Charles University in Prague
Faculty of Social Sciences
Institute of Economic Studies
Ondřej Sezemský
Determinants of Production and
Sales in Automotive Industry:
Evidence from ŠKODA AUTO
MASTER THESIS
Prague 2014
Author: Ondřej Sezemský
Supervisor: PhDr. Petr Teplý, Ph.D.
Academic Year: 2013/2014
Bibliography Reference
SEZEMSKÝ, Ondřej: "Determinants of Production and Sales in Automotive Industry: Evidence from ŠKODA AUTO." Prague, 2014, 82 p. Master Thesis (Mgr.) Charles University,
Faculty of Social Sciences, Institute of Economic Studies. Supervisor: PhDr. Petr Teplý,
Ph.D.
Abstract
Automobiles can be today considered as cultural icons which indicate the level of development of a particular state. This thesis provides analysis of the automotive industry in
general from the point of view of globalization, linkages, value chains and macroeconomic
environment. We will stress crucial challenges that face global original equipment manufacturers, depict situations on most important emerging markets and forecast development
that we can expect there. In the context of these information we will develop econometric
analysis which will widen current findings from microeconomic and macroeconomic perspective of a car manufacturer. ŠKODA AUTO represents the most important enterprise
in the Czech Republic and at the same time is a part of the biggest automobile concern in
the world. We will use internal data from the company for our research and give overall
recommendations. Central questions in this study are the following: What are the forecasted needs of the customers on emerging markets? How sensitive are companys’ costs
on the output? And finally, which role do the macroeconomic indicators play in delivering
of automobiles to end customers?
Key Words: automotive industry, costs, deliveries, emerging markets, suppliers, linkages
JEL Classification: C22, C32, C52, L22, L62, O11, O14
Extent of the Thesis: 126,855 characters (with spaces)
Bibliografický záznam
SEZEMSKÝ, Ondřej: "Determinanty produkce a prodeje v automobilovém průmyslu: případová studie ŠKODY AUTO." Praha 2014. 82 s. Diplomová práce. Univerzita Karlova v
Praze, Fakulta sociálních věd, Institut ekonomických studií. Vedoucí práce: PhDr. Petr
Teplý, Ph.D.
Abstrakt
Automobily jsou dnes považovány za symboly a kulturní ikony, které vypovídají o stupni
vývoje daného státu. V této diplomové práci analyzujeme automobilový průmysl z pohledu
globalizace, vazeb mezi výrobci a dodavateli, hodnotových řetězců a makroekonomického
prostředí. Budeme diskutovat základní výzvy, kterým čelí globální výrobci, popíšeme situaci
na nejdůležitějších růstových trzích a budeme se snažit předpovědět tamější vývoj v budoucnosti. V tomto kontextu vytvoříme ekonometrickou analýzu, která rozšíří současné poznatky z mikroekonomického a makroekonomického pohledu výrobců aut. ŠKODA AUTO
představuje nejdůležitější podnik v České Republice a zároveň je součástí největšího automobilového koncernu na světě. Pro náš výzkum použijeme interní data ze společnosti
a poskytneme patřičná doporučení. Hlavní otázky této práce jsou: Jaké jsou předpokládané potřeby zákazníků na růstových trzích? Jak jsou senzitivní náklady společnosti na
počet vyrobených aut? A konečně, jakou roli hrají makroekonomické indikátory v celkových
prodejích?
Klíčová slova: automobilový průmysl, náklady, dodávky zákazníkům, růstové trhy, dodavatelé, vazby
JEL klasifikace: C22, C32, C52, L22, L62, O11, O14
Rozsah práce: 126.855 znaků (včetně mezer)
Declaration
I hereby declare that I compiled this thesis independently, using only the listed resources
and literature.
I declare that this thesis was not used to acquire degree from any other institution.
I grant to Charles University permission to reproduce and to distribute copies of this thesis
document in whole or in part.
Prague, July 30, 2014
Signature
Acknowledgments
I would like to thank to my supervisor PhDr. Petr Teplý, Ph.D. for valuable suggestions
and support. I am also grateful to Ing. Jan Ryšavý from the ŠKODA AUTO for mediation
of interesting meetings with specialists from the company and provision of the required
company’s internal information. Apart from that I would like to thank my family, girlfriend
and friends for their support and encouragement.
Acronyms
ADF
Augmented Dickey-Fuller
AIC
Akaike Criterion
BIC
Bayesian Criterion
CEE
Central and Eastern Europe
CFR
Cost and Freight
CIF
Cost Insurance and Freight
CIS
Commonwealth of Independent States
CKD
Completely knocked down.
CZK
Czech Crown
DF
Dickey-Fuller
ECM
Error Correction Model
EU
European Union
EUR
Euro
FDI
Foreign Direct Investment
GDP
Gross Domestic Product
GPS
Global Positioning System
GVC
Global Value Chain
HQC
Hannan-Quinn criterion
IMF
International Monetary Fund
KPSS
Kwiatkowski–Phillips–Schmidt–Shin
MKD
Medium knocked down
NASDAQ National Association of Securities Dealers Automated Quotations
NYSE
New York Stock Exchange
vi
OEM
Original Equipment Manufacturers
OLS
Ordinary Least Squares
R&D
Research & Development
S&P
Standard & Poor’s
SAIC
Shanghai Automotive Industry Corporation
SAIPL
ŠKODA AUTO India Private Ltd.
SKD
Semi knocked down
SUV
Sport Utility Vehicle
TPCA
Toyota, Peugeot and Citroën Automobile joint venture
US
United states
USA
United States of America
VAR
Vector Autoreggression
VECM
Vector Error Correction Model
WTO
World Trade Organization
vii
Master Thesis Proposal
Student:
Ondřej Sezemský
Specialization:
Finance, Financial Markets and Banking
Supervisor:
PhDr. Petr Teplý, Ph.D.
Determinants of Production and Sales in Automotive
Industry: Evidence from ŠKODA AUTO
Topic Characteristics
We analyze global automotive industry and selected emerging markets and introduce reader
to the basic information about ŠKODA AUTO. We employ econometric methodology to
examine microeconomic and macroeconomic indicators on the performance of the company.
We decided to apply cointegration analysis and Error Correction Models. This concept
enables us to measure non-stationary time series. For estimating our models we use internal
company’s data and then data from Czech National Bank, Index Mundi database or IMF
World Economic Outlook Database.
Outline
• Analysis of Automotive Market
◦ Globalization
◦ Trends in Automotive Industry
◦ Development on important ŠKODA AUTO markets
• ŠKODA AUTO
◦ Brief History
◦ Organization
◦ Structure of the Costs
◦ SWOT Analysis
• Time Series Methodology
◦ Hypotheses
◦ Descrption of the Data
◦ Time Series Analysis
◦ Cointegration Analysis
viii
• Model of Production
◦ Data Set and Definitions
◦ Sensitivity Analysis on Input Variables
◦ Unit Root and Cointegration Testing
◦ Error Correction Model
• Model of Sales
◦ Data Set and Definitions
◦ Sensitivity Analysis on Input Variables
◦ Unit Root and Cointegration Testing
◦ Error Correction Model
Core Bibliography
Cipra, Tomáš (2008): "Finanční Ekonometrie." Praha: Ekopress, s. r. o.
Engle, Robert F. and Granger, Clive W.J. (1987): "Cointegration and error correction:
representation, estimation and testing."
Green, William H. (2002): ”Econometric Analysis.”
Johansen, Soren (1988): "Statistical Analysis of Cointegrating Vectors." Journal of Economic Dynamics and Control, Vol. 12.
Richet, Xavier (2002): "Restructuring and Competition in the Car Industry in Russia:
Conglomerate Control vs. Cooperation with Foreign Firms." Journal of Economics and
Business, Vol. 6.
Russo, Bill; Tse, Edward and Ke, Tao (2009): "The Path to Globalization of China’s
Automotive Industry."
Sturgeon, T. J.; Memedovic, O., Biesebroeck, J. V. and Gereffi, G. (2009): "Globalization
of the automotive industry: main features and trends." Int. J. Technological Learning,
Innovation and Development, Vol. 2.
ix
Contents
1 Introduction
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2 The
2.1
2.2
2.3
2.4
Automotive Industry
Globalization in Automotive Industry . . . . . . . . . . . . . . .
Trends in Automotive Industry . . . . . . . . . . . . . . . . . . .
Global Value Chains in Automotive Industry . . . . . . . . . . . .
Macroeconomic Development . . . . . . . . . . . . . . . . . . .
2.4.1 Financial Crisis . . . . . . . . . . . . . . . . . . . . . . .
2.4.2 Macroeconomic Forecast for Main Car Markets of ŠKODA
2.5 Development of the Markets with ŠKODA AUTO Branded Cars .
2.5.1 China . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.5.2 India . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.5.3 Russia . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4 Methodology
4.1 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Description of the Data . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Time Series Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3 ŠKODA AUTO
3.1 Brief History . . . . . . . .
3.2 Basic Information . . . . .
3.3 Organization . . . . . . . .
3.4 Structure of the Costs . . .
3.4.1 Material Costs . . .
3.4.2 Personnel Costs . .
3.4.3 Overheads . . . . .
3.5 SWOT Analysis . . . . . .
3.6 Motivation for the Research
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AUTO
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Contents
4.4
Cointegration Analysis . . . . . . . . .
4.4.1 Error Correction Model . . . .
4.4.2 Vector Autoregressive Model . .
4.4.3 Vector Error Correction Model .
5 The
5.1
5.2
5.3
Model of Production
Data Set and Definitions . . . . . . .
Sensitivity Analysis on Input Variables
Unit Root Testing . . . . . . . . . .
5.3.1 ADF Test . . . . . . . . . .
5.3.2 KPSS Test . . . . . . . . . .
5.4 Cointegration Testing . . . . . . . .
5.4.1 Engle-Granger Test . . . . .
5.4.2 Johansen Test . . . . . . . .
5.5 Vector Error Correction Model . . . .
6 The
6.1
6.2
6.3
Model of Sales
Data Set and Definitions . . . . . . .
Sensitivity Analysis on Input Variables
Unit Root Testing . . . . . . . . . .
6.3.1 ADF Test . . . . . . . . . .
6.3.2 KPSS Test . . . . . . . . . .
6.4 Cointegration Testing . . . . . . . .
6.4.1 Engle-Granger Test . . . . .
6.4.2 Johansen Test . . . . . . . .
6.5 Vector Error Correction Model . . . .
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8 References
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Appendix
i
xi
List of Figures
2.5.1 ŠKODA AUTO Deliveries to Customers in Emerging Markets . . . . . . .
2.5.2 ŠKODA AUTO Deliveries Percentage Change in Emerging Markets (y-o-y
percent growth) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.5.3 GDP development and Car Sales Growth Rates in China (y-o-y growth) . .
15
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5.2.1 Logarithms of Variables . . . . . . . . . . . . . . . . . . . . . . . . . . .
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6.2.1 Logarithms of Variables, LIBOR and Inflation . . . . . . . . . . . . . . . .
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0.1
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Production Model - Information Criteria .
Production Model - OLS Residuals . . .
Production Model - Johansen Test . . .
Production Model - VECM Summary . .
Production Model - VECM Coefficients .
Sales Model - Information Criteria . . . .
Sales Model - OLS Residuals . . . . . .
Sales Model - Johansen Test . . . . . . .
Sales Model - VECM Summary . . . . .
Sales Model - VECM Coefficients . . . .
xii
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15
List of Tables
2.1
2.2
2.3
Light Vehicle Density in 2012 . . . . . . . . . . . . . . . . . . . . . . . .
Car Markets Development (y-o-y growth) . . . . . . . . . . . . . . . . . .
ŠKODA AUTO Deliveries to Customers – Largest Markets and by Region .
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5.2
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Summary of Variable Production
ADF Test . . . . . . . . . . . . .
KPSS Test . . . . . . . . . . . .
Johansen Test . . . . . . . . . .
Estimated Coefficients . . . . . .
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Summary of the Variables (rounded to 2 decimal places)
ADF Test . . . . . . . . . . . . . . . . . . . . . . . . .
KPSS Test . . . . . . . . . . . . . . . . . . . . . . . .
Johansen Test . . . . . . . . . . . . . . . . . . . . . .
Estimated Coefficients . . . . . . . . . . . . . . . . . .
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1 Introduction
The automotive industry began 120 years ago and vast changes have taken place in
automobile manufacturing plants since that time. Car factories around the world have cut
costs per car and reduced man-hours, while offering many possibilities for consumers to
customize their cars. Today, more than ever, the design, comfort and purposefulness are
important. Car manufacturers and their suppliers are cooperating in revolutionizing the
entire production process. Vehicle manufacturers are the world leaders in research and
development (“R&D“) and spend many billions of Euros every year on R&D in order to
further improve existing concepts and to develop hybrids or alternative fuel vehicles using
natural gas, bio fuels, hydrogen and others.
The car industry is one of the most important economic sectors by revenue in the world
and is considered to be highly capital and labor intensive. Top managers have to precisely
estimate future needs of customers, profile the key products, streamline the operations of
the company and discover new emerging markets. The environment of turbulent automotive markets is highly competitive with more than 50 vehicle manufacturing groups around
the world. The the situation of car manufactures is even more complicated, because the
automotive industry is extremely sensitive to macroeconomic situation, especially on financial and banking crises. Bankruptcies at General Motors and Chrysler in 2009 enabled these
firms to shed debt, cut employees and dramatically reduce operating costs.
Despite recent slumps of car deliveries, future demand for vehicles will far outstrip former peaks, creating immense business opportunities. Globalization, the tendency of world
investment and businesses to move from national and domestic markets to a worldwide
environment, is a huge factor affecting the automotive market. The incomes are rising
in developing economies and the car makers want to introduce their low-cost options for
buyers in China or India. Eisenstein (2010) suggests, that one of every three global car
sales will be soon only in so-called ”BRIC” countries. Competing in Brazil, China, India
and Russia won’t be easy, but car makers trying to compete in only the major industrialized
markets will find increasing difficulties to survive.
The objective of this thesis is to analyze current automotive market and examine factors
influencing production and total deliveries to customers. We will begin with an analysis
of the global automotive market and value chains in automotive industry and then we will
1
1 Introduction
describe the main ŠKODA AUTO markets. We will employ econometrics methodology for
non-stationary time series data which helps us to study the sensitivity of the most relevant
factors influencing company’s performance on production and sales. We chose for our
analysis ŠKODA AUTO as the steadily largest Czech company by sales. We will determine
the root sources of company’s performance, their relative importance and then we will
summarize these findings and suggest possible improvements.
The motivation and the main arguments for this innovative research are the widening
of current findings, to be able to forecast development on automotive markets more effectively and to offer some possibilities for further research. It is important to know, which are
the essential challenges on automotive market, how to observe and measure variables that
influence performance of the company externally and how to manage internal processes to
reduce costs and make the production more efficient. We will analyze influence of macroeconomic indicators, the financial crises, changes in price of mineral resources, sensitivity
of particular internal costs or impacts of major events in the company. We believe that
these determinants will have a significant role in determining the structure and volume of
ŠKODA AUTO production and sales.
This thesis is structured as follows. The next Chapter provides review of current research and introduces worldwide automotive industry. We will focus on globalization, global
macroeconomics and main automotive markets of ŠKODA AUTO. Chapter 3 introduces
ŠKODA AUTO and provides general data about the company as well as the brief overview
of its long history. We will also describe the organization of ŠKODA AUTO for better
understanding of the company’s functioning and structure of the costs. In Chapter 4 we
state hypotheses to be tested, briefly describe the data and introduce methodology we use
in analyzing the collected data. The last two Chapters introduce the concrete data sets,
describe our variables, set and test the empirical models and discuss our results.
