Strategic Engineering Designing Systems for an Uncertain Future

1. Strategic EngineeringDesigning Systems for an Uncertain Future 21st Century COE ProgramSystem design: Paradigm Shift from Intelligence to Life Keio University June 10, 2006 Olivier L. de Weck deweck@mit.edu Assistant Professor of Aeronautics & Astronautics and Engineering Systems

2. Motivation: Iridium Satellite System 'Motorola unveils new concept for global personal communications: base is constellation of low-orbit cellular satellites', Motorola Press Release on Iridium, London, 26 June 1990. ‘Last week, Iridium LLC filed for bankruptcy-court protection. Lost investments are estimated at $5 billion.’ Wall Street Journal, New York, 18 August 1999. • Difficult to properly size capacity of large system • Market assumptions can change when 7-8 years elapse between conceptual design and fielding (1991-1998) Iridium Satellite

3. Outline • Customization of the F/A-18 Aircraft • Introduction to Strategic Engineering • Research Projects: • Staged Deployment of Satellite Constellations • Flexible Automotive Product Platforms • Time Expanded Decision Networks (TDN) • Engineering Education

4. Customization of the F/A-18 Aircraft

5. (1993) Swiss Mission interceptor land based 5000 flight hours 40 min average sortie max 9.0g positive ~30 year useful life “Redesign” (Switch) Modified Swiss F/A-18 C/D Configuration Mission (and Configuration) Change U.S. Navy Mission (1978) Standard U.S. Navy F/A-18 C/D Configuration fighter and attack aircraft carrier based 3000 flight hours 90 min average sortie max 7.5g positive ~15 year useful life

6. F/A-18 Redesign Strategy • specify new Swiss mission usage spectrum • apply new spectrum to existing U.S. Navy Configuration • identify and prioritize “hot spots” that most need change • redesign and implement local changes at “hot spots”

7. F/A-18 Wing Carry-Through Bulkheads

8. F/A-18 Center Barrel Section Y488 Y470.5 Wing Attachment Y453 74A324001

9. F/A-18 Center Fuselage Buildup (1)

10. achieved not expected or wanted Center Barrel Change Consequences • Substitution from Aluminum to Titanium • Intended Consequence: • Increased fatigue life of individual components from 3000  5000 hours • Unintended Consequences: • Increased aircraft empty weight by ~O(100) lbs • Shifted C.G. of aircraft by ~ O(1) inch • Stiffened fuselage (1st bending mode) ~(0.1) Hz • Rendered manufacturing processes obsolete

11. Manufacturing Processes Changed Original Change Fuselage Stiffened Flight Control Software Changed Center of Gravity Shifted Gross Takeoff Weight Increased F/A-18 Complex System Change F/A-18 System Level Drawing

12. F/A-18 Lessons Learned • Changes increased cost per aircraft by O(~$10M) • Changing a system after its initial design is • often required to accommodate new requirements • expensive, and time-consuming if change was not anticipated in the original design • Change propagation • some changes are local and remain local • other changes start local, but propagate through the system in complex, unanticipated ways • switching costs include: engineering redesign cost, change in materials, manufacturing changes, change in operational costs

13. Introduction to Strategic Engineering

14. What about the Future ? • Typical Engineering Design Mindset: • “Give me a set of requirements today, a timeline and a budget and I will design and deliver the best possible product/system/project for you by tomorrow.” • 90% of thinking and design effort is spent on this • But, in essence, we are always forecasting: • what customers will require in 18 months • what capacity our facility will need in 3 years • what variants we will produce in 8 years • how many missions we will fly in 12 years • What if our forecast is wrong? (it usually is) • Perhaps system will function technically …. • But system will not deliver optimal value, or architectural “lock-in” occurs, or it will fail financially if its configuration is not easily changed

15. Traditional (Systems) Engineering System Validation Customer Needs Marketing Requirements Definition Fielding/ Launch System Functional Testing Product System Ytarget Systems Engineering System Yactual Conceptual Design Subsystem Development Final Assembly Subsystem Ytarget Subsystem Yactual Component Design Preliminary Design Subsystem Integration System Operation Components Ytarget Components Yactual Detailed Design Component Testing

