A Case Study of the DSV Group Based on Fundamental Analysis

Author:

Andreas Berger Hjortholt – M.Sc. (cand.merc.) Finance and Strategic Management (FSM)

Supervisor:

Christian Würtz, Falck Denmark A/S, Group Business Development

Master Thesis – Copenhagen Business School 2014

Submission Date: 13th of November 2014

Number of pages and Total Characters: 79 pages equal to 179.961 characters Abstract

This thesis is a case study of the Danish transport company DSV Group A/S and its main objective is to address the following research question: what is the theoretical stock price for DSV Group based on an in- depth strategic and financial analysis. The freight forwarding industry is exposed to a number of issues. There is high market rivalry due to a fragmented market (from smaller domestic players to larger international competitors), governmental regulations, a customer universe that demands new product solutions, and constant negotiation of prices between the supplier (e.g. hauliers, shippers) and the customer. In addition, it is also an industry affected by broad economic measures (gross domestic product) and impacted by oil prices, freight rates, and currency fluctuations. The challenges are growing with the increase in intermodal solutions and customers that demand a worldwide network, reflected by the globalisation that has created an increase in product flow from important trade lanes between Asia-Europe, Europe-North America and Asia-North America. The high level of competition challenges the strategic agenda. The strategic path has to concentrate on a large range of parameters to distinguish itself from competitors, with specialisation in product type (automotive vs. pharmaceutical), routes and delivery type (full-truck load vs. less-than-truck load), terminal placement and warehouse services (picking and packing for customers). Another crucial factor is the ‘asset-light’ business model that is used among the international freight forwarders, helping to uphold operating margins in periods of volatility.

DSV have managed to obtain a strong market position through an attractive product mix combination (product type and routes etc.) in air and sea freight. The DSV’s profitability from transportation (gross- margin per tonne and TEU) and operating margins (EBIT and EBITDA) surpasses the representative peers. In addition, DSV have managed to bring down fixed and variable costs year-to-year, which suggests that they pursue a costs leadership strategy in the core three divisions: road, air, and sea freight. However, the constantly changing industry has challenged the DSV to focus on three growth strategies: market development, market penetration and product development, which is shifting year-to-year depending on whether an acquisition opportunity arises or if customers request new transportation solutions. Broadly speaking, with no particular change in the product mix from the three core business divisions, the forecasts of revenue growth drivers (line-item approach) are based on the assessment of promising growth prospects from market and economic indicators and a strong strategic position in the market. The sales-driven approach was applied to the balance sheet items according to previous-years observations. The costs of capital were applied together with the forecast predictions in the DCF models, which gave a theoretical stock price of 174,8 DKK as of 1st July 2014. Hence, the listed stock price of the DSV Group was overvalued according to the findings in this thesis.

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Table of Contents Preface 4 0. Introduction 4 0.1 Methodology 6 0.1.1 Research Approach 6 0.1.2 Validity and Reliability 7 0.1.3 Delimitation 8 0.1.4 Case Selection 9 0.1.5 Financial Statement Approach 10 0.1.6 Reorganisation of Peers Financial Statements 11 0.1.7 Valuation Approach 12 0.1.8 Definition of Product Mix 13

Chapter I The Freight Forwarding Industry and DSV Group A/S: An Introduction 14 1. Introduction to Chapter One 14 1.1 Introduction to the Freight Forwarding Industry in Europe 14 1.1.1 Freight Forwarding in Europe 15 1.1.2 Strategic Approaches of Third Part Logistics 15 1.2 Presentation of DSV 18 1.2.1 Principal Activities 18 1.2.2 Corporate Strategy and Culture 19

Chapter II Financial Analysis of Freight Forwarders 21 2. Introduction to Chapter Two 21 2.1 Profitability Analysis - Group Level 21 2.2 Financial Leverage and Net Borrowing Cost 24 2.3 Growth Analysis – Trend and Common Size Analysis 24 2.4 Segmental Analysis of freight forwarders 27 2.4.1 Profitability of Air & Sea Freight 27 2.4.2 Profitability of Road Freight 28 2.4.3 Benchmark of Transport Volumes 29 2.4.4 Tranport Ratios in Air & Sea Freight 31 2.4.5 Estimation of Road Freight 34

Chapter III Market Analysis of the Transport Service Industry 38 3. Introduction to Chapter Three 38 3.1 Segmental Fraction of the Transport Service Industry 38 3.2 Development and Trends - European Transport Service Industry 38 3.3 Development and Trends - Road Freight in Europe 39 3.4 Development and Trends - Sea Freight in Europe 40 3.4.1 Development and Trends - Sea Freight in Asia-Pacific 40 3.4.2 Development and Trends - Sea Freight in United States 40 3.5 Development and Trends - Airfreight in Europe 41 3.5.1 Development and Trends - Airfreight in Asia-Pacific 41 3.5.2 Development and Trends - Airfreight in United States 42 3.6 Concluding Remarks 42

Chapter IV Environmental Analysis of the European Transportation Industry 43 4. Introduction to Chapter Four 43 4.1 Political and Legal Factors Analysis 43 4.1.1 United Nations Framework on Climate Change 43 4.2 Economic Analysis - Insights from Macro Data 44

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4.2.1 Europe 2020: Smart, Sustainable and inclusive Growth 47 4.2.2 Fuel Costs Impact on Freight Forwarders 47 4.3 Social and Cultural Analysis 47 4.3.1 Corporate Social Responsibility 47 4.4 Technological-Analysis 48 4.5 The PEST-Influence Model 49

Chapter V Strategic Analysis of DSV Group A/S 50 5. Introduction to Chapter Five 50 5.1 Ansoff’s Growth Model 50 5.1.1 Diagnosis of Market development 50 5.1.2 Diagnosis of Product Development 51 5.1.3 Diagnosis of Market Penetration 52 5.2 Porter's Generic Strategies 54 5.2.1 Diagnosis of Differentiation Strategy 54 5.2.2 Diagnosis of Cost Leadership Strategy 54 5.3 SWOT Analysis 58

Chapter VI Forecasting of DSV Group 59 6. Introduction to Chapter Six 59 6.1 Length of Forecast Period 59 6.2 Terminal Growth Rate 60 6.3 Inflation 60 6.4 Forecasting Assumptions – Transport Ratios 61 6.4.1 Revenue Growth - Airfreight 61 6.4.2 Revenue Growth - Sea freight 62 6.4.3 Revenue Growth - Road Freight 63 6.4.4 Forecasted Income Statement 64 6.4.5 Forecasted Balance Sheet 66 6.5 Budget Control - Group Level 68

Chapter VII Valuation of DSV Group 69 7. Introduction to Chapter Seven 69 7.1 Weighted Average Costs of Capital 69 7.1.1 Costs of Equity Capital 69 7.1.2 Risk Free Rate 70 7.1.3 Risk Premium 70 7.1.4 Estimation of Beta 71 7.1.5 Capital Asset Pricing Model Applied to DSV Group 72 7.1.6 Cost of Debt Capital 72 7.1.7 Weighted Average Costs of Capital for DSV Group 73 7.2 DCF Valuation 74 7.3 Sensitivity Analysis 76 7.4 Multiples 76 7.5 Conclusion 77

8.0 Bibliography 80 9.0 Appendix 86

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Preface The aim of this thesis is to conduct a strategic and financial valuation of the Danish transport company DSV Group A/S – a public listed company with limited liability, which means that shareholders are not personally liable should the company become insolvent. My motivation for this choice is first and foremost due to the combination of the two main building blocks of my master studies: finance and strategy. A thesis based on a valuation makes it possible to use theories from both blocks and to apply them to a practical context. Myers (1984), among others, has debated how finance and strategy can mutually support each other’s shortcomings and create new standards and tools for managers in their decision-making. In order words, strategy can contribute to finance theory where the discounted cash flow model (DCF) enhances flexibility, as well as assisting in the forecasting of future values and growth.

0. Introduction In advanced economies, the allocation of capital from investors provides the oil to the wheels of an entrepreneur’s project or a corporation’s engine to invest in a pool of net present value projects (NPV). A basic requirement from the investor is an acceptable and efficiently-managed return on their investment. In other words, the return from investments should equal or surpass the opportunity costs of capital (Weighted Average Cost of Capital - later referred to as WACC). However, capital markets have constraints in the form of both incentive problems and information asymmetries. From the perspective of private investors, in a publicly listed firm (which are often small and passive) the directors (CEO) may not always work in the best interests of the investors. Jensen and Meckling (1976) describe this relationship, in their analysis of property rights, as the conflict of interests between the shareholder (the principal) and the director (agent): ‘If both parties to the relationship are utility maximizers there is good reason to believe that the agent will not always act in the best interests of the principal’ (pp. 305). In light of this likelihood for self-interested behaviour, the contract between the agent and principal is as such incomplete, meaning that the contract cannot fully acknowledge the self-interested behaviour of the agent. To solve this issue, three steps (costs) are identified by Jensen and Meckling (1976): 1) monitoring costs of managers’ internal acts, 2) bonding costs of the agent to increase incentives, and 3) residual costs.

In relation to the conflict of interest between the agent and principal, Michael Jensen (1986) has offered an alternative approach to solving this issue for firms with large free cash flows. He argues that directors of modern corporations with large free cash flow, sometimes referred to as “cash cows”, have a tendency to hold too much capital inside the corporation and to invest these on negative NPV projects – such as unnecessary acquisitions diversified outside the core operations. To solve this conflict, Jensen (1986) recommends that the corporation increase debt (change of capital structure). For example, dividend payouts – which allow the shareholders to find other projects that exceed their opportunity costs of capital. However,

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Jensen (1986) claims that the increasing debt can function as a control mechanism for the agents. In other words, the debt secures the most efficient use of the invested capital.

Consequently, there is an information asymmetry between the top management and shareholders, meaning that the investor needs to consider carefully which firm to invest in. Akerlof (1970) introduced the concept of markets with asymmetric information, and was the first to demonstrate how a seller of a specific product can take advantage of information asymmetry to mislead the buyer by offering him products of bad quality at higher than average market prices, known by the market as “lemons” (inferior quality products). To supplement this logic with corporate valuations, the managers have inside information about the quality of the assets and future earnings that investors do not (Myers and Majluf, 1984). Hence, the investor can run the risk of not receiving a fair return on the investment if he or she invests in a low-quality firm. However, a corporate valuation can, to some extent, function as a tool to help investors reduce information asymmetries and, as such, select the best firm to invest in.

In light of the claims that asymmetric information and incentive issues are a concern in corporations and, moreover, that a corporate valuation can support investors in their decision making, this thesis will attempt to determine the theoretical fair value of one specific firm, namely DSV Group A/S. This has led to the following research question:

’What is the theoretical fair value of DSV Group A/S stock based on a fundamental valuation analysis, according to the listed price on OMX NASDAQ Copenhagen as per 01.07.2014?’

A framework of underlying sub-questions will contribute to answering the abovementioned research question:

- What is freight forwarding in a European context and which growth strategies are commonly used among freight forwarders? What are the advantages and disadvantages of these different growth strategies? - How does the DSV Group perform financially-based time-series analyses (firms relative performance over time) and a cross-sectional analysis (comparison with selected peers)? - What are the current market expectations for the freight industry and which environmental conditions influence the DSV Group? - What growth strategy does the DSV pursue and which competitive advantages might appear within the Group?

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- How does the strategic and financial analysis influence the prospective analysis (forecast) and what impact does this have on the discount cash flow valuation approach? - How sensitive are the valuation approaches to changes in key drivers based on previous analysis?

0.1 Methodology 0.1.1 Research Approach The research method used in this thesis is based on a deductive case study framework since it explores existing theory applied to the DSV Group. A case study can be defined as a wide-ranging investigation of an individual, family, or organisation (White, 2000). This thesis tends to take a perspective of the whole organisation, looking firmly at the growth prospects of the DSV Group to set a theoretical fair value of the company. Yin (2009) defines a case study as an enquiry that uses multiple sources of evidence and that: “Investigates a contemporary phenomenon within its real life context when the boundaries between phenomenon and context are not clearly evident” (pp. 13). Thus, case studies can use a number of methods such as interviews and surveys investigations, which include quantitative data from questionnaires. However, case studies should not be seen merely as a means of data collection or a design feature but instead as a comprehensive research strategy (Yin, 2003). An advantage of using the case study approach is the possibility to gain in-depth knowledge of an entire situation compared to if only one research technique is used i.e. interviews, surveys. As such, the case study approach facilitates answering the research question of this thesis by gaining in-depth knowledge of the DSV Group.

In the literature of case study theory Yin (2003) distinguishes between four designs for case studies: single, multiple, holistic or embedded. This thesis uses a holistic single case study approach. The rationale for selecting this type of case study reflects the research question itself. In other words, I believe that a holistic case study investigates the DSV Group in appropriate detail and quality. However, to enhance the quality and detail of the enquiry, sections of this thesis do also approach an embedded single case study. In other words, the DSV Group is investigated as a single case scenario while the business activities within the Group are emphasised separately to value the growth prospects of each. Furthermore, the financial analysis is organised in such a way that it represents a multiple holistic case study, where the DSV Group is investigated as a single entity, and the peers of the DSV Group are examined as other entities independently and then compared with the DSV Group. Hence, the cases (peers) are first analysed separately to find specific patterns, then a cross case analysis is assembled to generalise on the strategic change (product mix change) and attractiveness (profitability) of the competitors.

In addition, this thesis distinguishes between an exploratory and descriptive research framework; relying largely on the former. These two types of case studies are seen as complementary and work well together (Yin, 2003). An exploratory case study involves challenging the status quo by asking questions such as:

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‘what’, ‘how’ and ‘why’. In other words, it helps to shed new light on a particular phenomenon or offer new insights into a topic. The exploratory research method is used to gain new knowledge and insights about the DSV Group to assess the growth prospects of the firm. However, this thesis also includes certain descriptive elements, which investigate former research (Yin, 2003). For instance, the first chapter of this thesis is partly a descriptive analysis of the freight forwarding industry in Europe. However, the descriptive essentials will, among other things, function as an introduction to each chapter to create a solid foundation of knowledge before moving on to the exploratory part.

0.1.2 Validity and Reliability The empirical information collected in this thesis draws upon secondary sources. The main objective was to gather extensive amounts of data and information from the DSV Group A/S and the peer group firms. This includes annual reports, articles from the most reliable newspapers, power point presentations, press releases, websites, Bloomberg terminal database, and so forth. Thus, all data and information is collected from secondary sources. Although interviews with leading employees who might be able to reveal strategic initiatives that would impact the forecast prospects – for example an ongoing acquisition – would have been exceptionally useful, unfortunately, in a public listed firm with fierce competition it is not possible to gain access to such insider information. Instead, the aim of this thesis is to build a strong analysis based on the secondary sources outlined above, which can reflect, as closely as possible, the prospects of DSV Group and other key factors. As for articles including interviews with the DSV Group director, one still has to be cautious about the reliability of his publically shared opinions about the Group’s future. Nevertheless, as such information is distributed to the shareholders it must be largely reliable, otherwise this could give rise to a conflict of interests between the partners.

A vital source includes the financial statements, which are considered reliable since they are produced under common laws and regulations. All the selected case firms are subject to the rules of ‘International Financial Reporting Standards’, known as IFRS.

Other types of material have been used and selected to uphold the reliability of the analysis; for example, economic reports from the IMF, World Bank and the European Commission, all of which are respected institutions. Furthermore, another major data institution, MarketLine, was chosen to set forth the future expectation of the market developments in air, sea, and road freight. However, economic and industry forecasts change continuously and cannot predict the future fully, but merely hint at how it may turn out. All judgemental forecasts are biased to some degree by the inherent unreliability of information collection of the judgement process (Stewart, 2001). This is referred to as ‘imperfect reliability’; i.e., humans are not consistent if a similar task is performed twice (Ibid). However, this lack of reliability cannot be solved. For

Page 7 of 127 instance, if another individual were to repeat the forecast the findings may vary from the first individual due to inattention, distraction or other factors (Ibid).

The forecasting in this thesis is based upon firmly reliable statistics, which combined are used to assess the future growth prospect of the DSV Group. In order to increase the reliability (repeating the case), the forecast assumptions are conducted with the following applied theoretical models: PEST framework, Du Pont model, Ansoff’s growth model, Porters generic strategies and SWOT analysis. These models are considered valid and are widely used in practice. However, the models may not be any more reliable or valid than the information and subjectivity used. As such, the DCF model is highly sensitive to any change in information. It has therefore been necessary to conduct a sensitivity analysis with change of key assumptions to secure the validity of the DCF model. Moreover, several checks have been implemented to enhance consistency including budget controls to ensure that historical rates such as return of invested capital (ROIC), profit margin (PM) and assets turnover (AT) are consistent with the historical levels. Moreover, since the majority of the forecasting items depend on revenue growth drivers, transport ratios are conducted for each business segment in the DSV Group, which can function as a control mechanism and enhance the accuracy of the forecasts.

0.1.3 Delimitation It has been necessary to enforce an endpoint/terminal point in the research question since there is a likelihood that information used in this thesis will change. If such a point is not established it can be difficult to catch new information during the writing process, and this may furthermore change key assumptions. For instance, economic and market data institutions publish reports every quarter, or even sooner. In addition, the DSV Group publish earnings announcements, new information regarding new consolidations and so on, which can change previous assumptions.

Another significant limitation is the validity and reliability of the sources used in this thesis, mentioned above in section ‘0.1.2’, meaning that this case study relies solely on information from external publically available sources and not from external advisors or internal DSV Group decision makers.

The DSV group is divided into four business divisions: air, sea and road freight, and solutions. The latter business division is minor compared to the others in terms of historical earnings. Its function is, in addition, as a supplement to the others in terms of logistic solutions. The other principal divisions air, sea, and road freight will therefore be the main focus of the analysis. In other words, the division ‘Solutions’ will not be investigated in this thesis.

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The reader may furthermore notice that this thesis is constrained to explore the main markets in which DSV operates, namely Europe. This is because nearly all the activities of the road division are in Europe. Given this fact, the European market will occupy a main part of this thesis. However, growth prospects from other emerging markets are also considered since these have a growing impact and role within air & sea freight.

0.1.4 Case Selection The cases (peer firms) are selected based on the universal character of this thesis:

1) Revenue has to be earned from the European continent in road freight. This decision is reasonable since the DSV Group have the principal market shares on the continent. 2) The selected peer cases must moreover operate within all the core business activities in which the DSV Group is represented, namely: road, air, & sea freight.

During the research process it was observed that representative peers in the industry do not disclose the accounting information required. An explanation for this is that many are subsidiaries of larger corporations. The companies that fulfilled the requirements, and gave transparent information are Deutsche Post DHL, Kuehne + Nagel International AG, Panalpina Holding AG, and DB Schenker Logistics. The two freight forwarders Kuehne + Nagel and Panalpina are the main peers throughout this thesis.

0.1.4.1 Introduction of Peer Companies Deutsche Post DHL Deutsche Post DHL is Europe’s leading mail and logistics service company with 435,520 employees worldwide. The integrated DHL is divided into four business divisions: express, mail, global forwarding, freight and supply chain. The global freight forwarding division is the largest of the four business divisions, equivalent to 27% of the aggregated revenue. The division is divided into air, sea, and road freight. The main geographical area DHL is present in is Europe, with 64% of the revenue (DHL, 13). The rest of the revenue earned comes from Asia and America.

Kuehne + Nagel International AG Kuehne + Nagel Group is a global transportation and logistics company based in Schindellegi, Switzerland, but it was originally established in Bremen, Germany, in 1890. The Group has a long heritage in the industry for freight forwarding and has been expanding globally since the 1950s. Today, it has a worldwide presence with operations in 1,000 locations in over 100 countries. The Group is divided into six business units: air and sea freight, road and rail logistics, contract logistics, real estate and insurance brokers. Air and sea freight, and road and rail logistics represent 79% of the aggregated revenue.

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Panalpina Holding AG Panalpina Group is a leading European freight forwarding and logistics provider with an international presence. The Group offers diversified services in air, sea and road freight where airfreight is the largest segment, equivalent to 45% of the aggregated revenue (Panalpina, 13). Their business activities are diversified across several key industries such as: automotives, healthcare, retail & fashion, hi-tech, telecom and oil & gas. Their contemporary vision is to grow in collaboration with their customers by creating tailor- made solutions. A large share of the Group’s activities are located in Europe with 59% of the aggregated revenue. The second and third largest geographic areas are America and Asia Pacific, based on revenue.

DB Schenker DB Schenker logistics AG is a subsidiary to Deutsche Bahn AG, which owns several business activities. DB Schenker logistics will thus merely be described. According to DB Schenker, it has a solid foothold in the European market and a world leading position within air, sea and road freight. Their vision is to become a leading transport and logistics provider. The company’s strategy is called DB2020, which consists of three dimensions: The strategy is to become 1) a ‘profitable market leader’, 2) experience ‘profitable growth’ – also called ‘Go for Growth´ and 3) become a ‘leader in quality and service’ (DB Schenker, 13).

0.1.5 Financial Statement Approach I believe that a brief discussion of the issue of ‘reorganising financial statements’ is necessary for the purposes of this thesis. During the research process it became clear that the valuation literature differs in terms of the classification of accounting items. It was a challenge to gain clarity about the consistency as the argumentation of the financial items, as well as the frameworks, can vary from book to book. Thus it was decided to choose one specific work, Petersen and Plenborg ‘Financial Statement Analysis’ (2012), first to avoid confusion and second to gain coherency.

In the book ‘værdiansættelse en praktisk tilgang’ by Ole Sørensen (2009), much effort is spent on explaining how to classify operations as core and non-core with the purpose of removing dirty surplus items that confuse operating and non-operating assets. The method is used to obtain value from core operations in the perspective of an equity shareholder (Sørensen, 2009).

Petersen and Plenborg have a somewhat a different framework. They look at which items have an effect on the invested capital, classifying these as interest-bearing and non-interest bearing items (operating items). For example, if it is a non-interest bearing item it will reduce the invested capital (Petersen and Plenborg, 2012). The method of Koller, Geodhart & Wessels (2010) of reorganising accounting items is consistent

Page 10 of 127 with those of Petersen and Plenborg (2012) by separating operating, non-operating and sources of financing to calculate the invested capital.

Moreover, certain textbooks undertake a reformulation of the equity statement, especially Ole Sørensen (2009). In my experience, other textbooks in the field are less technical and hence do not discuss the significance of reformulation of the equity statements. In next section 0.1.6.1 ‘classification of accounting items’, the way minority interests are treated (either as debt or equity finance) is discussed. They are typically the items described in Ole Sørensen’s (2009) approach as ‘dirty surplus items’, unlike in Petersen and Plenborg (2012) and Koller, Geodhart & Wessels (2010).

