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PENN S TATE

PENN S TATE

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PENN S TATE

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  1. Using Optimization to DetermineStrategic Platform Offerings* ME 546 - Designing Product Families - IE 546 Timothy W. Simpson Professor Mechanical & Industrial Engineering and Engineering Design The Pennsylvania State University University Park, PA 16802 USA phone: (814) 863-7136 email: tws8@psu.edu http://www.mne.psu.edu/simpson/courses/me546 *These slides are adapted, with permission, from Prof. Olivier de Weck at MIT. PENNSTATE © T. W. SIMPSON

  2. ? ? ? ? • Blue Factors: • mainly internal • have control over • Red Factors: • mainly external • uncertain, no control What Impacts Platform Strategy? Market Segmentation Manufacturing & Supply Chain Strategy Competition: New Products Mass Customization Platform Strategy Regulations & Standards Customers: Perceived Value Product Architecture Product Design New Technologies

  3. The Need for Platform Strategy • Competition: How to preempt or react quickly to new products from competitors? • Customers: What product features do all customers value highly? What product features are requested infrequently? • Technology: Can a product platform be designed such that new technologies can be “easily” infused? • Regulations and Standards: Can a platform be design to anticipate or meet future regulations (e.g. fuel economy and emission standards in cars)? Strategy: An adaptation or complex of adaptations (as of behavior, metabolism, or structure) that serves or appears to serve an important function in achieving evolutionary success. (Merriam-Webster, 2004)

  4. Poor Platform Strategy Source: R. J. Matson, St. Louis Dispatch, 7/17/2008

  5. An Enterprise Framework* Domain of Product Platform Variants Models Product Platforms to maps maps Platform Family Plan Chevrolet Malibu Platform A assigns Platform B places Saturn SL Platform C GMC Truck Sierra 1500 in Manufacturing Plan Marketing Plan to Corporate Strategy Production Plants (Facilities) Market Segments (Customers) chooses determines PLANT A SEDAN PLANT B SUV PLANT C PICKUP * Framework proposed by Dr. Olivier de Weck at MIT based on his collaborations with GM

  6. Product Architecture Business Strategy Organized to support architectural directions? (More than just an engineering question) Product Portfolio or Collection of Products? Does the system architecture match the evolution of key emerging technologies? (e.g., the Internet?) Open Systems? Platforms? or Cost-optimized Products Does the system architecture match the evolution of key surround business changes? (e.g., competing on cost or function? Changing product distribution channels?)

  7. Starting Assertions • Maximize profits …how? … offer family of diverse, competitive product variants, and minimize mfg cost • Platform strategy = program of deliberate reuse of components and processes within a family • How can commonality between products be quantified?  commonality indices • What components should be shared between products?  expensive ones with little effect on variant distinctiveness • What is the optimum amount of commonality?  difficult to answer in general, depends on market, firm …  moving target, changes dynamically from year-to-year • Without competition, no need for variants, no need for platforms  Ford Model T (one size fits all)

  8. Which strategy is best in a particular situation ? Platform Strategies Usually start with a market segmentation grid “Market Segment” No Leveraging Luxury Vertical Leveraging Horizontal Leveraging High-End Mid-Range Beachhead Approach Low-End Brand B Brand D Brand A Brand C

  9. Platform Portfolio Problem GM Platform Portfolio g b a N~20 • How many platforms (N) are optimal to support (V) variants? Optimize Ratio V/N • What is the optimal assignment of the V variants to the N platforms? Optimize assignment • How to deploy the V variants across M target market segments? Optimize Market Segment assignment • Determines Platform “Extent” A Y C Z Y Product Family Z V~100 For large product families, need more than one platform Ref also: Seepersad, Mistree, Allen, “A quantitative approach to designing multiple product platforms for an evolving portfolio of products”, 2002 ASME Design Engineering Technical Conferences, Paper No. DETC2002/DAC-34096