2
2 The Automotive Industry
In last decades, the car culture has spread over the entire planet. Automobiles have
influenced not only the global economy but also lifestyles of billions of people. Plunkett
Research (2013) claims that there were about 1 billion cars and light trucks on the road in
2013 around the world and this number is growing by roughly 40 million yearly.
The leading car original equipment manufacturers ("OEM") have to take into consideration vast number of opportunities and threats today. They are implementing technological
changes in order to enhance their fuel efficiency and are developing smaller, highly efficient
vehicles (hybrids, electric vehicles, etc.). Customers keenly demand high quality and fine
after sales service. European OEMs are facing challenges with high costs of production,
tough labor laws or daunting government regulations which finally led to slowdown in domestic sales in last years. On the other hand, there have arisen new emerging markets
with a huge potential. The low car density in these countries, improving infrastructure and
permanently rising level of education and income means that these markets are nowadays
the most attractive for car manufacturers.
Table 2.1: Light Vehicle Density in 2012
Population Light vehicle fleet Vehicle density
Country
million
million
per 1,000 people
United States
315.31
202.23
641
Germany
81.89
43.56
532
France
63.61
31.74
499
United Kingdom
63.24
31.55
499
Poland
38.32
18.17
474
Czech Republic
10.51
4.63
442
South Korea
48.59
14.61
301
Russia
143.00
37.22
260
Ukraine
44.94
8.04
179
Brazil
198.36
24.93
126
China
1,353.38
69.83
52
India
1,258.35
21.51
17
Source: Ernst & Young survey (2013)
The future will not develop in the same way in every country or type of car. The main
3
2 The Automotive Industry
growth will come from emerging markets and, to a lesser extent, the US, while Europe,
Japan, and South Korea will be stagnant in terms of profit growth. Analysts from McKinsey
stresses in their study from 2013 four key challenges for automakers in medium-term future
to be able to get a piece of future potential profitability:
• Complexity and cost pressure - Car makers will have to develop new technologies
for low-emission vehicles without knowing what will be the prevailing technology in
the future. At the same time, prices in established markets are likely to be flat and
governments will introduce new regulations with respect to environmental and safety
standards. These challenges will require significant investments. To reduce costs,
there will be more platform sharing systems to rise complexity.
• Diverging markets - Share of global sales and profits in emerging markets will rise
by 10 percent till 2020. Car manufacturers need to have sufficient local production,
supply base, after sales market and will have to have balanced product portfolio to
meet local demand.
• Digital demands - Digital channels are for today’s consumers the main source of
information when they want to buy a car. Online purchasing can mean an opportunity
but also puts a pressure on the existing dealership structure. Main requirements of
the customers are active safety and ease of use of the web page.
• Shifting industry landscape - Since car manufacturers will have to enhance their
facilities in emerging markets, they have to be sure if local suppliers are able to match
future market demands and their own production plans. Suppliers will also likely add
more value to the car. The car manufacturers in Europe will have to restructure their
business and deal with emerging Chinese players entering new segments and markets.
These challenges can rise future risk to automotive profit but the overall perspectives
for automotive markets are good. All leading institutions forecast sharp increase in sales in
following decades because people will have sufficient income to acquire the cars. Some of
them are talking even about 3 billion cars in year 2050. New traffic and safety measures may
smooth traffic flow, new technologies in highly efficient cars will strongly reduce air pollution
and fuel consumption. However, many questions reduce excessive enthusiasm: Will that
many consumers find automobile ownership to be desirable? Will public transportation, car
sharing systems, commuter trains and other alternatives to individual car ownership reduce
demand for personal automobiles? Will fuel, whether gasoline, hydrogen or electricity, be
affordable and readily available? Will roads, parking and other traffic infrastructure be
adequate to support car ownership on this scale?
4
2 The Automotive Industry
2.1 Globalization in Automotive Industry
One of the most important strategic topics in automotive industry is a globalization. This
term was first used in the 1980s, but the concept is known for decades, even centuries, if you
count the trading empires built by Britain, Netherlands, Portugal and Spain. Globalization
is an economic phenomenon, involving the increasing interaction, or integration, of national
economic systems through the growth in international trade, investment and capital flows.
Globalization has influenced every aspect of human life and it offers certain opportunities
and threats to each aspect of business. For better understanding of world automotive
industry functioning, it is needed to outline linkages between lead automakers and their
suppliers. We recognize three kinds of linkages:
• Rational – Design engineers from automotive company and its supplier company
works closely together to develop parts that will be used for competition of the whole
vehicle.
• Market – Engineers from automotive company develop all the vehicle parts alone and
then put the parts out for bids.
• Captive – Engineers from automotive company develop all the vehicle parts alone
and then put the parts out for bids and some specific investments from suppliers are
required (which they often are).
Market linkages allow the manufacturers to easily switch their suppliers, while relational and
captive linkages make supplier switching more difficult. Understanding of these linkages is
crucial to developing precise concept of today’s automotive industry.
Since the late 1980s, foreign direct investment ("FDI"), global production and crossborder trade have accelerated dramatically in many industries including automotive. New
World Trade Organizations’ liberal agreements have facilitated trade and investments in
countries with huge surplus of low-cost but skilled labour force and high potential market
growth such as China, India or Brazil. Outsourcing and bundling of more chain activities in
supplier firms is next feature of globalized industries. Sturgeon and Lester (2004) suggest
that the largest suppliers based in developed countries have become global suppliers with
multinational operations and an ability to provide goods and services to a wide range of
lead firms thus they increased their own involvement in FDI and trade. Globalization is of
course also great chance for suppliers from developing countries that have new vast opportunities to trade and to increase their capabilities. These features are similar also for other
industries like apparel or electronics.
The automotive industry is distinctive from other industries because of extremely concentrated firm structure which was enhanced by a wave of mergers and acquisitions in the
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2 The Automotive Industry
1990s. Eleven lead firms from the three countries (Germany, Japan and USA) dominate
production in main markets. The next distinctive globalization feature of automotive industry is that final vehicle assembly has been kept close to the final markets. Companies from
developed countries invest great amounts in regions with future potential growth and try
to build strong position to maximize future profits and sales. On the other hand, car manufacturers also develop strong regional-scale patterns of integration in their base countries.
The next distinctive feature is that there are not a lot of fully generic parts or subsystems
that can be used in a wide variety of end products without extensive customization. This
feature can be problematic for suppliers of leading firms because parts and subsystems tend
to be specific to particular vehicle models. The leading car manufacturers have been trying
to unify some parts that can be used for more types of vehicle as so called “platforms”.
Platforms can be shared only across the brands owned by a specific lead firm and usually
should not be visible to avoid having the same product design among several models. They
include mainly rolling chassis but sometimes also braking systems, suspension parts, engines or transmissions. Since there are few standardized parts in the automotive industry,
specifications must be developed for every part of each vehicle model. This raises costs
of suppliers that serve multiple customers, limits economies of scale, economies of scope,
even more ties them to lead firms and often creates captive character of linkages between
them and lead firms.
Krkoska and Spencer (2008) suggest that when global automotive market grew, OEMs
started to increase their production capacity. They chose the plant location according
to local network of suppliers, infrastructure or labour. A new efficient plant has to then
satisfy their business strategy for local markets and the location should be also suitable for
exporting to other markets. All of these features have resulted in the “nested” geographic
and organizational structure of the automotive industry:
• Local clusters – Activities tend to be concentrated within clusters of specialized activity, such as design or development, which are concentrated in, or near, the headquarters of the firms. Next example of local clusters can be assembly of new vehicles
that can be located for example in target market.
• National production systems – Domestic production of cars is still very strong and
still dominates many national markets.
• Regional production systems – Intra-regional finished vehicle and part flows are the
dominant operational pattern in auto industry.
• A global industry – Auto markets and global suppliers from buyer-supplier relationships
on a global scale. Intra-regional vehicle and parts trade is substantial but restricted
by political and operational considerations.
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2 The Automotive Industry
2.2 Trends in Automotive Industry
Global automotive industry has shown many specific trends during last decades which we
should summarize in the beginning of our study. Sturgeon et. al. (2009) suggest following:
The boom in vehicle production in the 1990s and 2000s
According to OICA, the global vehicle production has more than tripled since 1970. There
were produced 29 million cars and commercial vehicles in 1970 and 87 million in 2013.
The world vehicle production grew till 1990s on average rate of around 2 percent and from
this time it raised to around 3 percent. The main driver of this growth in last 20 years is
opening of new markets in Asia as China and India. The huge population and low level
of motorization resulted in large increase in investments in these countries and in rapid
production growth. The production shares of traditional leader producers as EU, Japan and
USA in total world production have lost in last years but still belong among the biggest car
manufacturers.
The continuing importance of core markets
As we already said, the leading car manufacturers has tried to expand their activities to
new emerging markets in last 15 years. Nevertheless, for many companies the home market
remains important. According to OICA, production and sales by the leading manufacturers
in Europe and North America remained concentrated in their home regions. The exceptions
are Japanese producers, which experienced the largest fall in concentration in the home
market.
Regional integration of production
Concentration of vehicles and vehicle parts production to particular regions in North and
South America, Europe, Asia and Southern Africa is in last decades the dominant trend
in automotive industry. The main reason is to lower transaction and operating costs.
There are several reasons that contribute to the importance of regional production. The
automotive industry is highly visible, high cost and powerful local manufacturers or parts
suppliers employ a lot of people. This can provoke strong backlash of population if they see
that imported vehicles account for too large share of vehicles sold and when local producers
are threatened by imports. This explains why automakers choose voluntarily to invest in
foreign regions (even in regions like USA where the employee expenses are much higher
than for example in Mexico) and do not strengthen the home production even the tariffs or
some other local content rules are not present or are scheduled to decline under WTO rules.
Next reasons to build vehicles and major heavyweight subsystems close to end markets can
7
2 The Automotive Industry
be high transportation costs of new vehicles to target market because the final product
is heavy and "fragile" or implementations of "lean" production techniques and increasing
product and module variety.
National and local elements
The regional integration of automotive industry is nowadays the most dominating trend
in automotive industry but we have to also discuss some national and local elements.
Consumer tastes and purchasing power, driving conditions, labour markets regulations,
standard requirements and public policy (incentives and taxation) can vary widely by country
(and even within countries). Consumer preferences reflect climate, characteristics of the
society or high & low income countries. In developed world customers demand more specific
features and on the other hand in developing countries we will find frequently fuel and roads
of poorer quality, so the most important is to adapt vehicle to local conditions (strengthening
the body, suspension, steering, etc.). Each country has also its own set of environmental
regulations (water regulations, air emissions, waste management, noise control, etc.) and
taxation policies, which can create demand for particular type of car.
2.3 Global Value Chains in Automotive Industry
To develop our context of automotive industry in more detail and to outline challenges
within this industry we use Global Value Chain ("GVC") analysis. In GVC analysis of
automotive industry we will recognize two broad types of firms: automakers and suppliers.
Automakers decide about product strategy, place orders, take financial responsibility for final
goods production, carry out most aspects of product design, produce most of the engines
and transmissions and assembly nearly all vehicle within their own facilities. They are large
employers, traders and innovators. They have substantial coordination and buying power
in the chain. But as we already mentioned, outsourcing has led to the creation of large
global suppliers since 1990s, which have taken on more responsibility in the field of design,
production and foreign investment. Memedovic (2007) described a simple automotive value
chain as follows:
1. Raw materials - Rubber, glass, plastics, steel and aluminum. Their costs have increased significantly, mostly due to oil and natural rubber price increases. Aluminum
and plastics increasingly replace steel to make automobiles lighter and more fuel
efficient.
2. Design and product development - These areas are mostly kept near the headquarters
of the automobile manufacturers. Design process has been significantly shortened by
8
2 The Automotive Industry
using computers. The designers are today factual chiefs most of the car companies.
3. OEM parts - Include bodies, electronic and mechanical components, tire and rubber
hoses, seats, windshields, air bags, lighting, batteries, engines, transmissions and
replacement parts.
4. OEM assembly - Today’s trend is using fewer parts in each vehicle component to
reduce industrial waste and pollution, and having parts delivered just-in-time, or lean
supply.
5. Marketing - OEM individual dealers work together to create national, regional and
local marketing strategies.
6. Distribution and sales - Work with automakers on vehicle strategies. May offer incentives to increase sales and collect customer feedback.
7. After sales service
These features are related and affect each other. From raw materials and product development we get OEM parts, product development and design have to cooperate with sales and
marketing to meet customers’ needs, production of parts is important part of also after sales
service etc. Only if everything cooperates perfectly, then the car makers can be globally
competitive.
Globalization of automotive industry has also meant globalization of suppliers. Today we
distinguish two classes of suppliers in car industry: global and local. The most of the top
suppliers now serve to the European, Japanese and US manufacturers. They have to have
global strategy, be able to offer platforms, be flexible, be able to follow automakers’ strategic
decisions and supply the same part with the same quality and price in every location in the
world. It often means immense investments to new facility in areas to which the customer
is expanding. However, building of a new facility is not required at each location. They can
concentrate production of some parts in one region and then ship them to customer’s final
assembly plants. This concentrated industry structure, vast requirements for engineering
hours for developing a new product and huge initial investment to become a global supplier
gives power to a few giant firms and is a big limit for smaller firms to enter the chain.
Value chain linkages between automakers and suppliers in the automotive industry are
typical with huge amount of power of automakers in the chain (even if the suppliers are
global). As Herrigel (2004) shows, the US lead firms (General Motors and Ford) had historically great tensions between them and their main suppliers. They had aggressive buying
practices to lower input costs as much as possible, did not often paid for whole services and
they switched suppliers with little advance notice. Result was oscillation between rational
9
2 The Automotive Industry
and market linkages. In Japan applied more captive linkages with suppliers based on trust
and long-term cooperation. As a result none of the major suppliers of Japanese automotive
companies have bankrupted in last 15 years in contrast to the suppliers of US automakers.
Japanese assemblers are the most loyal in the world, followed by Europeans on the other
hand Americans are the least loyal.
2.4 Macroeconomic Development
The more is the automotive industry global industry, the more is sensitive to fluctuations
of global economy. The review of main economic trends from last years is described in this
section.
The world GDP grows dynamically (with the exception of year 2009) for several decades.
This growth is strongly supported by Asian economics in last years. The world economy
grew more slowly in 2007 than in the previous years, mainly due to the slowdown in the USA
economy and beginning of housing bubble on the local market. To the global dynamics
then more and more increasingly contribute Asian economies, especially China and India.
Rising oil prices and the ongoing political instability in the Middle East in 2007 led to higher
prices of synthetic materials. The increase in consumption of raw materials in 2007 caused
a further increase in steel prices. The world economy in 2008 significantly slowed its growth
and in the end of the year experienced a decline. The main reason was the onset of a deep
financial crisis with all its negative impacts - the bankruptcy of a number of investment
banks (Lehman Brothers), uncertainty in the financial markets and the slowdown in many
world economies. To the global dynamics contributed more Asian countries, especially
China and India. EU economies slowed growth, some of them, such as Ireland, Spain and
Germany, have fallen into recession (they recorded two consecutive quarters of negative GDP
growth). The Global Financial and Economic Crisis influenced of course the all significant
industries such as the automotive industry. Besides the direct impact - financial market
instability - the most dramatic impact of the crisis was for the automotive industry large
slump in a demand. The vast majority of the world’s manufacturers had to face a significant
decline in sales which had to be necessarily reflected in the results of their operations. This
development logically resulted in the further slowdown in the global economy. In 2009 the
global economy was fully affected by the Global Financial Crisis. The problems of banks
resulted in a significant decline in world GDP after long time.
