DYNAMIC STRATEGIC PLANNING USING REAL OPTIONS TO IMPROVE

DYNAMIC STRATEGIC PLANNING USING REAL OPTIONS TO IMPROVE SYSTEMS DESIGN Richard de Neufville MIT Technology and Policy Program Slide 1 of 23

CONCLUSIONS l l “OPTIONS THINKING” WILL DEEPLY CHANGE THE WAY DESIGNERS THINK ABOUT SYSTEMS DESIGN “OPTIONS ANALYSIS” WILL ENABLE DESIGNERS, REALLY FOR FIRST TIME, TO VALUE FLEXIBILITY CORRECTLY AND THUS IDENTIFY WHAT KINDTO INSERT IN THEIR CREATIONS Richard de Neufville MIT Technology and Policy Program Slide 2 of 23

ORGANIZATION l l PART 1 -- WHAT IS THE POSITION OF OPTIONS ANALYSIS IN SYSTEMS DESIGN? PART 2 -- WHAT ARE ITS IMPLICATIONS FOR PRACTICE? Richard de Neufville MIT Technology and Policy Program Slide 3 of 23

A HISTORICAL PERSPECTIVE l l l “SYSTEMS ANALYSIS”, “SYSTEMS DESIGN” A PHENOMENON SINCE 1950 s DUE TO NEW TOOLS (COMPUTERS) AND METHODS (OPTIMIZATION, ETC. . . ) EARLIER, SYSTEMS IMPLEMENTED WITHOUT SYSTEMS ANALYSIS Ø Ø SYSTEM DIVIDED INTO INDEPENDENT BITS BRIDGE JOINTS; HIGHWAY LENGTHS Richard de Neufville MIT Technology and Policy Program Slide 4 of 23

3 DEVELOPMENT PHASES OF SYSTEMS ANALYSIS / DESIGN l l l OPTIMIZATION -- PRIMARY TO 1970 s DECISION ANALYSIS -- PRIMARY 1970 s TO 1990 s “REAL” OPTIONS ANALYSIS --2000 s Richard de Neufville MIT Technology and Policy Program Slide 5 of 23

OPTIMIZATION l l POWERFUL ANALYSIS OF Z = f(a. X) Subject to g(c. X) < B EXCELLENT ON IMPORTANT PROBLEMS BUT: LIMITED SENSITIVITY ANALYSIS -ASSUMES PARAMETERS KNOWN UNSUITED FOR UNCERTAIN CONTEXT Richard de Neufville MIT Technology and Policy Program Slide 6 of 23

UNCERTAINTY IS FUNDAMENTAL l l l “THE FORECAST IS ALWAYS WRONG” -- AMPLY DOCUMENTED, ALL FIELDS ANY SYSTEM WILL HAVE TO PERFORM IN BROAD RANGE OF CIRCUMSTANCES UNCERTAINTIES ARE: Ø Ø Ø TECHNICAL ECONOMIC (PRICES, ECONOMIC CYCLE…) INDUSTRIAL (STRUCTURE OF COMPETITION) POLITICAL (REGULATORY, LEGAL…) ETC. . . Richard de Neufville MIT Technology and Policy Program Slide 7 of 23

DECISION ANALYSIS l l FOCUS ON SEQUENCES OF CHOICES, FROM PRE-DETERMINED POSSIBILITIES NOTABLE LESSONS Ø Ø l FLEXIBILITY HAS VALUE SECOND-BEST SOLUTIONS OPTIMAL HOWEVER, NO PROCESS FOR Ø Ø DETERMINING DESIRABLE POSSIBILITIES RISK-ADJUSTED DISCOUNTED CASH FLOW Richard de Neufville MIT Technology and Policy Program Slide 8 of 23