From a holistic point of view Sørensen (2009), Petersen and Plenborg (2012) and Koller, Geodhart & Wessels (2010) do make somewhat similar arguments. The effects of choosing one method over another have relatively little impact for the end valuation. The respective methods reformulate the financial statements to examine the value drivers in the company, namely the operational activities that are understood as value creators in the company. Separating them from financial items and non-operating assets will unlock the core value creating drivers of the firm.

0.1.6 Reorganisation of Peers Financial Statements It is decided not to reorganise Deutsche Post DHL and DB Schenker’s financial statements as their business activities go beyond those of DSV, Kuehne + Nagel and Panalpina. For this reason they are not part of the Du Pont model on group level. Instead they are used as peers in the segmental analysis. The items from the balance sheets will be treated as equally as possible, but naturally there are some differences. All peer group companies follow the same accounting policies, i.e. the IFRS rules.

0.1.6.1 Classification of Accounting Items Appendix B offers an in-depth explanation of the classifications of each item from the reorganisation of the balance sheet for the DSV Group:  Minority interests: Interpreted as part of equity capital, since they endure similar risk as debt according to Petersen and Plenborg (2012).  Cash and cash equivalent: Treated as excess cash, since it is stable over time. Consequently it constitutes an interest-bearing asset.  Assets held for sale (and liabilities related to assets held for sale): Assets held for sale are treated as cash e.g. considered interest bearing.  Other securities and receivable: Other securities available for sale can be sold within a one year period and are therefore treated as interest-bearing (DSV, 13)

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 Investment in associates: Based on the DSV Group’s annual reports, associated companies seem to be involved in activities related to the core business. The associated companies can either support the large network or function as a springboard for a possible acquisition.  Pensions: Treated as interest-bearing debt. They are measured as value in use and as a financial expense in the income statement (discounted to present value).  Provisions: These follow a similar argument to the above explanation of pensions.  Operating leasing: Operating leases has been capitalised in a hypothetical balance sheet to demonstrate an ex-post and ex-ante situation of return on invested capital, in order to illuminate an accurate picture of the competitiveness among the cases (peer firms). Operating leases have not been accounted for in the forecast analysis, since they theoretically do not affect the intrinsic value of the DSV Group.

0.1.7 Valuation Approach 0.1.7.1 Present Value Approach Professional advisers distinguish primarily between four types of valuations approaches, all categories under the present value method: the dividend model, the discounted cash flow model (DCF), the economic value added model (EVA) and the adjusted present value model. Among those, the models that are most applied among bankers and private equity companies are either EVA or DCF (Plenberg, Petersen & Holm, 2005). These two models are in addition suggested by Koller, Goedhart and Wessels (2010) and Damodaran (2006) and are therefore used in this thesis. All the present value models are derived from the dividend discount model and are theoretically equivalent. This means they are based on identical inputs, which produce identical value estimates (Petersen and Plenborg, 2012). Both applied models have two stages, meaning that they are divided into two periods: an explicit forecast period (forecast horizon) complies with information from the applied theoretical models, revenue growth drivers and market analysis; and a terminal period reflecting the long-term growth rate in the industry (Ibid). There will not be any explanation for the mathematical backgrounds of the models in this thesis, but instead applied to the DSV Group. The models are as following:

퐹퐶퐹퐹 퐹퐶퐹퐹 1 Enterprise value = ∑푛 푡 + 푛+1 ∗ 0 푖=1 1+ 푊퐴퐶퐶푡 푊퐴퐶퐶−푔 (1+푊퐴퐶퐶)푛

Where FCFFt measures the free cash flow to the firm in period t. WACC represents the weighted average costs of capital, which will be estimated and elaborated later in this thesis. The next model in line is the EVA model measuring the economic profit as (ROIC – WACC ´ invested capitalt-1). The completed model follows below:

푛 퐸푉퐴푡 퐸푉퐴푛+1 1 EVAt = Invested capital0 + 퐼푛푣푒푠푡푒푑 푐푎푝푖푡푎푙0 + ∑푡=1 푡 + ∗ 푛 1+ 푊퐴퐶퐶 푊퐴퐶퐶−푔 (1+푊퐴퐶퐶)

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0.1.7.2 Relative Valuation Approaches In addition to the net present value approach, relative valuation approaches (often referred to as multiples) are applied across the benchmark firms (peers) in order to compare the attractiveness of each firm in the industry and to triangulate the results from the DCF approach. In other words, multiples support the plausibility of the cash flow forecasts since the DCF model is only as accurate as the inputs it relies on (Koller, Goedhart and Wessels, 2010).

The advantage with multiples is, according to Petersen and Plenborg (2012), their speed and low complexity, however they can also become complex and time consuming, like the DCF approach. For example, if an EV/EBITDA multiple is applied it requires that the firms have identical expected growth rate (g), costs of capital (WACC), profitability (ROIC) and tax rate. In addition, it also requires identical accounting principles (IFRS standards against GAAP). As such, comparable firms can lead to time-consuming tasks. Furthermore, the multiples are generally applied to companies in the same industries, which can help reduce the abovementioned shortcomings if they have identical economic characteristics and growth outlook. The multiple applied in this thesis is the EV/EBITDA since it explores the core operations in the firm and is commonly accepted in practice (Petersen and Plenborg, 2012).

0.1.8 Definition of Product Mix In this thesis ‘product mix’ will be characterised and understood by the complete setup that a freight forwarder utilises in their daily operations. This includes, primarily, the following:

 Product types: Industry specific solutions for the pharmaceutical or automotive industries, among others.  Transportation types (segments): full- or part-loads (container in sea freight), which distinguish in weight and load type where a full truck is normally oriented towards one customer, and part loads focus on several customers at once (Bain & Company, 2012). In other words, the frequencies of deliveries are distinguished.  Geographical routes: Distances distinguish, for example, Eastern Europe from Western Europe routes.

In the breakdown analysis of revenue growth in section 2.4 ‘Segmental Analysis of Freight Forwarders’ it is shown that a increase (or decrease) in revenue per unit is not necessarily due to an rise (or fall) in prices. Revenue per unit is also influenced by change in product mix.

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Chapter I The Freight Forwarding Industry and DSV Group A/S: An Introduction 1. Introduction to Chapter One This chapter explores the European freight forwarding industry by examining the size of the market, its operations and traditions; it also looks at the general business models. A definition of the term freight forwarding will be elaborated to ensure consistency. Furthermore, the strategic approaches, mergers and acquisitions, and respective advantages and disadvantages are discussed in relation to freight forwarders. In addition, this chapter will introduce the case study firm, the DSV Group, which will include a short description of the company’s principal activities and current strategic pillars, which are believed be responsible for the company’s strong foundations.

1.1 Introduction to the Freight Forwarding Industry in Europe Despite the long existence within transportation of goods and services, the transport industry is still growing with an annual increase in demand for services. Currently, the transport service industry (including freight forwarding) employs approximately ten million people, equivalent to 4.5% of the total employment in Europe, representing 4.6% of gross domestic product (GDP) (European Commission, 2013). Among the different transportation options, in the internal markets of Europe road freight is the dominant transport type accounting for 44% and employing roughly five million people, generating close to 2% of GDP (European Commission, 2012).

Essentially, freighting is the transportation of goods from one destination to another. These goods could be, for example, raw materials, such as wood or iron, which are afterwards processed in a plant. The raw materials comprise consumer industrial products, carried on to the wholesalers, or distribution centres (Woxenius & Bärthel, 2008). This process often consists of one or more traffic modes, if more modes are used it is called an intermodal process (see Figure 1), which varies depending on the transport specifications. It may either be domestic or foreign including overseas delivery. The chain of the transportation deliveries is separated into five stages: pre-haulage, transhipment, main haulage, transhipment and post-haulage (Savelsberg, 2007). Figure 1 shows an intermodal process. Figure 1:

Source: Author’s own illustration, inspired by Savelsberg 2007

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1.1.1 Freight Forwarding in Europe The three terms: freight forwarders, third party logistics service providers (3PLs) and carriers will appear throughout this thesis. For simplicity the three expressions will function as synonyms since the larger corporations in the transport industry operate within all areas. From a review of the literature, a common definition is used instead, which matches the operating activities of DSV Group. The definition is as follows: a freight forwarder functions as an ‘intermediary in the transaction between shippers and operators supplying physical transport and transhipment services’ (Woxenius & Bärthel, 2008 pp. 15). In other words, a freight forwarder services the corporation with physical transportation and administrative tasks of small or large consignments, documentation, warehousing and supplying both modal- and intermodal loading units (Woxenius & Bärthel, 2008). They organise the deliveries for shipping firms, hauliers or air charters but also directly with non-transport companies such as retailers or pharmaceutical companies. Freight forwarders do not own any assets such as ships, trucks or aircrafts. Instead, they lease the transport equipment by subcontracts. The leasing model is the dominant one and is known by the name asset-light (A-L). The A-L model creates agility in periods of high volatility with the possibility to maintain costs to the current activity e.g. hiring and firing people related to the activity (KPMG report, 2011).

The product mix offered by freight forwarders differs somewhat. The focus is either on differentiation (for example quality) – or cost focus strategies (for example economics of scale and scope). Specialisation in the field (either costs or quality) of freight can create competitive advantages. However, some can be specialised within product type: liquid bulk (gases, chemical products), solid bulk (agricultural and food products) and refrigerated transport (fresh food and pharmaceuticals) (Carbone & Stoner, 2005). In addition, the freight forwarders can specialise within transport type: full- and part-truck-, charter-, and container load, and moreover modal and intermodal transportation (the intermodal transportation between rail and road is a rising trend and is desired by more customers who want to diminish the emission of greenhouse gasses (Annual Reports, 13)). All the representative peers offer all types of transport within all geographic locations (Annual Reports, 13).

1.1.2 Strategic Approaches of Third-Party Logistics Carbone and Stones (2005) examine European third-party logistics (3PLs) heterogeneity and strategic behaviour. Their research paper finds that acquisitions and logistics alliances are the strategic behaviour adopted among the 20 leading 3PL firms. These two types of strategic approaches have, since the 1990s, became well known as useful instruments to increase market power, penetrate into new markets or enhance a firm’s capabilities in the form of new synergies (Hagedoorn & Duysters, 2002). Managing the process of acquisitions and logistics alliances can create distinct advantages for freight forwarders. Consequently, acquisition and logistics alliances will be elaborated to gain an understanding of these two methods as they are applied in the industry.

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1.1.2.1 Acquisitions by 3PLs Carbone and Stones (2005) have uncovered four factors that trigger 3PLs to make acquisitions: First, they benefit from a wider geographic coverage and control of important traffic routes, which allows freight forwarders to respond to customers’ increasing demand for global coverage. Second, it can create economies of scope, which can improve profit margins through business process optimisation and entry to new market segments. Third, the size of freight forwarders is important to withstand unexpected/expected investments in physical infrastructure (terminals, warehouses etc.). Finally, it can supplement synergies to the existing business model (Carbone & Stoner, 2005).

The size of the transaction market for the transport and logistics industry reached a value of 19bn dollars in 2013, equivalent to 27.4% growth from early 2012 (PwC statistics). Hence, 3PLs seems to favour acquisitions as a strategic path for growth. Nevertheless, it is not only in the industry for freight forwarding that acquisitions have become a commonly accepted method for growth. In 2013, acquisition transactions reached 646bn dollars in Europe, representing 10,943 deals. Figure 2 in Appendix C shows that the economic situation affects the acquisition negatively, where in 2007 Europe experienced an all time high revenue of 1.398bn dollars just ahead of the downturn caused by the financial crisis.

DSV Group has a remarkable track record with 36 completed transactions of companies since 1985 or 1.9 acquisitions (on average) per year. This means that DSV has used acquisitions as a strategic approach since its origin. The Group are acknowledged for acquiring firms with low asset valuation and redeploying them to achieve higher profitability by utilising their assets more efficiently – DSV optimise the acquired companies’ profit margin from 0 towards 4% (Zigler, 2013). For example, the Group focuses on small or medium sized family owned companies with growth potential. However, Family owned firms can be notoriously difficult to acquire and often need, among others, to be pressed on prices from fierce competitors. According to Lars Topholm (investment expert at Carnegie), small firms find it difficult to survive in a market where volume is necessary. In other words, the smaller family owned companies have practically no other choice than to sell their firm to a larger freight forwarder.

However, the acquisition process has causes and effects of its own, which have received lots of attention and curiosity from scholars (Barkema & Schijven, 2008). Studies have generally found that the firm did not increase in value, either in the short or long term (Haleblian et al., 2009). For example, Ravenscraft and Scherer (1987) have shown conclusive evidence on post-merger profitability. They define a successful merger by comparing product assortments ex-post (before the merger) with an estimate for what their

Page 16 of 127 performance would have been ex-ante (after the merger). The results following the merger show a declining operating income as a percentage of assets for the target group.

The adverse effects of acquisitions are numerous and can be challenging. Acquisitions demand an integration of resources such as knowledge and cultural differences. Today, the majority of the large consulting companies advise firms on how to most efficiently conquer the ex-post and ex-ante challenges of the acquisition process. Findings conclude that the initial stage of the process is crucial and many firms often underestimate the importance of getting the first months right. DSV has experienced such downside effects with its acquisitions. An example is the integration process of Frans Mass, in 2008, which turned out to be filled with unexpected issues related to IT-systems and terminals (Johnsen, 2008). The costs expected for the consolidation process had almost doubled from 250 DKKm to 450 DKKm (Beder, 2007). It took two years until the acquisition became profitable (Steen-Knudsen & Jensen, 2007).

1.1.3.1 Logistics Alliances Logistics alliances is another strategic approach that is favoured among freight forwarders. The strategic alliances have evolved due to increased competition, higher demand for quality from customers and increasing costs. It is defined as when two or more firms agree to share resources to follow a common strategy. Co-operation between two firms brings together their mutual skills and knowledge, and can supplement new learning opportunities for the respective partners. A logistic alliance can involve vertical alliance with customers and horizontal alliance with other 3PLs (Carbone & Stoner, 2005).

The logistics alliances strategy was initiated by shipping companies outsourcing parts of their transportation and distribution functions, and started with a narrow range of activities to broader value-added services, including packing and supply chain integration (Bagchi & Virum, 1998). At presence, the vertical logistics alliances include contracts with, in particular, retailers, food retailing, and the automotive sector. 3PLs facilitates retailer’s international expansion. For example, if a larger retailer decides to go overseas a logistic alliance can support the retailer with the sufficient knowledge and skills needed to deliver the goods to the market.

Moreover, the strategic alliances can also be horizontal. This means that it connects two 3PLs skills and facilitates, in turn, the expansion into new markets by strengthening the geographical network. This can create costs efficiencies and lower risk exposure since it is shared together with an alliance partner. When expanding into new geographical areas, distance issues – including social, cultural, economic and political distance – are often overlooked and with an alliance partner these issues can be diminished (Chemawat,

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2001). The approach of horizontal alliances is especially favoured when the costs of developing new services and solutions are high (Carbone and Stones, 2005).

Besides, if the intention is to acquire a target firm, horizontal alliances can help gain valuable knowledge before bidding on the firm, meaning that a horizontal alliance is used as a stepping-stone to a full acquisition. However, there are a number of shortcomings of entering into alliances: For example lack of knowledge transfer given the fact that knowledge is tacit - it cannot be spoken in words or written in sentences, but is rather in the hearts of the individuals (Nonaka, 1991). Furthermore, the partners can in a worst-case situation be protective and have self-interested behaviour. As a consequence the partners will unfortunately gain no new skills from the alliance. In addition cultural frictions can arise between the organisations and the connection between the partners will fail to build bridges between each other.

DSV Group utilises their expanision through logistics alliances in, for example, joint ventures (JV). For example, in late 2012 DSV acquired the rest of their stocks in DSV Latin America S.A. (DSV-GL) from their previous JV partner LOS INKAS S.A) (DSV, 12).

1.2 Presentation of DSV Group A/S DSV Group A/S is a leading freight forwarding company from Denmark with an international presence, and it has a global network of hauliers, warehouses, subsidiaries, partners and associates in more than 75 countries. The Group is divided into three main business divisions: air & sea freight, road freight and solutions. The Group’s journey began in 1976 when ten independent hauliers founded DSV, originally known by the name ‘De Sammensluttede Vognmænd af 13-7 1976 A/S’ (the united hauliers). At the time, the DSV was ahead of its competitors in Scandinavia. The Group provided quick and inexpensive transport in road freight with a business model that functioned as a cartage department for independent haulers who received a fixed percentage of the freight price paid by the customers. DSV earned the differences between the charges on customers and the transport task by the hauliers (Hyltoft, 2014a). Modifications to the business model were made to increase efficiency. This means that DSV, at present, leases trucks, warehouses and freight terminals in Europe (DSV, 13). However, it was certainly not the vision of the Group to function only within national borders: the aim was to go global. In order to achieve this goal, acquisitions endorsed the entrance to and coverage of new markets. This is a strategic method which, even today, is common for freight forwarders’ growth agendas.

1.2.1 Principal Activities The road freight division is the largest segment in the Group, equivalent to 47% of total sales in 2013. It offers full and part-full truckloads, model- and intermodal transport across Europe. In certain situations, the division provides services by rail and short sea crossings by ship. The division has high exposure to the

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European market with 42% in the Nordic countries, 12% in Southern countries and 46% in the rest of Europe. DSV road employs approximately 10,000 people and has more than 200 terminals in Europe. The division is specialised within temperature-controlled transport (i.e. pharmaceutical industry), bulk transport, hazardous cargo transport, tank container services, customs clearance, terminal network and storage operations (DSV, 2013). In Appendix D, Figures 3 and 4 show the development in revenue and change year-to-year in percentage points, respectively.

The air and sea freight division organises transport of cargo around the world. The division is the second largest in the Group, representing 42% of total sales in 2013. The division is represented in 75 countries including associated partnerships, which support locations where the division is yet manifested with own operations to offer the customers a truly global network. The air and sea freight division have a different geographic exposure with 58% of total sales from Europe, 16% from America and 21% from Asia Pacific. The division has approximately 6,300 employees worldwide and handles approximately 260,000 tonnes of airfreight and 770,000 TEU (twenty-foot equivalent unit) each year. Within sea freight the Group offers full- container-loads, and less-than-container-loads. In airfreight, it offers air charters of full planes, express and courier service. In Appendix D, Figures 5 and 6 show the development in revenue and volume in tonnes and TEU, respectively.

1.2.2 Corporate Strategy and Culture DSV Group has neither a corporate vision nor mission in their strategic planning agenda. Instead, DSV follows an asset-light strategy, a decentralised organisation structure that represents a performance culture and a customer centric strategy to enhance and develop services.

1.2.2.1 Asset-light Strategy to Preserve Margins and Increase Productivity The A-L model is an industry-wide strategic approach. The model is beneficial in industries exposed to volatility. It prevents capital from being tied up in assets, reduces fixed costs and raises the possibility to obtain a variable costs structure adjusting to the level of activity in the industry. As mentioned, the asset- light strategy is a common internal strength in the industry, at least all peer group companies advocate this strategy. Therefore, managing the A-L strategy can generate competitive advantages. For example, during the financial crisis the DSV managed to press new orders into the company without worrying about hiring new people, instead the employees had the ability to handle more movements. The result was an increase in productivity (Lunde, 2011).

1.2.2.2 Decentralised Organisation Structure and Management Culture Another major pillar is the decentralised organisation structure purposed by the Group. It has proved to be beneficial for the organisation in terms of more agility to new requests from customers and integration of newly acquired companies. According to Jens Bjørn Andersen, the CEO, it creates freedom and an

Page 19 of 127 entrepreneurial spirit within the organisation (Hyltoft, 2014). In other words, the decentralised organisation structure facilities, among others, the integration of new acquisitions since the decision making in placed lower in the organisational hierarchy. An additional differentiation from the peer firms (centralised organisation structure) is the Group’s corporate culture. The Group has no particular values on paper that the employees can see, instead they have a performance culture that is built into the minds of the employees according to Jens Bjørn Andersen. The management style is results-driven and tough, which partly has led DSV to their current strong market position (Hyltoft, 2014). However, the contemporary organisational structure is under pressure since the DSV has increased in size. It demands a change to a more centralised organisation with a different culture according to Jens Bjørn Andersen (Juel, 2014b)

1.2.2.3 Strong Risk Management Profile and Customer Centric Strategy The Group has subsidiaries in more than 75 countries and as such it is important to have a strong risk management department. DSV’s focus is on transparency and accuracy due to the risk of easily made mistakes, as both financial (frustration of currency and oil prices) and operational risks (IT-systems, legal issues) can occur (Juel, 2014). According to DSV this is a core motive in which they can achieve high profit margins. Besides a strong risk management setup, DSV promotes the importance of having a strategy that is oriented toward the customers’ needs. Therefore, continuous initiatives have been implemented to follow the trends in the market (DSV, 14).

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Chapter II Financial Analysis of Freight Forwarders

2. Introduction to Chapter Two This chapter examines the level of profitability and growth through a time-series and a cross-sectional analysis. The analysis will include a quantitative peer-group comparison with a sampling of representative freight forwarders, as introduced in section 0.1.4 ‘Case Selection’. The analysis is twofold, initiated with a top-down approach. First, the peer group firms are analysed on group-level and afterwards on segmental level of the three business units: air, sea and road freight. The findings of the following sections will contribute to the later analysis in Chapter five.

2.1 Profitability Analysis – Group Level The profitability analysis is based on the Du Pont model, which measures the overall operating profitability, the return on equity (ROE) and its decomposed components (cf. Appendix F). The model is divided into three levels where each level explains the effects of the previous level. 1) ROE is explained by Return on Invested Capital (ROIC), Financial Leverage and Spread. 2) ROIC is explained by the revenue and expenses relation (Profit Margin) and Asset Turnover whereas 3) Profit Margin and Asset Turnover is explained by their underlying value drivers. This section will only explore profitability and growth on a group level. Figure 9: ROE, Level 1 2010 2011 2012 2013 DSV Group (DK) 19.7% 24.4% 26.7% 27.0% Kuehne + Nagel Group (CH) 23.2% 22.7% 20.1% 24.5% Panalpina Group (CH) -2.8% 13.1% -8.5% 1.6% Author’s own creation: red = worst, green = best It is clear from Figure 8 that the development in ROE has stabilised prior to the crisis effects on the transport sector. DSV has since 2010 increased ROE gradually to new heights from 19.7 to 27% in 2013. Kuehne + Nagel’s ROE has during the period performed below DSV, with more fluctuations year-to-year. For example, ROE falls from 23.2% in 2010 to 20.7% in 2012. From 2012 to 2013 the development in ROE alters and reach new heights, representing 24.5%. In other words, both DSV and Kuehne + Nagel had a positive development in ROE since 2010. On the other hand, the third peer group participant, Panalpina had a negative ROE in 2010 and 2012, equivalent to 2.8- and 8.5%, respectively.