The Global Economic Crisis had a harmful effect on the automotive industry. All the
major brands had to face double-digit declines in sales which resulted in the reduction of
earnings and redundancies with subsequent effect on suppliers. After a significant decline in
2009, in 2010 the global economy recorded a recovery. Most countries returned to growth
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2 The Automotive Industry
in GDP. Economic recovery and rising demand led to an increase in prices of precious metals
and steel in this year. The main consequences of the crisis, which influenced the automotive
industry, were following:
• Debt crisis in various countries
• High volatility on financial markets
• High volatility in exchange rates
• High prices of raw materials
2.4.1 Financial Crisis
In this thesis we will discuss impacts of financial crisis on car deliveries in global automotive market. Thus it is important to define financial crisis for our purposes and find the
most important drivers. Current literature appears to be unclear in specification of financial
crisis definition. There are several major directions. The first, represented by monetarists
(e.g. Schwartz, 1987), sees the true reason of financial crises only in the banking panics,
since they believe those are the ones causing severe downturns in aggregate economy. The
other definition is quite broad. As e.g. Kindelberger and Aliber (2011) and Minsky (1970)
suggest, the financial crisis includes substantial decline in asset prices, deflation, defaults of
financial and non-financial companies, disturbances in exchange markets, disinflation, or its
combinations. Mishkin (1992) defines entirely different approach. He uses the asymmetric
information assumption, which says that in financial transactions one party usually has a
superior information set than the other. Based on this assumption, the author derives that
a financial crisis is a situation, when the adverse selection and moral hazard phenomena
become much worse, which results in inefficient channeling of funds through the financial
system.
Reinhart and Rogoff (2010) identify three different types of financial crisis in another
study:
• Banking crisis - Situation when the bank is no longer solvent or is not able to maintain
liquidity (the trust of public declines and people start withdrawing their deposits, a
bank invests to a particular industry in a higher than optimal proportion and the
industry performance decreases).
• Currency crisis - Defined as a rapid exchange rate depreciation (at least 15 percent
per annum). Periods of high inflation (more than 20 percent per annum) are also
included in this type of crisis, because a currency depreciation and high inflation often
go hand in hand (based on empirical evidence).
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2 The Automotive Industry
• Debt crisis - Failures to meet debt obligations incurred in a foreign country, repudiations, or the restructuring of debt into worse terms to the creditors (external debt
crisis is usually much more noticeable than a domestic one).
2.4.2 Macroeconomic Forecast for Main Car Markets of ŠKODA
AUTO
Global economic projection of GDP growth is approximately 3 percent and inflation 5
percent. According to International Labour Organization (2013), the global unemployment
will slightly rise up to 6.5 percent (from today’s 5.9 percent unemployment rate) in next 5
years, because of an uncertain economic outlook, which has weakened aggregate demand,
holds back investment and hiring employees. The situation of youth unemployment is
expected to improve slightly in developed economies over the next 5 years, but it is expected
to rise in emerging economies in Eastern Europe, East and South-East Asia and the Middle
East.
In advanced economies we can see stagnation of GDP or slight growth; inflation will be
probably low and under 2 percent. The expected GDP growth for China for the next 5 years
is according to both International Monetary Fund and World Bank (2014) around 7 percent
and inflation around 3 percent. Russia is showing slight positive results of GDP growth but
inflation outlook is relatively high - over 5 percent in next years. The expected GDP growth
for India for the next 5 years is little bit losing to previous year’s growth but will surely remain
over 4 percent with relatively high inflation around 6.5 percent. Unemployment rate in India
are in average 7.6 percent and decreased from all time maximum 9.4 percent in 2010 to
3.8 percent in 2011. The unemployment is supposed to even more decline to around 1.2
percent in 2015. However there is unstable economic and monetary situation with high
interest rates around 8 percent in last years. These high interest rates are also predicted
for near future.
2.5 Development of the Markets with ŠKODA AUTO
Branded Cars
The global car markets steadily grew from late 1990s till the Financial Crisis in 2008. The
impacts of the Crisis on the banking markets led to the restraint on automotive markets.
The decline was also amplified by rising prices of raw materials and energies. Above-average
declines in sales recorded particularly regions of North America and Western Europe. On
the other hand the positive development continued in the markets of Central and Eastern
Europe and also in Asia but with significantly reduced growth rate. In 2009 the sharp
12
2 The Automotive Industry
decline in sales was recorded mainly in regions of North America, Central and Eastern
Europe. In contrast, the positive trend continued in Asia. The sales in 2010 were strongly
influenced by the ending of scrappage program which was implemented as an economic
stimulus to increase market demand in the industrial sector during the global recession that
began in 2008. Significant increase in sales was recorded mainly in Eastern Europe and also
in Asian markets. The recovery that had begun in 2010 on the global automobile markets
continued also in 2011 and 2012. This trend was mainly driven by the positive developments
in markets as India, China and the Russia and total global automobile sales increased by
4.5 percent and 6.3 percent year-on-year respectively. In 2013 grew the markets of China,
North America and Middle East. Earlier dynamically growing markets in Russia and India
recorded a decline in demand in 2013. In following Table is the summary of year-on-year
global car market development in most important ŠKODA AUTO car markets.
Table 2.2: Car Markets Development (y-o-y growth)
2007
2008
2009
2010
2011
World
+4.2%
-5.8%
-6.1% +11.4% +4.5%
Central Europe
+8.5%
+4.7% -16.5%
-3.5%
-3.3%
Western Europe +0.2%
-8.4%
+0.5%
-5.1%
-1.4%
Eastern Europe
+36%
11.4%
-52.5% +2.3% +35.2%
China
+22.3% +7.8% +53.9% +35.1% +8.1%
India
+16%
+2.1% +17.8% +29.8% +5.6%
Russia
+37.5% +15.4% -50.3% +27.9% +39.8%
Source: Scotiabank (2014) and ŠKODA AUTO Annual Reports
2012
+6.3%
-2.1%
-8.2%
+7.7%
+9.3%
+11.1%
+10.9%
2013
+4.6%
+1.1%
-1.9%
-4.9%
+17%
-6.7%
-5.7%
We can see deliveries to customers on 12 biggest markets of ŠKODA AUTO and in each
of four regions in the table below. China and Russia are from year 2010 the first and
third biggest markets of ŠKODA AUTO respectively and together with India represent the
markets with biggest future potential. Germany, United Kingdom, Czech Republic, Poland,
France, Austria, Switzerland, Belgium and Slovakia belong among very important markets
for the company with relatively stable results. Central Europe region has stable markets
and evinces slight growth, while Western Europe is more sensitive on economic recessions
and its performance vary. Eastern Europe and Overseas/Asia regions with their growing
Russian, Chinese and Indian markets play an important role in the ŠKODA growth strategy
and steadily grow. Therefore we will focus on them further.
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2 The Automotive Industry
Table 2.3: ŠKODA AUTO Deliveries to Customers –
2008
2009
2010
China
59,284
122,556 180,515
Germany
112,504 162,328 113,323
Russia
50,733
33,002
45,577
United Kingdom 37,072
36,012
41,632
Czech Republic
58,001
56,504
58,033
Poland
33,986
38,305
37,918
India
16,051
14,535
20,019
France
19,480
20,313
20,394
Austria
16,700
17,500
18,803
Switzerland
n.a.
n.a.
14,320
Belgium
14,130
12,358
15,756
Slovakia
17,809
14,613
13,650
Central Europe
131,084 119,923 121,063
Western Europe 315,571 345,185 333,261
Eastern Europe 123,630
60,464
73,747
Overseas/Asia
104,245 158,654 234,529
Total
674,530 684,226 762,600
Source: ŠKODA AUTO Annual Reports
Largest Markets and by Region
2011
2012
2013
220,089 235,674 226,971
128,011 132,580 136,415
74,074
99,062
87,456
45,282
53,249
66,029
58,202
59,674
60,042
38,116
36,307
38,710
30,005
34,265
22,563
22,536
22,022
20,400
21,208
22,300
20,073
16,298
17,830
16,984
18,900
17,530
15,482
15,182
15,902
14,827
123,156 124,012 126,481
361,777 358,439 369,598
108,423 137,057 125,359
285,828 319,694 299,312
879,184 939,202 920,750
On the figure below, we can see that since 2006 the ŠKODA AUTO deliveries to customers have been increasing year by year for China, Russia and India as well with exception
of the year 2013. Although Russia experienced a major decline due to the financial crises
in 2009, it has recover quickly and ŠKODA AUTO delivered almost 10 percent of the total
production on local market in 2013.
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2 The Automotive Industry
Figure 2.5.1: ŠKODA AUTO Deliveries to Customers in Emerging Markets
Source: ŠKODA AUTO Annual Reports, own calculations
The figure below depicts the percentage change over the past years of deliveries to
customers in emerging markets plotted into the total world deliveries. We can see, with
exception of the year 2009 and 2013, that the growth of deliveries in the emerging markets
is above the ŠKODA AUTO worldwide average.
Figure 2.5.2: ŠKODA AUTO Deliveries Percentage Change in Emerging Markets (y-o-y
percent growth)
Source: ŠKODA AUTO Annual Reports, own calculations
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2 The Automotive Industry
In the year 2013 slight decline in deliveries to customers was recorded. In 2013, ŠKODA
AUTO sold 920,750 vehicles worldwide (year 2012: 939,202; -2.0 percent). Deliveries to
customers have been affected by changes in production due to the comprehensive model
renewal. With eight new or completely revised models, 2013 is ŠKODA AUTO’s year
dedicated to the biggest model offensive in corporate history. The Czech automaker could
not also fully escape the weak market environment. But in September 2013 ŠKODA
AUTO recorded an increase of 3.3 percent over the same month in 2012, further increased
its market share in Europe and growing trend lasted till the end of the year. Also current
incoming orders are developing very positively. The model campaign is finally paying off
and on the basis of these indicators, ŠKODA AUTO expects similar progress in the future.
In next Subsections we will look closely to the main emerging ŠKODA AUTO’s markets.
2.5.1 China
China’s Economy
After the collapse of the Republic of China and formation of the People’s Republic of
China in 1949 came into power Communist party of China and they started to transform the
country into a modern and powerful socialist nation. In economic terms, these objectives
meant industrialization, improvement of living standards, narrowing of income differences
and production of modern military equipment. The reforms that mostly influenced the
boom of automotive production were so-called Chinese economic reforms starting in 1978.
The Party decided that the centrally planned economy had failed to produce efficient economic growth and started to shift a centrally planned to a socialist market economy. They
reduced the government planning and direct control and added market mechanisms in the
system thus this economic model is based on dominance of the state-owned sector and an
open-market economy. China experienced rapid economic, industrial and social development after these reforms. This boom was also enhanced by China’s entry into the World
Trade Organization in 2001.
The socialist market economy of China with a population of 1.3 billion became recently
the world’s second-largest economy and is increasingly playing an important and influential
role in the global economy. It is the world’s fastest growing economy, with growth rates
averaging 10 percent over the past 30 years and fastest growing consumer market. China is
also the largest manufacturing economy, the largest exporter and second largest importer of
goods in the world. Coastal regions of China tend to be more industrialized, while regions in
the hinterland are less developed. The predictions for China are continuing strong economic
growth and high inflation in next years. However, China still remains developing country
because its per capita income is still very low and almost 100 million people live below
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the national poverty line. There is high inequality in the country, the market reforms are
incomplete, they challenge environmental sustainability, rapid urbanization and external
imbalances. According to Accenture (2013) report, there are 14 modern metropolises with
population of more than 10 million and 150+ cities with populations of more than one
million. The percentage of population living in urban areas is still supposed to rise from
51 percent in 2011 to 60 percent in 2020 and also the income of households is going
to rise greatly. Significant policy adjustments are required in order for China’s growth to
be sustainable. According to World Bank (2014), the last Five-Year Plan (2011-2015)
highlights development of services, settings targets to reduce pollution, to improves access
to education and healthcare, addresses environmental and social imbalances and expands
social protection. The quality of infrastructure has improved significantly in last years
while expansion of road systems is going to continue. The target growth is 7 percent and
predicted inflation is relatively low.
China’s Car Industry and Market
China’s passenger cars and commercial vehicles manufacturing industry has become the
number one automaker in the world in 2009 with an annual production of more than 22
million units in 2013. Russo et al. (2008) suggest in their report that most of the cars
manufactured in China are sold within China. Even if Chinese firms have learned very quickly
how to assemble cars and develop supply chains, they are very inexperienced at the vehicle
development and synthesis process. They often draw inspiration from foreign brands and
their ability to compete on foreign markets is insufficient. As a result, exports reached only
977,300 units in 2013. Native player’s share of the passenger car market in China was 40
percent in 2013 and the rest were produced by joint ventures with foreign car makers. The
production plant in China’s industrial metropolis of Shanghai was inaugurated in 1984 as a
joint venture of Volkswagen and the Chinese firm Shanghai Automotive Industry Corporation
("SAIC"). In 2005, the production of the plant expanded also by ŠKODA AUTO models;
Octavia was the first, later followed also the Fabia and Superb. ŠKODA AUTO further
produces Rapid in plant in Yizheng.
Chinese automotive market has been largest single-country market in the world since 2010
and is also supposed to be the market with the biggest potential for ŠKODA AUTO cars.
In 1975 only 139,800 automobiles were produced annually, but as a result of the reforms
from 1978, by 1985 production had reached 443,377, then jumped to nearly 1.1 million
by 1992 and during the next 10 years reached 2.3 million and has continued growing. We
can see sustainable growth of sales in Table 2.2. In 2008, dynamics of growth significantly
slowed down, particularly because of increased fuel prices and implementing of measures
to control inflationary tendencies. The growth of China’s car market is undeniable but has
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experienced great volatility of growth rate in last 20 years. Year-on-year car sales growth
rate ranged from 0 to 70 percent (see following Figure) which can be one of the challenges
for car makers on China’s market.
Figure 2.5.3: GDP development and Car Sales Growth Rates in China (y-o-y growth)
Source: IHS Automotive Insight (2013) and IMF (2014)
In 2011, number of registered automobiles in China exceeded 100 millions. Cars account
for almost half of the demand of motor vehicles (automobiles, motorcycles, and others).
The dominant player on Chinese market is Volkswagen but other brands (like Chery, Greely,
SAIC or Toyota) are strongly catching up. According to China Auto Web (2013) ShanghaiVolkswagen was with more than 1.5 million sold cars a close second on China’s passenger
car market behind Shanghai-General Motors in 2013.
There are several consumer segments according to Accenture (2013). First tend to buy
domestic brands either because of the loyalty to the local brands and country or because their
low income is insufficient to afford foreign cars. Other customer segments are interested
in foreign brands. The wealthier of them keen on exclusivity and high-end products, other
aspire to buy trendier and more sophisticated brands or brands with long tradition on
Chinese market.
