Welcome to the - EARPA · 10/19/2016 · ... Mini, Rolls Royce Daimler ... The model structure requires a hybrid approach of macro and micro modelling with ... Environment Agent Micro

© ika 2016 · All rights reserved 2016/10/19 Slide No. 2 #150 · 16nm0042.pptx

Brussels, October 19th 2016

Dipl.-Wirt.-Ing. Nils Robert Neumann

Winning the CO2 Challenge Optimisation of OEM Product Portfolios by Market Modelling

EARPA FORMForum 2016

Institute for Automotive Engineering


The European CO2 legislation bears high economical risks for OEMs and suppliers due to necessary investments with unknown customer acceptance at the same time.

A reliable picture of future customer demands is the key for market success of OEMs and suppliers in Europe.

Holistic market modelling is able to provide this information and helps OEMs and suppliers to meet the challenges

arising from the European CO2 legislation.

Status-quo

Market modelling as a solution

The framework for the EU automotive industry is changing drastically.

Introduction of stringent CO2 reduction legislation

Highly volatile and increasing fuel prices (in the long term)

OEMs and suppliers react by establishing technologies for higher efficiency and

introduction of electrified vehicle concepts.

Very high investments are necessary for each technology.

Future consumer demand for respective technologies is highly uncertain.

The framework conditions bear high economical risks for OEM and suppliers.

Cost-driven approaches are insufficient since customers determine fleet composition.

3 1 2 4


All OEMs face a major challenge in complying with future CO2 fleet emission targets. However, their initial situation is very different.

120

110

180

170

1,900 1,800 1,700 0 1,600

160

150

140

1,300 1,200 1,100 1,500 1,400 2,100 2,000

100

90

0

130

M0 updated

CO

2 E

mis

sio

n [

g C

O2/k

m]

M0 initial

Vehicle Mass [kg]

VW Group: Audi, Porsche, Seat, Skoda, Volkswagen, Bugatti, Bentley, Lamborghini OEM Group: OEM, Mini, Rolls Royce Daimler: Mercedes-Benz, Smart FCA: Fiat, Alfa Romeo, Chrysler, Dodge, Ferrari, Jeep, Lancia, Maserati GM: Opel, Vauxhall, Chevrolet, Buick, Cadillac Hyundai Kia Automotive Group: Hyundai, Kia Renault Pool: Nissan, Renault, Infiniti, Dacia, Lada PSA: Peugeot, Citroen Tata Motors, Jaguar, Landrover Toyota-Daihatsu Group: Toyota, Daihatsu, Lexus

Pooling

Source: EEA 2014

95 g

123 g

130 g

Future indicative target range: 2025: 65 - 80 g/km (NEDC eq.) - 2030: 50 - 70 g/km (NEDC eq.)

3 1 2 4


Agenda

1. Motivation

2. Methodological Approach

3. Exemplary Results

4. Summary


The complete model structure represents the complex

interactions in the market

All relevant actors and interdependencies have been integrated

in the model

Complete Model Structure

The methodological approach is a holistic market model that is capable of simulating OEM performance and hence improving product strategies in different environmental conditions.

Market environment

EU

vehicle

market

Vehicle

manufacturers

Vehicle

customers

Competition Social

interaction

National

legislation

EU

legislation

Energy

sector

Supplying

industry

Vehicle

homologation/

testing

Vehicle

taxation/

incentives

Customer

incentives

Market

information

Market

information Market

information

Vehicle

demand

Assets Information Regulation

Three types of actors with direct or indirect connections

Actors exchange different assets

OEM, customers and the market are regulated by the EU and

national governments

Main effects are CO2 regulation, taxation and

incentives

Various information is exchanged by market actors internally

and externally

Model represents a complete description of the relevant

interactions within the EU new vehicle market

Full integration of interaction requires high effort towards model

setup and parameterization, e.g.

Socio-demographics of customers

National (vehicle) taxation schemes of EU member

countries

Vehicles

(product

portfolio) Vehicle

usage All interactions except the product portfolio variable are

transferred in model algorithms and scenario-based assumptions

The product portfolio remains as the single model command

variable

Various performance indicators can be defined e.g.