The second driver in the Du Pont model is ROIC. It shows how profitably a firm utilises its operations (Petersen and Plenborg, 2012). All else being equal, a higher ROIC will lead to higher company value. In order to assess whether ROIC is within an acceptable level it can be compared with WACC (Weighted Average Costs of Capital) or alternatively one can perform a cross-sectional analysis with representative peers (Ibid). The latter approach will appear throughout this chapter. As illuminated in Chapter one, operating leases fill a vast fraction of freight forwarders’ business models. DSV and Kuehne + Nagel has

Page 21 of 127 operating leasing equipment equivalent to 23.7- and 24.6% of total assets on average, respectively (cf. Appendix E). According to Koller, Goedhart and Wessels (2010) and Sørensen (2009) it is crucial to make adjustments for operating leases since the item is not recorded on the balance sheet, it merely appears as a rental expense in the income statement. Hence, the freight forwarding companies gains an artificial boost in ROIC, but this do not affect the intrinsic value if it is incorporated accurately in the free cash flow, cost of capital (WACC) and debt according to Koller, Goedhart and Wessels (2010). Therefore, the ratios are only adjusted for leases to give a more accurate picture of the competitiveness in the industry. Three hypothetical balance sheets and income statements are conducted for DSV, Kuehne + Nagel- and Panalpina (cf. Appendix E). Figure 10: ROIC, Level 1 2010 2011 2012 2013 DSV Group 11.4 % 13.0% 11.9% 13.2% Kuehne + Nagel Group 30.9 % 28.7% 23.8% 32.5% Panalpina Group -12.3% 24.3% -12.8% 2.9% Author’s own creation: red = worst, green = best Figure 10 illustrates ROIC before adjustments for operating leases. In the figure it is clear that Kuehne + Nagel’s ROIC surpasses both those of DSV and Panalpina during the period. With such a large difference in ROIC it is evident that especially DSV and Kuehne + Nagel follow different accounting policies with regards to the classification of operating leases. Kuehne + Nagel has double the size compared to DSV. In 2010, Kuehne + Nagel obtains a ROIC of 30.9% and even with certain fluctuations during the period they manage to achieve a ROIC equal to 32.5% in 2013. The unnaturally high ROIC is partially because of Kuehne + Nagel’s asset-light balance sheet. DSV Group does not have the same characteristics with a lower ROIC. For example, the ROIC is below ROE in all years during the period while the opposite scenario is seen at Kuehne + Nagel and Panalpina. In general, DSV ROIC shows a constant pattern through the historical period and tops at 13.2% in 2013 – a slight improvement. Figure 11: ROIC, Adjusted for Leases 2010 2011 2012 2013 DSV Group 8.7% 9.7% 8.8% 9.6% Kuehne + Nagel Group 15.4% 14.3% 12.0% 15.1% Panalpina Group -4.2% 13.4% -3.6% 3.3% Author’s own creation: red = worst, green = best It is evident from Figure 11 that ROIC falls significantly when adjusting for operating leases. Compared with the non-adjusted ROIC, Kuehne + Nagel and Panalpina have changed the most after the adjustments of operating leases. In 2010, Kuehne + Nagel had an ROIC of 15.4% compared with 30.9 before adjustments – equivalent to almost half in all years. A similar pattern is observed for Panalpina. However, DSV has not been affected to the same degree as the competitors when adjusting for operating leases. In fact, ROIC has fallen by only three percentage points. For example in 2013, DSV has a ROIC of 9.6% while before the leasing adjustments the Group had a ROIC of 13.2%. The industry average for ROIC is 7.6%, which is below DSV – both before and after adjustments for leases (Sørensen, 2009).

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In order to better understand the development in ROE and ROIC, decomposition of its underlying drivers are conducted. The first driver, Profit Margin (PM), explains the development in ROIC by assessing the revenue and expenses relation. All things being equal, it is attractive with a high PM. The PM is calculated as NOPAT divided by revenue. Figure 12 below shows the PM for the DSV and the peers. Figure 12: Profit Margin, Level 2 2010 2011 2012 2013 DSV Group 3.7% 4.0% 3.6% 3.9% Kuehne + Nagel Group 3.6% 3.7% 2.8% 3.5% Panalpina Group -0.9% 2.0% -1.0% 0.2% Author’s own creation: red = worst, green = best It is evidential from Figure 12 that the PM for all peers has followed a somewhat similar pattern as ROIC. DSV Group has performed well compared to the peers, exceeding both Kuehne + Nagel and Panalpina during the period analysed. In 2011, DSV achieved the highest PM, equivalent to 4.0% whereas Kuehne + Nagel has 3.7%. Panalpina suffers the most with a negative PM in 2012, equivalent to -1.0%. However, since adjustment on leases changes the PM, it is decided not to describe and show these in the main text given that the effects are not significant (cf. Appendix F) Figure 13: Asset Turnover, Level 2 2010 2011 2012 2013 DSV Group 3.1 3.2 3.3 3.4 Kuehne + Nagel Group 8.6 7.8 8.4 9.3 Panalpina Group 13.5 12.0 13.1 12.5 Author’s own creation: red = worst, green = best In Figure 13 the Asset Turnover (AT) is illustrated and shows that DSV’s increase in ROIC is driven by a moderate PM and AT. The AT explains the utilisation of invested capital. During the period the Group has a considerably lower AT compared to the peers, which tops in 2013 with 3.4%. The development shows an improvement of invested capital since the AT increases slightly year-to-year. Panalpina’s high AT and low PM may explain the very high and low ROIC in the period. Kuehne + Nagel have accomplished a moderate PM and at the same time a very high AT, which has ensured their high ROIC during the period. However, this picture changes when adjusting for operating leases in the balance sheet, which diminish the high AT considerably. Figure 14: Asset Turnover, Adjusted for Leases 2010 2011 2012 2013 DSV Group 2.1 2.2 2.2 2.2 Kuehne + Nagel Group 3.8 3.4 3.7 3.8 Panalpina Group 6.5 5.5 5.6 5.2 Author’s own creation: red = lowest, green = highest As a result of the adjustments for operating leases, Kuehne + Nagel has cut their AT by more than half, as shown in Figure 14. The AT is 3.8 in 2013, whereas before adjustments it was 9.3. A similar pattern is seen for Panalpina which experiences a noticeable decrease in AT. In other words, the generation of invested capital diminishes significantly as a result of the adjustments. However, the DSV pattern is different from the peers with a smaller decrease in the AT and ROIC.

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2.2 Financial Leverage and Net Borrowing Cost Figure 15 shows the decomposition of ROE with regards to the financial leverage impact on profitability. It furthermore shows an estimate of the firms net borrowing costs (NBC), but it does not verify the absolute borrowing rate (Petersen and Plenborg, 2012). The NBC is as it suggests a synonym for borrowing rate. The difference between deposit and lending rates, as well as currency effects and gains and losses on securities will affect NBC and as such it will seldom match the firm’s borrowing rate completely (Ibid.). Figure 15: Financial Leverage, Level 3 2010 2011 2012 2013 ROE 19.7% 24.4% 26.7% 27.0% Return on Invested Capital (ROIC) 11.4% 13.0% 11.9% 13.2% Net borrowing costs (NBC) 4.9% 4.1% 2.2% 2.8% Spread 6.4% 8.9% 9.8% 10.3% Financial Leverage (FLEV) 1.3 1.3 1.5 1.3 Author’s own creation It is clear from Figure 15 that DSV is able to earn a satisfactory return from debt. The Spread between ROIC and NBC express whether the financial leverage has a positive or negative effect on ROE. During the period the Spread shows a positive development as it surpasses the ROIC for all years with a continuously larger spread year-to-year. This is partly reflected by diminishing NBC and increasing ROIC during the period. In other words, DSV has been eager to reduce debt, which today can be measured positively. In sum, financial leverage has contributed to the positive development in ROE in the period. Figure 16: Financial Leverage, Adjusted for Leases 2010 2011 2012 2013 ROE 19.7% 24.4% 27.7% 27.0% Return on Invested Capital (ROIC) 8.5% 9.5% 8.6% 9.4% Net borrowing costs (NBC) 3.6% 3.2% 2.0% 2.5% Spread 4.9% 6.3% 6.5% 7.0% Financial Leverage (FLEV) 2.3 2.4 2.8 2.5 Author’s own creation In Figure 16 it is possible to see how financial leverage becomes less attractive when ROIC is adjusted for operating leases. However, ROIC is not affected much by the change from leases when compared to the competitors (cf. Appendix F). Before the adjustments from leases the Spread between ROIC and NFE was 10.3% in 2013 and after the corrections it diminished to 7.0% as a result of more debt from operating leases. In other words, the financial leverage has increased to double the size than before the modifications were made.

2.3 Growth Analysis – Trend and Common Size Analysis The completed trend and common size analysis of revenue and expenses for DSV and its peers can be found in Appendix G. Figure 17 illustrates the trend analysis over the period 2010 – 2013 for key selected financial measures. The analysis is without leases adjustments since the effects are minor on the income statement. It is clear from Figure 17 that Kuehne + Nagel has managed to outperform the competitors over the period, with the highest revenue growth, equivalent to 28% while the costs of goods sold (COGS) has only increased by 25%. This must, all else being equal, affect the PM positively during the period. However, DSV’s increase in revenue is less spectacular than the peers with 7.4% whereas costs of goods sold have increased

Page 24 of 127 similar. However, DSV’s operating margins (EBITDA, EBIT and NOPAT) illustrate a larger increase than revenue during the period, suggesting that DSV has managed to decrease operating costs (fixed costs). This might explain DSV’s continuous claims to be improving the efficiency and productivity of their operations (DSV, 13). This has been on the strategic agenda since 2009 after the acquisition of ABX Logistics in 2008 (DSV, 09). In regards to the peers, Kuehne + Nagel has, on the other hand, increased their operating costs since EBITDA, EBIT and NPOAT has increased less than revenue in the period. This might explain Kuehne + Nagel’s expansion strategy – with many new acquisitions within perishables logistics (transportation of flowers, fruits etc.). At Panalpina, one can notice the large increase in operating margins, which is due to their fluctuations year-to-year. Figure 17: Trend analysis over the period 2010 - 2013 DSV Group Panalpina Group Kuehne + Nagel Group Revenue Growth 7.4% 18% 28% Direct costs (COGS) 7.4% 14% 25% Gross profit growth 7.3% 32% 31% EBITDA growth 12.2% 141% 20% EBIT growth 15.9% 291% 25% NOPAT growth 13.4% -130% 25% Author’s own creation In order to deepen the analysis of the revenue and expense relation, Figures 18 and 19 further summarise the development and trends. Figure 18 illustrates the common size that shows revenue as percentage of direct costs, gross profit and so on. It is evident that DSV has the highest cost of goods sold (COGS) margin in 2013, equivalent to 78.1%. It appears that Kuehne + Nagel has a significantly lower COGS margin, representing 63.5%. This suggests that they have a different variable cost structure setup. In the freight forwarding industry it is considered to be beneficial to have a higher amount of your costs as variable, so that in periods of volatility the firm is not harmed as much. Additionally, it appears that DSV’s operating margins surpass the peers, which can be explained by their lower fixed costs structure. Figure 18: Common size analysis, percentage of revenue 2013 DSV Group Panalpina Group Kuehne + Nagel Group Direct costs (COGS) 78.1% 76.9% 63.5% Gross profit 21.9% 23.1% 36.4% EBITDA 6.7% 1.8% 5.6% EBIT 5.3% -0.2% 4.4% NOPAT 3.9% -0.2% 4.4% Author’s own creation In Figure 19, average figures are used instead to touch upon the development from 2010 – 2013. It is verified that DSV has on average the highest COGS margin, shared with Panalpina. Kuehne + Nagel has the lowest average COGS margin. However, once again DSV has the highest operating margins compared to peers, equivalent to 5.3%. By comparing Figures 18 and 19 it appears that DSV has improved over the period analysed. To see which items that have contributed to the positive development, a detailed common size analysis is illustrated in Figure 20.

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Figure 19: Common size analysis, percentage of DSV Group Panalpina Group Kuehne + Nagel Group revenue average from 2010 to 2013 Direct costs (COGS) 77.8% 77.8% 64.1% Gross profit 22.2% 22.2% 35.9% EBITDA 6.7% 1.6% 5.6% EBIT 5.3% -0.12% 4.3% NOPAT 3.8% 0.09% 3.4% Author’s own creation The detailed common size analysis in Figure 20 illustrates how the COGS margin fluctuates over the period. From 2010 to 2011, it improves slightly with an increase from 78.1 to 77.5%. This can be partially explained by changes in freight rates. The margin is unchanged from 2011 to 2012, which has been characterised as a challenging year with volatile market conditions in road, air and sea freight. From 2012 to 2013, the COGS margin increases as a result of, among other things, the fierce price competition e.g. price pressure in the market (DSV, 13). The most noticeable measure from the common size analysis is ‘other external expenses’ and ‘staff costs’, which has improved during the period. In 2011, ‘other external expenses’ represented 4.8% and in 2013 it was reduced to 4.4%. With regards to ‘staff costs’, the DSV has managed to reduce these from 10.9% in 2011 to 10.8%. Overall, the common size analysis shows a fairly constant development with some small fluctuations year-to-year.

Figure 20: Common size analysis, percentage of revenue - DSV Group 2010 2011 2012 2013 Revenues 100.00% 100.00% 100.00% 100.00% Direct costs 78.10% 77.50% 77.60% 78.10% Gross profit (COGS) 21.90% 22.50% 22.40% 21.90% Other external expenses 4.60% 4.80% 4.70% 4.40% Staff costs 10.90% 10.90% 10.80% 10.80% EBITDA before special items 6.40% 6.80% 6.80% 6.70% Amortisation and depreciation 1.20% 1.30% 1.20% 1.10% Special Items. net 0.00% 0.00% -0.60% -0.30% EBIT 5.20% 5.60% 5.00% 5.30% Taxes on EBIT 1.40% 1.50% 1.50% 1.40% NOPAT 3.70% 4.00% 3.60% 3.90% Net financial expenses before tax -1.30% -1.00% -0.50% -0.70% Tax shield 0.40% 0.30% 0.20% 0.20% Net earnings 2.60% 3.20% 3.10% 3.40% Author’s own creation

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2.4 Segmental Analysis of Freight Forwarders As a result of previous analyses on group level a segmental analysis will now examine each principal business divisions volume development and transport ratios comparison with peers.

2.4.1 Profitability of Air & Sea freight The following analysis assesses the PM for the business division air & sea freight. It has not been possible to separate air and sea freight in this matter. Figure 21: Profit Margin, Air & Sea freight 2010 2011 2012 2013 Average DSV (DK) 6.3 % 7.2 % 7.1 % 6.9 % 6.9 % Kuehne + Nagel (CH) 4.8 % 5.2 % 4.0 % 4.6 % 4.7 % Panalpina (CH) - - 1.6 % 2.5 % 2.0 % DHL Logistics (DE) 3.3 % 3.5 % 3.9 % 4.0 % 3.7 % DB Schenker logistics (DE) 2.6 % 3.3 % 3.5 % 2.9 % 3.1 % Average 4.3 % 4.8 % 4.0 % 4.5 % Author’s own creation: red = worst, green = best In Figure 21 it appears that DSV exceeds all peers within air & sea freight. In 2013, the Group obtains a PM of 6.9%, a little below the its highest in 2011, of 7.2%. According to DSV, this is somewhat reflected by a deterioration of average freight rates compared to previous years, and hence DSV earns more per average freight. From 2011 to 2013, the PM falls slightly, which is, to some extent, due to a new inflow of capacity, between the crucial routes of Asia Pacific and Europe (sea freight), which increases the freight rates. The freight rates pressures the prices of the services in sea freight between the shippers and freight forwarders. On the other side of the bargain table, the freight forwarder has to offer customers acceptable prices without losing too much per freight. The Group cannot fully outsource the surcharge from shippers to the customers, although the DSV makes great efforts to continuously adjust for any seasonal developments in freight rates, which has, among others things, allowed them to retain high margins.

It is noticeable in Figure 21 that Kuehne + Nagel is, among the selected peers, close to the DSV’s contemporary level. In 2011, the Group achieved a PM of 5.2%, which diminished to 4.6% in 2013. The fluctuations during the period can partially be explained by their focus on expansion within high-quality transportation of pharmaceutical and perishables products (K+N, 11; 12; 13). The acquisition of firms, particularly in the perishable industry, firmly explains the diminishing and volatile margins during the period 2011-2013. Similar to the DSV, external effects have furthermore influenced Kuehne + Nagel in the same period, namely: new capacity inflow (new smaller vessels) in the trade lanes between Asia Pacific and Europe, increase in oil prices and volatile freight rates (K+N, 12). Overall, the low margins suggest tough market conditions in air & sea freight.

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2.4.2 Profitability of Road Freight Figure 22: Profit Margin, Road Freight 2010 2011 2012 2013 Average DSV 3.7 % 3.7 % 4.1 % 4.1 % 3.9% Kuehne + Nagel -0.6 % -0.4 % -0.5 % -0.3 % -0.5% Panalpina n.a. n.a. -4.4 % -4.3 % -4.3% DHL Logistics 0.8 % 1.2 % 0.6 % 1.4 % 1.0% DB Schenker Logistics 1.7 % 2.1 % 2.2 % 1.4 % 1.9% Average 1.4 % 1.6 % 0.4 % 0.2 % Author’s own creation: red = worst, green = best From Figure 22 it is evident that DSV achieves a higher PM compared to peers. Although there is a small diminishment in PM within air & sea freight (of minus 0.3 percentage points between 2011 and 2013), road freight improves towards the end of 2013. The improvements from 2011 to 2013 represent an increase of 0.4 percentage points, equivalent to 4.1%. According to DSV, this can be explained by a unique network setup, which supplement to achieve a vast utilisation of each consignment (DSV, 13). By comparison to the peers, DSV manage to keep a less volatile margin. In addition this can, to some extent, reveal their strong focus on risk management within financial and operating activities of the asset-light strategy.

During the period from 2010 to 2013, Kuehne + Nagel and Panalpina have struggled with negative margins, equivalent to 0.3 and 4.3% in 2013, respectively. Strong price pressure and oil price fluctuations are some of the indicators of this (K+N, 10; 13). However, Kuehne + Nagel’s development during the period has improved slightly from minus 0.6 in 2010 to minus 0.3% in 2013. In order to reverse the current conditions Kuehne + Nagel initiated a strategy in 2013 called ´Road 2 Profit’, to improve profitability. 1) Full- and part- load traffic, 2) special distribution for the pharmaceutical industry and other high-value goods and 3) industry-specific solutions. The same scenario is observed at Panalpina. The Group realise the need to alter strategy to improve the negative margins, which is the worst among the peers. Their European road project consists of a new procurement programme to create streamlined processes and a standardised IT platform, making better utilisation of transportation (Panalpina, 11). The overall low PM, among peers, represents even tougher market conditions – for example, the high price pressure of road freight.

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2.4.3 Benchmark of Transport Volumes 2.4.3.1 Trends and Development in Airfreight Volume Figure 23 below shows the volume development in airfreight. It illustrates how DSV and the competitors have grown in volume during the period. Notice that the DSV has the lowest volume compared with the larger competitors, which indicate additional potential for growth in this segment. Figure 23: Volume in Airfreight, Tonnes 2010 2011 2012 2013 Average DSV 248.797 262.362 259.057 259.365 257.395 - Change y/y pct. 29.2% 5.5% -1.3% 0.1% 8.4% Kuehne + Nagel 948.000 1.073.000 1.093.000 1.134.000 1.062.000 - Change y/y pct. 25.0% 13.2% 1.9% 3.8% 11.0% Panalpina 892.000 848.000 801.000 825.000 841.500 - Change y/y pct. 22.0% -4.9% -5.5% 3.0% 3.6% DHL Logistics 4.435.000 4.378.000 4.147.000 3.949.000 4.227.250 - Change y/y pct. 18.8% -1.3% -5.3% -4.8% 1.9% DB Schenker Logistics 1.225.000 1.149.000 1.095.000 1.092.000 1.140.250 - Change y/y pct. 18.7% -6.2% -4.7% -0.3% 1.9% Market growth pct. 19% -1% -1.5% -1.5% Author’s own creation: red = worst, green = best The development in airfreight volume was affected by the turbulent year of 2010, which was caused by the after-effects of the financial crisis. The initiating recovery of the global economy and restocking of goods accelerated the freight volumes with a market increase equal to 19%. The freight rates increased from a historical low in 2009 to an all-time high, caused by low capacity that struggled to keep-up with the high market demand (DSV, 09). In 2011, the freight forwarders have slowly recovered and growth has stabilised somewhat, to more accurate levels. However, this picture alters in 2012 where the market conditions appear more difficult where the market volume declines compared to 2011, which emerge for all peers apart from Kuehne + Nagel. The difficult market conditions can partially be related to volatile freight rates and the after affects of the financial crisis. Particularly tough markets are the routes from North American to Europe, and from Europe to North American and Asia (K+N & DSV, 11; 12; 13).

Kuehne + Nagel have managed to achieve the highest growth in volume between 2010 and 2013. The Group initiated a new focus specialising on industry-specific solutions. It is concentrated on tailor-made services for the automotive and pharmaceutical industry and other high-value activities such as perishables logistics. A crucial factor for the increasing volume is the expansion of the perishables logistic segment with new acquisitions. In 2011, Kuehne + Nagel completed three new acquisitions in South America: Translago S.A.S. and Agencia de Aduanas Excelsia Ltda. in Colombia and Mastertransport S.A. in Ecuador (K+N, 11). Moreover, in Europe Kuehne + Nagel acquired a Dutch Company, J. van de Put, to strengthen its activities in perishables logistics. In 2012, Kuehne + Nagel acquired two new firms in Australia and Canada to extend its perishables network further.