16. Implicit Assumptions of TSE • The customer knows what his/her needs are • The requirements are known and time-invariant • The system or product can be designed as one coherent whole and is built and deployed in one step • There is only one system or product designed at once • The system will operate in a stable environment as far as regulations, technologies, demographics and usage patterns are concerned

17. But reality tells us that … • Customer knows some of his/her needs but not all • The true requirements often change after the system is fielded and experience is gained • Constraints on capital expenditures and operating budgets frequently only allow a “piecemeal” implementation • Often multiple variants of a system must be designed and built, possibly based on some common standard • Environment is not static, but dynamic • macro economic/budgetary changes (e.g. prime interest rate) • regulatory changes (e.g. new CAFÉ standards) • new technologies emerge (e.g. hydrogen fuel cells for cars) • demographic shifts (e.g. aging population in Western nations) • changing customer preferences (e.g. weighting of fuel economy) • disruptive events (natural, man-made)

18. Strategic Engineering • Strategic Engineering is the process of designing systems and products in a way that deliberately accounts for customization and future uncertainties such that their lifecycle value is maximized.

19. Baseline System Baseline System Gen 2 Baseline Gen 2 Baseline … Variant B Variant B Variant B2 Variant B2 … Variant C Variant C Variant C2 Variant C2 … Space … … Strategic Engineering Framework - CDI – - Operate - – RDI – – Operate - Time Operations Operations Development Development (Stage 2) (Stage 1)

20. robust risk averse opportunistic flexible Interested in how to do 3. Strategic Engineering Alternatives • Ignore the future and design for `optimal’ immediate or short-term use (= TSE) • Come up with a `best guess’ of the most likely future scenario and design to it (= forecasting + TSE) • Develop a range of potential future outcomes and design such that the system will be • optimal on `average’ across all future scenarios • protected against the worst case scenario • take advantage of the `best case’ scenario • most flexible to adapt to any scenario

21. Strategic Engineering “Toolbox” • Traditional Systems Engineering Methods (QFD, DSM,…) • Forecasting, • Change Propagation Analysis • System Architecting Principles, “Illities” • Modularity, Flexibility, Scalability, Reconfigurability,… • Real Options “in” Projects • Standardization • Product/System Platforms • Staged Development and Deployment • Optimization: Dynamic Programming, Multiobjective, … … all these attempt to address part of the problem, when do these methods apply, is there a unifying framework …?

22. de Weck Research Approach theory Time-expanded decision networks Non-dimensional lifecycle analysis Meta-platforming Generic Lifecycle Cost Modeling Generic System Modeling (OPM) Comparative Analysis NASA: Launch Vehicle Selection & Evolution DARPA/AFRL: Space Tug Mission Scenarios BP: Exploration & Production Standardization NASA: Inter-planetary Supply Chain & Logistics ARM: Hydrogen Enhanced Combustion Engine Iridium and Globalstar: Staged Deployment GM: Flexible Automotive Product Platforms BP: Commercial Office Building Staging application

23. Staged Deployment of Satellite Constellations • Funded by Alfred P. Sloan Foundation • Reference • de Weck, O.L., de Neufville R. and Chaize M., “Staged Deployment of Communications Satellite Constellations in Low Earth Orbit”, Journal of Aerospace Computing, Information, and Communication, 1, 119-136, March 2004

24. C: 'walker' h: 2000 emin: 12.5000 Pt: 2400 DA: 3 MA: 'MFCD' ISL: 0 X1440= Design (Input) Vector X Design Space Constellation Type: C Orbital Altitude: h Minimum Elevation Angle: emin Satellite Transmit Power: Pt Antenna Size: Da Multiple Access Scheme MA: Network Architecture: ISL Astro- dynamics Satellite Design Network This results in a 1440 full factorial, combinatorial co design space

25. Objective Vector (Output) J • Performance (fixed) • Data Rate per Channel: R=4.8 [kbps] • Bit-Error Rate: pb=10-3 • Link Fading Margin: 16 [dB] • Capacity • Cs: Number of simultaneous duplex channels • Cost • Lifecycle cost of the system (LCC [$]), includes: • Research, Development, Test and Evaluation (RDT&E) • Satellite Construction and Test • Launch and Orbital Insertion • Operations and Replenishment