Compliance with CO2 legislation

Operating profit / margins

3 1 2 4


The model structure requires a hybrid approach of macro and micro modelling with an integrated system dynamics and agent-based modelling structure.

Hybrid modelling

Causal-loop-chains model self-reinforcing or self-regulating

effects (analogy to closed loop control)

Sources and abatements as interface towards environment

+ Reproduction of complex systems with only a few

components

+ Analysis of influences of exogenous factors on the

environment

- Detailed comprehension of system necessary

- Effects on micro-level not visible

Macro-modelling: System Dynamics (SD)

sold xEV

Production costs of

xEV-components

B +

-

Developments of systems can be traced back to microscopic

effects (emergence)

+ System-coherencies must not be known a priori

+ high level of detail, as every agent possesses a limited

amount of proprietary methods

- High computational effort

- Exogenous parameters cannot be stated

Static

Dynamic

Behaviour

Rules

Decision

Perception

Attributes Methods

Information

Activities

Environment Agent

Micro-modelling: agent based modelling(ABM)

System environment (example)

Total production of

xEV-components +

Learning effects Economies of scale

-

+ +

3 1 2 4


Agenda

1. Motivation



4. Summary


The assumed baseline strategy for the investigated OEM fails to meet the target limits from 2020-2023 and again in 2030, implying high penalty payments and reputational damage.

24%

11%

10%7%

10%

5%

7%

17%

11%17%58%

36%

11%17%

13%

11%18%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Ma

rke

t sh

are

in

OE

M p

ort

folio

[%

]

2030 2029 2028 2027 2026 2025

2%

5%

2024 2023 2022 2021 2020

1%

8%

1%

FCEV

BEV

CNG

PHEV-D

FHEV-D

MHEV-D

ICEV-D

PHEV-P

MHEV-P

ICEV-P

40

50

60

70

80

90

100

110

120

130

140

150

160

1.900 1.800 1.700 1.600 1.500 1.400 1.300 1.200

2020

2016

2015 (P)

2014 2013

2012 2011 2010

CO

2 fle

et e

mis

sio

ns in

cl. s

upe

rcre

dits [g

CO

2/k

m]

Average vehicle mass [kg]

1445

1397

1417

1372

2030

2025

2020/1:

95

2025:

78

2030:

60

Simulation, non-compliant

Simulation-compliant M0 (market Ø vehicle mass)

OEM historic

3 1 2 4

Exemplary

targets

[g/km] 2015:

130

Powertrain Market Share CO2 Fleet Emissions


Powertrain Market Share

PHEV-focused strategy leads to full target compliance even with significantly decreasing diesel shares. The fleet emissions curve has a safety reserve in 2025 and 2030.

8%

14%

16%

41%

11%

16%

65%

44%

22%

9% 8%

7%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

3%

2030

2%

1%

2029 2028 2027 2026 2025

3%

1%

2024

Ma

rke

t sh

are

in

OE

M p

ort

folio

[%

]

2023 2022 2021 2020

1%

10%

3%

13%

FCEV

BEV

PHEV-D

MHEV-D

ICEV-D

PHEV-P

MHEV-P

ICEV-P

CO2 Fleet Emissions

40

50

60

70

80

90

100

110

120

130

140

150

160

1.900 1.800 1.700 1.600 1.500 1.400 1.300 1.200

CO

2 fle

et e

mis

sio

ns in

cl. s

upe

rcre

dits [g

CO

2/k

m]

Average vehicle mass [kg]

2030

2025

2020

2016

2015 (P)

2014 2013

2012 2011 2010

1445

1397

1417

1372 2020/1:

95

2025:

78

2030:

60

OEM historic M0 (market Ø vehicle mass)

Simulation-compliant

Exemplary

targets

[g/km] 2015:

130

3 1 2 4


2016 2018 2020 2022 2024 2026 2028 2030

0.0

10.5

11.0

9.5

11.5

12.0

9.0

12.5

8.5

0.5

10.0

Pro

fit M

arg

in [%

]

Profit Margin

Penalty payments are completely avoided while sales and turnover are even slightly increased with significantly improved profit margins compared to baseline strategy.