Customers in China predominantly rely on four factors when they decide which car to
buy: advice from family members, opinions shared via social media, recommendations from
friends and colleagues and they also often look on manufacturer websites. Social media
nowadays have significant role on Chinese consumers’ purchasing behaviors. They use
social media to learn about companies’ products and services, share positive or negative
comments or communicate with companies about their product or customer service. The
digital marketing can strengthen relationships and brand loyalty. Chinese customers want
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2 The Automotive Industry
more access to carmakers’ products (e.g. mobile-enabled websites), more content and
digital features (clearer pricing, online customization or online chat with a dealer) and a
digital experience that is simple to navigate.
A Perspective on the China’s Car Market
China’s automotive market grew at a compound average rate of 24 percent a year between
2005 and 2011 and is expected that the growth will continue. Chinese customers are more
sophisticated now and their tastes are evolving so automakers just need to understand their
needs and which segments will be most important. They need to translate those consumer
insights into quick action. Prices of cars are falling and this trend is expected to continue
over the next five to ten years. China is a huge country and the tastes of the customers
may also vary depending on the region where they live. Fundamental drivers for demand
growth are increasing urbanization, rising household income, low car penetration rates and
infrastructure improvements. Wang et al. (2012) mention in their paper following trends
on Chinese auto market in the next 10 years which will drive growth:
• Going bigger - Consumers in China are going to buy bigger cars. Sedans will remain
the largest segment but sales of sport utility vehicles ("SUVs") will triple. Wealthy
customers will consider SUV more likely as an upgrade from sedan and they will
show off heir personal tastes and needs by some big car. But despite high growth
in this segment, the share of sedans in 2020 will be still only 20 percent and sedans
will command 70 percent. But even among sedans the customers will likely choose
bigger cars as long as they can afford it. However, in segment of small cars is also
great potential. Chinese market development will be likely consistent with US and
European markets, where a lot of families in urban areas buy second cars just for
commuting.
• Trading up - The segment middle-priced cars (80,000 to 250,000 CNY) will remain
the dominant price segment with 60 percent of the market in 2020. Rising demand
for small and medium-sized cars will support as well as rising household income will
underpin demand in this price segment. But customer will be more likely to buy
high-priced cars (250,000 to 800,000 CNY) because there will be more second-time
buyers and aggressive marketing strategies of leading car companies will strongly
influence a selection of a new car. There are also more and more environmentally
conscious customers which will look for cars with green technologies, alternative fuels
and fuel-economy engines. Additional in-vehicle technologies, like Wi-Fi connectivity,
GPS or access to real-time traffic data, will also generate added revenue per car sold.
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In last 20 years, the automotive market in China has had to face some unfavorable developments that can influence further market growth. Wang et al. (2012) mention in their
paper following:
• Global economic uncertainties will likely have negative impact on rate of urbanization
and thus on number of jobs.
• Governments could introduce policies that might restrict car use in more than 20 big
cities in order to reduce air pollution.
• Emerging alternatives to new car ownership as used car market, improved public
transportation or cheap car rental.
• Next challenges are to satisfy evolving customers’ tastes and reduce disruption of the
landscape.
Despite these uncertainties, the new car sales in China are forecast to contribute 35 percent
of the world’s car market growth between 2011 and 2020. The China auto market growth
is expected to slow but it will still be in average 7 to 8 percent a year in next 10 years but
the volatility observed in last twenty years as likely to continue. Regional importance of
the sales is going to be crucial because increase in sales in next 8 years in smaller cities is
around 60 percent. Automakers should invest in order to understand local customers needs,
select effective marketing strategy in particular region and have to understand, in which
regions are customers more sensitive on price or rather on design. But crucial for sales will
be still big cities which will have major share of newly sold cars.
Chinese people appreciate German quality of vehicles and ŠKODA AUTO benefits from
it. The growth of ŠKODA AUTO’s cars have been steadily above the growth of the market
and this growth is expected to continue. The ŠKODA AUTO’s goal on Chinese market is
to deliver 500,000 vehicles to the customers around year 2020 to accomplish the company’s
growth strategy.
2.5.2 India
Indian Economy
The reforms that have had the biggest impact on car market in India in last decades
were established in 1991. The period from 1947-1991 is called Pre-liberalisation period
and is characterized by the protectionism, with a strong emphasis on import substitution,
industrialization, economic interventionism, a large public sector, business regulation, and
central planning inspired by Soviet Union, while trade and foreign investment policies were
relatively liberal. In 1991 the new government initiated economic liberalization. The reforms
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reduced tariffs and interest rates and then brought foreign competition in Indian market, led
to the privatization of certain public sector industries, improved infrastructure and ended
many public monopolies, allowing automatic approval of foreign direct investment in many
sectors. According to IMF (2014), the GDP of India has then risen rapidly since 1991
growing on rates 4-10 percent per year with a peak in 2010. By the turn of the 21st
century, India had progressed towards a free-market economy, with a substantial reduction
in state control of the economy and increased financial liberalization.
India is the largest democracy in the world with 1.2 billion people and steadily growing
middle class. The Indian economy is fourth-largest economy in the world according to
purchasing power parity. India is nowadays the 3rd largest investor in the world and rapidly
invests to infrastructure. The country will soon have the largest and youngest workforce in
the world and now is in the midst of a massive wave of urbanization. To create jobs, housing
and infrastructure, massive investments are needed. According to ACMA (2013), the total
planned infrastructure investments from 2007-2012 were $500 billion and infrastructural
spend is targeted to double to $1 trillion for 2012-2017.
The great problem in India are social inequalities. Poverty rates are especially in some
regions incredibly large. The level of education is still not enough to to compete in today’s
changing job market, despite primary education has largely been generalized. The forecast
for Indian economy is continuing strong economic growth but high inflation in next years.
India now has a great opportunity to lay the foundations for a truly prosperous future and
to improve the quality of life of its citizens.
Indian Car Industry and Market
The automotive industry in India is economically and demographically considered as
well-positioned for growth and for satisfaction of domestic demand and also export. It is
one of the key drivers of India’s economy, accounting for around 4 percent of India’s GDP
and over 200,000 jobs. India’s automobile exports are expected to cross $12 billion by 2014.
India’s passenger cars and commercial vehicles manufacturing industry is the sixth largest
in the world, with an annual production of almost 3.9 million units in 2013. The majority
of India’s car manufacturing industry is based around three clusters in the south (Chennai), west (Mumbai and Pune) and north (National Capital Region). In 2011, there were
10 Indian automotive companies and more than 30 foreign automotive companies which
produced their vehicles in 3,695 factories across India. ŠKODA AUTO produces its cars
in Volkswagen’s greenfield facility in Pune (Fabia, Rapid) and owns assembly plant in Aurangabad (ŠKODA AUTO India Private Limited), where the company produces Octavia,
Superb and Yeti and where also Audi cars are produced.
Indian car market is supposed to be one of the car markets with the biggest potential
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and is characterized by rapidly growing presence of global OEMs in last years. In Table
2.2 we can see sustainable year-on-year growth of new cars sales. But we have to discuss
that the dynamics of this growth strongly depends on many external factors. In 2008
the potential of Indian auto market was not fully utilized because of rising fuel prices and
implementing government measures to control inflation. In 2011 the expensive petrol and
rising interest rates prevented the car market from higher growth. In the next year was
unstable exchange rate of rupee. The unstable economic and monetary situation led finally
to decline in steadily growing Indian market in 2013.
India is a vibrant economy with more than 40 million vehicles. The current vehicle
demand in Indian market is quite different from other top automotive markets. Around ¿
of the demand is for two-wheelers and for passenger vehicles is the demand only around 15
percent.
The biggest players on Indian automotive market with passenger vehicles cars are MarutiSuzuki (44 percent), Hyundai (14 percent) and Tata Motors (11 percent). We can mention
other car companies as General Motors, Toyota, Honda, Ford, Volkswagen Group or Mahindra which has around or less than 5 percent market share. The ruler of the commercial
vehicles market in India is Tata Motors with more than two thirds of all sales.
A Perspective on the Indian Car Market
As we already mentioned, the predicted investments to roads, ports, airports, railways,
telecommunication or utilities will increase rapidly in following 5 years.
According of ACMA (2013), the estimated yearly production of cars is 5 million passenger
vehicles in 2015 and 10 million in 2020 (in 2013 it was approximately 3.3 million) and annual
passenger vehicles sales are projected to increase to 4 million by 2015.
There were adopted some frameworks for automotive industry in India - Auto Policy 2002
and Auto Mission Plan 2006-16. These frameworks include manufacturing and imports free
from licensing and approvals, WTO compliant policies (no import restrictions and reduced
tariff levels), robust legal system and stable foreign exchange regime, increased budgets for
R&D activities and others. There rules should create a robust Indian automotive industry
in few years.
The important feature of Indian automotive industry is creating of technological and
developing alliances in manufacturing clusters which enable the automakers to manage their
financial risks and access to the global experience and technology. KPMG study (2010)
suggests that there will be two broad themes in Indian automotive industry in medium-term:
• Growth - In the context of the unique characteristics of the Indian automobile market,
growth is expected to be driven by following:
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◦ Affordability - The average disposable income in India is still much lower than
average price of a passenger car. This is the reason why two-wheelers have more
than ¿ of the market. But there is changing demography in India. Increasing
number of educated people entering the working age should be the driver of
double digit growth of passenger car market in following years. Furthermore, as
there is further economic development in India, the two-wheeler users of today
are likely to be tomorrow’s first-time car buyers. But as usual, these forecasts are
strongly dependent on taxation, legislation, infrastructure and global conditions.
◦ Fuel economy - The automotive market leaders in India are companies which
have been able to offer products with the lowest consumption. Fuel economy
will also be an important factor in the truck sector.
◦ Alternative fuels - Passenger cars and commercial vehicles running on alternative
fuels (e.g. compressed natural gas or electricity - “CNG”) are gaining popularity
because of their low operating costs. The problem is to radically improve the
fueling infrastructure before these vehicles become more mainstream.
◦ Rural market - The focus of market players in India is in last years on rural
areas and on expanding their dealer networks to these places. Previously had to
people from these areas go to cities and towns to their car dealers.
◦ Niche products - India recorded growth of the sales of luxury vehicles or gear-less
scooters in last years.
• Consolidation - With the liberalization, many new car manufacturers entered Indian
market over the last two decades. Players from across the world see it as natural
extension of their business domain and creating of the alliances plays significant role
in generating economies of scale.
But we have to consider also two important trends in India from last years. The gradual
legislative is heading for green vehicles and public investments to metro systems and buses
in major cities to lower the impact of booming car market on environment. We discuss the
following trends in long-term view:
• Green revolution - The segment of vehicles with alternative fuels has been growing
rapidly in last decade. The importance of saving the environment was supported by
various government measures across the world. The global innovators of the green
sector are Toyota and Honda but all important car companies are trying to catch
up this trend. The development of green vehicles in India began with a regulatory
push for CNG buses and three-wheelers in New Delhi more than a decade ago. Each
car manufacturer has been then developing several technologies (hybrids, hydrogens,
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2 The Automotive Industry
CNG, bio fuel, LPG) because they don’t know which will be most successful in the
future. This lack of technological consensus may be obstacle in creation of particular
fueling/charging infrastructure. Government alone is not able to construct infrastructure for every green technology and it is the reason why the government should make
some blueprint for the manufacturers concerning introduction of greener vehicles. In
the last years there are 2 main paths to move towards the green vehicles in India:
◦ CNG/Dual fuel vehicles - It is believed that at least 5 percent of new car buyers
would choose CNG variant when it is available. This could grow in the future
as the demand increases for vehicles with lower running costs. The dual fuel
vehicles have higher purchase price but it is compensated by lower cost per
kilometer.
◦ Electric/Hybrid vehicles - On Indian market only one manufacturer, Reva, whose
sales account for less than 1percent of all passenger cars sold in India, offers
hybrid electric vehicles. But this situation is caused by bad reputation the entire
electric vehicle industry. In two-wheelers sector is the situation changing but in
sector of commercial vehicles the situation is not clear. The opportunities for
Indian automotive industry in green vehicles despite some significant obstacles
can be export hub, R&D hub or sourcing hub for parts for green vehicles.
• Mobility revolution - The alternative transport represents the other interest in automotive industry but there is a question whether it represents opportunity or thread.
However, most of the experts nowadays agree that extensive building urban mass
mobility will not negatively influence car sales.
The overall prognosis for Indian car market industry is good. Rising prosperity, substantial
adopting new public measures to support free market economy, growing middle class and
current low car penetration (see Table 2.1) give good prospects to the car manufacturers
in the future.
2.5.3 Russia
Russian Economy
Russian economy went through dramatic changes in the nineties of 20th century. They
moved from Soviet Union’s centrally planned economy to more market based and globally
integrated economy through reform characterized as shock therapy. Richet (2002) pointed
out the root reasons of difficulties in transition between the industrial organization of the socialist economy and a fully-fledged market economy. There were deeply entrenched socialist
systems and the powerful administrative mechanisms that allowed this economy to fulfill its
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2 The Automotive Industry
planned targets. The differences between the two regimes were ones of space, allocation
of natural resources, economic objectives and also openness. Newly implemented policies
resulted in an economic collapse, hyperinflation, large declines in year-on year GDP growth,
millions were plunged into poverty and corruption and crime started to spread rapidly.
The subsequent privatization has been radical, rapid, extensive and unprecedented in the
world and led to the enrichment of narrow group of people. The state enterprises should
have been equally owned by all citizens but instead of this they fell into the hands of a few,
who became immensely rich. Blasi et al. (1996) mention that 90% of industrial output and
80% of industrial enterprises went to private hands but only minority of them were able to
finance their further development out of their profit. The companies were in 1996 in need
of radical restructuring and the management had strong relationships with politicians.
The Russian ruble crisis in 1998 strongly affected sales in automotive sector. In 1999,
Russia stabilized financial markets, devaluated its currency and started period with substantial yearly GDP growth from 5 percent till 8 percent. This growth was also supported
by increasing oil prices from 1998. The industry then grew by 75 percent, investments
increased by 125 percent and other sectors of the economy increased as well.
According to World Bank (2014), Russia is considered as developed country with highincome commodity-driven economy. It has been the eight-largest economy in the world
since 2013. The country has an abundance of natural resources which lead to high economic growth from 1999 till 2008 because of increasing in raw materials’ prices. In 2008
Russia was strongly affected by the global financial crisis since oil prices dropped from $140
per barrel to $40 per barrel or capital flow reversed from $80 billion of in-flows to $130
billion in out-flows. The economic growth slowed down mainly because of the lack of more
comprehensive structural reforms which has led to the erosion in businesses’ and consumers’
confidence, but today the forecasts are still favourable. Russia’s GDP growth from recent
years is driven mainly by rising household consumption, supported by fast credit, the increase in Russian exports and wage growth. However, without growth-supporting structural
reforms and after Crimea crisis, growth will remain low accompanied by high inflation in
next years.
Russian Car Industry and Market
The Russian car industry in is the most developed and the most important among states in
Central and Eastern Europe ("CEE") and in Commonwealth of Independent States ("CIS").
Automobile production is a significant industry of the country, directly employing around
600,000 people and the supporting around 2-3 million people in related industries. Citizens
in some areas also heavily depend on the social services provided by automotive companies.
In recent years, many foreign car manufacturers have established their facilities in Russia, as
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2 The Automotive Industry
in one of the fastest growing automotive markets in the world. These companies started to
collaborate with local producers in order to lower transportation costs and created sufficient
mass to encourage an entry of many types of component suppliers. This reflects the rapidly
growing importance of Russian car market in world’s automotive industry.