26. Multidisciplinary Simulator Structure Constants Vector Input Vector p x Constellation Spacecraft Cost Launch Module Link Budget Capacity Satellite Network Number of spot beams Output Vector Satellite Mass J Number of gateways Number of Satellites Launch vehicle selection Number of orbital planes Note: Only partial input-output relationships shown

27. Governing Equations – Satellite Simulator Energy per bit over noise ratio: a) Physics-Based Models (Link Budget) b) Empirical Models (Spacecraft) Scaling models derived from FCC database Springmann P.N., and de Weck, O.L. ”A Parametric Scaling Model for Non-Geosynchronous Communications Satellites”, Journal of Spacecraft and Rockets, May-June 2004

28. If actual demand is below capacity, there is a waste If demand is over the capacity, market opportunity may be missed Demand distribution Probability density function waste under cap Traditional Systems Engineering • The traditional approach for designing a system considers configurations (architectures) to be fixed over time. • Designers look for a Pareto Optimal solution in the Trade Space given a targeted capacity. 1 10 Iridium actual Iridium simulated Lifecycle Cost [B$ FY 2002] Globalstar actual Pareto Front Globalstar simulated 0 10 3 4 5 6 7 10 10 10 10 10 Global Capacity Cs [# of duplex channels]

29. Staged Deployment • Adapt to uncertain demand with a staged deployment strategy: • A smaller, more affordable system is initially built • This system has the flexibility to increase its capacity if demand is sufficient and if the decision makers can afford additional capacity • Economic Advantage • Some capital investments are deferred to later • The ability to reconfigure and deploy the next stage is a real option

30. Step 1: Partition the Design Vector Constellation Type: C Orbital Altitude: h Minimum Elevation Angle: emin Satellite Transmit Power: Pt Antenna Size: Da Multiple Access Scheme MA: Network Architecture: ISL Rationale: Keep satellites the same and change only arrangement in space Astro- dynamics xflexible Satellite Design xbase Network Stage II Stage I C: 'polar' h: 1000 emin: 7.5000 Pt: 200 W DA: 1.5 m MA: 'MFCD' ISL: 1=yes C: 'walker' h: 2000 emin: 12.5000 Pt: 200 W DA: 1.5 m MA: 'MFCD' ISL: 1=yes = xIIbase xIbase

31. h= 400 km • = 35 deg Nsats=1215 • h= 400 km • = 20 deg Nsats=416 • h= 2000 km • = 5 deg Nsats=24 • h= 800 km • = 5 deg Nsats=54 • h= 400 km • = 5 deg Nsats=112 Step 2: Search Paths in the Trade Space family Lifecycle cost [B$] Constant: Pt=200 W DA=1.5 m ISL= Yes Total: 40 Paths System capacity

32. Step 3a: Model Uncertainty [GBM] D - demand Dt – time period e- SND random variable m, s - constants • Demand can go up or down between two decision points • Infinitely many scenarios can be generated based on this model 5 x 10 Geometric Brownian Motion Model 1.6 GBM model, Dt = 1 month, Do = 50,000, m = 8% p.a., s = 40% p.a. – 3 scenarios are shown 1.4 1.2 Demand [Nusers] 1 0.8 0.6 0.4 0 5 10 15 Time [years]

33. Step 3b: Binomial Lattice Model Total 25=32 scenarios p p p Sample scenario Discretized Random Walk (1-p) 1-p

34. Cap2 Cap1 Deploy 2nd stage Step 4: Calculate cost of paths • We compute the costs of a path with respect to each demand scenario • We then look at the weighted average of every allowable path for cost over all scenarios • Decision rule: We always adapt to demand when demand exceeds capacity • The costs are discounted: the present value of LCC is considered Costs wait wait wait Initial deployment

35. For a given targeted capacity, we compare our solution to the traditional approach Our approach allows large savings (30% on average) LCC of rigid design E [LCC(pathj)*]= Best Deployment Strategy Step 5: Identify optimal path Traditional design E[DLCC]=$650 million value of real option