RISK-ADJUSTED DISCOUNT RATE l l HIGHER RATE FOR HIGHER RISK (CAPM CAPITAL ADJUSTED PRICING MODEL REFLECTS RISK AVERSION) THROUGH TIME, ACCORDING TO EVENTS, RISK LEVEL CHANGES NO SINGLE DISCOUNT RATE APPLIES DECISION ANALYSIS WITH CONSTANT DISCOUNT RATE IS INACCURATE Richard de Neufville MIT Technology and Policy Program Slide 9 of 23

OPTIONS ANALYSIS l PROVIDES CANONICAL MEANS TO ACCOUNT FOR VARYING RISK (BLACK -SCHOLES, WIENER PROCESS) BOTH TECHNICAL AND MARKET RISK l NOBEL PRIZE WINNING EFFORT l l FOCUS ON PRICING OF FLEXIBILITY, OF “OPTIONS” Richard de Neufville MIT Technology and Policy Program Slide 10 of 23

WHAT IS AN OPTION? l l A PRECISE MEANING -- NOT “CHOICE” OPTION IS “RIGHT, NOT OBLIGATION” TO TAKE AN ACTION, A CAPABILITY ACQUIRED AT SOME EFFORT Ø Ø l CALL CONTRACT TO BUY STOCK AT $X CONTRACT TO BUY EXPANSION SITE “REAL” OPTIONS ARE PHYSICAL Ø Ø R&D TO PERMIT PRODUCT LAUNCH DUAL-FUEL BURNERS FOR POWER PLANTS Richard de Neufville MIT Technology and Policy Program Slide 11 of 23

“REAL” OPTIONS ANALYSIS l l l IDENTIFIES VALUE OF DESIGN ELEMENTS PROVIDING FLEXIBILTY GIVES DESIGNERS ANALYTIC BASIS FOR DESIGN CHOICES DIFFERS FROM RELIABILITY ANALYSIS - INCLUDES MARKET RISKS Richard de Neufville MIT Technology and Policy Program Slide 12 of 23

EXAMPLE: VALUE OF R&D? l TRADITIONAL ANALYSIS Ø l WHAT IS EXPECTED VALUE OF EFFORT OPTIONS ANALYSIS Ø Ø Ø R&D IS AN OPTION CAN BE EXERCISED IF MARKET IS POSITIVE IF MARKET OR TECHNOLOGY POOR, DROP BECAUSE POOR OUTCOMES DROPPED VALUE AS OPTION > EXPECTED VALUE Richard de Neufville MIT Technology and Policy Program Slide 13 of 23

PRACTICAL IMPLICATIONS l GREATER VALUE, THUS EMPHASIS ON FEATURES NOT TRADITIONALLY CONSIDERED AS OPTIONS Ø Ø l RESEARCH, PRODUCT DEVELOPMENT DESIGN CHOICES (FACILITY SIZE) DESIGN CONFIGURATION (INTERNET) DESIGN OPERATION (SHARED FACILITIES) OPTIONS “THINKING” Ø EXPLICIT FOCUS ON FLEXIBILITY Richard de Neufville MIT Technology and Policy Program Slide 14 of 23

EXAMPLE -- PRODUCT DEVELOPMENT AT FORD l R&D INVESTMENTS Ø l AS OPTIONS ON THE POSSIBILITY OF A NEW PRODUCT, NOT PRODUCT DECISIONS, R&D IS AROUND 20% MORE VALUABLE (Neely) BALLARD, FUEL CELL VEHICLE Ø Ø NESTED OPTIONS, ON MARKETS FOR VEHICLES AND POWER SOURCES INVESTMENT GOOD -- EVEN IF ON AVERAGE FC CAR NOT REASONABLE (Oueslati) Richard de Neufville MIT Technology and Policy Program Slide 15 of 23

EXAMPLE -- FLEXIBLE PRODUCTION l DUAL-FUEL POWER PLANT (Kulatilaka) Ø Ø Ø l l DEVICES TO PERMIIT OIL/GAS SWITCH COST VALUE IS USE OF CHEAPER FUEL DEPENDS ON FUTURE MARKETS CAR MANUFACTURE (Toyota, Komatsu) AIRPORT DESIGN (Shared Gates) Ø Ø ENABLING FLEXIBLE PRODUCTION => TRACKING OF VOLATILE DEMANDS Richard de Neufville MIT Technology and Policy Program Slide 16 of 23