Russian passenger cars and commercial vehicles manufacturing industry is the eighth
largest in the world, with an annual production of almost 2.2 million units in 2013. The
light vehicle market is characterized by the expanded production of Russian made foreign
brands. While sales of Russian brands, as a share of the total light vehicle market, have
declined in last years, sales of Russian-made foreign brands have risen steadily. The biggest
market share by brand in terms of volume has still Lada, followed by Chevrolet, Renault, KIA,
Hyundai and Volkswagen. Other brands have 5 or less percent of the market. Commercial
vehicles market is dominated by domestic players but foreign brands has been gradually
increasing their share of sales in recent years.
After dissolution of the Soviet Union, the automotive industry in Russia faced a crisis
due to competitive foreign imports. Richet (2002) suggests that restructuring of Russian automotive sector has meant changing in the industrial organization by focusing on
core businesses in which car makers have their competitive advantage, setting up of jointventures, building up new sectors of suppliers and distributors, upgrading the production
and integrating new technologies which the Western producers use. The tariffs in Russia
were relatively low in the beginning of the millennium, compared to other emerging markets, but sufficient to protect local market and encourage foreign investors to enter local
market and replace with their assembly plants substitute imports. However, after Russia’s
accession to WTO in August 2012, many protectionist measures were revisited and in and
it may in long term indicate potential danger for local car makers. But after recovery of
the economy in early 2000s the market started booming. In 2005 Russian government enacted legislation with the aim of encouraging investments by foreign automotive companies.
The Toyota, General Motors or Nissan got extensive support to build their plants in areas
of Saint Petersburg and Leningrad, Volkswagen then in the areas of Kaluga and Nizhny
Novgorod. ŠKODA AUTO produces cars in these Volkswagens’ plants as well.
The risks for car manufacturers producing in Russia are related to the general economic
environment. The current global macroeconomic volatility has potentially negative impact
both on emerging markets and on commodity prices. Also the automotive market was
strongly affected by the financial crisis. In second half of 2008 (and especially at the end
of the year), the dynamics of the growth sharply declined. The market for new passenger
cars then has dramatically dropped from January 2009 due to the lingering effects of the
financial and economic crisis. Production of passenger cars dropped from 1,470,000 units
in 2008 to just 597,000 units in 2009. In the Table 2.2 we can see huge decline of sales
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about 50 percent. Then the car market came back to life (also because of an effective car
scrappage scheme) but in 2013 ŠKODA AUTO recorded decline about 5.7 percent again. It
can be understood as a signal that the local passenger car market in the short term peaked
and the rapid growth of recent years will not be repeated.
A Perspective on the Russian Car Market
The Russian automotive industry and economy are stabilizing and foreign car makers and
government support steer automotive industry toward stable growth path. According to
Ernst & Young study (2013), the Russian passenger car market will remain one of the most
attractive markets in Europe due to low car density (see Table 2.1) and advanced age of
car fleet (the average age of vehicles in Russia is 12 years; in Europe, 7 years), growing
number of car lending programs provided by banks jointly with OEMs and development
of infrastructure and highways. The long-term prospects are therefore optimistic assuming
that realized and declared investments will help to change the current situation in the
industry and make it more competitive in the next few years.
Krkoska and Spencer (2008) and Ernst & Young study (2013) suggest that the key drivers
for passenger car production in Russia, which are most important for viable automotive
industry in next years, are following:
• Foreign producers should have links to local car producers which would significantly
help to transfer skills and know-how, most importantly in management
• Expansion of OEMs’ assembly plants and moving their production over 100,000 units
of single model per year
• Improving of logistics and infrastructure for the automotive industry (infrastructure
for automotive industry in ports, industrial parks for components suppliers etc.)
• Automotive companies should be able to utilize the comparative advantage of Russia
(cheap energy and natural resources) and focus also on exports to other countries
• Creation of new production facilities
• Extension of the product range of Russian-made foreign brands
• Russian economic recovery
• Government support under the 2020 Development Strategy aimed at replacing imports with domestic production
• Trade policy aimed at domestic production and establishing a vehicle scrapping fee
(mandatory vehicle utilization levy)
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2.6 Conclusion
In this Chapter, we analyzed current situation on global automotive market. We described
role of globalization, linkages between car manufacturers and their suppliers, global value
chains and role of macroeconomics. On the basis of existing researches we forecasted
probable development of main emerging markets which ŠKODA AUTO focuses. We will
continue in our analysis with introduction to ŠKODA AUTO company and then we will
employ econometric methodology for non-stationary time series to widen current findings
about automotive industry on the basis of company’s internal data and macroeconomic
indicators.
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3 ŠKODA AUTO
The Czech automotive industry represents one of the most developed automotive markets
in the Central and Eastern Europe. It succeeded in transitioning into an European advanced
automotive company because of its unique technology know-how, highly developed Research
& Development ("R&D") sector with more than thirty world leader R&D companies and
robust supplier base of nearly 900 companies. According to Czechinvest (2012), the yearly
production of passenger cars in 2010 exceeded one million units and in 2013 Czech Republic
was 13th largest global passenger car producers by volume in the world with 1,128,473
produced units. The Czech automotive industry employs more than 260,000 people and
accounts for more than 20 percent of both Czech manufacturing output and Czech exports.
Among major car makers in the country belong besides ŠKODA AUTO also TPCA (Toyota,
Peugeot and Citroën Automobile joint venture) and Hyundai Motor Manufacturing Czech.
3.1 Brief History
Car production has long tradition in the Czech Republic and ŠKODA AUTO belongs
among the world’s oldest car makers. The origins of what became ŠKODA AUTO go back
to 1895, when two keen cyclists, Mr. Klement and Mr. Laurin, designed and produced
their first bicycle and this firm laid the foundations of the more than one hundred year-long
Czech automotive tradition. They gradually continued with production of motocyclettes
and motorcycles. In 1905, first vehicle was constructed in Mladá Boleslav and this act was
the origin of the biggest manufacture on the Czech territory. Two years later, the company
had already 600 employees and the product supply was increased. After a great fire in one of
production halls, Mr. Klement and Mr. Laurin decided to merge their firm with the concern
Škoda in Pilsen with validity from 1st January 1925. After the Second World War, during
which the car production in Škoda factories was replaced by war material production, Škoda
concern was nationalized and divided into two smaller companies, one based in Pilsen and
the second one being based in Mladá Boleslav. At the same time the industrial factories in
Vrchlabí and Kvasiny were incorporated into Mladá Boleslav-based Škoda company. During
the years 1960-1970, new plants had been launched. This expansion can be considered as
the beginning of the mass car production with 120,000 cars a year.
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3 ŠKODA AUTO
The reputation of Škoda was after centrally planned period quite poor. In 1989, the
relatively small company was looking for the strategic partner in order to succeed in the
European competition. After several negotiations with world leading car companies, the
contract was signed with German concern Volkswagen on 28th March 1991 because of its
strength, quality of the products and reliability. The objective was Škoda’s production of
400,000 cars a year by the end of the century. In 1991, Škoda finally celebrated 5 million
produced vehicles and started to belong among strong and profitable world car brands.
The merger opened access to Volkswagen’s know-how. The cooperation in both Škoda’s
and Volkswagen’s units significantly accelerated and improved technical development as
well as management approaches. The biggest impact was on the Quality and Technical
Development departments. During past 20 years, ŠKODA AUTO has been constantly
proving its exceptional position within the Volkswagen concern, its potential and ideas
which are continuously offered to its customers. Company’s sales have more than tripled
during this time. ŠKODA AUTO is now rapidly expanding its market share in the most
demanding car markets in whole Europe and in emerging markets in Asia
3.2 Basic Information
ŠKODA AUTO is a company operating in global automotive market and as well the
biggest exporter in the country with market share over 30 percent in the Czech Republic.
The company is developing, manufacturing and selling high-quality and environmentally
friendly automobiles, genuine parts and accessories. Today, ŠKODA AUTO is a joint-stock
company and the only shareholder is Volkswagen International Finance N.V. The main
statutory organ is seven-memebered board of management composed from the chiefs of
single areas. The company comprises from the parent company ŠKODA AUTO a.s., its fully
consolidated subsidiaries ŠKODA AUTO Deutschland GmbH, ŠKODA AUTO Slovensko
s.r.o., ŠKODA AUTO India Private Ltd. ("SAIPL"), and associates.
On 5th February 2013, ŠKODA AUTO has produced its overall 15 millionth vehicle since
1905. The 15 million car threshold underlines the dynamic growth of the brand. Until the
2018, annual ŠKODA AUTO sales are planned to increase to at least 1.5 million units.
In order to accomplish this goal, ŠKODA AUTO has begun the largest model offensive of
its history. The company has increased its model series from 1 to 7 since 1991. At the
moment, Škoda Citigo (from 2011), Škoda Fabia II (from 2007), Škoda Rapid (from 2012),
Škoda Octavia III (from 2013), Škoda Superb II (from 2008), Škoda Roomster (from 2006)
and Škoda Yeti (from 2009) are being produced. In the coming years, ŠKODA AUTO
wants to introduce one new or completely revised model in the market every six months on
average. Furthermore, the company wants to strengthen the position in European markets
30
3 ŠKODA AUTO
and continue the consistent internationalization of the brand, particularly focusing on strong
growth in the emerging markets of China, India and Russia.
The headquarters and the biggest plant of ŠKODA AUTO is in Mladá Boleslav. The
factory includes assembly lines of models Fabia, Rapid, Octavia and Seat Toledo, 3 stamping
factories, 4 weld assemblies, 2 painting facilities, 2 steel mills, assembly of some components
and others. Three models are currently rolling off the line at Kvasiny: the Superb, Yeti and
Roomster. Further there is 1 stamping factory and 1 weld assembly. The third company’s
plant is in Vrchlabí, where the gearbox DQ 200 is being assembled1 . Except Czech Republic,
ŠKODA AUTO has manufacturing plants in India, and also produces cars in China, Russia,
Slovakia, Ukraine and Kazakhstan. Into these states only dismantled cars are often exported
which are subsequently assembled in the particular assembly line2 .
3.3 Organization
ŠKODA AUTO is divided into 7 Areas, circa 60 Organizational Units and has 26,509
employees worldwide3 including loaned personnel.
1. Area “G”, Board Chairman (1,163 employees), includes some very important organizational units like Quality (812 employees), Product Management or Audit.
2. Area “E”, Commercial Affairs (849 employees), is responsible for a wide range of
activities in the company. It provides effective financial management in the company
in order to ensure long-term economic stability of ŠKODA AUTO. Main units are
Controlling (222 employees), Accounting or Legal Affairs.
3. Area “P”, Sales and Marketing (1,149 employees), is responsible for the sales of both
new and used cars, accessories and post-sales services. Among the aims of this Area
belong the improvement of the image of the company’s brands on the present markets
as well as introducing the company’s brands to the new markets. ŠKODA AUTO is
well known for its "Human Touch" philosophy thanks to this Area. Main units are
Sales Management and Marketing (153 employees).
1
There were assembled also vehicles in Vrchlabí before 2013, but due to rising demand for gearboxes DQ
200 within the concern the operation of this plant was transformed in the manufacture of components
only.
2
The reason behind this procedure are high tariffs on import of finished product in these countries;
therefore, ŠKODA AUTO usually concludes an agreement with particular state that it will invest money
in the country and create new working opportunities. In exchange for that, ŠKODA AUTO receives
lower tariffs on imported parts. There are three stages of dismantlement: SKD – Semi knocked down,
MKD – Medium knocked down and CKD – Completely knocked down.
3
Report on the state of personnel, intern material of Human Resources Planning, situation at 31.10.2013.
31
3 ŠKODA AUTO
4. Area “V”, Production and Logistics (20,704 employees), produces vehicles and components and includes also brand logistics, brand planning and brand management.
5. Area “T”, Technical Development (1,731 employees), is responsible for coordinating
the developments of designs, vehicles, interiors and electronic systems.
6. Area “Z”, Human Resources Management (682 employees), follows the motto: "Recruit, Develop and Retain Motivated Employees". It plans human resources, selects
and trains employees, supports innovations and cares about company’s employees
abroad.
7. Area “N”, Purchasing (205 employees), is responsible for purchasing and operating
materials. Its main objective is to reduce costs of materials and determine and
optimize the supplier structure.
3.4 Structure of the Costs
There are a lot of types of costs in ŠKODA AUTO with a high degree of decomposition.
The situation is even more complicated because particular department is responsible only
for monitoring of a certain type of data. The main structure could look like following:
• Variable costs - material (Area N), personnel (agency personnel), overheads, energy
consumption, logistic
• Fixed costs - developmental (Area T), personnel (permanent personnel), advertising
(Area P), administration, installments
Other possible division of costs is on direct (directly attributable to the production) and
indirect (not directly accountable to a product) costs. In our analysis of car production we
will use so-called factory costs. These costs are monitored by Controlling Production and
Logistics only for plants in Mladá Boleslav and Kvasiny and consist from indirect acquisition
costs, direct and indirect personal costs, overheads, scrapping costs4 , depreciation, activated
tools5 , other income and other costs. These costs are only for vehicles that pass through
the ZP8 point6 (not for CKD vehicles).
Three most noticeable types of costs in production of automobiles are material costs,
personnel costs and overheads.
4
Costs on scrapping of low-quality material resulting from some damage in the production process or
planned technological rejects from production for verification of technology and quality.
5
The tool shop in plant Mladá Boleslav produces this tools for Production Area.
6
ZP8 is acceptance of finished cars (car conformity control, visual body control focusing on gross surface
deformations and gross painting defects, car functions test - after functioning description, control of
gaps and mutual parts positioning, rework quality control, car control card check and affirmation).
32
3 ŠKODA AUTO
3.4.1 Material Costs
Material costs consist of material costs on basic vehicle7 , specific equipment for particular
country8 and special equipment defined by a customer. Material costs belong to the administration of Purchasing Area and are monitored by Controlling of Purchasing, Production
and Inventories.
3.4.2 Personnel Costs
We can divide personnel costs in the company by follows:
• Direct personnel costs - permanent production workers and agency staff
• Indirect personnel costs - overhead and technical-economic workers
Direct staff is directly involved in production of vehicles and constitutes circa 90 percent of the company’s personnel. Temporary workers are hired temporarily to supplement
the numerical status of production workers during their illness, holidays or transient increase of production. Under the term overhead workers we understand the profession as a
measurement mechanic, mechatronics, adjuster, driver, controller, dispatcher, electricians,
locksmiths service, storekeeper, toolmaker, etc. Among the technical-economic workers
we consider specialists, coordinators, technicians, supervisors, production control officers,
planners, etc.
3.4.3 Overheads
Overheads are indirect costs that arise due to activities of the department. The predominant types of cost are e.g. overhead materials (chemicals, protective equipment etc.),
energies, water, company cars, other vehicles (vehicles in logistics), tools, maintenance,
software and hardware, leasing (copiers, large containers), the costs of waste disposal or
advertising costs.
3.5 SWOT Analysis
SWOT analysis is overall non-financial analysis of the company. It consists from analysis
of internal environment (strong and weak sides of the company) and analysis of external
environment (opportunities and threats). It is a good start in our research, because it is
7
Material costs on basic vehicle cover approximately 90 percent of all material costs. It is basically mobile
car without additional equipment.
8
For example steering wheel on the right side for Great Britain etc.