36. Takeaway from Satellite Project • Identified best initial configuration, as well as potential growth stages • Previous work focused on optimal coverage for static requirements only, arrive at very different solution • Requires extra upfront investment (e.g. extra fuel, tunable antenna patterns), technical details remain Stage A1 21 satellites 3 planes h=2000 km Stage A2 50 satellites 5 planes h=800 km Stage A3 112 satellites 8 planes h=400 km

37. Flexible Automotive Product Platforms • sponsored by General Motors 2003-2005 • Suh E.S., de Weck O.L., Chang D., “Flexible Product Platforms: Framework and Case Study”, Research in Engineering Design, submitted Nov.2, 2005

38. Research Context & Questions • Sharp increase in number of models (variants) offered in the U.S. automotive market [Detroit News, Jan 2005]: • 1947: 33 • 1990: 198 • 2009: 277 (estimate) • Sales volumes per variant drop on average • Market fragmentation • Platform strategy adopted by most manufacturers • Many uncertainties: • Styling & performance preferences shifting, regulations, new technologies  future sales volumes are uncertain • How to design platforms to be flexible to respond to future developments? Model 3-4 years Model 3-4 years Model 3-4 years Platform ~ 10-15 year life

39. Typical Vehicle Architecture (Platform) – General Motors Unique Carryover Modified Common “Platform” • Traditional product platform concept: • Unique Elements: Variant-specific customized elements • Common Elements: Commonly shared elements among product family • Rise of new elements class • Flexible (“Cousin”) Elements: Elements used (with modification) in more than one variant to satisfy variant-specific requirement

40. Design Automotive Platforms to accommodate future changes in styling and demand of individual variants Identify flexible elements Developed 7-step process Change Propagation Analysis BIW Change Propagation Network Body-in-White Platform Key Design Variables

41. Embed Flexibility W27 Flexible/Unique Upper Passenger Compartment *Assume it meets quality, manufacturing, and safety requirements H122 H50 L48 Flexible Lower Rear Passenger Compartment Common Lower Front Passenger Compartment Critical Components (Example) Flexible BIW Design Inflexible BIW Design Unique Unique Body Outer Panel Common Unique Unique Body Inner Panel Flexible (Blanking) Common

42. H122 H5 L48 Cost of Design Alternatives Length Change Above Belt Line W27

43. Takeaway Automotive Platforms • Product Platforms …. • “Bandwidth” can be increased by carefully embedding flexibility in the design • Key is to propagate exogenous, functional uncertainties into design variables and find critical physical components • Critical components are those that are change multipliers, or whose change would cause large switching costs • Design for flexibility might cause larger upfront investment and larger variable costs • Crossover between rigid and flexible design as a f(uncertainty) typically occurs

44. Wrap-Up

45. wait wait switch switch chance node decision node state node start end … Time-expanded Decision Networks Period 1 Period 2 Period N

46. max NPV, min LCC, … start end … Path Optimization in TDN For each uncertain scenario, find the optimal path through the TDN example Period 1 Period 2 Period N

47. Principles of Strategic Engineering • A rigid design will be optimal (max NPV) if future events unfold exactly as forecasted • A robust design can minimize the standard deviation of outcomes (reduce risk), but will usually also lower the expected NPV and max achievable NPV • The larger the degree of uncertainty, the more valuable flexibility will be. Flexible designs can increase the E[NPV], while limiting downside and maximizing upside • The larger the switching costs from one configuration to another the more likely that the current system will be • continued due to “architectural lock-in”, despite operational sub-optimality

48. “we are betting the farm” Strategically Redesign Flexible Design “we can adapt” Optimize for Expected Requirement Robust Design “we will be ok no matter what” “we know what’s coming” Strategic Engineering Map Degree of NPV Uncertainty s E[NPV] Relative Switching Costs DC/LCCr

49. Future Work: Where do various systems fall ? Degree of NPV Uncertainty ? s communication satellites E[NPV] commercial aircraft wireless sensor networks automotive platforms consumer products highway infrastructure water supply system Relative Switching Costs DC/LCC

50. The migration of strategic thinking Warfare Management ~500 A.D. Sun Tzu The Art of War Carl von Clausewitz (1780-1831) since ~1960s Michael E. Porter Competitive Strategy: Techniques for Analyzing Industries and Competitors Engineering since 2000? target domain: System/ Product Army Firm