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2.4.3.2 Trends and Development Sea freight in Volume Figure 24: Volume in Sea freight, TEU 2010 2011 2012 2013 Average DSV 707.193 727.861 725.806 772.142 733.251 - Change y/y pct. 18.9% 2.9% -0.3% 6.4% 7.0% Kuehne + Nagel 2.945.000 3.274.000 3.473.000 3.578.000 3.317.500 - Change y/y pct. 16.0% 11.2% 6.1% 3.0% 9.1% Panalpina 1.241.000 1.310.000 1.388.000 1.495.000 1.358.500 - Change y/y pct. 13.0% 5.6% 6.0% 7.7% 8.1% DHL logistics 2.772.000 2.724.000 2.840.000 2.807.000 2.785.750 - Change y/y pct. 5.7% -1.7% 4.3% -1.2% 1.8% DB Schenker logistics 1.647.000 1.763.000 1.905.000 1.891.000 1.771.667 - Change y/y pct. 15.7% 7.0% 8.1% -0.7% 7.5% Market growth pct. 11% 5.5% 1% 2.5% Author’s own creation: red = worst, green = best Figure 24 shows the development in sea freight measured in number of containers (TEU). The high growth from 2010 to 2011 is due to the after-effects of the financial crisis. Kuehne + Nagel have again managed to outperform the peers with the largest increase in volume during the period. From 2010 to 2011 the volume increases by 11.2%, which is clearly a result of network expansion in crucial trade lanes between Asian and non-European countries. In addition, the segment Less-Than-Container-Load performed very well and contributed to the overall volume growth. Another indicator is the acquisition of Cooltainer from New Zealand, to further enhance their expansion of perishable goods in Australia and surrounding islands in the South Pacific. In 2012, the container market became more challenging with market growth of 1%. Despite the low growth, Kuehne + Nagel continued outperforming the market with 6.1%, a drop from the previous year, and is according to Kuehne + Nagel, principally due to challenging market conditions with volatile freight rates and slowdown in export from Asia to North America and routes between Asia and Europe. In 2013 they experienced an additional drop of 3.0%, but it still surpasses the market growth.

DSV’s development from 2011 to 2013 has shown no significant improvements. In 2011, DSV perform below market conditions, which might indicate it has lost growth opportunities to other competitors. In 2012, the trade lanes between Asia and Europe declined as a result of decreasing imports in Europe. DSV exposure to these routes is relatively high with approximately 40% (DSV, 12). In order to strengthen the network in growth countries, DSV acquired Swift Freight to supplement the sea freight segment in Africa and Asia. The surpassing volume increase from 2012 to 2013 can be explained by the acquisition. In 2013, DSV acquired three new companies, all supporting the air & sea freight segment.

From 2010 to 2011, Panalpina performs in line with market growth, representing 5.6%. In 2011, the Group started to focus insistently on less-than-container-loads with 50 new point-to-point services supporting the important Intra-Asia market and trades between Asia and Europe. In same year they acquired Grieg Logistics, specialised within logistics services in oil and gas. These facts can partially explain volume growth from 2011 to 2012, equivalent to 6.0%. In 2012, Panalpina continued to launch their less-than-container-loads activities, with approximately 40 new point-to-point services in Intra-Asia. From 2012 to 2013, Panalpina

Page 30 of 127 experienced another large growth in volume in which it surpassed peers and market rates, equivalent to 7.7%. This clearly indicates the Group’s continuous focus on less-than-container-loads, with approximately additional 50 new point-to-point services in the Intra Asia market.

2.4.4 Transport Ratios in Air & Sea freight In this part of the peer group analysis transport ratios are conducted to assess the development and change in product mix. The analysis will follow the past chronology, by assessing airfreight and sea freight. The transport ratios consist of 1) revenue divided by tonnes and TEU; 2) direct costs divided by tonnes and TEU; and 3) gross profit divided by tonnes or TEU. These ratios illuminate why an increase in volume does not necessary signify a gain in revenue, gross profit or an increase in costs per tonne transported. The correlation varies depending on external factors (oil, freight rates and price pressure) and changes of product mix. Hence, revenue and costs are analysed independently. These ratios will be forecasted separately as shown in this analysis to achieve a more detailed forecast to obtain an accurate theoretical fair value of DSV Group.

2.4.4.1 Transport Ratios in Airfreight Figure 25 below shows the revenue divided by tonnes in airfreight. Basically it shows how much revenue the firm has earned per tonne handled. Figure 25: Airfreight, Revenue/Tonnes (DKK) 2010 2011 2012 2013 Average DSV 32.878 31.773 31.785 31.608 31.608 - Change y/y pct. 4.9% -3.4% 0.0% -0.6% 0.3% Kuehne + Nagel 29.314 28.757 31.390 31.483 30.236 - Change y/y pct. 23.7% -1.9% 9.2% 0.3% 7.8% Panalpina 19.374 20.911 23.481 22.877 21.661 - Change y/y pct. 5.8% 7.9% 12.3% -2.6% 5.9% DHL logistics 9.118 9.480 9.912 9.365 9.469 - Change y/y pct. 15.3% 4.0% 4.6% -5.5% 4.6% Author’s own creation: red = worst, green = best It is clear from Figure 25 that Panalpina experienced a positive development from 2010 to 2011 in revenue per tonne, equal to 7.9%. This trend continues and from 2011 to 2012 the Group’s revenue per tonne increases by 12.3%, equivalent to 23.481 DKK. In 2013, the Group experienced a decrease in earnings per tonne, representing 2.6%. In comparison with the peers, the Group has grown the most from 2011 to 2013 whereas their focus is on increasing revenue per tonne instead of increasing volume. From 2010 to 2012, a model shift (product categories) from airfreight to sea freight occurred, which is where firms (the customers of the freight forwarders) change their transportation types (supply chains) at the expense of airfreight (Panalpina, 12). This could, presumably, indicate the small shift in the product mix at Panalpina from 2010 to 2012. For example, in 2011 and 2012, Panalpina recognised a trend for smaller and lighter shipments, largely in the technology sector (Ibid).

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Kuehne + Nagel developed positively during the period with an average increase of 7.8%. In 2010, the Group earned 29.314 DKK per tonne which increased to 31.483 DKK per tonne in 2013. This can indicate a small change in product mix, for example perishables logistics. In 2013, Kuehne + Nagel’s earnings are just below DSV’s, equal to 31.608 DKK per tonne. This clearly shows that the two firms pursue somewhat similar product mix. DSV has developed constantly during the period, with fewer fluctuations in earnings per tonne, compared to peers. This indicates that the Group has not demonstrated no particular change in the product mix.

In Figure 26 the changes in costs per tonne are presented. Direct costs, often referred to as costs of goods sold, encompasses costs related to the revenue for the year. The direct costs include settlement of accounts with haulage contractors, shipping companies and airlines. Other costs include staff salaries (DSV, 13). In other words, these costs vary alongside an increase or decrease of tasks fulfilled to the customer. The ratio isolates the price mechanism from the product mix and thereby gives an indication of which peer group firm pursues similar product mix. However, it is assumed that costs per unit are less flexible compared to revenue per unit, since fuel and wages to hauliers vary along with revenue per unit. Revenue per unit can be affected by price effects such as bargain discounts on the product mix.

Figure 26: Airfreight, Direct Costs/Tonnes (DKK) 2010 2011 2012 2013 Average DSV 25.985 24.905 24.601 24.672 25.041 - Change y/y pct. 10.4% -4.2% -1.2% 0.3% 1.3% Kuehne + Nagel 25.416 24.752 26.752 26.706 25.907 - Change y/y pct. 22.7% -2.6% 8.1% -0.2% 7.0% Panalpina 15.685 16.525 18.732 18.150 18.150 - Change y/y pct. 8.4% 5.0% 13.4% -3.1% 5.9% Author’s own creation: red = worst, green = best It is evident from Figure 26 that DSV and Kuehne + Nagel had similar costs per tonne in 2011, with 24.905- and 24.752 DKK respectively. From 2011 to 2012, Kuehne + Nagel’s costs increase by 8.1%. This relatively small change may be influenced by their increasing focus on perishables logistics, acquired by new acquisitions in Asia and South America. The transformation could indicate a small change in the product mix since transportation of perishables products is assumed to be more costly. On the other hand, DSV have constant costs indicating no change in product mix. In sum, it can be said that DSV and Kuehne + Nagel may pursue a similar product mix. Last, Panalpina differs with much lower costs per tonne (as well as earnings), which can indicate a different product mix setup. The Group’s change in costs may be a result of the swift of freight modes, as mentioned.

It is beneficial for the firm if earnings per tonne increase relatively more than the costs per tonne. Hence, costs viewed in isolation do not reveal profitability. Figure 27 below thus shows gross profit divided by tonnes. In other words, the ratio measures how much the peers have earned after costs related to transportation. By using this method it is possible to interpret the profitability.

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Figure 27: Airfreight, Gross profit/Tonnes (DKK) 2010 2011 2012 2013 Average DSV 6.893 6.868 7.184 6.936 6.970 - Change y/y pct. -11.7% -0.4% 4.6% -3.4% -2.7% Kuehne + Nagel 3.898 4.004 4.638 4.777 4.329 - Change y/y pct. -11.0% 2.7% 15.8% 3.0% 2.6% Panalpina 3.689 4.386 4.749 4.728 4.388 - Change y/y pct. -2.7% 18.9% 8.3% -0.5% 6.0% DHL logistics 1.572 1.826 1.850 1.811 1.765 - Change y/y pct. n.a. 16.2% 1.3% -2.1% 5.1% Author’s own creation: red = worst, green = best It is clear from Figure 27 that DSV’s development on average has diminished by 2.7%. This is due to the almost unchanged earnings but increasing costs during the period. In addition, the figure shows how DSV differs from Kuehne + Nagel and Panalpina in terms of profitability. For example, in 2013 DSV obtains a gross profit per tonne equal to 6.936 DKK, while Kuehne + Nagel and Panalpina obtains 4.777- and 4.728 DKK, respectively. Nonetheless, Kuehne + Nagel and Panalpina managed to increase profitability during the period. For example, Kuehne + Nagel experience a remarkable growth from 2011 to 2012, equal to 15.8%. This can be explained by the high increase in revenue per tonne, which might be related to the new strategic initiatives changing the product mix towards high value added products (perishables logistics).

2.4.4.2 Transport Ratios in Sea Freight As demonstrated in the section ‘Transport Ratios in Air Freight’ a similar analysis will now be undertaken for sea freight. Figure 28: Sea freight, Revenue/TEU (DKK) 2010 2011 2012 2013 Average DSV 15.871 14.549 16.013 15.537 15.493 - Change y/y pct. 18.5% -8.3% 10.1% -3.0% 4.3% Kuehne + Nagel 14.211 12.680 14.491 14.059 13.860 - Change y/y pct. 13.4% -10.8% 14.3% -3.0% 3.5% Panalpina 11.016 9.544 11.411 11.492 10.866 - Change y/y pct. 4.4% -13.4% 19.6% 0.7% 2.8% DHL logistics 9.406 9.689 9.806 9.367 9.567 - Change y/y pct. 34.6% 3.0% 1.2% -4.5% 8.6% Author’s own creation: red = worst, green = best Figure 28, above, confirms that DSV has the highest earnings per TEU during the period. For example in 2013, DSV earns 15.537 DKK per TEU compared to Kuehne + Nagel which earns 14.049 DKK per TEU. As proven in airfreight, with regard to the alignment of product mix between Kuehne + Nagel and DSV, this can to some extent be acknowledged again. In sum, it can be observed that the development in sea freight fluctuates more year-to-year than airfreight. It also indicates that none of the peers has changed significantly during the period. Figure 29: Sea freight, Direct Costs/TEU (DKK) 2010 2011 2012 2013 Average DSV 12.930 11.406 12.693 12.301 12.333 - Change y/y pct. 27.3% -11.8% 11.3% -3.1% 5.9% Kuehne + Nagel 12.160 10.610 12.268 11.851 11.722 - Change y/y pct. 12.5% -12.7% 15.6% -3.4% 3.0% Panalpina 9.215 7.731 9.404 9.460 8.952 - Change y/y pct. 8.3% -16.1% 21.6% 0.6% 3.6% Author’s own creation: red = worst, green = best

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Figure 29 shows the development in costs per TEU. It is evident that DSV’s direct cost per TEU is the highest among the peers with 12.693 DKK in 2012. In the period from 2011 to 2013, costs per TEU increased from 11.406- to 12.301 DKK, representing an increase of 7.8%. In 2013, this trend reverses and the costs fall by 3,1 %, equivalent to 12.301 DKK. The rising costs in 2012 can partially be explained by the volatile market conditions with rising and falling freight rates, a pattern observed for all competitors. Kuehne + Nagel’s costs per TEU is similar to that of the DSV with 11.851 DKK in 2013, which can indicate that they have a somewhat identical product mix. In 2011, the costs per TEU were 10.610 DKK and increase during the period to 11.851 in 2013, equivalent to 11.7%. In comparison, Panalpina’s costs per TEU differ from DSV and Kuehne + Nagel, which can indicate that they have a different product mix focus. In sum, it is clear that all the peers follow similar patterns year-to-year, but to a different degree.

Figure 30: Sea freight, Gross profit/TEU (DKK) 2010 2011 2012 2013 Average DSV 2.941 3.143 3.318 3.236 3.160 - Change y/y pct. -9.0% 6.9% 5.5% -2.4% 0.2% Kuehne + Nagel 2.050 2.070 2.223 2.208 2.138 - Change y/y pct. -7.7% 1.0% 7.4% -0.7% 0.0% Panalpina 1.801 1.812 2.007 2.032 1.913 - Change y/y pct. -12.1% 0.6% 10.7% 1.3% 0.1% DHL logistics 1.572 1.826 1.850 1.811 1.765 - Change y/y pct. n.a. 16.2% 1.3% -2.1% 5.1% Author’s own creation: red = worst, green = best Figure 30 shows profits related to services. It is clear that DSV’s profitability per TEU exceeds all peers. In 2011, they earned 3.143 DKK per TEU in which increased to 3.236 in 2013, representing a growth of 3.0%. Their competitor Kuehne + Nagel achieved a profit per TEU of 2.208 DKK in 2013. In sum, the development has been unchanged among the peers during the period.

2.4.5 Estimation of Road Freight Road freight is the core of DSV’s business activities and is consequently the most protected due to the highly competitive environment. As such, it has been necessary to create my own estimates of the road freight segment to assess the development for strategic and forecasting purposes. The segmentation breakdown is identical to the air & sea freight by splitting the analysis into volume (number of consignments based on one single average estimated consignment in DKK) to calculate earnings, costs and profit per consignment. Given that a single average consignment is estimated, earnings per consignment will be identical among the peers. However, costs will be different among the peers due to product mix configurations, but not accurate since the estimates are based on a market consignment. However, it can reveal which of the peers used in the analysis are more closely aligned than others in their operations, if it is assumed that they have earned the same per consignment. For example, Kuehne + Nagel and Panalpina are international freight forwarders with same country of origin (Switzerland). Hence, they may operate with identical product mix. It has only been possible to gather information about the change of total consignments from DSV’s annual report. These numbers can give accurate information about the change in the transport ratios. Hence, change in

Page 34 of 127 consignments from the other peers is unfortunately not reflected in the calculations (market increase affects the development in revenue per consignment).

To elaborate on the intuition behind the logical reasoning it was possible to find information of total revenue in bn. dollars from the European road freight market and volume of freight tonne kilometres. Dividing the two variables gives the road freight tonne kilometres in DKK. The European commission for road transport published a report in 2011 in which they highlighted and debated transport efficiency in Europe. In the report, average distances for hauliers (610.5 kilometres) from EU27 countries and average truckload for both national and international driving (13.6 tonnes) are revealed (cf. Appendix H) (European Commission, 2010a). These two variables (average distance and average truckload in tonnes) are multiplied by road freight tonne kilometres in DKK and as a result an average consignment for the total market is achieved. The next step was then to divide the average consignment in DKK by revenue, direct costs and gross profit respectively for the years from 2010 to 2013.

Reflections on the road estimations have led to thoughts of how to incorporate company specifics by investigating their region exposure and then weighting them as regards distance of freight. At DSV their region exposure consists mostly of northern countries, equal to 42%, rest of Europe 46% and southern Europe 12%. This could indicate that DSV’s routes are shorter than the average distance, giving that Nordic countries including the main markets Germany, Denmark, Norway, Sweden and England have shorter routes (DSV, 10). The Baltic countries and eastern countries have in general longer routes, likewise Southern countries. For simplicity this resulted in the decision to use average numbers. The competitors do not disclose any information on their region exposure why average numbers are used.

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2.4.5.1 Volume in Road Freight Figure 31, below, gives an indication of the total numbers of consignments in road freight for similar peers. The volume is calculated by dividing revenue by an estimated average market consignment. Although the change in volume is not accurate, it can still give an indication. At DSV where the Group provides the change in consignments year-to-year, the development can be interpreted and will thus be later used in the forecast of the road freight segment. Figure 31: Volume total number of consignments 2010 2011 2012 2013 Average DSV 1.731.731 1.873.733 1.892.470 1.968.169 1.866.526 - Change y/y pct. 12.0% 8.2% 1.0% 4.0% 6.3% DHL logistics 7.660.933 8.283.133 7.928.799 7.843.835 7.929.175 - Change y/y pct. 8.1% -4.3% -1.1% 0.9% Panalpina 286.331 360.116 368.538 403.800 354.696 - Change y/y pct. -2.6% 25.8% 2.3% 9.6% 8.8% Kuehne + Nagel 1.202.661 1.538.856 1.701.555 1.833.987 1.569.265 - Change y/y pct. 28.0% 10.6% 7.8% 15.4% Market Growth 8.0 3.0 -2.0 0.0 Author’s own creation: red = worst, green = best It is evident from Figure 31 that DSV perform above market growth in all years in which they declare that they have increased their market shares during the period. The volume growth from 2010 to 2011 can partially be contributed by the northern and eastern countries while the southern European countries delivers lower growth potential, hardly due to the previous crisis damaging effects on these countries. In addition, the growth is partly due to slightly higher than average freight rates (DSV, 11). The increase in volume from 2012 to 2013 is unquestionably due to the acquisition of Ontime Logistics (DSV, 13). The acquisition strengthens their strong position in the Nordic countries (Raun, 13).

From 2010 to 2011, Kuehne + Nagel recorded a favourable volume development of 28%. The main impetus for the above-average volume growth came from the important acquisition of British groupage provider RH Freight. RH Freight is located in 17 locations in United Kingdom and handles roughly 425,000 shipments per year (K+N, 11). Another acquisition was made in late 2011 of Carl Drude GmbH & Co., which facilitated access to new highly capable hubs in Bad Hersfeld, Germany. Kuehne + Nagel also started to grow outside their main territory in Europe with new acquisitions in emerging economies such as Brazil. As such, the Brazilian company Grupo Eichenberg was acquired and made an entry in South America. The company is one of the leading providers of overland transport services (Ibid.). In addition, Kuehne + Nagel’s previous investments in full- and part-loads business started bearing fruit. In 2012, Kuehne + Nagel performed with above market rates, with 18.3% growth, even in the unstable market conditions in southern Europe. This shows that the implementation of the acquisitions from 2011 is starting showing its full potential and the full- and part-loads performance continues this positive development.

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2.4.5.2 Transports Ratios in Road freight Figure 32: (Revenue / total consignments) 2010 2011 2012 2013 Average DSV 12.186 12.083 11.971 11.745 11.996 - Change y/y pct. -0.8% -0.9% -1.9% -1.2% DHL logistics 13.939 13.592 14.721 14.081 14.083 Kuehne + Nagel 13.939 13.592 14.721 14.081 14.083 Panalpina 13.939 13.592 14.721 14.081 14.083 Author’s own creation In Figure 32 the numbers illustrates the average revenue calculated as a market consignment. In 2010, DSV illuminate the year-to-year growth rate in consignments and these are used instead. Holding the average estimated revenue fixed to the market development would, as pointed out, not reflect the real earnings or the costs (the costs may be higher or lower with regard to the real earnings per consignment). All things being equal, DSV development shows a small decrease in revenue per consignment during the period. Figure 33: Direct Costs/ number of consignments (DKK) 2010 2011 2012 2013 Average DSV 9.816 9.799 9.674 9.561 9.712 - Change y/y pct. -1.1% -0.2% -1.3% -1.2% -1.0% DHL logistics 10.742 10.393 11.141 10.613 10.722 - Change y/y pct. n.a. 3.3% -7.2% 4.7% 0.3% Kuehne + Nagel 10.555 10.582 11.564 11.044 10.936 - Change y/y pct. 10.6% 0.3% 8.5% -4.7% 3.7% Panalpina 7.908 8.343 8.524 7.386 8.041 - Change y/y pct. n.a. 5.5% 2.2% -13.4% -1.9% Author’s own creation When reflecting on the costs one can see how the costs have developed during the period. Figure 33 verifies a reversing pattern in the costs development. DSV is distinguished from the peers in costs per consignment. For example, the DSV’s costs per consignment are 9.561 in 2013 while Kuehne + Nagel equals 11.044 in costs per consignment. In sum, a stable development is identified for DSV who manage to diminish costs year-to-year, by 1.0% on average, which indicate no change in product mix during the period. Figure 34: Gross profit/ number of consignments (DKK) 2010 2011 2012 2013 Average DSV 2.370 2.284 2.296 2.184 2.284 - Change y/y pct. -10.8% -3.8% 0.5% -5.1% -4.8% DHL logistics 3197 3.199 3.580 3.469 3361 - Change y/y pct. 0.1% 11.9% -3.1% 2.2% Kuehne + Nagel 3.384 3.010 3157 3038 3147 - Change y/y pct. 7.9% -12.4% 4.7% -3.9% -0.9% Panalpina 6.030 5.249 6.197 6.695 6.043 - Change y/y pct. -14.9% 15.3% 7.4% 2.0% Author’s own creation Figure 34 illustrates the development in profit per consignment. It is clear that DSV saw a stable development during the period. From 2011 to 2012, the development in gross profit per consignment increased by 0.5%. In 2013, this trend reverses with a small decrease of -5.1%.

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CHAPTER III Market Analysis of the Transport Service Industry

3. Introduction to Chapter Three This chapter will examine the market expectations of the freight forwarding industry in all three principal business divisions. I have previously narrowed this thesis by placing the focus on Europe. However, the growth prospects within sea & airfreight in the Asia-Pacific region and United States will be investigated due to their global position in the trade regime and high route exposure on DSV operations. The findings will contribute to the forecasts.