33
3 ŠKODA AUTO
important to recognize internal and external issues to avoid losing sales and market share
and it will also provide transparent overview of company’s situation.
Strengths:
• Satisfaction of its customers - according to their strategy "Clever engineering with
human touch", they build cars that their owners would enjoy and do not focus only
on maximizing sales of a product
• Global presence - the company operates on more than 100 markets worldwide
• Volkswagen Group - ŠKODA AUTO is a part of the third biggest car manufacturer in
the world (according to OICA 2012), use Volkswagen’s know-how and produce cars
in Volkswagens’ plants in China, India, Russia or Slovakia
• Long tradition – one of the four companies in the world that has over 100 years long
tradition
• Wide range of products - availability of many car variants, both petrol and diesel
• Strong presence in China - China is an emerging economy, the largest automotive
market that grows steadily and it is also the biggest market for ŠKODA AUTO
vehicles
• High performance in international racing events
• Seven consecutive years of growth
• Emphasis on green production
Weaknesses:
• Prohibition of competition - to be part of the concern can also bring some difficulties,
because companies within the Volkswagen Group can not compete among each other
on the same market
• The dependence of the concern - the company is apart of the Volkswagen Group and
therefore has a limited ability in the decision-making process
• Research and development risks - new products carry the inherent risk that customers
might not accept them, failure to launch production start-ups within the scheduled
time line, in the required quality and with target expenditures
34
3 ŠKODA AUTO
• Weak position in the US passenger car market - US is the second largest automotive
market in the world and weak company’s position there results in comparably lower
sales
• Perceptions of the brand - especially on Western markets, ŠKODA AUTO could be
still considered as lower class brand
• Quality risks and human resources risks - basic risks common for many businesses
• Information technology risks - protect itself against risks involving data availability,
confidentiality and integrity
Opportunities:
• Emerging markets - as witnessed over the past years, there is a great demand for
ŠKODA AUTO vehicles in Asian markets such as in China, India and Russia
• Promotional campaigns - differentiation of the brand and correction of old perceptions
of the brand
• Changing customer needs - introduction of more fuel-efficient models that also emit
much less CO2
• Increasing fuel prices - consumers are very sensitive to rising fuel prices and when
prices go up, their demand tends to grow for fuel-efficient and hybrid cars
• Gaps in the market for new products or services
Threats:
• Competition - since there is immense amount of various model derivatives on the
market, it is important to give clear and powerful message to target customers
• Economic, political and legislative risks - high level of public debt, high rates of
unemployment, new emission standards or fluctuations in prices of precious metals,
oil and plastic can mean significant risks for global business
• Demand risk - low real wages and psychological factors can cause lower demand
• Purchase risks - late delivery, failure to deliver or quality defects are challenges in
which ŠKODA AUTO must succeed
• Financial risks - exchange rate fluctuations, impact of trends in interest rates or
liquidity risk
35
3 ŠKODA AUTO
• Legal risks - legal disputes against suppliers, importers, dealers and customers on
various markets
• Natural disasters, epidemics and other threats
3.6 Motivation for the Research
Automotive industry is highly competitive even there is relatively small number of sellers. There is a high degree of operating leverage9 in this business. Car producers faces
greater danger from forecasting risk because they are more dependent on each individual
sale. ŠKODA AUTO is a part of the largest automotive company in the world (measured
by revenue). This is a good base for stability and future prospects. However, in the world
where western automotive markets are stagnating and new emerging markets are arising, it
is difficult to set optimal strategy for such a big company. Managers have to take into consideration many variables from internal and external environment and this paper should help
to determine, what factors drive company’s performance most. We will discuss macroeconomic and microeconomic factors influencing production and deliveries in automotive
industry based on internal data of ŠKODA AUTO and macroeconomic indicators.
9
A measurement of the degree to which a firm incurs a combination of fixed and variable costs. A business
that makes relatively few sales, with each sale providing a very high gross margin, is said to be highly
leveraged.
36
4 Methodology
4.1 Hypotheses
At the very beginning of our investigation we will summarize our expectations and state
some hypotheses to confirm.
• Production will be more sensitive to change in overheads than to change in personnel
costs.
• Start of production of a new model significantly influences total production of the
plant.
• Total deliveries to customers will be affected more by changes in exchange rates than
in interest rates.
• Total deliveries to customers will be more sensitive on oil prices than on iron ore
prices.
4.2 Description of the Data
We are going to combine two types of data:
• Internal data of ŠKODA AUTO
• Commonly accessible data (macroeconomic indicators, prices of commodities, etc.)
Internal data of ŠKODA AUTO were obtained from specialists from production, controlling and marketing. For analysis of macroeconomic indicators we use data from public
databases. The development of raw materials prices is from Index Mundi database1 . We
chose EUR/CZK exchange rate for our analysis from the database of Czech National Bank2 .
The risk from exchange rate fluctuations against the Czech crown and their impact on cash
1
2
http://www.indexmundi.com/commodities/
https://www.cnb.cz/en/financial_markets/foreign_exchange_market/exchange_rate_fixing/currency_average.jsp?code=EUR
37
4 Methodology
flows and the financial and economic performance of the ŠKODA AUTO is of primary importance. Volatility of S&P 100 index is from Yahoo Finance3 , historical LIBOR rates from
Fed Prime Rate4 and inflation data from US Inflation Calculator5 .
4.3 Time Series Analysis
Empirical research in economics is largely based on time series. In our study we will
view economic time series as realizations of stochastic processes and this view economic
time series as realizations of stochastic processes and this approach allows us to to use
statistical inference in constructing and testing equations that characterize relationships
between economic variables. This Section is based mainly on Baltagi (2008), Green (2002)
and Wooldridge (2002). We have to establish rules for verifying and testing data utilized
in the analysis. They are ordered temporarily and they constitute sequences of random
variables indexed by time (stochastic process). The general static model6 is according to
Wooldridge (2002) defined as following:
yt = β0 + β1 xt1 + β2 xt2 + ... + βk xtk + ut
t = 1, 2, ..., T
(4.3.1)
where T stands for a number of time periods, β0 is an intercept, parameters βj , j =
1, ..., k are to be estimated and ut is an error term.
Many economic time series have a common tendency of growing over time but if two
series are trending, we can not say that the relation is casual. The biggest problem is
phenomenon of the spurious regression, where trending factors that affect yt are correlated
with explanatory variables. We find relationship between two or more trending variables
simply because each is growing over time (spurious correlation problem). Adding a linear
trend term t to a regression is the same as using "detrended" series a regression. Detrending
involves regressing each variable in the model on t. Basically, the trend has been partial
led out. Big advantage is that detrending the data involves the calculation of goodness
of t. Time-series regressions with trending variables will have very large R2 as the trend
is very well explained. But the R2 from a regression on detrended data will tell us how
well the xt explain yt . Seasonality is next phenomenon occurring in time series, while it
is observed at monthly or quarterly intervals. In our analysis we will face seasonality for
example in production, which is significantly lower in summer months because of annual
plant shutdown. Seasonality can be dealt with by adding a set of seasonal dummies.
3
http://finance.yahoo.com/q/hp?s=%5EOEX&a=00&b=1&c=2000&d=01&e=29&f=2014&g=m
http://www.fedprimerate.com/libor/libor_rates_history.htm
5
http://www.usinflationcalculator.com/inflation/historical-inflation-rates/
6
We want to study change in x at time t has an immediate effect on y, and tradeoff between y and x.
4
38
4 Methodology
Stationarity is essentially a restriction on the data generating process over time. It means
that the fundamental form of the data generating process remains the same over time and
meets the requirement of the time invariant mean, covariance and autocorrelation structure.
The impact of the shocks on the non-stationary series can be permanent, while it is only
temporary in the stationary one. Wooldridge (2002) suggests, that a stationary time series
process is one, whose probability distributions are stable over time in the following sense:
if we take any collection of random variables in the sequence and then shift that sequence
ahead h time periods, the joint probability distribution must remain unchanged. If the data
are not stationary, variables have no clear tendency to return to a constant value or a linear
trend, and spurious regression problem occurs. A trending series can not be stationary,
but as long as a trend is included in the regression, everything is correct. A very different
concept is that of weak dependence, which places restrictions on how strongly related the
random variables xt and xt+h can be as the time distance between them, h, gets large.
Based on Wooldridge (2002), a stationary time series process {xt : t = 1, 2, ...} is said to be
weakly dependent if xt and xt+h are ”almost independent” as h increases. In other words,
as the variables get farther apart in time, the correlation between them becomes smaller
and smaller. Moreover, it has constant mean, constant variance, and it is asymptotically
uncorrelated.
The next feature of time series processes that we will study in our statistical inference
are unit roots. They evolve through time and it is said that linear stochastic process has
an unit root if one is a root of the process’s characteristic equation. According to Baltagi
(2008), the time series has an unit root if e.g. an autoreggressive model of order one, i.e.
xt = ρxt−1 + ut , is a random walk (ρ = 1). Therefore, a test for non-stationarity is a test
for ρ = 1 or a test for an unit root. There are several tests used for testing stationarity,
respectively unit roots. We can use Dickey-Fuller test if the errors are uncorrelated. If
the equation exhibits serial correlation, the standard Dickey-Fuller ("DF") statistic will
be wrong, thus we use Augmented Dickey-Fuller ("ADF") test with null hypothesis of
non-stationarity. We will utilize also Kwiatkowski–Phillips–Schmidt–Shin ("KPSS") test to
test stationarity under opposite null hypothesis.
4.4 Cointegration Analysis
In most cases, the linear combination of non-stationary series is again non-stationary and
result in spurious regression. However, for some time series we are able to make such a
linear combination of originally non-stationary series that is stationary. According to Cipra
(2008), this effect is called cointegration and it can be interpreted as a long run equilibrium
relationship between variables. If these variables are cointegrated, spurious regression no
39
4 Methodology
longer arise.
Nobel prize winners Engle and Granger defined cointegration in their pioneering study
from 1987: "The components of the vector yt are said to be cointegrated of order d, b,
denoted yt ∼ CI (d, b), if (i) all components of yt are I (d); (ii)there exists a vector α 6= 0
so that zt = α · yt ∼ I (d − b) , b > 0. The vector α is called the cointegrating vector."
Consider the non-stationary time series are said to be cointegrated if they are e.g. I (1)
and if there exist their nontrivial combination that is I (0). I (1) indicates a unit root
process integrated of order one and means that process is stationary after first differencing.
Process integrated of order zero I (0) is a stationary and weakly dependent process.
If the series are cointegrated, they move together in the long run. If we difference
I (d) data, we lose long run information and estimate only short run model. However,
if we find the evidence of cointegration, we can model the series using error correction
mechanism. In this case, we may model both short run and long run relationship jointly
without loosing the long run relationships among variables. For testing of cointegration we
will use Engle-Granger test and Johansen test.
4.4.1 Error Correction Model
In many studies with time series with cointegrated data, the authors use an error correction approach. The error correction estimation method uses cointegration between explained
and explanatory variable. The basic idea is that a proportion of the disequilibrium from one
period is corrected in the next period. Engle and Granger (1987) suggest that for any set
of I (d) variables the error correction and cointegration are equivalent representation.
According to Cipra (2008), the model is defined as:
∆yt = β0 + β1 ∆xt + γ (yt−1 − δ1 − δ2 xt−1 ) + εt ,
(4.4.1)
where ∆ indicates the first difference operator, thus ∆yt = yt − yt−1 , intercept β0 is
a deterministic linear term, parametersβj , j = 1, ..., k describe short-term relationships
among variables, γ 6= 0 characterizes the speed of adjustment to the equilibrium and
parameters δ1 , δ2 describe the long-term cointegration relationships among the variables and
are written into the cointegration vectors. The component (yt−1 − δ1 − δ2 xt−1 ) is known
as the error correction term. It corrects past deviations from the long-term equilibrium in
the short-term adjustment. In other words, if the long-term equilibrium is steady, there
exists a tendency, represented by the error correction term, pushing any deviation from
the equilibrium backward. For that reason, the coefficient γ, which measures the error
correction speed, is expected to be negative.
The single-equation ECM is estimated by the Engle-Granger (1987) two step estimator.
40
4 Methodology
Before own estimations we we employ the Engle-Granger test to test the I (1) variables for
the cointegration. In the cointegrating regression we have
yt = δ1 + δ2 xt + ut
(4.4.2)
ut = ut yt − δ1 − δ2 xt ⇒ ut−1 = yt−1 − δ1 − δ2 xt−1
(4.4.3)
If xt and yt are cointegrated, than ut−1 is I (0) and hence stationary, and ∆xt and
∆yt are stationary since xt and yt are I (1). Then all variables used in equation 4.4.1 are
stationary and in the first step the regression can be consistently estimated by OLS method.
In the second step, equation 4.4.1 is estimated by OLS, but the error correction term is
replaced by the one lagged residuals from the cointegration regression 4.4.2. By using
Engle-Granger procedure we have not a situation that we compare in one model stationary
and non-stationary members, which usually causes major problems in the construction of
such a model. The drawback of Engle-Granger approach is that it is only single equation
model and that there could be more than one cointegrating variables (if there are more than
two variables). The drawback can be tackled by Johansen procedure described in section
4.4.3.
4.4.2 Vector Autoregressive Model
Vector autoreggression ("VAR") models are natural generalization of autoregressive models into a multivariate space. Green (2002) defines an vector autoreggressive model as:
yt = µ + Γ1 yt−1 + ... + Γp yt−p + εt ,
(4.4.4)
where εt is a vector of non-autocorrelated disturbances (innovations) with zero means and
0
contemporaneous covariance matrix E εt εt = Ω. The notation of individual equations
are:
ymt = µm +
p
X
j=1
(Γj )m1 ·y1,t−j +
p
X
(Γj )m2 ·y2,t−j +...+
j=1
p
X
(Γj )mM ·yM,t−j +εmt , (4.4.5)
j=1
where (Γj )lm indicates the l, m element of Γj . Variables ytm are stationary and residuals
mutually correlated.
Among pros of VAR models belong easy implementation and estimation (all variables are
endogenous), empirical experience shows that VAR can lead to better results than simultaneous equations, this theory has richer structure than one-dimensional autoreggressive
modes and allows data to talk about themselves without almost any binding constraints
41
4 Methodology
imposed by the theory. On the other hand, among cons belong large number of coefficients
(n variables, k lags ⇒ n + n2 k coefficients), absence of particular theory to choose appropriate variables and lags, application of VAR is very theoretical without deeper foundation
and encounter difficulties when it is applied to non-stationary or integrated processes.
Baum (2013) suggests that if the series are both I(1), it would be statistically correct
to model their interrelationship by taking first differences of each series and including the
differences in a VAR model. However, this approach would be suboptimal if it was determined that these series are indeed cointegrated. In that case, the VAR model designed for
differentiated values would not have to reveal the long run equilibrium relationship between
the original (undifferentiated) variables. This implies that the simple regression in first
differences is misspecified. In our analysis we will examine theory of cointegration in the
context of VAR models.
