R&D Selection Methods Automotive R&D in New Materials / Processes

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R&D Selection Methods
for New Materials and Processes
Elicia Maine
Centre for Technology Management,
University of Cambridge
October, 1999
Automotive R&D in New Materials /
Processes
• Why do OEMS and suppliers support R&D?
• Creating Profit Surplus
• Medium to long term view
• Methods to Create Profit Surplus through
new Materials and Manufacturing Processes
• Lower cost production through process innovation
• Raising market demand curve through marketable
performance enhancement
R&D Portfolio Planning
Technical Risk:
35%
0%
Market Risk:
100 %
30%
100 %
0%
Potential Reward:
15%
0%
100 %
Competitive Position:
85%
Risk / Reward Balance
Strategic
Increasing Risk
Unattractive
Bread and Butter
Increasing Reward
No Brainer
Selection of Automotive Materials R&D Projects
Viability
• Grasp opportunity!
Value
Analysis
st /
Co
Technical
Cost Model
Material in Basic Form
Shaped Material
Market
Assessment
Cost-Performance
Balance
e
lum
Vo
it
Un
• Lower risk by systematically
assessing project at an early
stage
Exchange constants
Exchange constants
Market Size Assessment
Substitution Time
Approach
Performance
Technical
Feasibility
• Method to provide
communication between
Marketing and R&D
Most suitable markets
Application Requirements
Material Selection
EMAM and MFA, University of Cambridge, Sept. 99
Performance Enhancements
Technical
Feasibility
Application Requirements
Material Selection
Polyurethane (0.024)
Foam selection
for head protection
1.
Melamine Foam
(75% of energy absorbed by
elastic deflection of A-pillar)
Minimum foam thickness x (m)
Polyurethane (0.08)
Polyethylene (LD18)
Polyurethane (0.16)
Polyethylene (PE80)
0.1
Polyethylene (HD115)
Polyurethane (0.35)
Polyurethane (0.53)
Aluminium-SiC (0.16)
Polyurethane (0.70)
1.e-002
Polyurethane (1.05)
Aluminium-SiC (0.27)
Polystyrene (0.800)
Aluminium-SiC (0.41)
1.e-003
Aluminium (0.5)
4.5 kg head, impact velocity 11.2 m/s (40 km/hr) knock down factor 0.25
1.
10.
100.
Deceleration a* in units of g
Aluminium (1.0)
1000.
10000.
Forecasting Cost / Unit
Technical
Cost Model
$ / Unit
Material in Basic Form
Shaped Material
100
90
80
70
60
50
40
30
20
10
0
New Material
Incumbent
0
20
40
60
80
100
120
140
160
180
200
Production Volume (Thousands of Units / Year)
Performance Cost Trade off
100
Value
Analysis
Greater Life
Cost
Cost-Performance
Balance
10
C
Index M1
B
1
Tradeoff Curve defined
by Exchange Constant
A
0.1
D
Lower Life
Cost
0.01
0.01
0.1
1
Index M2
10
R&D Selection Conclusions
• Better way to Assess New Materials and
Processes for Automotive R&D
• Differentiation
• Lower Costs
• Material Suppliers and Industry Consortiums are
ALL going to tout their material
• Need for in house assessment and prioritisation
• OR standardised methods
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