  10. Profit Maximization Actual Sales Price Sales volume Maximize product family profit, subject to investment cost constraints, by determining the optimal - number of platforms N - assignment vector - platform design vector set - variant design vector set Total Cost revenue cost Sum over all V product variants de Weck O., Suh E. S. and Chang D., ”Product Family and Platform Portfolio Optimization”, Paper DETC03/DAC-48721, Proceedings of DETC’03, 2003 ASME Design Engineering Technical Conferences, Chicago, Illinois, 2-6 September, 2003

  11. BOA BOM BOP Product (Variant) Modeling Framework Engineering Model Value Model x J V design vector value performance Architecture Model Market Model P price c D components demand Pr Manufacturing Model Financial Model C net profit cost maximize requires 6 models

  12. Bi-Level Optimization Methodology Platform Model • Steps • Create 6 models • Split product architecture into platform and variant components • Select N=1 platforms • Perform bi-level optimization for Pr • Set N=N+1 if N<V • Repeat steps 4. and 5. until N=V Variant Model Pr Variant Optimizer Platform Optimizer optimize for each N=1,2,…V

  13. TRCK LXSD SUV VAN MDSD SPTR LOWC Sedans Sports Utility Automotive Case Study (hypothetical) • What is the “optimal” platform strategy for a family with 7 variants ? • One product (vehicle) per market segment • Basic vehicle architecture is always BOF • Market segments operate independently • Competitors continue to offer the same • MSRP corresponds to actual sales price • Use 2001 database for North America • The platform consists of the chassis Assumptions Market Segmentation Grid high mid low How many platforms? How to optimally assign variants to those platforms?

  14. Vehicle Data Set for Case Study We are a (new) automotive manufacturer and want to compete successfully in these market segments: Symbol Name #vhc Size Mean Price LOWC Compact Car 30 2,357,802 $13,427 MDSD Medium Sedan 33 4,198,028 $19,844 LXSD Luxury Sedan 65 1,591,438 $34,238 SPTR Sports-Roadster 34 514,837 $23,424 SUVC SUV 56 3,519,461 $25,146 PUPT Truck 51 2,800,104 $22,805 MVAN Van 24 1,589,958 $24,986 16,571,628 Total U.S. Market 2001 ca. 16.8 M/year New Vehicle Sales What is the right strategy? Source: NIADA National Market Report - 2002

  15. Architecture Model WT chassis (common) ED body WB engine Architecture vehicle HT Chassis Engine Body Design Vector

  16. Vehicle Design Vector (genotype = vehicle “DNA”) Units [in] [in] [ccm] [in] [-] DV=[ 108.2 61.3 2990 58 1.0 ]T Example: DV=[ WB WT ED HT SF ]T Engine Body Platform PDV(k) =[ WB WT]T MDV(j) =[ ED ] DV’ = [ kj 1400 1.0 ] decode binary encode DV’’=[ 0 1 1 | 1 1 1 | 1 0 1 0 0 1 1 1 | 0 1 1 1 0 1 0 1 ]

  17. SUV SUV 28 6000 26 5500 24 5000 22 4500 Fuel Economy [mpg] 20 Curb Weight [lbs] 4000 18 3500 16 3000 14 2500 12 2000 10 2000 2500 3000 3500 4000 4500 5000 5500 6000 90 100 110 120 130 Curb Weight [lbs] Wheelbase [in] Engineering Model (e.g. WB to FE) x(1)=WB J(3)=FE Instead of detailed CAD/CAE-simulation model: - Response Surface Modeling (RSM) - Neural Network Regression Models

  18. Value Model Attributes LOWC MDSD LXSD SPTR SUV TRCK VAN 0.1 0.15 0.15 0.4 0.1 0.15 0.05 0.1 0.1 0.15 0.3 0.25 0.35 0.1 0.4 0.2 0.05 0.05 0.05 0.10 0.05 0.3 0.4 0.45 0.2 0.3 0.05 0.4 0.1 0.15 0.2 0.05 0.3 0.35 0.4 AC - Acceleration HP - HorsePower FE - Fuel Economy PV - Passenger Vol. CV - Cargo Volume Preference weight matrix Performance Vector J (Perceived) Value = Aggregate performance relative to the market segment leader Relative Price Value= Relative Performance