4.4.3 Vector Error Correction Model
Many economic series appear to be first-difference stationary. The most sophisticated
theory of ECM is in the context of VAR models. This extension of VAR is called vector
error correction model ("VECM") and there is an evidence of cointegration among two or
more series. Cipra (2008) offers following definition of a VECM: "Suppose a m + 1 vector
of I(1) variables yt , then the VECM takes the form:
∆yt = Πyt−1 + Γ1 ∆yt−1 + ... + Γp−1 ∆yt−p+1 + εt ,
(4.4.6)
P
Pj=p
where Π = j=p
j=1 Aj − Ik and Γi = −
j=1+i Aj and Aj are coefficients of the corresponding VAR model. If a rank r of the matrix Π applies to inequality 0 < r < m, where
m is a number of the considered time series, then according to the Granger’s representation
theorem exist matrices α and β (both dimension of m × r), so that Π = αβ 0 and each
component of the vector β 0 yt is I (0)." In other words, there exist r cointegrating vectors
and the VAR in first differences is misspecified as it excludes the error correction term. If
the rank of Π = 0, there is no cointegration among the non-stationary variables, and a
VAR in their first differences is consistent. If the rank of Π = m, all of the variables in yt
are I (0) and a VAR in their levels is consistent.
In VECM, every variable must be integrated of the same order. The model is fit to the
first differences of the non-stationary variables, but a lagged error correction term is added
to the relationship. The right side of the equation 4.4.6 may also contain deterministic
terms such as constant, trend, etc.
42
5 The Model of Production
In this chapter, we will present model based on evidences from ŠKODA AUTO. We start
our empirical research with description of the data, definitions of the variables and model.
Then we test the data for unit roots and cointegration and we run the regression on the
basis of employed methodology. We estimate the long-run relationship between number of
produced cars and companys’ costs.
5.1 Data Set and Definitions
The file contains monthly data from Controlling and from internal databases of ŠKODA
AUTO from January 2007 to December 2013 thus we have 84 observations. They were
obtained mostly after some appointments with specialists on request and from annual
reports. Data collected include information about factory costs from Production Area and
also information about material costs from Purchasing Area. However, we will use only
selected variables from all available data. We decided to drop some variables after we tried
several specifications and statistical tests. The main reasons for excluding these data were
insignificance in different models and low proportion of the total costs. The data discussed
in our analysis are given below:
• Production (prod) - total amount of produced cars in particular month in Czech
Republic and SAIPL that pass through the ZP8 point
• Material Costs (mat) - material costs include every product delivered by suppliers
(steel, aluminum, dashboards, seats, tires, etc.)
• Indirect Acquisition Cots (iac) - these costs are for example costs on packaging or
costs on transport of the vehicle from a plant to customer
• Direct Personal Costs (dperson) - these workers directly produce vehicles in assembly
plants, stamping factories or weld assemblies (permanent production workers and
agency staff)
43
5 The Model of Production
• Indirect Personal Costs (iperson) - overhead workers ensure service of the machines
in the plant (robots, lines, etc.) and technical-economic workers are service for the
production and include people from offices like managers, coordinators or specialists
• Overheads (ovh) - overheads refer to an ongoing expense of operating the production
and data in our data set include also information about consumption of energies
(water, gas, electricity, heating, etc.)
• Depreciation (depr) - in ŠKODA AUTO, they allocate the costs of their tangible
assets over it’s useful life from accounting purposes and depreciation in Production
Area means loss in value of robots, buildings or vehicles
Data about the costs are confidential so we will give summary only about variable prod:
Table 5.1: Summary of Variable Production
Variable
Mean
Std. Dev. Min.
Max.
prod
48,559.49 10,436.1 17357 72013
5.2 Sensitivity Analysis on Input Variables
We are examining impact of most important costs in the plant on the production of
cars in plants Mladá Boleslav, Kvasiny and Vrchlabí that pass through the ZP8 point.
We will employ elasticity model analysis of our variables by putting them in the natural
logarithm from several reasons. The first reason is easier interpretation of our results.
Our endogenous and exogenous variables are in different units (number of sold cars and
Czech crowns respectively). Then the interpretation will be that a 1 percent increase
in costs increases/decreases the number of produced cars about βk percent - the usual
interpretation of elasticity. The next reason is that costs are in millions of crowns and we
want to decrease our scale of numbers. The general model for this section is defined as
following:
log prodt = β0 + β1 log matt + β2 log iact + β3 log dpersont +
β4 log ipersont + β5 log ovht + β6 log deprt + εt
(5.2.1)
However, we have to consider also other important factor affecting production. The
important event is a start of production of a new vehicle that causes decline in overall
production of a month. In the analysis we consider start of production of completely new
or revised model. We integrate a dummy variable for start of production of a new model
44
5 The Model of Production
Figure 5.2.1: Logarithms of Variables
(start) to handle these problems. In Figure 5.2.1 we can see that all time series have a
non-zero mean and don’t exhibit a trend so we don’t have to add any other variable. The
final form of our then model look like:
log prodt = β0 + β1 log matt + β2 log iact + β3 log dpersont + β4 log ipersont +
β5 log ovht + β6 log deprt + β7 startt + εt
(5.2.2)
5.3 Unit Root Testing
Most of the economic time series are non-stationary. As we have already mentioned,
using non-stationary series can lead to the spurious regression. Calculated t − statistics
then does not have (asymptotically) t − distribution as well as calculated F − statistics
does not have (asymptotically) F − distribution.
It is necessary to distinguish two types of non-stationarity. The series with deterministic
trend is made stationary simply by elimination of this trend. Eagle and Granger (1987)
offer second option of holding stationarity assumption in which we apply differences. If we
differentiate the series d times, the series is said to be I (d). Equivalently, it said that the
series has d unit roots. Thus we will test differentiated variables to see if they have the
same order of integration.
An important point for running unit root tests is the specification of the lag order p. If the
p is too small, the serial correlation of the residuals may bias the test. On the other hand,
if p is too large, the test may suffer from the low power. For determining the number of
added autoregression members are recommended to apply information criteria., i.e. Akaike
45
5 The Model of Production
criterion ("AIC"), Bayesian criterion ("BIC") and Hannan-Quinn criterion ("HQC"). Cipra
(2008) defines AIC in following way:
2 (k + l + 1)
,
(5.3.1)
n
2
where σ̂k,l
is variance of white noise of ARM A (k, l) process and n denotes number of
observations. If we have different results for different criteria, we rely on AIC.
2
AIC (k, l) = logσ̂k,l
+
5.3.1 ADF Test
ADF test is an augmented version of the Dickey-Fuller test. This extension was suggested
to allow residuals to be serially correlated. According to AIC we take into account 4 lags in
our research (see Appendix). Now, we perform the test for our seven time series. According
to Cipra (2008), resulting statistics (τ − statistics) are derived from following formula:
H0 :
∆yt = ψyt−1 +
P
X
γi ∆yt−i + εt ,
(5.3.2)
i=1
where yt is observed time series and p is a lag order. The null hypothesis of this test is
that the variable is not stationary or got a unit root (ψ = 0), whereas the alternative is that
the series has no unit root (ψ 6= 0). According to AIC, we selected lag order p = 4 (see
Appendix). The results of the test in absolute values are summarized in following table.
Variable
log prod
log mat
log iac
log dperson
log iperson
log ovh
log depr
∆log prod
∆log mat
∆log iac
∆log dperson
∆log iperson
∆log ovh
∆log depr
Table 5.2: ADF Test
Critical values
Test statistics
1%
5%
10%
3.084
3.539 2.907 2.588
2.391
3.539 2.907 2.588
3.567
3.539 2.907 2.588
2.131
3.539 2.907 2.588
3.243
3.539 2.907 2.588
2.219
3.539 2.907 2.588
0.008
3.539 2.907 2.588
6.032
3.541 2.908 2.589
6.323
3.541 2.908 2.589
5.115
3.541 2.908 2.589
8.655
3.541 2.908 2.589
10.429
3.541 2.908 2.589
6.748
3.541 2.908 2.589
4.970
3.541 2.908 2.589
If the test statistic is lower than critical value, we do not reject the null hypothesis of
46
5 The Model of Production
unit root. For our variables we can reject null hypothesis for variable log iac which seems to
be stationary. For variables log prod and log iperson we have a border situation since test
statistics lies between 1 and 5 percent confidence intervals. Other time series seem to have
unit root and thus are not stationary. From the ADF test applied on differentiated series
we can say with certainty that all non-stationary series are I (1) because we can strongly
reject the null hypothesis. Their first difference is then I (0).
5.3.2 KPSS Test
We employ also KPSS test because ADF test has insufficient information in some cases
and thus weak resolution power. KPSS test was therefore designed so that the H0 and
H1 are hypotheses are the opposite of ADF test (thus H0 tests stationarity and H1 nonstationarity). It is recommended to use these two tests together and confirm stationarity
only if we reject H0ADF and do not reject H0KP SS and non-stationarity if we we do not
reject H0ADF and reject H0KP SS . We select again lag order p = 4. The results of the test
are given below:
Variable
log prod
log mat
log iac
log dperson
log iperson
log ovh
log depr
∆log prod
∆log mat
∆log iac
∆log dperson
∆log iperson
∆log ovh
∆log depr
Table 5.3: KPSS Test
Critical values
Test statistics
10%
5%
1%
0.504798
0.350 0.467 0.732
0.471556
0.350 0.467 0.732
0.123305
0.350 0.467 0.732
0.909123
0.350 0.467 0.732
1.116140
0.350 0.467 0.732
1.107400
0.350 0.467 0.732
0.616261
0.350 0.467 0.732
0.036488
0.350 0.467 0.732
0.047332
0.350 0.467 0.732
0.035386
0.350 0.467 0.732
0.054845
0.350 0.467 0.732
0.04323
0.350 0.467 0.732
0.07067
0.350 0.467 0.732
0.29085
0.350 0.467 0.732
In KPSS test, if the test statistic is lower than critical value, we do not reject the null
hypothesis of stationarity. Variable log iac seems to be again stationary. We will exclude
it from the model because according to Eagle and Granger, all variables in the model must
be non-stationary and integrated of the same order. We can reject the null hypothesis of
stationarity for variable log prod at 5 percent and for log iperson even for 1 percent critical
47
5 The Model of Production
value so the variables have unit root. So we decided to keep them in our regression model.
For other variables, the KPSS test confirms results of ADF test. The first differences
of non-stationary series have unit root, thus we can say that all of them are I (1). The
cointegration itself between the I (1) variables must be examined through the tests.
5.4 Cointegration Testing
We must test our I (1) variables for the cointegration before estimation of the model.
Testing for cointegration basically means determination of a number r of cointegrating
relations in particular VAR model for construction of error correction model. Cointegration
is confirmed if r > 0.
5.4.1 Engle-Granger Test
For our purpose of estimating ECM models we employ the Engle-Granger test (1987),
which examines the stationarity of the residuals from the regression. These residuals must
be I (0).
We decided to include also dummy variable start to final model because even if dummy
variables are not I (1), variable start improves significance of variables in the model and
gives reasonable results. After exclusion of variable log iac, we examine residuals from
following regression:
log prodt = β0 + β1 log matt + β2 log dpersont + β3 log ipersont
+β4 log ovht + β5 log deprt + β6 startt + εt
(5.4.1)
So we modify the standard DF test and we test following hypothesis:
H0 :
∆εt = ψεt−1 + ut
(5.4.2)
The only difference from standard DF test is that we work with already estimated residuals. So we reject null hypothesis of non-stationarity if the p − value is lower than significance level and similarly we do not reject H0 if p − value is higher than significance level.
The residuals of an OLS regression have no constant and no trend (see Appendix). Our
p − value = 0, 1266 so we can not reject H0 , the residuals are stationary and variables in
equation 5.4.1 are not cointegrated. The cointegration is the main requirement for using
ECM, thus we should not estimate the coefficients in this way.
48
5 The Model of Production
5.4.2 Johansen Test
The most used today’s test for estimating VECM presented Johansen (1988). In this
framework, deterministic terms can appear in the mean of cointegrating relationship or in
the means of differenced series. The purpose of Johansen’s paper is to derive maximum
likelihood estimators of the cointegration vectors for an autoregressive process, and to
derive a likelihood ratio test for the number of cointegrating vectors. This technique is
based on a full system equation and can eliminate simultaneous equation bias and increase
the efficiency in relation to single-equation methods. Johansen provides two test statistics:
•
λtrace (r) = −n
k
X
log (1 − λi ) ,
(5.4.3)
i=r+1
where n is the sample size, the λi are the eigenvalues, k is the full rank and r is the
hypothesized number of cointegrating vectors. The null hypothesis is that the number of
cointegrating relationships is maximum r and alternative is that number of cointegrating
vectors is higher than r. We reject H0 if λtrace (r) is higher than particular critical value.
•
λmax (r) = −n log (1 − λr+1 )
(5.4.4)
The null hypothesis in this case is than number of cointegrating relationships is r against
alternative that it is r + 1.We reject H0 if λmax (r) is higher than particular critical value.
Our I (1) variables are log prod, log mat, log dperson, log iperson, log ovh, and log depr.
The lag length of the VECM is determined on the basis of relevant VAR. We must take into
consideration that if the relevant VAR is signalized to have p lags, the VECM will include
p − 1 lags. The Akaike information criterion indicates that the VAR for our variables should
contain four lags (VECM three lags). We can see the results of Johansen test in following
table:
Maximum
0
1
2
3
4
5
Table 5.4: Johansen Test
Rank Trace Statistics 5% Critical Value
130.04
94.15
82.57
68.52
47.32
47.21
23.06*
29.68
5.19
15.41
0.02
3.76
If we have maximum rank zero, we have no cointegration among variables. When the
trace statistics is more than 5 percent critical value, we can reject our H0 of no cointegration.
49
5 The Model of Production
In this case, the null hypothesis is gradually equal to 0, 1, 2, 3, 4 and 5. So we can reject
the null hypothesis of zero cointegrating vectors because r > 0 via the λtrace . We cannot
reject the null of three cointegrating vector in favor of r > 3. Thus, we conclude that
there are three cointegrating vectors, our variables are cointegrated and we can run VECM
regression.
5.5 Vector Error Correction Model
Since the assumptions of the cointegration hold only for Johansen test, we interpret the
trade elasticities estimated by VECM. We can see the estimated coefficients in table 5.5.
Table 5.5: Estimated Coefficients
Variable
VECM Coeff. VECM Std. Err.
cons
-1.7017
log mat
-0.8423
0.0579
log dperson
-1.0534
0.1346
log iperson
1.0481
0.0904
log ovh
0.1934
0.0413
log depr
0.3758
0.0564
start
-0.3899
0.0461
This study is pioneering in its focus so we can not compare our results with some other
similar papers. All of our variables are significant at 1 percent significance level. Some of our
variables has the sign as expected, some do not. The variables log mat and log dperson
have different signs than we expected. A 1 percent increase in material and direct personnel costs decrease the number of produced cars about 0.84 percent and 1.05 percent
respectively. This can signalize poor and unconvincing results of the model. The possible
explanation for negative sign in front of the variable log mat can be that material is bought
in advance and production takes it from its inventories. The material costs then do not
have to copy number of produced cars in particular months.
Variables log iperson, log ovh and log depr have a positive sign and then confirm the rule
that the more we invest, the more cars we produce. Positive elasticity of these variables can
be explained as following: for higher production we need more indirect personnel (overhead
workers, technical-economic workers, etc.), we consume more energies and the equipment
of the plant wears out more. Variable start has reasonable value and sign since start
of production of a new model lowers total production of the plant because of required
adjustment and modification of the assembly lines.
We did not confirm our hypothesis that overheads are more sensitive on change in production than personnel costs. Change in log prod about 1 percent means change in log ovh
50
5 The Model of Production
about 0, 19 percent and at the same time change in both log dperson and log iperson
about 1 percent. Start of production of a new model has a significant impact on overall
production so we confirmed second of our hypotheses for this model.