3.1 Segmental Fraction of the Transport Service Industry The transportation service industry consists of different modes of transportation, namely: road, rail, air and sea. In the European transportation industry, road freight is the most lucrative generating revenues of 600bn dollars, equivalent to 74% of the aggregated value. Sea freight is the second largest accounting for 12.4% and airfreight represents 5%. In 2013, a somewhat similar segmental pattern is observed in the United States for transport services, where road freight constitutes 80 % of the aggregated value, equivalent to 971,9bn. In the Asia-Pacific transport sector this picture changes somewhat, where sea freight stands out having 25% of the Group’s aggregated value, equivalent to 226bn dollars. By comparison, Europe and the United States have 12.4 and 8.6% in sea freight, respectively (cf. Appendix I, figure 37). The large outflow of production and demands from the West could explain the difference in sea freight attractiveness.

Figure 35: Market Segmentation of Transport Service Industry Figure 36: Market Segmentation of Transport Service Industry in in Europe, per cent Asia-Pacific, per cent Air freight Air freight Rail freight Rail freight 5% 6% 8% 6% Sea freight 13%

Sea freight 25% Road freight Road freight 63% 74%

Road freight Sea freight Rail freight Air freight Road freight Sea freight Rail freight Air freight

3.2 Development and Trends - European Transport Service Industry Forecasts for the European transport service industry and the development in gross domestic product is conducted with the purpose to compare the statistics (cf. Appendix I). The numbers indicate that the freight forwarding and logistics industry is continuing to grow, as the rates will still possibly outperform that of global GDP – a promising situation given the current economic climate in the European Union (see Chapter

Page 38 of 127 four). The adverse economic situation in Europe is predicted to recover in 2013, toward the end of the forecast period. In 2018, the European transportation service industry is estimated to have a value of 657.2bn dollars, an increase of 9.5% since 2013.

Figure 38: European Transportation Service Industry, $bn 4% 700 3,6% 3,6% 3,4% 680 3% 2,8% 660 2,1% 2% 1,9% 1,9% 1,9% 1,7% 1,6% 1,8% 640 1% 620 0,2% 600 0% -0,1% -0,3% 580 -1% -1,1% 560 -2% 540 2011 2012 E2013 E2014 E2015 E2016 E2017 Growth, percent GDP change y/y, pct.

3.3 Development and Trends - Road Freight in Europe It is evident from Figure 39 that the European road freight industry shows signs of recovery following the spectacular downturn after the financial crisis in 2008. The recovery was estimated to make slow progress in the beginning of 2013, and is predicted to accelerate from 2015 towards the end of 2017. In the five-year period of 2012-2017 road freight is expected to grow by 15.1%. The compound annual growth rate (CAGR) in the same period is estimated to be 2.9%. If the forecasts are expected to hold true then the road freight industry in Europe looks promising (cf. Appendix I).

In Figure 40 it is clear that the volume consumption stabilises towards 2012 where it reaches a total value of 1.961,2bn freight tonne kilometres (FTK). From 2013, a reverse trend alters the development and the volume consumption is expected to rise to 2269,1bn FTK by the end of 2017, indicating a GAGR of 3% in the period 2012-2017. The future prospects for road transportation measured in bn. FTK is also promising (cf. Appendix I).

Figure 39: European Road Transportation Service Industry, Figure 40: European Road Transportation Service Industry, $bn Volume FTK bn 14% 520 5% 2300 4,7% 12% 12,1% 2250 500 4% 3,9% 2200 10% 3,4% 3,4% 3% 2,9% 2150 8% 480 2100 6% 2% 460 2050 1,3% 4% 4,0% 3,9% 3,6% 4,0% 1% 2,7% 2000 2% 440 0% 1950 -0,3% 0% 0,1% 1900 420 -1,6% -1% -2% -1,3% 1850 -4% 400 -2% 1800 2010 2011 2012 E2013 E2014 E2015 E2016 E2017 2010 2011 2012 E2013 E2014 E2015 E2016 E2017 DSV considers expanding into the United States for road transportation if possibilities for acquisitions emerge (Duelund & Mortensen, 14). It is questionable as to when this will prove realistic and as such it is difficult to incorporate this in the forecast. Growth prospects for the American road market are therefore avoided.

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3.4 Development and Trends - Sea Freight in Europe The sea freight industry use two approaches when shipping goods, namely, ocean and inland waterways, where the ocean segment is the most lucrative with the lion’s share of 92.5%, equivalent to 68bn dollars, in 2013. The inland waterways account for the remainder, i.e. 7.5%, equivalent to 5.6bn dollars. Figure 41 illustrates how much growth the industry generates. In 2012, sea freight declined by 1.6% followed by prompt growth from 2012 to 2014. A rapid growth of 19.6% is experienced between 2013-2018 with an anticipated CAGR of 3.6%. Unfortunately, it has not been possible to find any information on volume forecasts within sea freight.

Figure 41: Seafreight Service Industry in Europe, $bn 6% 95,0 5% 5,1% 90,0 4% 3,8% 3,9% 3,6% 3,6% 85,0 3% 3,3% 2% 80,0 1% 75,0 0% 0,2% 70,0 -1% -1,6% -2% 65,0 2011 2012 2013 E2014 E2015 E2016 E2017 E2018

3.4.1 Development and Trends - Sea freight in Asia-Pacific The growth opportunities in sea freight is larger in the Asia-Pacific region, which has an anticipated CAGR of 5.6% between 2013-2018. The rapid growth is expected to reach an overall market value of 297 bn. dollars by the end of 2018. China is the most lucrative market and accounts for 45.1% of the Asia-Pacific freight industry value, and will over the same period, as mentioned for sea freight in Europe, have an CAGR of 8.5% reaching a value of 153,1bn dollars in 2018. In other words, the Asian market for sea freight is more lucrative than the European one, with larger growth opportunities and market shares to conquer (cf. Appendix I).

3.4.2 Development and Trends - Sea freight in United States In 2013, the market for sea freight in the United States is estimated to account for 53,1bn, dollars, representing CAGR of 3.2% between 2009-2013. A comparison with the European and Asia-Pacific markets suggests that they are estimated to grow by 2.8 and 5.5% respectively. By the end of 2018, the market for sea freight in United States will realise a total revenue of 63bn., an anticipated CAGR of 3.5% for the five-year period of 2013-2018. Yet again, a comparison of the European and Asian-Pacific markets forecasts CAGR growth of 3.6 and 5.6%, respectively over the same period, reaching respective values of 89bn and 297bn dollars in 2018 (cf. Appendix I).

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3.5 Development and Trends - Airfreight in Europe Airfreight is among the smallest of the transportation modes and is known to be a costly way to transport goods and services. In periods of economic downturns airfreight are generally affected the most since buyers are not willing to pay the extra mark-up for their deliveries of goods and services, and consequently they substitute the deliveries to sea freight. The opposite situation characterises economic upturns, when buyers are willing to pay the additional costs for faster deliveries. Nevertheless, airfreight grows less in periods of moderate economic development than road and sea freight. The period between 2009-2013 has been affected by an economic downturn with no significant development, representing a CAGR of 0.4% and equivalent to 30bn dollars in 2013 (cf. Appendix I). Figure 42 below illustrates the future outlook for the sector, which is predicted to increase during the five-year period 2013-2018 with an estimated value of 32.6bn dollars in 2018 and with an anticipated CAGR of 1.4%.

In the category of volume consumption, volume declined by 0.1% CARC (compound annual rate of change) between 2009-2013 and reached an anticipated value of 35.4bn FTK by the end of 2013. Figure 43 illustrates the forecasts for 2013-2018, where volume is estimated to increase to 37bn FTK with an expected CARC of 0.9% during the period. The development in volume is spikier than in bn. dollars, which has a more stable flow.

Figure 42: Airfreight Service Industry in Europe, $bn Figure 43: Airfreight Service Industry in Europe, volume FTK bn 10% 33 5% 375 8,8% 32,5 4,2% 8% 4% 370 32 3% 6% 365 31,5 2% 2,2% 4% 1,7% 1,6%360 31 1% 2,3% 2% 2,0% 1,8% 0,4% 355 30,5 0% 0,8% -0,3% 0% 350 -0,2% 30 -1% -0,8% -1,0% -1,7% -2% 29,5 345 -2% -2,2% -4% 29 -3% 340 2011 2012 2013 E2014 E2015 E2016 E2017 E2018 2011 2012 2013 E2014 E2015 E2016 E2017 E2018

3.5.1 Development and Trends - Airfreight in Asia-Pacific The Asia-Pacific airfreight sector remains balanced in 2013 and a moderate growth rate is expected in the forecast period. In 2013, the Asia-Pacific airfreight sector is estimated to generate revenues of 56.2bn dollars indicating a CARC of 1.8% between 2009-2013. By the end of 2018 the airfreight sector generates a value of 61.5bn dollars, equivalent to 9.1% increase between 2013-2018. In the category of volume consumption the forecast predicts that between 2013-2018 air volume will increase with a CAGR of approximately 0.7%, equal to 87.8 billon FTK in 2018 (cf. Appendix I).

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3.5.2 Development and Trends - Airfreight in United States The United States airfreight sector is expected to recover since the meltdown of the financial crisis. In the forecast period development is moderate with small annual increases. In 2013, the air transportation sector achieves an overall value of 39,2bn dollars, representing a CARC of 1.2% from 2009-2013. During the forecast period 2013-2018, it is expected that the air transportation will reach 38.9bn dollars. On the other hand, the consumption sector, measured in volume, obtain a value of 97.4 billion FTK in 2013, representing an increase of 2.5 % CAGR between 2009-2013. Forecasts predict a rise of 0.8% between 2013-2018, reaching a total value of 101.2 billion FTK (cf. Appendix I).

3.6 Concluding Remarks The market analysis has contributed insights into the growth potentials of the transport service industry. A promising outlook has been verified with a strong indication of moderate and stable growth in all three transportation modes. In Figure 44, below, each division’s CAGR and end of year revenue is given for 2013 and 2018. The last column on the right assesses the growth potential relatively in the industry: low, moderate or high.

Figure 44: Overview of the Transportation Service Industry Value bn. dollars CAGR (2009- Value bn. dollars CAGR - forecast Relative growth Type of transport modes 2013 2013) 2018 (2013-2018) expectations All transportation modes in Europe 600 2.8% 657 1.8% Moderate

*European road freight 445 -3.4% 513 2.9% Moderate European sea freight 74 2.8% 89 3.6% High European airfreight 31 0.4% 33 1.4% Low

Asia-Pacific airfreight 56 1.8% 61 1.7% Low Asia-Pacific sea freight 226 5.5% 297 5.6% High

United States airfreight 39 1.2% 39 0.5% Low United States sea freight 53 3.2% 63 3.5% High Value bn. FTK CAGR (2009- Value bn. FTK CAGR Relative growth Type of transport modes 2013 2013) 2018 (2013-2018) expectations *European road freight 1961 -1.9 2269 3.0 Moderate European airfreight 35 -0.1% 37 0.9 Low

Asia-Pacific Air 83 0.7 88 1.1 Low

United States Air 97 2.5 101 0.8 Low *Period change, instead 2012-2017. Note in the column all transport modes rail freight is included. Source: Author’s own creation, Market line database

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Chapter IV Environmental Analysis of the European Transportation Industry 4. Introduction to Chapter Four This chapter will address the business environment that influences the performance in DSV Group. A vast number of external factors can affect a firm’s performance and it is vital to build a framework that supports the long-term forecasts of the thesis. The factors that influence the firm vary in degree depending on the industry the firm operates within. A prerequisite is to distinguish the crucial from the merely important (Grant, 2010). The analysis will consider various factors systematically and, as far as possible, individually, after which the most significant factors – ‘the key drivers of change’ – will be identified at the end of the analysis. A theoretical approach to analysing the environment includes the political, economic, social, and technological factors (PEST).

4.1Political and Legal Factors Analysis 4.1.1 United Nations Framework on Climate Change

The awareness of global warning caused by CO2 emissions has become an increasing concern among world politicians. This attention has affected the European transportation industry wherein possible new regulations can affect the prices of transports. Normally, freight forwarders ‘pass on’ such costs to their customers, for example costs caused by new regulations, but given the fierce competition in the sector this can only be done to a degree that is accepted by the customer universe and governmental restrictions. Consequently, greenhouse gasses need to be taken into consideration. Climate change discussions fill a larger part of freight forwarders strategic agenda due to the issues emphasised (for a detail description, refer to section ‘4.3.1

Social and Culture Analysis’). Therefore, the extent of CO2 emissions is illuminated to get a perceptive of the current situation in Europe and the impact from the transport industry for future concerns.

Contemporary evidence verifies a decreasing trend in the emission of greenhouse gasses (GHG) from EU-28 and EU-15 member countries, all submitted under the Kyoto Protocol – an agreement whereby each country signed a mutual commitment to reduce CO2 emission by 8% between 2008 and 2012 (European Commission). Figure 45 shows a decreasing trend in the emission of GHG. In 2011 and 2012, data confirms a CO2 emissions reduction of 30 million tonnes (European Commission).

However, even if the emission of GHG decreases in the European Union, they are still large users of fossil fuels in order to power the engines of the transport sector. The main energy supply is oil fuels and accounts for 96% of the total market. The road subdivision has the majority share. According to the European Commission 71% of transportation emissions are related to road transportation, others such as air and sea transportation account for 14 and 13% respectively. It is clear from Figure 46 that road freight has increased dramatically since 1990. This concern is mutually agreed on by the Europe Commission where statistics

Page 43 of 127 moreover verify a reverse trend for road transportation compared to other transport substitutes, as well as industries (cf. Appendix J, Figure 47 and 48).

Figure 45: CO2 - emission EU-28 and EU-15 Figure 46: Mt CO2 Equivalent 1.400 6000 1.200 5500 1.000 5000 800

4500 600

4000 400 3500 200 3000 0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Emissions - EU28 - Tg (million tonnes) Road transportation Civil aviation Navigation Railways (excluding electrified) Emissions - EU15 - Tg (million tonnes) Other transportation International maritime transport International aviation

The statistics warn about possible future policy regulations from the European parliament. The latest press release from the European commission at the end of May 2014 attacks heavy-duty vehicles (HDVs) in particular. As Climate Action Commissioner Connie Hedegaard said: “We first regulated cars and vans, and we can now see the results: emissions have been reduced, air pollution in cities in decline, and more innovative, fuel-efficient vehicles are now available to consumers. That is why we turn now to trucks and buses” (Press release, May 2014). Without action of polices HDVs emissions of GHGs will between 2030- 2050 remain close to current levels. It is discussed if mandatory restrictions on newly registered HDVs such as new state-of-art technologies should be considered. Studies show a decrease of approximately 30% of

CO2 emission if the technologies are implemented (Ibid).

4.2 Economic Analysis - Insights from Macro Data Gross Domestic Product (GDP) is possibly the factor most affecting DSV’s operations and the freight forwarding industry in the long term. Figure 49 on page 46 illustrates the development in GDP derived from EU-28 countries compared with changes in organic growth (excluding acquisitions) of the DSV Group and the industry consisting of all subsectors: road, air, sea and rail freight. A moderate and correlated trend between the measures verifies the development, where DSV and the industry outperform that of GDP. Moreover, the development is firmly established during the forecast period and future prospects look more promising.

However, the economy can be characterised as a mechanical wheel that needs oil to contribute growth in GDP. Consequently, private consumption, public consumption, and exports of goods and services contribute to growth in GDP, but vary in degree. Private and public consumption accounts for 58.4 and 21.6% of GDP in 2012, equivalent to 7.797 and 2.873bn Euros. Figure 50 shows the two accelerators of GDP: private and

Page 44 of 127 public consumption in EU-28, which gives an indication of renewed confidence in the system. An upward trend signals a European economy stabilising and moving against pre-crisis growth.

Figure 51 shows the exports of goods and services for the European economy. The outlook predicts larger foreign demand of European products indicating optimisation in the economy and a larger demand for transport. The exports from EU-28 are expected to increase by 4% in 2014 and 5.1% in 2015. A similar pattern is expected for import, which is estimated to growth by 3.8 and 5.5% respectively. The contribution for net exports indicates that transportation of goods and services will be positively affected.

The labour market conditions improve slowly. Figure 52 verifies the unemployment rate for EU-28 and Eurozone together with the growth in employment and measures an upward activity in Europe, which accelerates demand for products. Consequently, a recovery in unemployment helps stabilise the private consumption with greater disposable income. The unemployment rates started to stabilise in 2013 after reaching close to 12%. The forecasts show a fall from 12 to 11.8% in 2014 and will continue to decrease with the expectation of recovery in sight. Notably, unemployment rates remain high in southern Europe, Spain (25.1%.), Portugal (14.3%) and Italy (13.6%) (Eurostat, May 2014).

The financial markets have regained previous strength supported by, among others, central banks from the US, the UK and the Eurozone keeping interest rates low until the economy proves healthier. Figure 53 shows that the ECB interest rate (refinancing rate – interest rate banks have to pay when they borrow money) is at a record-low level of 0.15 %. In general, when ECB lowers interest rates it believes that the economy needs help, expanding the supply of money in the hope of lowering unemployment. When the economy starts to stabilise the ECB raises the rates, diminishing the money supply to keep inflation in form. The last chart Figure 54 shows inflation (consumer prices), which is expected to rise a little in 2014 and 2015 (cf. Appendix J). Nevertheless, a low interest rate and inflation still signal an economy brought to its knees.

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Economic Outlook of Growth Indicators

Figure 49: Growth in GDP, Industry and DSV Corelates

20 15 10 5

0 2007 2008 2009 2010 2011 2012 2013E 2014E 2015E -5 -10 -15 -20 -25

Real GDP, pct. y/y DSV Group organic growth, pct Transport Europe, pct. y/y

Figure 50: Private and public consumption: upward trend Figure 51: Exports is expected to growth, EU signals confindence 15% 7.000 2,5 6.000 2 10% 5.000 1,5 5% 1 4.000 0% 0,5 3.000 0 -5% 2008 2009 2010 2011 2012 2013E 2014E 2015E 2.000 -0,5 -10% 1.000 -1 -1,5 -15% 0 -2 Private consumption, real pct. y/y Exports of goods and service, bn. Euro pct. y/y Public consumption, real pct. y/y

Figure 52: Unemployment declines but remains high Figure 53: ECB interest rates remain at a record-low 2,0% 14 4 1,5% 12 1,0% 3,5 10 0,5% 3 8 0,0% 2,5 6 -0,5% 2 4 -1,0% 1,5 2 -1,5% 1 -2,0% 0 0,5 0 Employment rate, EU pct. y/y Uemployment rate EU pct.

Uemployment rate Euro-area, pct. Author’s own elaborations: Statistics from Eurostat, ECB, World Bank, MarketLine and DSV annual reports

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4.2.1 Europe 2020: Smart, Sustainable and inclusive Growth In the beginning of 2010, the European Commission introduced a new strategy called ‘Europe 2020’. It consists of three mission-targets to improve the internal markets of Europe: smart, sustainable and inclusive growth. The 2020 strategy was initiated due to low growth and productivity compared to other developed countries, as well as a wake-up call to the damage of the financial crisis. The aim of the strategy is to make Europe a world leader, not only in growth measures such as GDP (European Commission, 2010b). The three targets are defined as:

 Smart growth: developing a country based on knowledge and innovation  Sustainable growth: securing more resource efficiency, greener and more fierce competition  Inclusive growth: promoting high employment, taking care of social and urban district

The three targets are to be accomplished by 2020. To be precise, ‘Europe 2020’ wants to accomplish the following: 75 per cent of the population should be employed (from the current 69 per cent); 3 per cent of the

EU’s GDP should to be invested in R&D; there should be a 20% reduction of CO2 emissions, school leavers to be 10%, and a minimum of 40% of the young generation to have tertiary degrees. Finally, 20 million fewer people should risk their poverty.

4.2.2 Fuel Costs Impact on Freight Forwarders The fuel costs impact on the freight forwarding industry is considered high. Oil prices affect all transport modes and unless these costs are hedged, they can fluctuate from quarter to quarter. In other words, if the oil prices reach new high levels DSV will try to transmit the additional costs from the rising oil prices (Zigler, 2008). The oil prices are at the current situation estimated to stabilise with a downward trend according to the World Bank estimates.

Figure 55: Oil price (USD per barrel) 106 104

102

100 98 96 94 2012 E2013 E2014 E2015 E2016

4.3 Social and Cultural Analysis 4.3.1 Corporate Social Responsibility Increased attention towards corporate social responsibility (CSR) has emerged in the corporate world and is today a central part of DSV’s strategic planning. The Group experiences a vast number of requests from

Page 47 of 127 customers who want to see reports validating a decrease of CO2 emissions from transportation of goods and services. In 2010, the DSV target was to bring down CO2 emissions per consignment by 15% by 2015 – this has almost been achieved. In collaboration with subcontractors, particularly hauliers, they are requested by the DSV to take action on the causes of CO2 emissions. Given the size of DSV group, the bargain of such a request is easier to get through to the hauliers compared if it were a smaller actor.

4.4 Technological-Analysis As discussed in section 4.1 ‘Political and Legal Factors Analysis’ the European commission has just presented a strategy to reduce HDV fuel consumption and CO2 emissions. A computer simulation tool called

VECTO measures CO2 emissions from HDVs in order to monitor emissions. Among the alternatives to reduce HDV CO2 emissions, technical improvements to the motor (including heat recovery), transmission, aerodynamics, tyres and auxiliaries and lightweight trucks are all being discussed (European Commission). Mandatory costs are uncertain, as is the degree to which these will have an impact on DSV operations.

It has not been possible to find any information related to truck efficiency, for example the utilisation of fuel throughout previous years. However, it is believed that the explicit forecast horizon will not accomplish a lower year-to-year decrease in fuel costs per kilometres transported than the past related to truck efficiency. Moreover, fuel costs does not change country-to-country and as such it will not be strategically beneficial to consume fuel in one country rather than another (European Commission, 2014b). Labour costs (wages) may have an impact, but that impact is difficult to quantify. DSV currently has many of their hauliers from Eastern Europe, and hope that the Danish government will liberalise the market for domestic driving in the future due to pressure from the EU (Ritzau, 2014). If the Danish government allow DSV to use foreign drivers for domestic transport services it will have a current impact, as wage is a main cost driver. However, the European countries are converging more, which suggests that the wage and fuel costs will unite country to country in the very long term (European Commission, 2014b).

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4.5 The PEST-Influence Model The “PEST-Influence Model”, which assesses a range of environmental factors, is applied to identify the key drivers of change, meaning the factors that have the largest influence on the industry and thus independently impact on the analysed firm. Column (A) indicates how much each factor influences the industry DSV operates within and column (B) indicates how much the DSV will be affected. The conclusions are showed in the “Note Column”.