EXAMPLE -- NATURAL RESOURCE EXTRACTION l OIL PLATFORMS (Hibernia / Smets) Ø Ø l TRADITIONAL: DESIGN TO TARGET PRODUCTION RATE OPTIONS ANALYSIS: LARGER SIZES GIVE OPTION ON FASTER EXTRACTION DESIGN OF MINE DEVELOPMENT (Peru) Ø Ø EXPLORATION, INFRASTRUCTURE PROVIDE OPTION ON EXTRACTION WHAT TO BUILD, WHEN? Richard de Neufville MIT Technology and Policy Program Slide 17 of 23

EXAMPLE -- COMPUTER ARCHITECTURE l MODULARITY (Baldwin and Clark) Ø Ø l HOW MANY MODULES? COST VS. VALUE OF FLEXIBILITY LOCATION OF NETWORK INTELLIGENCE Ø Ø CENTRALLY -- AS IN TELEPHONE COMPANY AT EDGES -- EXTRA EXPENSE CREATES OPTION ON INNOVATION -- USERS CAN EASILY CHANGE DISTRIBUTED DEVICES Richard de Neufville MIT Technology and Policy Program Slide 18 of 23

OPTIONS THINKING… l IF OPTIONS ANALYSIS IMPRACTICAL? Ø Ø l MARKETS POORLY UNDERSTOOD HISTORICAL RECORDS ABSENT OPTIONS THINKING Ø Ø Ø USE DECISION ANALYSIS AS A PROXY EXTENSIVE SPREADSHEET ANALYSIS EXAMPLE -- KODAK (See Faulker) Richard de Neufville MIT Technology and Policy Program Slide 19 of 23

CONCLUSIONS l l “OPTIONS THINKING” WILL DEEPLY CHANGE THE WAY DESIGNERS THINK ABOUT SYSTEMS DESIGN “OPTIONS ANALYSIS” WILL ENABLE DESIGNERS, REALLY FOR FIRST TIME, TO VALUE FLEXIBILITY CORRECTLY AND THUS IDENTIFY WHAT KINDTO INSERT IN THEIR CREATIONS Richard de Neufville MIT Technology and Policy Program Slide 20 of 23

REFERENCES -- theory l l Trigeorgis, L. (1996) Real Options, Managerial Flexibility and Strategy in Resource Allocation, MIT Press, Cambridge, MA. Mc. Donald, R. (2000) “Real Options and Rules of Thumb in Capital Budgeting, ” in Project Flexibility, Agency and Competition, Brennan and Trigeorgis, Oxford Univ. Press, Oxford, UK, pp. 13 -33 Richard de Neufville MIT Technology and Policy Program Slide 21 of 23

REFERENCES -- applications l l Amran & Kulatilaka (1999) Real Options, Managing Strategic Investment in an Uncertain World, Harvard Business Sch. Faulkner T. W. (1996) "Applying Options Thinking to R & D Valuation, " Research Technology Management, May, 50 -56. Nichols, N. (1994) "Scientific Management at Merck: An Interview with Judy Lewent, " Harvard Business Review, Jan. 89 -99. Baldwin and Clark (2000) Design Rules, the power of modularity, MIT Press. Richard de Neufville MIT Technology and Policy Program Slide 22 of 23

RECENT THESES l NEELY -- Ph. D. -- Practical Method for Valuing “Real Options” Ø Ø l OUESLATI -- Method for Valuing Real Options for Multiple Markets Ø l Applied to Research Projects at Ford (with J. Clark, D. Lessard) Applied to Ford’s Investment in Fuel Cells SMETS -- Application to Hibernia Platform Richard de Neufville MIT Technology and Policy Program Slide 23 of 23