51
6 The Model of Sales
In this chapter, we will present model based on evidences from ŠKODA AUTO and
publicly available sources. We again start our empirical research with description of the
data, definitions of the variables and model. Then we test the data for unit roots and
cointegration and we run the regression on the basis of employed methodology. We estimate
the long-run relationship between number of total deliveries to customers and changes in
raw material prices, volatility, exchange rate and others.
6.1 Data Set and Definitions
The data file contains monthly data from internal and public databases form January
2000 to February 2014, thus we have 170 observations. Data collected include information
about actual raw material prices, exchange rate, volatility of indexes, LIBOR interest rates
and inflation. We chose for our analysis of sales the most important raw materials for
automotive industry, for modeling volatility of the market we chose the Standard & Poor’s
("S&P") 100 index1 . The representative of exchange rates is EUR/CZK, of interest rates
twelve-month LIBOR and we chose inflation of USA as inflation of the biggest economy in
the world. According to IMF databases, USA inflation has also had similar development as
aggregated inflation of advanced economies and countries in Eurozone. The descriptions
of variables are given below.
• Deliveries to Customers (deliver) - there are total deliveries from dealers to final
customers and these data include also deliveries of cars produced in China, India,
Russia, Slovakia, Ukraine and Kazakhstan
• Crude Oil Prices (oil) - crude oil (petroleum), simple average of three spot prices;
Dated Brent, West Texas Intermediate, and the Dubai Fateh, Euro per Barrel
1
The S&P 100 is based on the market capitalization of 100 largest and most established companies in the
S&P 500 (considered as the definition of the U.S. market). It has common stock listed on the NYSE
or NASDAQ and represents almost 45% of the market capitalization of the U.S. equity markets. This
index includes also some big American car manufacturers as Ford or General Motors.
52
6 The Model of Sales
Table 6.1: Summary of the Variables (rounded to 2 decimal places)
Variable
Mean
Std. Dev.
Min.
Max
deliver
52505.82 16478.35
26417
95227
oil
49.55
20.42
20.75
89.23
iron
0.49
0.41
0.11
1.37
alu
1578.53
262.69
1026.26 2233.58
copper
3934.83
1834.05 1425.98 7240.35
rubber
0.74
0.39
0.25
2.06
EU R/CZK
28.74
3.53
23.53
36.56
S&P 100
590.26
101.02
348.21
827.41
libor12M
2.76
1.95
0.55
7.45
inf
2.42
1.31
-2.10
5.60
• Iron Ore Prices (iron) - China import Iron Ore Fines 62 percent iron spot, Cost
Insurance and Freight2 ("CIF") Tianjin port, Euro per Dry Metric Ton
• Aluminum Prices (alu) - aluminum, 99.5 percent minimum purity, London Metal
Exchange spot price, CIF United Kingdom ports, Euro per Metric Ton
• Rubber Prices (rubber) - Singapore Commodity Exchange, No. 3 Rubber Smoked
Sheets, Euro per Pound
• Exchange Rate EUR/CZK (EU R/CZK) - monthly averages of Czech National Bank
exchange rates fixing
• S&P 100 Prices (S&P 100) - monthly averages of historical prices
• Twelve-month LIBOR Prices (libor12m) - monthly Eurodollar London Inter Bank
Offered Rates
• Inflation (inf ) - historical monthly US inflation rates
6.2 Sensitivity Analysis on Input Variables
In this model we will examine impact of macroeconomic factors and prices of commodities
on ŠKODA AUTO total deliveries to customers in the whole world. We will employ elasticity
model analysis of our variables as in previous model from similar reasons. Logarithms of
the data again do not show any trend (see Figure 6.2.1) so we define the general model for
this section as following:
2
Shipper/Trader has to pay the Cost of shipment up to the ship, Insurance cost of cargo and Freight cost
up to destination port.
53
6 The Model of Sales
log delivert = β0 + β1 log oilt + β2 log iront + β3 log alut + β4 log rubbert +
β5 log EU R/CZKt + β6 log S&P 100t + β7 libor12m + β8 inft +
(6.2.1)
εt
Figure 6.2.1: Logarithms of Variables, LIBOR and Inflation
6.3 Unit Root Testing
We will proceed equally as in the previous chapter. We start with ADF test for unit
roots.
6.3.1 ADF Test
This test allows residuals to be serially correlated. According to AIC we will include 2
lags in our analysis (see Appendix). Now, we perform the test for our nine time series.
54
6 The Model of Sales
Table 6.2: ADF Test
Variable
Test Statistics
log deliver
log oil
log iron
log alu
log rubber
log EU R/CZK
log S&P 100
libor12m
inf
∆log deliver
∆log oil
∆log iron
∆log alu
∆log rubber
∆log EU R/CZK
∆log S&P 100
∆libor12m
∆inf
1.653
1.431
0.603
2.398
1.729
1.868
1.836
2.143
3.275
10.376
6.859
7.729
6.188
6.671
6.894
6.390
6.021
7.182
Critical Values
1%
5%
10%
3.488 2.886 2.576
3.488 2.886 2.576
3.488 2.886 2.576
3.488 2.886 2.576
3.488 2.886 2.576
3.488 2.886 2.576
3.488 2.886 2.576
3.488 2.886 2.576
3.488 2.886 2.576
3.488 2.886 2.576
3.488 2.886 2.576
3.488 2.886 2.576
3.488 2.886 2.576
3.488 2.886 2.576
3.488 2.886 2.576
3.488 2.886 2.576
3.488 2.886 2.576
3.488 2.886 2.576
If the test statistic is lower than critical value, we do not reject the null hypothesis of
unit root. For our variables we can reject null hypothesis on 5 percent confidence interval
for variable inf which seems to be stationary. Other time series seem to have unit root
and thus are not stationary. From the ADF test applied on differentiated series we can say
with certainty that all non-stationary series are I (1) because we can strongly reject the null
hypothesis. Their first difference is then I (0).
6.3.2 KPSS Test
We test our variables with both ADF and KPSS test at the same time to confirm stationarity and non-stationarity. We selected again lag order p = 2. The results of the test
are given below:
55
6 The Model of Sales
Table 6.3: KPSS Test
Variable
Test Statistics
log deliver
log oil
log iron
log alu
log rubber
log EU R/CZK
log S&P 100
libor12m
inf
∆log deliver
∆log oil
∆log iron
∆log alu
∆log rubber
∆log EU R/CZK
∆log S&P 100
∆libor12m
∆inf
5.2430
4.7975
5.4225
0.2888
4.6979
5.0923
0.5560
2.2517
0.4543
0.0244
0.0307
0.1128
0.0790
0.1486
0.2852
0.3129
0.2338
0.0295
Critical Values
10%
5%
1%
0.349 0.464 0.738
0.349 0.464 0.738
0.349 0.464 0.738
0.349 0.464 0.738
0.349 0.464 0.738
0.349 0.464 0.738
0.349 0.464 0.738
0.349 0.464 0.738
0.349 0.464 0.738
0.349 0.464 0.738
0.349 0.464 0.738
0.349 0.464 0.738
0.349 0.464 0.738
0.349 0.464 0.738
0.349 0.464 0.738
0.349 0.464 0.738
0.349 0.464 0.738
0.349 0.464 0.738
In this test, if the test statistic is lower than critical value, we do not reject the null
hypothesis of stationarity. Variable inf seems to be again stationary. We will exclude it
from the model because according to Eagle and Granger, all variables in the model must
be non-stationary and integrated of the same order. Another variable, which seems to be
stationary, is log alu. We will exclude this variable from our model as well because for
confirming non-stationarity we have to do not reject H0ADF and reject H0KP SS at the same
time.
For other variables, the KPSS test confirms results of ADF test and they are nonstationary. The first differences of non-stationary series have unit root, thus we can say
that all of them are I (1). We will continue with cointegration tests between the I (1)
variables.
6.4 Cointegration Testing
We must again test our I (1) variables for the cointegration before estimation of the
model. We will determine the number r of cointegrating relations in particular VAR model
for construction of an error correction model.
56
6 The Model of Sales
6.4.1 Engle-Granger Test
This test examines the stationarity of the residuals from the regression. If they are I (0),
we can estimate the model by ECM. After excluding our stationary variables, we examine
residuals from following regression:
log delivert = β0 + β1 log oilt + β2 log iront + β3 log rubbert +
β4 log EU R/CZKt + β5 log S&P 100t + β6 libor12m + εt (6.4.1)
In this test we reject null hypothesis of non-stationarity if the p − value is lower than
significance level. The residuals of an OLS regression have no constant and no trend (see
Appendix B). Our p − value = 0.07494 so we can reject H0 on 5 percent confidence
interval, the residuals are stationary and there is no cointegration in equation 6.4.1. Thus
we should not estimate the coefficients by ECM.
6.4.2 Johansen Test
Our I (1) variables are log deliver, log oil, log iron, log rubber, log EU R/CZK, log S&P 100
and libor12m. The Akaike information criterion indicates that the VAR for our variables
should contain two lags. We can see the results of Johansen test in following table:
Maximum
0
1
2
3
4
5
6
Table 6.4: Johansen Test
Rank Trace Statistics 5% Critical Value
172.16
124.24
76.29*
94.15
43.61
68.52
25.21
47.21
12.19
29.68
4.11
15.41
0.55
3.76
If the trace statistics is more than 5 percent critical value, we can reject our H0 of no
cointegration. In this case, the null hypothesis is gradually equal to 0, 1, 2, 3, 4, 5 and 6.
So we can reject the null hypothesis of zero cointegrating vectors because r > 0 via the
λtrace . We cannot reject the null of three cointegrating vector in favor of r > 1. Thus, we
conclude that there one cointegrating vector, our variables are cointegrated and we can run
VECM regression.
57
6 The Model of Sales
6.5 Vector Error Correction Model
Since the assumptions of the cointegration holds only for Johansen test, we interpret the
trade elasticities regressed by VECM. We can see the estimated coefficients in table 6.5.
Table 6.5: Estimated Coefficients
Variable
VECM Coeff. VECM Std. Err.
cons
-10.5085
log oil
-0.1921
0.0761
log iron
-0.1672
0.0271
log rubber
0.0838
0.0473
log EU R/CZK
0.6199
0.2418
log S&P 100
-0.2920
0.0836
libor12m
0.0218
0.0063
Neither this model we can compare our results with another studies. We selected representatives of phenomenons which in our opinion have influence on global car sales. Variables
log iron, log EU R/CZK, log S&P 100 and libor12m are significant at 1 percent significance level, variable log oil is significant on 5 percent level and variable log rubber on 10
percent level. The model signalizes that 1 percent increase in oil and iron prices decreases
the number of delivered cars about 0.19 percent and 0.17 percent respectively. The change
in rubber price has according to our model very low impact on sales, about 0.08 percent.
Variable log EU R/CZK has a bigger impact. The 1 percent increase in EU R/CZK
exchange rate means 0.6 percent increase of deliveries to customers. In other words weakening of the Czech crown or strengthening of the Euro means higher deliveries. We can
explain by the fact that strong Czech crown means advantage for exporting companies
and ŠKODA AUTO is the biggest Czech exporter. Strong Euro can than indicate good
conditions on foreign markets.
For modeling of market volatility we chose S&P 100 index. In our analysis 1 percent
increase of this index means 0.3 percent decrease in sold cars. Negative can be relevant for
this variable because if companies on the American market strengthen, it can have negative
impact on ŠKODA AUTO since the company has weak representation on local market. We
tried to include in the equation also prices of other indexes and the important information is
that examined indexes are very significant in our analysis. This can be challenge for further
monitoring and research. Variable libor12m has then a positive sign and signalizes slight
increase in sales while LIBOR increases.
We confirmed both our hypotheses. Sales are more sensitive on change in the exchange
rate than in the interest rate. Sales are then more sensitive on change in oil prices than in
iron ore prices. However, the difference between coefficients is very small in second case.
58
7 Conclusion
In this paper, we analyzed global automotive market on the basis of current research and
employed new methodology to examine factors influencing production and sales. To introduce the context of automotive industry, we described how globalization changed conditions
in the industry, development of automotive markets and we analyzed the challenges in the
context of global macroeconomic situation. We can conclude that Western car markets will
be stagnating and the potential lies in emerging markets of BRIC countries which expect
rise in wages, level of education and vast investments to infrastructure and urbanization.
The challenges for car makers in following years are lowering of the costs (for example
by widening of complexity and platform systems), developing new green and economical
technologies, making reasonable investments on growing emerging markets, focus on online
sales, establish new relationships with their suppliers or making links to local producers
which could help to transfer skills and know-how in particular region. They will also make
extensive surveys of local customers’ needs because they can vary greatly in different regions.
Satisfying of rising customers requirements and improvement of after sales services will be
crucial for success in tough competition. Monitoring of new governments policies have to be
also take into consideration. The trend will be the lowering of air pollution and prevention
from crowded roads in important metropolis.
After summarizing the main information about ŠKODA AUTO we came up with empirical
analysis in order to examine non-stationary time series processes. We were looking for a
long run relationship among variables and for this purpose we employed methodology for
cointegration analysis.
We presented two models in this thesis. The first model was the Model of Production
examining elasticities of variables. The model is based on data of produced cars and
confidential data about costs from ŠKODA AUTO from January 2000 to December 2013.
Although we found relevant significance and reasonable results, we have to admit some
problems. The most serious problem is with the results of material and direct personnel
costs which came out with negative sign. It can imply poor and unconvincing results of
the model. On the other hand, the signs and values of remaining coefficients seems to be
fine. We showed that costs are increasing with number of produced cars and that start of
production of a new model has a significant negative impact on output of the production.
59
7 Conclusion
In the Model of Sales we used data set of raw material prices and economic indicators.
The collected data are from January 2000 to February 2014. This model shows some
interesting relationships between deliveries to customers and selected explanatory variables.
The raw material prices seems to be significant and mostly shows negative relationship
to sales. The exchange rate EUR/CZK seems to be also significant and this relationship
tells us that weakening of Czech crown or strengthening of Euro means higher deliveries
to customers. As a representative of volatility of the market we chose S&P100 index.
This variable shows strong significance and negative relationship with ŠKODA AUTO sales.
According to the model, various interest rates are very significant but have a low impact
on sales. In our case, twelve-month LIBOR shows slight positive effect.
We can conclude that we mostly obtained relatively acceptable and plausible estimates
of selected determinants of production and sales, but we have to keep in mind that the
methodology used is not straightforward and may still suffer from many problems. We
believe that this paper may be used as transparent summary of today’s automotive market
and lays a foundation for a further research. This approach can be used also for another car
maker to confirm evidence from ŠKODA AUTO. The further research could also include if
developing of prices of various stock indexes, which can be often considered as barometers
of the market, has a deeper relationship with car makers’ global deliveries to customers.
60
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64
Appendix
Figure 0.1: Production Model - Information Criteria
Figure 0.2: Production Model - OLS Residuals
Figure 0.3: Production Model - Johansen Test
i
Appendix
Figure 0.4: Production Model - VECM Summary
Figure 0.5: Production Model - VECM Coefficients
Figure 0.6: Sales Model - Information Criteria
ii
Appendix
Figure 0.7: Sales Model - OLS Residuals
Figure 0.8: Sales Model - Johansen Test
Figure 0.9: Sales Model - VECM Summary
iii
Appendix
Figure 0.10: Sales Model - VECM Coefficients
iv