The PEST-Influence Model Influence on Influence on the Key drivers of change DSV (-5 to A x B Notes industry (0-10) +5) A B Economic outlook 2014 Overall, the economic health of Europe - Upward Trends support promising growth has improved and indicators forecast for the economy in a short and medium upward trends in growth of GDP, labour perspective activity and exports in the following years. The positive indicators will in the 8 4 32 medium-term have a positive impact on DSV Group.

Europe’s 2020 strategy plan: In the long-term Europe wants to grow - Smart, sustainable and inclusive growth, a beyond GDP and develop an even long-term view stronger internal market (stronger governance). Targets focus on: 1) Increasing R&D spending, 2) educational attainment 3) reduce national poverty for over 20 million people, 4) decrease emission of 7 3 21 greenhouse gases by 20 per cent, 5) increase resource efficiency by stronger networks. All in all, the long-term targets set forth a stronger Europe, which will possibly benefit DSV with increasing activity and more focus on efficiency in the market by developing better environmental conditions for transportation. CO2 emissions from commercial vehicles Statistical evidence suggests commercial are projected to increase towards 2030 and transportation, and especially road 2050 if no actions are taken. transport, will increase significantly in the future, conspicuous as the only industry in Europe. The European Commission will now take action on heavy trucks. An action plan should first monitor and report on the vehicles, for 4 -3 -12 example the VECTO programme. In the medium term, mandatory limits on average emissions by newly registered HDVs are a current ongoing discussion. The argument is strong: with less or no actions the emission levels will not show the expected results. Mandatory intervention can hurt DSV’s operations.

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Chapter V Strategic Analysis of DSV Group A/S 5. Introduction to Chapter Five This chapter endeavours to analyse the DSV’s competitive advantage and growth strategies based on the findings from Chapter 1 (Section: ‘Strategic Approaches of Freight Forwarders’) and Chapter 2 (‘Financial Analysis’). Ansoff’s growth model and Porter’s generic strategies are used to analyse the strategic advantage and growth directions of the DSV. All findings from this chapter will contribute to the internal part of the forthcoming SWOT-analysis and supplement information when estimating the growth prospects.

5.1 Ansoff’s Growth Model Ansoff’s growth matrix distinguishes between four growth strategies that a company pursues according to the nature of the industry and firm-specific characteristics (cf. Appendix K). The strategic path may vary depending on the situation of the company. For instance, if the customer desires new product solutions, DSV needs to carry out ‘internal acts’ in order to avoid losing their customer to a competitor. I would argue that this is the case with the DSV. The Group is continuously shifting between three types of growth strategy (market development, product development, and market penetration) due to a highly competitive environment, and in order to stay on course the company cannot commit its focus to just one area of the growth matrix. In other words, it appears that the DSV follows an overall corporate strategy which is revised year-to-year depending on the conditions of the environment and the opportunities in the market. In addition, the corporate strategy changes depending on division: for example, DSV Road has a different growth strategy to DSV Air & Sea. The three types of growth strategies will be elaborated below, division-by- division.

5.1.1 Diagnosis of Market development 5.1.1.1 Market Development – Air & Sea freight DSV Group has started to focus on new markets in South America (DSV-GL Latin America SA covering Argentina, Chile and Peru) and North America, and has recently expanded into Africa and Asia with the acquisition of Swift freight group in 2012. Jens Bjørn Andersen, DSV CEO, has declared that future orientations for air & sea freight are on emerging markets (Duelund & Mortensen, 14). Andersen further clarifies that the Group are looking for possible acquisitions outside Europe on overseas markets (Shippingwatch, 13). As such, DSV must also pursue a market development strategy in a future context – expansion into new geographic areas, according to Ansoff’s (1985) four-dimension matrix model. However, the DSV is not alone when it comes to expansion into emerging markets. The two larger competitors Kuehne + Nagel and Panalpina are also positioned in Asia, Africa and South America within air & sea freight on a larger scale. As was argued in section 2.4.3, ‘Benchmark of Transport Volume’, the DSV volume capacity

Page 50 of 127 was much lower by comparison to its peers. At present, DSV are more equipped to develop in overseas markets than before due to their growing size internationally. According to Jens Bjørn Andersen it is less challenging to consolidate with firms within air & sea freight, which facilities the expansion strategy for DSV in overseas markets (Duelund & Mortensen, 14). Therefore, it evident that DSV will continue to grow through its expansion in emerging markets.

5.1.1.2 Market Development – Road Freight In the market for road freight, DSV is highly concentrated in the European market and their activities are very small in other parts of the world. With a strong and a more dominant position in Europe, DSV may be ready to expand into other continents. Jens Bjørn Andersen states that the focus is currently on North America and South East Asia for the road division (Duelund & Mortensen, 14). However, Kuehne + Nagel has already made its first move outside Europe with its important acquisition of Grupo Eichenberg in Brazil in 2011. Entering new markets will require more effort to succeed. In other words, the acquisition of firms in the United States creates different challenges than acquisitions in the European markets. For example, in 2001, Deutsche Post acquired DHL in order to enter the American market. This was also a case with many failures and unexpected costs. From the beginning of the US market entry, in 2011, Deutsche Post DHL found the American market far tougher and with a very different competitive landscape, consisting of two strongly positioned companies: FedEx and UPS. It also consisted of a different road infrastructure, which affected the service quality. This forced DHL to shut down its express-delivery service in America in 2008 (The Economist, 2008). Based on this discussion, it is expected that DSV Road will grow less in new markets.

5.1.2 Diagnosis of Product Development In addition, it is claimed that product development strategies are a part of the DSV growth strategy by focusing on existing customers. Competition for customers is an ongoing circle with demand for continuous improvements in transport services. To maintain and conquer new customers in existing markets the pressure forces DSV to offer diversified services and attractive prices including: transport quality (frequency, goods comfort etc.), environmental impact (emissions, noise, accidents etc.) and marketing channels (CSR branding). Hence, customers value a number of parameters that can have the ending effect in the selection of freight forwarder. As the DSV literature says, “Customers are the cornerstone of DSV, and we want to grow and develop with our customers” (Annual report 2013, pp. 6). Furthermore, DSV pronounce, “The transport and logistics market is constantly moving, which poses great demands on a company like DSV to continuously refine and improve products and services” (Annual Report 2012, pp. 5). For example, DSV have introduced a new delivery system called ‘Daily Pallet’ offering daily product deliveries across their European network (DSV, 13).

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5.1.3 Diagnosis of Market Penetration Besides an upcoming focus on emerging economies, the European market is nonetheless the most important for the Group. Market penetration is explicitly understood by taking market shares from competitors in which, to some extent, can be quantitatively verified by comparing volume increases at DSV with market rates. Therefore, DSV are compared with the market (competitors are included in the charts). However, volume growth can also be derived from new markets, and it is important to emphasise where DSV has made their recent acquisitions to make sure that volume growth is from current markets where DSV are positioned.

5.1.3.1 Volume Growth – Road Freight The road division indicates an increase in consignments. Figure 57 shows how DSV outperform market rates, which signals that DSV surpasses the market rates development. Increase in volume compared to market growth must, all else being equal, indicate expansion in Europe with recent acquisitions of Ontime Logistics from Norway in 2013 and AWT Cechofrachts from the Czech Republic in 2012. Figure 58 illustrates the total number of consignments calculated for a quantity of freight forwarders (refer to section 2.4.5 ‘Estimation of Road Freight’). Based on volume, DSV and Kuehne + Nagel have a similar capacity while Panalpina have the smallest volume capacity. However, the number of consignments is not exact but estimated to give an illustration.

Figure 57: Consignments y/y change, pct. Figure 58: Number of total consignments

14,0% 2.500.000 12,0% 10,0% 2.000.000 8,0% 1.500.000 6,0% 1.000.000 4,0% 2,0% 500.000 0,0% 0 2010 2011 2012 2013 -2,0% 2010 2011 2012 2013 DSV Market DSV Panalpina Kuehne+Nagel

5.1.3.2 Air & Sea Freight – Remain High Concentration on Europe The picture changes somewhat in air & sea freight where the focus is twofold: 1) market penetration and 2) market development. Firstly, from an historical point of view the market volume has focused on the European market with geographical exposure of 63% of total sales. The remaining part is obtained from Asia-Pacific and America with 21 and 16%, respectively. In 2013, DSV acquired Airmar Cargo SA from South America generating 45 million DKK in revenue yearly. This indicates that the air & sea freight seek expansion outside Europe. However, the focus is still on the existing markets in Europe. For example, in 2013 DSV consolidated with two other firms to supplement the divisions activities, namely: Seatainers in

Page 52 of 127 which has a yearly turnover of 1.000 million DKK and SBS Worldwide with a turnover of 450 million DKK, in Europe.

Figure 59 demonstrates airfreight volume. Kuehne + Nagel exceeds peer- and market rates with an average growth rate of 11% during the period 2010 to 2013. Kuehne + Nagel are closely followed by DSV with an average growth rate of 8.4% in the same period. DB Schenker Logistics and DHL Logistics have negative year-to-year growth signalling a loss of market shares. In fact, DSV has been taking market shares during the period as they exceed market growth. A different pattern is observed in sea freight. In Figure 60 Kuehne + Nagel exceeds both peers and market rates on average, equivalent to 9.1% from 2010 to 2013. Panalpina grows by 81% on average and DSV grows by 7% on average in the same period. Consequently, DSV captures new market shares within sea freight but is still positioned below its peers (see section 2.4.5 for a detailed description).

Figure 59: Airfreight volume change y/y. Figure 60: Sea freight volume change y/y. 35,0% 20,0% 30,0%

25,0% 15,0% 20,0% 15,0% 10,0% 10,0% 5,0% 5,0% 0,0% 2010 2011 2012 2013 0,0% -5,0% 2010 2011 2012 2013 -10,0% -5,0% Kuehne +Nagel Market growth tonnes Kuehne +Nagel Market growth tonnes DB Schenker logistics DSV DB Schenker logistics DSV Panalpina DHL logistics Panalpina DHL logistics

Figure 61 shows airfreight in tonnes from 2010 to 2013. The figure verifies that the two other peers surpass DSV in volume. Kuehne + Nagel is the largest and leading player in the market. In 2013, Kuehne + Nagel achieve yearly transport of 1.134.000 tonnes in airfreight and 3.578.000 TEU in sea freight. However, DSV is below the two international competitors. In 2013, DSV achieve volumes of 259.365 tonnes in airfreight and 772.142 TEU in sea freight. This indicates that DSV has potential for growth in volume.

Figure 61: Airfreight volume, tonnes Figure 62: Sea freight volume, TEU 1.200.000 4.000.000 3.500.000 1.000.000 3.000.000 800.000 2.500.000 600.000 2.000.000 1.500.000 400.000 1.000.000 200.000 500.000 0 0 2010 2011 2012 2013 2010 2011 2012 2013 Panalpina DSV Kuehne + Nagel Panalpina DSV Kuehne + Nagel

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5.2 Porter’s Generic Strategies Michael Porter has defined three generic strategies, where each supports distinct directions to obtain competitive advantage. The transport industry is primarily standardised in moving products from one place to another, which indicate that DSV must pursue costs efficiency in order to increase profitability. According to Grant (2010) mature industries have less scope for differentiation advantages due to buyer knowledge, product standardisation, less production innovation, sluggish demand growth and international competition. Instead, mature industries exploit economies of scale to bring down variable costs (Grant, 2010). That means every level in the organisation needs to work to bring down costs. On the other hand, the tasks within the transport industry are somewhat complex. Among many things the transport industry contains many niche suppliers, which differentiate through specialisation within a specific industry (retail, pharmaceuticals etc.), through customer requirements (comfort, transit time and CSR desires) or geographic areas/routes (Eastern European, Southern Europe). This verifies the possibility of a differentiation strategy to a certain level, but cost is still the main strategic factor.

5.2.1 Diagnosis of Differentiation Strategy Relating to previous findings from Ansoff’s growth model it was concluded that DSV pursues a product development strategy. The development strategy is a result of customer’s reliability and comfort requirements. This is only done to stay align with competitors in the fragmented market with continuous pressure on prices and costs. As such, differentiation strategies are not the DSV’s competitive advantage but function as a supplement to the overall corporate strategy. Instead, it is assessed that the competitive advantage is cost-focused due to the correlation of increasing volume identified and lower costs change year- to-year.

5.2.2 Diagnosis of Cost Leadership Strategy To verify DSV’s cost leadership as a competitive advantage, a holistic approach is conducted. The two figures below illustrate the profit margins for road, air and sea freight. Both figures confirm that DSV has higher operating margins than its peers. Figure 63 shows the profit margin for air & sea freight. It verifies that DSV has an upward trend of around 7% compared to the main competitors, which have around 4%. Figure 64 illustrates that DSV has an increasing profit margin within road freight of around 4%, which surpasses its competitors. The relatively high margins are either a result of lower costs effects (costs focused strategy) or higher mark-up on services (differentiation strategy). Low costs can be achieved by increase in efficiency and productivity while a higher mark-up on services is achieved by differentiated services. In section 2.3 ‘Growth Analysis - Common Size and Trend Analysis’ it was demonstrated that DSV’s ‘staff costs’ and ‘other external costs’ are lower compared to peers, which explained their high margins.

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Figure 63: Profit Margins air & sea freight, Figure 64: Profit Margins road freight

5,0% 8,0% 4,0% 7,0% 3,0% 6,0% 2,0% 5,0% 1,0% 4,0% 0,0% 3,0% 2010 2011 2012 2013 -1,0% 2,0% -2,0% 1,0% -3,0% -4,0% 0,0% 2010 2011 2012 2013 -5,0%

DSV (DK) Kühne + Nagel (CH) DSV (DK) Kühne + Nagel (CH) Panalpina (CH) DHL logistics (DE) Panalpina (CH) DHL logistics (DE) DB Schenker logistics (DE) DB Schenker logistics (DE)

Besides the attention to fixed costs, it is moreover vital to investigate the development in costs per unit. According to Michael Porter, a company which has the lowest costs in the industry per unit (i.e. consignment) pursue a costs leadership strategy (Grant, 2010). The costs per unit depend on the product mix, transport routes and frequencies, storage location and size. The development in direct costs per consignment, tonnes and TEU for road, air and sea freight will now be interpreted. However, costs per consignment are not accurate for all peers, therefore DSV costs development is the only one interpreted.

5.2.3.1 Costs Road Figure 65 measures costs per consignment. On average DSV has a small change in variable costs per year, equivalent to -1.0% on average from 2010 to 2013, which verifies their costs focus. DSV states in their annual reports how they: 1) enhance transport routes by relocation terminals at the right spots 2) integrate and optimise new IT-systems, 3) implement new strategies such as ‘operational excellence 1-2’ and 4) focus on financial risk management. In addition to this, the DSV claims that: “process optimisation and tight cost management have always been of high priority at DSV” (DSV, 12 pp. 5).

Figure 65: Costs per consignment, change y/y pct. Figure 66: Costs per consignment, DKK.

15,0% 14.000 12.000 10,0% 10.000 5,0% 8.000 0,0% 6.000 2010 2011 2012 2013 4.000 -5,0% 2.000 -10,0% 0 -15,0% 2010 2011 2012 2013 DSV DHL logistics Kuehne+Nagel Panalpina DSV DHL logistics Kuehne+Nagel Panalpina

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The competitive advantages are evidently the cost advantages since the focus is on decreasing costs year-to- year. Hence, the focus is on economy of scale (lower costs per unit, decreases with experience) and scope (higher volume), suggested by the amount of acquisitions to achieve volume. For example, DSV has announced that they want to turn around small and middle-sized family owned companies in the European road market into more profitable companies (Zigler, 2013). However, DSV only consolidate with firms if they have the right potential and costs synergies to support the exiting business model and product mix focus (Rasmussen, 2014). Hence, many firms are avoided even though the Group has the resources.

5.2.3.2 Costs: Air & Sea Freight The costs development from airfreight is demonstrated in Figure 67. The development shows that DSV has diminished costs per tonne in comparison to its peers. DSV follows the same pattern as the peers but DSV costs decrease more year-to-year, except from 2012 to 2013 where costs increase. As previously mentioned, Jens Bjørn Andersen says that DSV will improve productivity and efficiency in both air & sea freight. By comparison to road, more new initiatives will be implemented to optimise air & sea freight since the segment has been less prioritised compared to the road division (Duelund & Mortensen, 2014). In sea freight all peers follow the same pattern year-to-year, which can indicate the impact of freight rates and oil prices.

Figure 67: Airfreight cost per tonne, change y/y pct. Figure 68: Sea freight cost per TEU, change y/y pct. 25,0% 30,0% 20,0% 20,0% 15,0% 10,0% 10,0% 5,0% 0,0% 0,0% 2010 2011 2012 2013 2010 2011 2012 2013 -10,0% -5,0% -10,0% -20,0% Panalpina DSV Kuehne +Nagel DSV Kuehne +Nagel Panalpina The two figures below show the development in volume for air & sea freight in cost per tonne and TEU. In airfreight, DSV has the highest cost per tonne handled in 2010 compared to its peers. In 2013, the costs are slightly reduced compared to Kuehne + Nagel in which costs increase during the period. Regarding sea freight, DSV surpasses peers for all years in the period. This shows that DSV on average pays more per TEU than peers during the period. Generally, Kuehne + Nagel and DSV utilise the same cost per unit for both sectors. This could indicate that they pursue a similar product mix, for example the same routes between Asia and Europe where DSV has the highest exposure with 40% of the total routes.

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Figure 69: Airfreight cost per tonne (DKK). Figure 70: Sea freight cost per TEU (DKK)

30.000 14.000 25.000 12.000 20.000 10.000 8.000 15.000 6.000 10.000 4.000 5.000 2.000 0 0 2010 2011 2012 2013 2010 2011 2012 2013 Panalpina DSV Kuehne + Nagel Panalpina DSV Kuehne +Nagel

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5.3 SWOT Analysis Findings from previous chapters will enable a SWOT-Analysis. The SWOT-Analysis functions as a collection tool and a summary of the assignment’s environmental, industry, and internal analyses, and identifies the most important strategy drivers for change. The following SWOT-Analysis states the current strengths, weaknesses, opportunities, and threats of the DSV and its environment. Figure 71 Internal Strengths Weaknesses Road freight: Road freight: - Costs leadership in Road freight - High concentration on the European market and low focus o Attractive development in consignment y/y, pct. on emerging economies. For example, peers look outside o Low fixed costs (tight costs structure) Europe with consolidation overseas - Superior volume development compared to market - - Strong network in Europe Sea freight: o Attractive location of warehouses and terminals - Contemporary high exposure to trade lanes between Asia and Europe (40 pct.) Sea freight: - Highest gross profit per TEU compared to peers Group level: o Indicate strong product mix - Contemporary low concentration on air & sea freight - Change in organisation structure from decentralised to Airfreight: centralised - Highest gross profit per tonne compared to peers o Increase in transaction costs can be expected. o Indicate strong product mix - Costs leadership in airfreight o Development y/y, pct. superior to peers - Superior volume development compared to market

Group - Efficient and optimised cost structure in comparison to peers - profit margin surpass peers o Indicating a streamlined organisation as well as a successful asset-light strategy. o Strong risk- and operational management - Magnitude track-record of successful consolidations - Differentiation through CSR initiatives (CO2 emission) External Opportunities Threats Road freight Road freight - Horizontal integration e.g. use joint ventures as a springboard - Hidden costs in the integration process of consolidations. approach to consolidate with firms outside Europe - Low growth from the European markets - Future regulations on HDV’s (road transportation) and Sea freight hence increasing costs on operations - Increase in ship capacity (larger vessels equals lower costs) - Rail transportation more attractive in regards to CO2 emission – long term threats of some cannibalization Airfreight - Price pressure from a highly competitive market - Fuel optimisation of planes Group Solutions - Fluctuations in currencies (oil prices), commodities, - Increase of intermodal transportation freight rates, and the economy as a whole - European economy is still unhealthy. Group - Statistics emphasis on economic Europe a promising development - Europe is constantly focused on enhancing the internal markets – 2020 Strategy - Acquisitions within air & sea freight in emerging markets - Vertical integration with third-parties (for example retailers) - Increase of intermodal transportation

Source: Author’s own creation Page 58 of 127

Chapter VI Forecasting of DSV Group

6. Introduction to Chapter Six This chapter aims to forecast the long-run value drivers that are consistent with the investigations in previous chapters which analysed the growth potential of the DSV Group and the freight forwarding industry over the past few years. Hence, the key findings are utilised in this chapter with the purpose of providing a detailed forecast of DSV’s future growth. The explicit forecast period is set to five years where each year is thoroughly and individually examined by analysing previous developments and trends. The terminal period uses a perpetuity discount model, also known as the Gordon growth model, and is applied from 2019 and 2020 where it is assessed that DSV has reached ‘steady state’ (constant growth). Moreover, the terminal period applies a static and simplistic approach due to the high uncertainty that cash flows continue eternally. The structure of this chapter starts by forecasting revenue drivers for each division to conduct thorough and most accurate forecasts according to previous chronology. Consequently, forecasts for the income statement and balance sheet are conducted on group level.

6.1 Length of Forecast Period Numerous articles in the literature debate the length of time that is most appropriate to apply in the explicit forecast period. In a survey investigation by Holm, Petersen and Plenborg (2005) of non-public listed companies the participants (consultants, investment bankers, private equity and auditors) were asked which length is normally applied before the company reaches ‘steady state’ - the terminal period. The answer is twofold and varies from participant to participant. However, in generally it is recommended to adjust the length with the growth opportunities exiting in the industry (Holm, Petersen & Plenborg, 2005). In other words, if the industry has reached a mature state, for example it exhibits constant ROIC, as seen at the DSV Group, the length of the budget period is shorter compared to a growth industry. However, Koller, Goedhart and Wessels (2010) suggest a forecast period of 10 years to increase the accurateness of the valuation. This framework can result in some challenges if a line-item approach of revenue growth is applied in the valuation, as it is in this thesis of air, sea and road freight. Moreover, Damodaran (2006) suggest a forecast period of 5-10 years depending on the size of the firm, existing growth rate and competitive advantage. Consequently, of this discussion it is assessed that a forecast period of five years reflects the growth potential of DSV Group. Additionally, the historical evidence supports this claim with a constant ROIC and Profit Margin, which are partly influenced by the matured market conditions and strong market position. Contrary to the DSV Group, B&O a Danish high premium television and high-tech corporation, are exposed to constantly cyclical movements and a turnaround condition (Møller, 2005). As such, it can be difficult to

Page 59 of 127 reach steady state mode within five years where it would be preferable to use 10 years as suggested by Koller, Goedhart and Wessels (2010).