-3-1-3-1-2-10

1

-2

0102

0

24

-4

-2

02

21

31

4110

-7

90

30

20

120

110

100

0

10

-10

-20 2028 2016

-7

2026

-2

2018 2024

14

2020 2022

+11%

2030

No

rma

lise

d V

alu

e [

%]

6

∆ net profit (rel. to baseline)

Net profit (base: 2016)

Penalties [% of op. profit]

Op. profit (base: 2016)

∆ sales (rel. to baseline)

Vehicle sales (base: 2016)

Profit margin (baseline)

Profit margin (actual) Increase of margin

Loss of margin

3 1 2 4

Sales and Profit


Agenda

1. Motivation



4. Summary


Summary

3 1 2 4

Model simplification which allows to increase the number of simulated technology options and optimisation iterations

Development of a scenario tool to assess and optimise regulation strategies on various levels, e.g. local (entry

restrictions), national (taxation and incentives) and EU-wide (future fleet emission standards)

Key learnings

Outlook

The EU CO2 legislation bears high economical risks for OEM and suppliers. These risks can

be reduced by models and simulation.

However, cost-driven approaches are insufficient since customers determine fleet composition.

Holistic market modelling is able to provide this information and helps OEMs and suppliers to

meet the challenges arising from the European CO2 legislation.

The developed simulation tool can be used both to generate scenario-specific powertrain forecasts

for the complete market and for the optimisation of OEM strategies under various boundary

conditions and criteria.

An in-depth analysis regarding the contribution of specific vehicles or technologies is also

possible.

Suppliers are able to estimate future OEM demand for specific technologies.

120

110

180

170

1,9001,8001,7000 1,600

160

150

140

1,3001,2001,100 1,5001,400 2,1002,000

100

90

0

130

M0 updated

CO

2E

mis

sio

n [

g C

O2/k

m]

M0 initial

Vehicle Mass [kg]

Source: EEA 2014

95 g

123 g

130 g

Market environment

EU

vehicle

market

Vehicle

manufacturers

Vehicle

customers

Competition Social

interaction

National

legislation

EU

legislation

Energy

sector

Supplying

industry

Vehicle

homologation/

testing

Vehicle

taxation/

incentives

Customer

incentives

Market

information

Market

informationMarket

information

Vehicle

demand

Assets Information Regulation

Vehicles

(product

portfolio)Vehicle

usage

2016 2018 2020 2022 2024 2026 2028 2030

0.0

10.5

11.0

9.5

11.5

12.0

9.0

12.5

8.5

0.5

10.0

Pro

fit

Ma

rgin

[%

]

Profit Margin

-3-1-3-1-2-10

1

-2

0102

0

24

-4

-2

02

21

31

4110

-7

90

30

20

120

110

100

0

10

-10

-20

20282016

-7

2026

-2

2018 2024

14

2020 2022

+11%

2030

No

rma

lise

d V

alu

e [

%]

6

∆ net profit (rel. to baseline)

Net profit (base: 2016)

Penalties [% of op. profit]

Op. profit (base: 2016)

∆ sales (rel. to baseline)

Vehicle sales (base: 2016)

Profit margin (baseline)

Profit margin (actual) Increase of margin

Loss of margin

Sales and Profit


Phone

Fax

Email

Internet www.ika.rwth-aachen.de

Institute for Automotive Engineering (ika)

RWTH Aachen University

Steinbachstr. 7

52074 Aachen

Germany

Contact

Dipl.-Wirt.-Ing. Nils Robert Neumann

Strategy & Consulting

+49 241 80 25686

+49 241 80 22147

[email protected]

Documents

Welcome to the - EARPA · 10/19/2016 · ... Mini, Rolls Royce Daimler ... The model structure requires a hybrid approach of macro and micro modelling with ... Environment Agent Micro