used for in Technical R&D Programs • Risk is never modeled as a quantitative value • Financial models don’t capture technical decisions or flexibility to change direction • High discount rate for long-term uncertain payoff or high risk programs result in negative NPV • Sparse market data and projections • Inability to collaborate technical and financial models, especially large programs

  4. Some Boeing Investment and Risk Methods • Quantitative Technical Risk Modeling • Investment vs. Risk Modeling • Real Options • Demand (Price & Quantity) Modeling • Structured (Object-Oriented) Spreadsheet Modeling • Portfolio Analysis

  5. 70% probability $100 $115 The most likely value does not represent the average value. Average Value = 70% x $100 + 30% x $150 = $115 30% probability $150 • Statistical models capture the range of possible outcomes $115 150 100 Why Model Uncertainty • Estimates compress reality into a single value: theMost LikelyValue

  6. Profit/Loss Profile Diagrams for Unfolding Future A Decision Treewithuncertainty generates a statistical probability

  7. Quantitative Technical Risk ModelingBuilding Uncertainty into Models ExcelspreadsheetwithCrystal Balladd-in

  8. Risk Eliminatedby Investment Risk Management through Targeted Investments • Today’s projection of future asset (Red distribution) • Without investment today, production will commence with great cost uncertainties. Project discount rate is high. • Investment today in Technology Development increases the predictability of program costs. This reduces program cost risks. (Blue distribution). Project discount rate lowers.

  9. Investment vs. Risk Modeling • Quantitatively rank investment decisions to risk reduction • Estimate reduction from current risk levels to target production-ready risk levels Mathews Ratio* Calculates Investment Effectiveness: Change in Risk Investment *“Mathews Ratio, Copyright (c) 2001, The Boeing Company, All Rights Reserved."

  10. The Value of Project Management Flexibility Concept of Real Options Profit/Loss Profile Greater Profits Most likely Value (NPV Value)    Worse Losses Time

  11. Simplified Real Options The Datar-Mathews Method* • Intuitive and transparent • Easily integrated into spreadsheets with simulation methods • Facilitates strategic planning • Extensible to many option types • A Boeing strategic advantage *“Datar-Mathews Method for Quantitative Real Option Valuation, Copyright (c) 2001, The Boeing Company, All Rights Reserved."

  12. Quickly Deriving Market DemandThe Boeing Demand Curve* Method • Key to quantifying primary project risk: Price and Quantity • Uses sparse data inputs • Easily integrated into spreadsheets with simulation methods • Two methods have been developed • Non-differentiated markets (such as commodities, prices are public) • Differentiated markets (such as durable goods sold by contract) • Calculates optimum price and quantity to maximize profits • An adjunct to project real option valuation *“The Boeing Demand Curve, Copyright (c) 2001, The Boeing Company, All Rights Reserved."

  13. Demand ModelingThe Boeing Demand Curve* Method $8B $140M Demand (Market Price) $120M $6B Cost Gross Profits $100M $4B $2B $80M Price Profits $0B $60M -$2B $40M Units Sold 0 200 400 600 800 1000

  14. Some Demand Model ResultsMaximizing Project Profits • Clear visualization of interplay of variables • Optimum Price is a narrowly defined price range • Easily integrated into real options simulation model

  15. Structured (Object-Oriented) Spreadsheet Modeling for Large-Scale Business Cases CPML UML Structured (OO) Model • Provides ability to exchange or add components efficiently • Allows objects created by different parts of a corporation to be tied together • Makes it easy to incorporate new ideas and methods given specific inputs and outputs • Key Capabilities: • Facilitates Knowledge Management • Monitors Data Flow • Extensibility

  16. $     Risk,  Portfolio Analysis • Combining independent models into portfolio • Use Sharpe Ratio, ($/) to determine investment exposure and returns Platform Risk, NPV, Cash Flow, Economic Profit, Option Value, Risk Adj. Profit, Sensitivity Asset 2 Asset 1 Low-Level Dependencies Asset 4 Asset 3

  17. End of Presentation