6.2 Terminal Growth Rate When a detailed framework has conducted the growth prospects in the forecast period, the second step of the model is the terminal period. The terminal period accounts on average for 60 – 80 per cent of the valuation if a DCF model is applied (Petersen & Plenborg, 2012). Consequently, the growth rate has a magnifying effect and one should be cautious when estimating the eternal growth rate due to high uncertainty of future earnings. The available equations related to computing the terminal period assumes abnormal returns in perpetuity, which are theoretically impossible in the long-term. Empirical investigations support the claim of abnormal returns to converge over time (Dechow, Hutton & Sloan, 1999; Huyett and Viguerie, 2005) and thereby oppose available models estimating the terminal value. The applied model to estimate the terminal value in this thesis relies on the Gordon growth model and the assumptions behind it. In order to determine Gordon’s growth rate, it is vital that this does not exceed the economy’s long-term equilibrium for the nominal growth rate, at least in the end the company (DSV) will become the whole economy – means if DSV continues surpasses the growth of GDP (Møller, 2005). Another concern is related to the time period of the terminal period since it typically falls in the first year. A two-year period is favoured instead since it reflects a more accurate estimate in perpetuity (Petersen and Plenborg, 2012). The terminal growth rates are estimated to be 2.5%, a low nominal growth rate. The real growth rate would equal 1.5%1 with an inflation of 1.3% – just above the real GDP growth of 1.3% in the eurozone in 2013 (European commission, spring report 2014).

6.3 Inflation As discussed in the last section, the investigation of Holm, Petersen and Plenborg (2005) assessed that the forecasts and terminal growth rate applied by practitioners depends on the growth from the industry (real growth) and the inflation rate. This means that nominal terms are favoured in the forecast period, which is in addition suggested by Koller, Goedhart and Wessels (2010). The historical terms from the market analysis in Chapter three are assumed to be in nominal terms. This is a reasonable assumption given that the inflation rate is at a low level equal to 1.3% (cf. Appendix J). To be consistent between the free cash flow and the discount rate, WACC are in nominal terms. Consequently, the risk free rate is also in nominal terms.

1 (1 + the nominal rate) / (1 + the real rate), equals to (1 + 2,5) / (1 + 1,3) – 1 * 100

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6.4 Forecasting Assumptions – Transport Ratios In section 2.4 ‘Segmental Analysis of Freight Forwarders’, transport ratios of the freight forwarding industry were conducted to distinguish volume, revenue and direct cost relations. As previously mentioned, an increase in volume does not necessarily lead to an increase in revenue or costs per unit, neither does an increase in revenue lead to a likewise (or constant) increase in costs per unit, it depends, among other things, on the product mix. Hence, volume and transport ratios are forecasted independently of each other based on the historical evidence of developments and trends as verified in section 2.4. In other words, a line-item approach is favoured to a sales approach after direct costs, as suggested by Petersen and Plenborg (2012) and Koller, Goedhart and Wessels (2010). A sales-driven approach reflecting the expected level of activity and ensures better connection between investments and expenses than a line-item approach in which assess items individually (Ibid). As such, the combine method will comprehend more value drivers than a classic template and benefit with a more refined forecast (cf. Appendix L). On the other hand, Koller, Goedhart and Wessels (2010) distinguish between a top-down (market shares, product prices and total market) and bottom-up (demand from customers) method. The top-down approach is recommend for mature industries (Ibid). However, many thoughts have come to mind when assessing the forecasts. In this context, I decided to avoid forecasting revenue based on geographical level since these will assumedly vary in product mix, market from market. In addition, it has not been possible to find volume from continent to continent. The likelihood estimating future growth, continent from continent will, as a result, increase the uncertainty of the forecasts. This particularly concerns the air and sea freight division, which have activities outside Europe and is advocated to increase their role overseas as argued in section 5.1.1.1 ‘Market Development – Air & Sea freight.

6.4.1 Revenue Growth - Airfreight It has been verified in section 2.4.4.1 ‘Transport Ratios in Airfreight’ that the airfreight industry has stabilised to pre – crisis conditions, which mean more shipments swifts from sea- to airfreight again. The historical evidence has concluded that the Group’s air division has an unchanged product mix with revenue and costs per TEU, representing 0.3 and 1.3% change on average. Furthermore, the development in volume shows a constant development above market growth equally to 8.4% average. The airfreight division currently pursues a twofold strategy: market penetration and market development strategy with the strongest focus on the first mentioned. Based on previous facts the strategic growth agenda will be somewhat unchanged. In other words, the main growth markets will be in Europe, and the Group will continue to initiate consolidations with firms in overseas markets. As previously initiated, DSV Group has an attractive product mix. The strategic assessment has concluded that it will be as such in the forecast period. With no particular change in product mix, the volume is expected to increase due to economic stability and positive market prospects prior to the previous period, as emphasised in the ‘Market Analysis’ Chapter three and

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‘Environmental Analysis’ Chapter four. Figure 72 illustrates the explicit forecast and terminal period based on the above claims. It should be noticed that the average between 2010 and 2013 are highly influenced by the results from 2010.

Figure 72: Avg. Airfreight (DKKm) 2010 2011 2012 2013 '10-13 E2014 E2015 E2016 E2017 E2018 E2019 E2020 Revenue 8.180 8.336 8.234 8.198 8.237 8.387 8.580 8.778 8.981 9.188 9.306 9.417 - Change y/y pct. 35.6% 1.9% -1.2% -0.4% 9.0% 2.3% 2.3% 2.3% 2.3% 2.3% 1.3% 1.2% Direct costs 6.465 6.534 6.373 6.399 6.443 6.514 6.631 6.750 6.871 6.995 7.142 7.285 - Change y/y pct. 42.7% 1.1% -2.5% 0.4% 10.4% 1.8% 1.8% 1.8% 1.8% 1.8% 2.1% 2.0% Gross profit 1.715 1.802 1.861 1.799 1.794 1.873 1.950 2.028 2.110 2.193 2.165 2.132 - Change y/y pct. 14.2% 5.1% 3.3% -3.3% 4.8% 4.1% 4.1% 4.0% 4.0% 4.0% -1.3% -1.5% Chapter two - financial analysis Volume tonnes 248.797 262.362 259.057 259.365 257.395 264.552 269.843 275.240 280.745 286.360 291.514 296.762 - Change y/y pct. 29.2% 5.5% -1.3% 0.1% 8.4% 2.0% 2.0% 2.0% 2.0% 2.0% 1.8% 1.8% Revenue/Tonnes 32.878 31.773 31.785 31.608 31.608 31.703 31.798 31.893 31.989 32.085 31.925 31.733 - Change y/y pct. 4.9% -3.4% 0.0% -0.6% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% -0.5% -0.6% Direct costs/Tonnes 25.985 24.905 24.601 24.672 25.041 24.622 24.573 24.524 24.475 24.426 24.499 24.548 - Change y/y pct. 10.4% -4.2% -1.2% 0.3% 1.3% -0.2% -0.2% -0.2% -0.2% -0.2% 0.3% 0.2% Gross profit/Tonnes 6.893 6.868 7.184 6.936 6.970 7.080 7.225 7.369 7.514 7.659 7.425 7.185 - Change y/y pct. -11.7% -0.4% 4.6% -3.4% -2.7% 2.1% 2.0% 2.0% 2.0% 1.9% -3.1% -3.2% Chapter three - market analysis Market growth: - Asia Pacific 2.8% 3.6% -0.9% 1.8% 1.8% 3.0% 1.5% 0.4% 2.1% 1.7% 1.7% - Europe -4.1% 8.8% -1.7% -1.0% 0.5% 0.8% 2.3% 2.0% -0.2% 1.8% 1.3% - United States 2.9% 7.4% -2.0% -3.3% 1.3% 3.6% 1.9% -3.4% 0.0% 0.8% 0.6% Market growth. volume: - Asia Pacific 3.0% -1.3% -1.2% 2.3% 0.7% 0.9% 1.2% 0.3% 1.9% 1.1% 1.1% - Europe -2.6% 4.2% -2.2% 0.4% 0.0% -0.8% 2.2% 1.7% -0.3% 1.6% 0.9% - United States 18.7% -0.6% -3.5% -3.1% 2.9% 3.8% 2.1% -3.2% 0.2% 1.0% 0.8% Chapter four - environmental analysis GDP Change y/y 2.0% 1.7% -0.3% 1.2% 0.9% 1.6% 1.8% 1.9% 1.9% 1.9% 1.8%

6.4.2 Revenue Growth - Sea freight It was proven in section 2.4.4.2 ‘Transport Ratios in Sea freight’ that DSV has performed slightly above market rates on average, representing a growth of 7.0% from 2010 to 2013. In addition, it has been shown that the sea freight division has a firmly unchanged product mix during the period with revenue and costs per TEU equivalent to 4.3 and 5.9 % on average. Compared to the other two divisions in the Group. The volume development in sea freight has performed with less lucrative rates compared to market development. Similar trends are identified for the peers (refer to section 2.4.3.2 ‘Trends and Development Sea Freight Volume’). Based on those facts. The sea freight segment will follow the historical development and trend patterns in regards to product mix since it is already superior to the competitors, see section 2.4.4.2 ‘Transport Ratios in Sea Freight’. It is expected that volume growth will continue to develop above market rates and with higher rates due to the future predictions of higher growth during the forecast horizon. In Chapter five, ‘Strategic Analysis of DSV Group A/S’, it was shown that sea freight was pursuing a twofold growth strategy: market development and market penetration strategy. This growth strategy will remain constant within sea freight and DSV will therefore continue expanding in emerging markets. Figure 73 below shows the forecast and the terminal growth rate for sea freight.

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Figure 73: Sea Avg. freight (DKKm) 2010 2011 2012 2013 '10-13 E2014 E2015 E2016 E2017 E2018 E2019 E2020 Revenue 11.224 10.590 11.622 11.997 11.358 12.467 12.967 13.501 14.070 14.677 15.029 15.389 - Change y/y pct. 41.0% -5.6% 9.7% 3.2% 12.1% 3.9% 4.0% 4.1% 4.2% 4.3% 2.4% 2.4% Direct costs 9.144 8.302 9.214 9.498 9.040 9.811 10.144 10.498 10.875 11.277 11.593 11.931 - Change y/y pct. 51.4% -9.2% 11.0% 3.1% 14.1% 3.3% 3.4% 3.5% 3.6% 3.7% 2.8% 2.9% Gross profit 2.080 2.288 2.408 2.499 2.319 2.656 2.823 3.003 3.195 3.400 3.436 3.459 - Change y/y pct. 8.2% 10.0% 5.2% 3.8% 6.8% 6.3% 6.3% 6.4% 6.4% 6.4% 1.0% 0.7% Chapter two - financial analysis Volume TEU 707.193 727.861 725.806 772.142 733.251 799.167 827.937 858.571 891.196 925.953 949.102 972.829 - Change y/y pct. 18.9% 2.9% -0.3% 6.4% 7.0% 3.5% 3.6% 3.7% 3.8% 3.9% 2.5% 2.5% Revenue/TEU 15.871 14.549 16.013 15.537 15.493 15.599 15.662 15.724 15.787 15.851 15.835 15.819 - Change y/y pct. 18.5% -8.3% 10.1% -3.0% 4.3% 0.4% 0.4% 0.4% 0.4% 0.4% -0.1% -0.1% Direct costs/TEU 12.930 11.406 12.693 12.301 12.333 12.276 12.252 12.227 12.203 12.178 12.215 12.264 - Change y/y pct. 27.3% -11.8% 11.3% -3.1% 5.9% -0.2% -0.2% -0.2% -0.2% -0.2% 0.3% 0.4% Gross profit/TEU 2.941 3.143 3.318 3.236 3.160 3.323 3.410 3.497 3.585 3.672 3.620 3.555 - Change y/y pct. -9.0% 6.9% 5.5% -2.4% 0.2% 2.7% 2.6% 2.6% 2.5% 2.4% -1.4% -1.8% Chapter three- market analysis Market growth: - Asia Pacific 9.2% 4.8% 3.3% 4.9% 5.6% 5.3% 5.3% 5.7% 5.9% 6.1% 5.7% - Europe 7.9% 5.1% -1.6% 0.2% 2.9% 3.6% 3.3% 3.6% 3.8% 3.9% 3.6% - United States 7.8% 2.6% -2.0% 4.4% 3.2% 2.7% 3.4% 3.7% 3.7% 3.8% 3.5% Chapter four - environmental analysis GDP Change y/y 2.0% 1.7% -0.3% 1.2% 0.9% 1.6% 1.8% 1.9% 1.9% 1.9% Author’s own creation

6.4.3 Revenue Growth - Road Freight In section 2.4.5.2 ‘Transport Ratios in Road Freight’, it was concluded from historical evidence that the road freight division has an unchanged product mix during the period. This was verified by the development of revenue and costs per consignment on average, equivalent to -1.2 and -.9%, respectively. Moreover, the development in volume shows a stable development above market growth, representing 5.6% on average in the period. Based on the findings, this trend will remain unchanged in the forecast period if the Group perseveres with its main growth strategy towards the European markets – a market penetration strategy. With regards to expansion in markets outside Europe it is currently unknown whether or not the Group will pursue a market development strategy, with North America as a potential target. Recall from section 1.1.2 ‘Acquisitions’ DSV that indicates future consolidations of European small and middle-sized family owned freight forwarding companies, with the purpose of developing their assets into higher profitability. The European market is highly fragmented and the growth opportunities within in Europe are still significant. DSV’s market share in 2012 was approximately 2.1% on the global scale (Journal of Commerce, as cited in DSV annual report 2013) and 1.8% in the European market for road freight according to DHL estimates (DHL, 13). In other words, the Nordic and European markets remain potential growth targets for the DSV road segment (Raun, 13). Figure 74, below, shows forecasts for road freight.

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Figure 74: Road Avg. freight (DKKm) 2010 2011 2012 2013 '10-13 E2014 E2015 E2016 E2017 E2018 E2019 E2020 Revenue 21.103 22.641 22.654 23.116 22.379 23.969 24.852 25.769 26.771 27.811 28.673 29.561 - Change y/y pct. 8.7% 7.3% 0.1% 2.0% 4.5% 3.7% 3.7% 3.7% 3.9% 3.9% 3.1% 3.1% Direct costs 16.998 18.361 18.308 18.817 18.121 19.531 20.271 21.040 21.880 22.753 23.434 24.111 - Change y/y pct. 11% 8% 0% 3% 5.3% 3.8% 3.8% 3.8% 4.0% 4.0% 3.0% 2.9% Gross profit 4.105 4.280 4.346 4.299 4.258 4.438 4.581 4.729 4.891 5.058 5.239 5.449 - Change y/y pct. 1.1% 4.3% 1.5% -1.1% 1.5% 3.2% 3.2% 3.2% 3.4% 3.4% 3.6% 4.0% Chapter three - financial analysis Volume 1.731.731 1.873.733 1.892.470 1.968.169 1.866.526 2.046.896 2.128.772 2.213.923 2.306.907 2.403.798 2.480.719 2.560.102 - Change y/y pct. 12.0% 8.2% 1.0% 4.0% 6.3% 4.0% 4.0% 4.0% 4.2% 4.2% 3.0% 3.2% Revenue/ consignment 12.186 12.083 11.971 11.745 11.996 11.710 11.675 11.640 11.605 11.570 11.558 11.547 - Change y/y pct. -0.8% -0.9% -1.9% -1.2% -0.3% -0.3% -0.3% -0.3% -0.3% -0.1% -0.1% Direct costs/ consignment 9.816 9.799 9.674 9.561 9.712 9.542 9.522 9.503 9.484 9.465 9.447 9.418 - Change y/y pct. -1.1% -0.2% -1.3% -1.2% -0.9% -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% -0.3% Gross profit/ consignment 2.370 2.284 2.296 2.184 2.284 2.168 2.152 2.136 2.120 2.104 2.112 2.129 - Change y/y pct. -10.8% -3.8% 0.5% -5.1% -4.8% -0.7% -0.7% -0.7% -0.7% -0.7% 0.3% 0.8% Chapter four - market analysis Market growth: - Europe 2.0% 1.7% -0.3% 0.2% 0.9% 2.7% 3.9% 3.6% 4.0% n.a. 2.8% Market growth. volume: - Europe 4.7% -0.3% -1.3% 1.3% 1.1% 2.9% 3.4% 3.4% 3.9% n.a. 3.6% Chapter five - environmental analysis GDP Change y/y 2.0% 1.7% -0.3% 1.2% 0.9% 1.6% 1.8% 1.9% 1.9% 1.9% Author’s own creation

6.4.4 Forecasted Income Statement At this point, the study of individual items has been conducted and the focus will now shift towards a sales- driven approach with focus on main costs drivers such as operating expenses, including: ‘other external expenses’, ‘staff costs’, ‘amortization and depreciations’ and ‘special items’. However, one can notice special items, which are often referred to as restructuring costs related to acquisitions and which are considered a non-operating expenses and non-recurring items. However, it is verified empirically that new consolidations will likely appear in the future and therefore special items be will forecasted along with other costs drivers as a proxy from historical information equal to 0.02% of revenue.

It was confirmed in section 2.3 ‘Growth Analysis - Trend and Common Size Analysis’ that the DSV Group operating margins (EBIT, EBITDA and NOPAT) surpass their peers, due to lower operating expenses. Factors that can explain their higher performance include the Group’s efficient network, excellent costs programmes, and optimal asset-light business model. It is clear that the relation between operating expenses and revenue has improved slightly from 2010 to 2013. However, it is assessed that the historical development has yet to experience its full potential. The Group initiated its business strategy ‘operational excellence 1’ in 2012, and ‘operational excellence 2’ in 2013. The main focus of the two strategic initiatives are to reduce fixed costs (reduce the amount of employees) and optimisation of the network (routes). As such, each cost driver will be elaborated in detail below.

‘Other external expenses’ include marketing, IT, other rent, education, travel, and so forth. These costs are strongly related to revenue and will therefore be forecasted as a percentage of revenue. It is expected that these will follow the historical performance, representing 4.6% on average.

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It has been verified that ‘staff costs’ (excluding production staff e.g. hauliers) develop at a constant rate with a 0.1 average percentage point improvement during the period. In 2010 and 2011 the level of revenue is 10.9% and in 2012 and 2013 it is 10.8%. It is assessed that DSV can diminish staff costs further through optimisation of the workforce through initiatives such as ‘operational excellence 2’ since operational 1 has proven successful. The staff costs are expected to represent 10.7% of revenue during the forecast period.

Koller, Goedhart and Wessels (2010) recommend two methods to forecast ‘amortization and depreciations’, either as a percentage of revenue or percentage of property, plant and equipment (PP&E) where the first mentioned is preferred. It is reasonable to assume that the amortization and depreciation will be equal to the historical evidence, representing 1.3% of revenue on average, since there is no indication of any upcoming change.

The effective tax rate is straightforward since it has been close to the marginal tax rate, with the exception of 2009 where it was 58 %. However, there are some deviations between the marginal tax rate and the effective tax rate. A main explanation for this is that operating losses can be passed forward and consequently the Group can save taxes in future periods (Petersen and Plenborg, 2012). DSV Group has both deferred tax assets and liabilities, which might explain why the effective tax rate is different from the marginal tax rate. Given that the Group are taxed by the rules of corporate tax law in Denmark, the marginal tax rate is favoured in this regard. In Denmark, the corporate tax rate is currently 25%, and it is expected to diminish gradually towards 2016. It will diminish to 24.5% in 2014; 23.5% in 2015; and 22% in 2016.

The net financial expenses consist of ‘interest expenses’ and ‘interest income’. Net-borrowing costs are calculated by taking ‘net financial expenses’ divided by ‘net-interest bearing debt’. According to Koller, Goedhart and Wessels (2010), forecasting net financial expenses is conducted by taking net financial expenses as a percentage of net-interest bearing debt (total debt) in order to avoid circularity that leads to implementation problems. During the period, the average is 5%. It is assessed that the past historical development on average will continue. Figure 75 below summaries the above-mentioned.

Figure 75: Sales-driven approach F2014 F2015 F2016 F2017 F2018 F2019 F2020 Cost of goods sold as percentage of revenue 78.1% 78.0% 77.9% 77.8% 77.7% 77.9% 78.2% Other external expenses as a percentage of revenue 4.6% 4.6% 4.6% 4.6% 4.6% 4.6% 4.6% Staff costs as a percentage of revenue 10.7% 10.7% 10.7% 10.7% 10.7% 10.7% 10.7% EBITDA-margin 6.6% 6.7% 6.8% 6.9% 7.0% 6.8% 6.5% Special items -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% Amortisation and depreciations as a percentage of revenue 1.3% 1.3% 1.3% 1.3% 1.3% 1.3% 1.3% EBIT as a percentage of revenue 5.1% 5.2% 5.3% 5.4% 5.5% 5.3% 5.0% Net financial expenses percentage of revenue 0.7% 0.6% 0.6% 0.6% 0.6% 0.6% 0.6% Efficient tax rate 24.5% 23.5% 22.0% 22.0% 22.0% 22.0% 22.0% Depreciation as % of intangible and tangible assets 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% Author’s own creation

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6.4.5 Forecasted Balance Sheet With a solid foundation of the forecast assumptions with regards to the income statement, attention is now turned towards the balance sheet. Assets and liabilities, non-current and current, and equity will be described in depth.

6.4.5.1 Non – current Assets ‘Intangible assets’ and ‘property, plans and equipment’ (PP&E) are forecasted as a percentage of revenue suggested by Koller, Goedhart and Wessels (2010). ‘Intangible assets’ record, among other things, goodwill and acquired intangibles when the price paid for an acquisition surpasses the targets book value (Ibid). Forecasting future acquisitions is a difficult task and Koller, Goedhart and Wessels (2010) suggest it is to be avoided due to existing literature documenting how a typical acquisition fails to create value. According to them, goodwill should be held constant. However, if one chooses to believe that acquisitions are recurring and, in addition, add growth to the firm, a percentage fraction can be estimated. In general, the ‘intangible assets’ as a percentage of revenue had been constant during the period, equal to 20% on average. It is assessed that the ‘intangible assets’ will continue at this level and therefore equal 20% in the forecast period. With regards to ‘PP&E’, the margin has diminished gradually over the analysed period. In 2010, the PP&E margin is 11% and in 2013 it is 8%. This may indicate that DSV has sold parts of their warehouses or terminals. The level from 2013, a margin of 8%, is believed to remain for the rest of the forecast period. This is a reasonable assumption due to previous evidence suggesting that the DSV group will continue to strengthen their asset-light business model.

