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Using Optimization to Determine Strategic 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

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slide1

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

what impacts platform strategy

?

?

?

?

  • 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

the need for platform strategy
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)

poor platform strategy
Poor Platform Strategy

Source: R. J. Matson, St. Louis Dispatch, 7/17/2008

an enterprise framework
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

product architecture business strategy
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?)

starting assertions
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)

platform strategies

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

platform portfolio problem
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

profit maximization
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

product variant modeling framework

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

bi level optimization methodology
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

automotive case study hypothetical

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?

vehicle data set for case study
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

architecture model
Architecture Model

WT

chassis (common)

ED

body

WB

engine

Architecture

vehicle

HT

Chassis Engine Body

Design Vector

vehicle design vector
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 ]

engineering model e g wb to fe

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

value model
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

example segments

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

sweet spot market model

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

demand sensitivity curve
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
cost manufacturing model
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:

simulation optimization framework
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

resulting optimal platform strategy

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

platform strategy evolution
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?

architecture progression 2003 to 2013
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 ?