  19. Honda Civic Ford Explorer Example Segments Sports Utility Vehicles - SUV Compact Cars - LOWC Sales Volume vs MSRP - SUVs Compact Cars -Sales vs MSRP $80,000 $25,000 $70,000 $20,000 $60,000 $50,000 $15,000 MSRP $40,000 MSRP $10,000 $30,000 $20,000 $5,000 $10,000 $0 0 100000 200000 300000 400000 500000 0 100000 200000 300000 400000 Sales Volume Sales Volume Who are the leaders? Source: AutoPro

  20. II - Overscoped III - Noncompetitive I - Contender IV - Underscoped “Sweet Spot” - Market Model Relative Position w.r.t Leader - LOWC $1.60 $1.50 $1.40 $1.30 $1.20 Dw,i $1.10 Relative Price Prel $1.00 $0.90 $0.80 $0.70 $0.60 0.800 0.900 1.000 1.100 1.200 Value = Relative Performance

  21. Demand Sensitivity Curve Demand Sensitivity - MDSD 450000 400000 350000 300000 250000 Demand = Sales Volume 200000 150000 100000 50000 0 0.00 0.20 0.40 0.60 0.80 1.00 Weighted Distance from Leader Dw • Using common components (e.g. platforms) reduces design freedom • Reduced design freedom increases distance from the “sweet spot” • Sales Volume (Demand) drops as we increase the sweet spot distance • Demand Sensitivity Curve quantifies penalty due to platforming

  22. Cost (Manufacturing) Model - x% Market Leader MSRP Vehicle Cost 100-x% 100% Margins x%: LOWC 5% MDSD 10% LXSD 20% SPTR 15% SUV 15% Truck 25% Van 15% 45% 30% 25% Frame Body Engine Include Learning Curve Effect Total Product Family Cost:

  23. Simulation/Optimization Framework Bi-Level Optimization Framework Family Level opt 2 Vehicle Level mkt cost profit nnet opt 1 i=1,..,7 portfolio platform # of platforms

  24. 2 1.8 1.6 1.4 Product Family Profit [B$] 1.2 1 0.8 0.6 0.4 0.2 1 2 3 4 5 6 7 Number of Platforms variants platform 7/3=2.3 Horizontal/Vertical No Levering SUV VAN LXSD SPTR LOWC MDSD TRCK Resulting “Optimal” Platform Strategy TRCK LXSD SUV VAN MDSD SPTR LOWC Utility Sports Sedans V N a 1 1 1 1 1 1 1 a,b 2 2 2 2 2 1 2 a,b,c 3 3 2 3 2 1 2 a,b,c,d 4 3 2 4 2 1 2 a,b,c,d,e 4 3 2 4 2 1 5 a,b,c,d,e,f 4 3 2 6 2 1 5 a,b,c,d,e,f,g 4 3 7 6 2 1 5 Increasing # of platforms “Optimal” variant- platform assignment matrix

  25. Platform Strategy Evolution BOM Platforms Vehicle Platforms Suspension Engine ... a b c d e f sfl smp srs ei1 ei2 ei3 ev2 ev3 Vehicle X1 Vehicle X2 Current Vehicle Family Vehicle X3 Vehicle Y1 Vehicle A4 Vehicle C2 Newly proposed vehicle ? ? ? Platform consolidation proposal What is the “best fit” existing platform for this new vehicle ? What are the consequences of consolidating a platform?

  26. Architecture Progression: 2003 to 2013 Reducing the number of architectures, while maintaining or increasing the number of models (variants)  Architecture “bandwidth” must increase, but how, where ?