‘Other non-current assets’ consist of ‘deferred taxes’ and ‘investment in associates’. Since both items are recognised as an operating asset, they are forecast as a percentage of revenue. Deferred taxes show an upward trend during the period equal to 0.9 as a percentage of revenue. For simplicity, this fraction will be held constant during the forecast period (Petersen & Plenborg, 2012). Investment in associates is a minor item and is generally hard to forecast (Ibid). This is also forecast as a constant fraction, equal to 0.04 as a percentage of revenue during the period.

6.4.5.2 Current Assets ‘Trade receivables’, ‘forwarding in progress’ and ‘other receivables’ are forecasted by using a percentage of revenue. Each item has fluctuated during the period, which is why average terms for the period between 2010 and 2013 are applied as a forecast approximation.

6.4.5.3 Equity The DSV group is highly cash generating and the excess cash is distributed to the shareholders primarily through share buybacks and a minor part as dividends. Consequently outstanding shares have diminished

Page 66 of 127 from 210.4 million in 2010 to 180 million shares by 2013. On the other hand, dividends have increased gradually each year since 2010 from 105 million DKK to 270 million DKK in 2013 (DSV, 13). Accordingly, this increases the payout ratio (dividends per share divided by net earnings per share) (cf. Appendix M). Given that dividends and share buybacks are part of total equity, including other comprehensive income and net earnings, it increases the complexity to forecast the change in equity. For example, ‘other comprehensive income’ consists of hedging and currency instruments, which can be difficult to predict. As such, forecasting dividends is avoided, as well as share buybacks and other comprehensive income, as suggested by Koller, Goedhart and Wessels (2010). Hypothetically, if forecasts were going to be conducted in this regard, it is believed that dividends and share buybacks will increase in the future (cf. Appendix M hypothetical forecasts).

6.4.5.3 Current and Non-current Liabilities Current operating liabilities such as ‘trade and other payables’, ‘forwarding in process’, and ‘deferred tax liabilities’ will be treated as percentages of revenue. The average margin over the historical period is preferred and therefore applied, representing 14.9%, 3.0% and 1.1%, respectively. In the analytical balance sheet ‘interest bearing assets’ consist of, among others, ‘cash and cash equivalents’, which are forecasted as a margin of invested capital, as suggested by Petersen and Plenborg (2012). Furthermore, it is suggested that ‘interest bearing liabilities’ (including ‘financial liabilities’ and ‘pensions and provisions’) are forecasted as a percentage of invested capital. If you take the differences between interest bearing liabilities and interest bearing assets you obtain net-interest bearing debt (NIBD). Therefore, NIBD is applied as a percentage of invested capital, representing a margin of 58 percent. Figure 76 summarises the elaborated forecast factors from the balance sheet:

Figure 76: Investment drivers F2014 F2015 F2016 F2017 F2018 F2019 F2020 Intangible and tangible assets as percentage of revenue 28.0% 28.0% 28.0% 28.0% 28.0% 28.0% 28.0% - Tangible assets as percentage of revenue 8.0% 8.0% 8.0% 8.0% 8.0% 8.0% 8.0% - Intangible assets as percentage of revenue 20.0% 20.0% 20.0% 20.0% 20.0% 20.0% 20.0% Other non-current assets as a percentage of revenue 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% - Deferred taxes 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% - Investments in associates 0.04% 0.04% 0.04% 0.04% 0.04% 0.04% 0.04% Non - current assets as a percentage of revenue 29.0% 29.0% 29.0% 29.0% 29.0% 29.0% 29.0% - Trade receivables 17.0% 17.0% 17.0% 17.0% 17.0% 17.0% 17.0% - Forwarding in progress 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% - Other receivables 2.1% 2.1% 2.1% 2.1% 2.1% 2.1% 2.1% Current - assets 20.5% 20.5% 20.5% 20.5% 20.5% 20.5% 20.5% - Trade- and other payables 14.9% 14.9% 14.9% 14.9% 14.9% 14.9% 14.9% - Forwarding in process, liabilities 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% - Deferred tax liabilities 1.1% 1.1% 1.1% 1.1% 1.1% 1.1% 1.1% - Corporate tax 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% Net working capital as a percentage of revenue 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% Financial drivers Net interest bearing debt as percentage of invested capital 58.0% 58.0% 58.0% 58.0% 58.0% 58.0% 58.0% Net financial expenses as a percentage of NIBD 5.0% 5.0% 5.0% 5.0% 5.0% 5.0% 5.0% Author’s own creation

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6.5 Budget Control For Group Level The purpose of this section is to illuminate the forecasted value drivers to ensure consistency between the historical development and the assessment from the strategic and financial analysis. Figure 77 shows the budget control for DSV Group.

Figure 77: Budget control 2010 2011 2012 2013 E2014 E2015 E2016 E2017 E2018 E2019 E2020 Revenue Growth 17.9% 2.7% 2.4% 2.3% 3.5% 3.6% 3.7% 3.8% 3.8% 2.5% 2.5% ROIC before tax 11.5% 13.2% 11.8% 13.1% 12.9% 13.2% 13.7% 13.9% 14.2% 13.6% 13.0% EBIT margin 5.16% 5.55% 5.04% 5.30% 5.1% 5.2% 5.3% 5.4% 5.5% 5.3% 5.0% Turnover ratio 3.09 3.28 3.32 3.34 3.31 3.31 3.31 3.31 3.31 3.31 3.31 Author’s own creation

The revenues are expected to rise, which stems from previous assumptions of increase in economic activity (GDP) and the claim that transport activity will follow same promising trend above GDP. The growth at the DSV increased above GDP and the transport activity and it is believed to do likewise in the budget period.

ROIC are estimated to perform better than the historical development. Since invested capital is a constant fraction of revenue, the PM explains the positive change in ROIC. In my opinion, the slight improvements in the operating profit are due to the fact that DSV will continue follow its cost leadership strategy with further reduction in costs through strategic initiatives as seen from the historical period. For example, ‘staff costs’ and improvements in the respective division’s activities. The ROIC and PM falls in the terminal period, f2019 and f2020, to what it is believed that the DSV Group will perform and earn in eternity.

The turnover rate is held as a constant ratio equal to 3.3 during the period. This is due to the assumption that net interest bearing debt will remain as a percentage of revenue during the period equal to 58% – with the assumption based on the average development from 2010 to 2013.

Figure 78: ROIC, pct. Figure 79: Profit-Margin (EBIT), pct. Figure 80: Turnover rate 14,5% 5,80% 3,40 14,0% 5,60% 3,35 13,5% 5,40% 3,30 13,0% 5,20% 3,25 12,5% 5,00% 3,20 12,0% 4,80% 11,5% 4,60% 3,15 11,0% 4,40% 3,10 10,5% 4,20% 3,05 10,0% 4,00% 3,00

2011 2012 2013

2011 2012 2013

E2015 E2014 E2016 E2017 E2018 E2019 E2020

E2014 E2015 E2016 E2017 E2018 E2019 E2020

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Chapter VII Valuation of DSV Group

7. Introduction to Chapter Seven The last chapter of this thesis explores the factors necessary to conduct a theoretical fair value of DSV. As has previously been described, in order to value a firm by using the enterprise value approach one must discount the free cash flow by the weighted average cost of capital (WACC) - the opportunity cost of capital. The focus of this chapter will be to give a detailed explanation behind the calculation of WACC, since it has a large impact on the theoretical stock price of the DSV Group.

7.1 Weighted Average Costs of Capital WACC represent, as the name implies, the costs of capital that the investor requires as a minimum on their investments in the firm. In other words, it is the required rate of return the firm generates on its investments in assets. Theoretically it accounts for the risk of both the after-tax cost of debt and cost of equity. The following formula defines how WACC is calculated (Petersen and Plenborg, 2012):

푁퐼퐵퐷 퐸 푊퐴퐶퐶 = ∗ 푟 ∗ (1 − 푡) + 푟 (푁퐼퐵퐷 + 퐸) 푑 (푁퐼퐵퐷 + 퐸) 푒

Where, ‘NIBD’ represents net interest-bearing debt, ‘e’ represents equity, ‘rd’ represents required rate of return on debt, ‘re’ represents the required rate of equity and finally ‘t’ represents the corporate tax rate. Each component will be elaborated in detail. First, the costs of equity are estimated and afterwards the costs of debt.

7.1.1 Costs of Equity Capital The costs of equity are estimated by using a favoured asset-pricing model. The best-known asset pricing model is the ‘Capital Asset Pricing Model’ (CAPM) developed by Sharpe (1964), Lintner (1965) and Mossin (1966). The model emphasises that an investor should consider two important things when investing in a security: the risk premium of the portfolio and secondly the security beta. In general there is strong support among Chief Financial Officers (CFOs) to use the capital asset pricing model (CAPM) according to Graham & Harvey (2001). They surveyed 392 CFOs and one of the findings revealed that the majority of the survey participants (73.5%) strongly favoured the use of CAPM model. However, this does not prove the model to be the best one to estimate the cost of capital, according to Fama & French (1992) this is the three-factor model. Given that CAPM establish simple means for investors (relatively easy to use) and given the central purpose of this thesis there will not be any further discussion relating to the shortcomings of the assumptions

Page 69 of 127 behind CAPM and so forth. The CAPM is determined by three factors: the risk-free rate (Rf), the risk premium (Rm - Rf ) and the systematic risk on equity (βi – systematic risk or leveraged beta) and are calculated as following: Re = Rf + βe*(Rm - Ri). Each parameter of the CAPM formula will below be elaborated:

7.1.2 Risk Free Rate According to Koller, Goedhart and Wessels (2010), Petersen and Plenborg (2012) and Damodaran (2006), it is suggested to apply a government bond as a proxy for the risk free rate. There is general support for using a 30-year or 10-year government bond. The 10-year government bond is applied since it reflects yield to maturity more efficiently than a 30-year government bond, which can vary due to illiquidity over the period (Petersen and Plenborg, 2012). DSV Group is strongly concentrated on the European market, which is why a 10-year German Eurobond is applied as a risk-free rate. In addition, this bond has high liquidity and low default likelihood compared to other European countries (Koller, Goedhart and Wessels, 2010). As of July 2014, the 10-year government benchmark bond yields 2.2% (ECB, 14).

7.1.3 Risk Premium The risk premium is the extra return an investor can achieve by shifting the investment from a risk free asset to an averagely risky asset. The more risk adverse the investor is the more he or she demands in premium. There is in theory high uncertainty about the ¨true value¨ of the risk premium and such rate does therefore not exist in practice (Petersen and Plenborg, 2012). There are generally two approaches in the literature to finding the risk premium: survey studies targeting investors, academics and institutional investors (professionals) explaining which risk premium they prefer, and studies relating to the historical return approach from stock indexes. Studies show that especially the risk premium varies from country to country (for example USA against Japan), and that risk premium varies depending on how it is estimated. For instance, the historical return of stocks, treasury bonds and treasury bills can be computed by use of the arithmetic (simple mean average) or geometric (compound average) mean where the geometric generally compute lower returns (Koller, Goedhart and Wessels, 2010). In addition, the time period can have a biased effect since some periods might experience crisis and other conflicts.

For those interested, Damodaran’s paper ‘Equity Risk Premiums (ERP): Determinants, Estimation and Implications’ (from 2013) explores the literature in depth. For simplicity, Dimson, Marsh and Staunton (2013) study is applied in this thesis whereas the survey study of Fernandez, Aguirreamallo & Pablo (2013) of academics, analysts and managers was highly considered. Dimson, Marsh and Staunton (2013) estimates the risk premiums for 17 markets in the period 1900 – 2012, representing a risk premium of 5.1% in Europe if an arithmetic average is selected. This rate seems reasonable since Koller, Goedhart and Wessels (2010)

Page 70 of 127 believes the market risk premium continuously lies between 4.5 and 5.5%. Further discussion related to the risk premium downside- and bias effects will not be included in this thesis.

7.1.4 Estimation of Beta The final input of the CAPM model is beta, often referred to as the firm specific risk. Beta measures the co- variation between the company stock returns and the market portfolio returns. In other words, it quantifies a company’s stock sensitivity to market movements. Beta can be measured within an interval of 0 and 1. If beta for the specific firm is above 1 it tends to magnify the overall movements of the market. Conversely, if beta is in the range between 0 and 1 it tends to move in the same direction as the market, but not completely (Brealey, Meyers and Allen, 2011).

Beta is estimated using a regression analysis comparable with the market. The raw regression will include monthly returns to avoid systematic biases and the period will include five years of observations according to Koller, Goedhart and Wessels (2010). However, Alexander and Chervany (1980) assess the optimal interval and stability of beta. In their research they conclude that the optimal interval to estimate beta is between four and six years, which is consistent with findings of previous studies (Gonedes, 1973). Therefore, it is a reasonable assumption to use an interval of five years with monthly returns in order to reduce standard errors when estimating beta through a regression analysis. The time period is from January 2009 to May 2014 with data gathered from Bloomberg.

A well-diversified market index such as the MSCI World Index (high correlation with S&P 500 Index) is chosen instead of a local country index since this may be exposed to heavily weighted industries. For example, in the Danish equity market wherein DSV is listed, healthcare is weighted with 60% of the index’s total and industries 18% (Credit Suisse, 13). Based on the above discussion a Single-Index Model estimates the best linear relation between the historical returns on the asset (RDSV) and the market portfolio (RMSCI World

Index). The regression model is as following: 푅퐷푆푉 =∝ +훽푅푀푆퐶퐼 푊표푟푙푑 퐼푛푑푒푥 + 휖 See appendix N for regressions.

However, estimating beta can be an inaccurate process due to high standard errors in the regression analysis. To improve the accuracy of the beta estimation, an industry beta is computed to enhance the validity. It is suggested to compute an unlevered industry beta and hereafter relevering the industry beta to DSV’s current capital structure (Koller, Goedhart and Wessels, 2010). Unlevering shows what a firm’s beta would be if it were all equity, namely the operating risk of the firm. Modigliani and Millers (1963) (M&M) beta relation formula undo the effects of leverage. The model assumes that debt is constant in perpetuity, and there will be no growth. M&M’s beta relation is as following: βL = βU*(1+(1-Tc) * D/E). Alternatively, the beta relation of

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Harris & Pringles (1985) can be applied: βL = βU * (1+D/E). The model is based on the assumption that debt in relation to equity capital is constant over time (constant capital structure) (Hansen and Plenborg, 2003). M&M beta relation is chosen since this is widely applied among professionals according to Koller, Goedhart and Wessels (2010). Figure 81 shows the different regression betas derived from Kuehne + Nagel, Panalpina, DHL and DSV. All betas above 1 indicate that freight forwarders are exposed to a larger risk than the market. This confirms previous observations made throughout this thesis about the industry’s operating and financial risk exposure to broad economic factors and a fiercely competitive landscape.

Figure 81: (DKKm) Regression betas D/E Ratio Tax rate Beta equity (Unleverage) DSV 1.41 0.23 0.25 1.20 Kuehne + Nagel 0.75 0.04 0.18 0.73 Panalpina 1.13 0.07 0.18 1.07 DHL 1.13 0.17 0.30 1.01 Average 1.1 0.13 0.22 1.01 Author’s own creation

When the respective betas are unleveraged using the equation: βu = βL/(1+Tc)*D/E), the average unleveraged beta equals 1.01. As such, the average unleveraged beta (equity financed) is used as a proxy when estimating the leveraged beta for DSV: βL= 1.0*(1+(1+0.245)*0.23 which gives a beta estimate equal to 1.18. This is the sensitivity of the return on DSV stocks to the return on the MSCI Index. Hence, DSV’s stock returns are not perfectly correlated with the market returns. It is subject to specific risk. Other things being equal, for each percentage point the market return increases (or decreases), the return on DSV stocks will increase (or decrease) by 1.18 percentage points. Therefore, DSV’s beta shows higher sensitivity (risks) compared to the movements in the market. This is accordance with the strategic analysis since DSV business risk are exposed to a large amount of risk factors such as freight rates, oil prices and a fierce competitive environment that pressures prices on transportation.

7.1.5 Capital Asset Pricing Model Applied to DSV Group When all the parameters from the CAPM model are estimated the costs of equity for DSV Group can be calculated as following 2.2+1.18*5.1 = 8.2 per cent. Using the CAPM model the expected return on DSV’s stock equals 8.2%. On the security market line, beta would symbolise the slope and the expected returns are placed on the vertical axis on the coordinate plane.

7.1.6 Cost of Debt Capital In order to compute the interest rate on debt the following formula is suggested by Plenborg and Petersen

(2012): rd = (rf + rs)*(1-t). Here, rd represents the required rate of return on NIBD, rf represents the risk free rate, rs the credit spread and t represents the corporate tax rate. The findings of the respective factors will be emphasised below.

7.1.6.1 Credit Spread The return of debt measures the current costs to fund loans from banks and credit institutions to finance the firms’ assets (Damodaran, 2006). An important factor when estimating the return on debt is the computation

Page 72 of 127 of the credit spread (risk premium on debt), which measures the additional compensation from the risk-free interest rate for a loan e.g. the risk premium on debt. Credit rating agencies such as Standard and Poor’s (S&P) and Moody’s compute firms ratings regularly. Petersen and Plenborg (2012) have demonstrated a method to calculate the credit spread on the basis of a firm’s credit rating by computing financial ratios (cf. Appendix O). Using this method, DSV achieve a credit rating equivalent to an “A” security. The average spread of an “A” rated security is 2.2% according to Petersen & Plenborg (2012). Furthermore, DSV announce the yearly weighted average effective interest rate from bank loans and credit institutions, representing 2.0% on average from 2010 to 2013. The effective interest rate is applied by professionals according to Plenberg, Petersen & Holm (2005). It is assessed that a credit spread equal to 2.2 is a reasonable assumption.

7.1.6.2 Tax Rate The last term required to calculate the return on net interest bearing debt is the tax rate. Damodaran (2006) suggests using the marginal tax rate instead of the efficient tax rate – the difference between the marginal tax rate and efficient tax rate has been discussed in the forecast section. The margin tax rate of 24.5% is used in the calculation of WACC.

7.1.6.3 Cost of Debt for DSV Group

The cost of debt is computed by utilising the above-mentioned variables: Rd = (0.022+0.022)*(1-0.245) = 3.3%. It can be concluded that the DSV Group yields an after costs of debt equivalent to 3.3%. However, there are alternative ways to estimate the costs of debt. Koller, Goedhart, and Wessels (2010) define costs of debt as the yield to maturity on the long-term debt if a company has issued corporate bonds. Another approach is to estimate the net borrowing costs but this is not an accurate method according to Petersen and Plenborg (2012), as previously discussed in section 2.2 ‘Financial Leverage and Net Borrowing Cost’. However, if the net borrowing costs are adjusted for operating leases, then the DSV Group obtains 3.0% on average in the period 2010 to 2013, which indicates that the costs of debt for the DSV Group is based on reasonable assumptions.

7.1.7 Weighted Average Costs of Capital for DSV Group The factors to estimate WACC have been analysed and are now ready to be applied. Using the equation from section 7.1, a WACC of 6.7% is represented:

7.427 32.058 WACC = ∗ 3.3 ∗ (1 − 0.245) + ∗ 8.2 39.485 39.485

The net interest bearing debt consists of the book value from 2013, which are assumed as market values. The total value of DSV is calculated by taking the total shares outstanding multiplied by the DSV stock price as

Page 73 of 127 of the 1st July. A target WACC is suggested by Koller, Goedhart and Wessels (2010), which either can be an estimate or based on the contemporary capital structure. DSV’s capital structure has changed continually since 2009 where, for example, the debt to value (D/V) ratio has change from approximately 30% to 19% (cf. Appendix P). It is believed that the contemporary capital structure is a reasonable assumption to apply. However, the capital structures of the competitor do range from DSV with much less debt, which is primarily are due to their asset-light balance sheets.

7.2 DCF Valuation The aim of this section is to conduct the valuation of DSV by use of the enterprise value and economic value added valuation models advocated in section 0.1.7 ‘Valuation Approaches’. The valuation models will consist of the estimates from the forecast analysis in Chapter six. The logic behind the DCF and EVA approach are to convert the forecasts of future earnings into an expected market value of DSV.

The enterprise value approach represents the free cash flows to the firm (FCFF), which are discounted to present value. The free cash flow is calculated according to the method of Petersen and Plenborg (2012) (cf. Appendix P). In order to find the FCF, the operating income (NOPAT) is adjusted for no cash flow effects (depreciations) and changes in net working capital; hence one obtains the cash from operating activities. The next step includes the investments in non-current assets, computed by the change in intangible and tangible assets minus depreciations. These are as such adjusted for operating activities to achieve cash flow after investments, also referred to as the firm’s free cash flow. In order to calculate the present value of the FCFF, the free cash flows are discounted with the WACC rate of 6.7%. The discount factor is calculated as 1/(1+WACC)^n. For example, in 2014 the formula would equal 1/(1+6.7)^0, representing 0.94 as a discount factor for year 1, 0.88 for year 2, and so forth. Discounting the FCFF in all years of the forecast horizon equals 7.497 DKKm. Moreover, the terminal rate is calculated according to the Gordon Growth model as elaborated in section 6.2 ‘Terminal Growth Rate’. The terminal year f2020 are computed by FCFF (1.840) divided by WACC minus the assumed terminal growth rate of 2.5%. The terminal period represents 29,984 DKKm. The sum of the forecast horizon and terminal period yields an estimated enterprise value of 37,480 DKKm. To calculate the expected market value of equity the net interest bearing debt is deducted from the enterprise value and equals 30.053 DKKm. DSV only has common stocks outstanding, which means there is no need to adjust for preferred shares from the total equity value. Figure 82 shows the elaborations:

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Figure 82: DKKm Forecast horizon Terminal period Discounted cash flow model 2014f 2015f 2016f 2017f 2018f 2019f 2020f Free cash flow to the firm (FCFF) 1.229 1.437 1.553 1.639 1.735 1.885 1.840 WACC 6.7% 6.7% 6.7% 6.7% 6.7% 6.7% 6.7% Discount factor 0.94 0.88 0.82 0.77 0.72 0.68 0.64 PV, FCFF 1.152 1.263 1.279 1.266 1.257 1.280 PV of FCFF in forecast horizon 7.496 PV of FCFF in terminal period 29.984 Estimated enterprise value 37.480 Net interest-bearing debt 7.427 Market value of group equity 30.053 Number of shares, mil. 180 DSV stock price pr. share 167,0 Author’s own creation A similar procedure is conducted for the EVA valuation approach. However, there are some differences when calculating the enterprise value. The value is determined by the invested capital and present value of future EVA’s. Figure 83 shows the forecast horizon, terminal period and the market value of equity.

Figure 83: DKKm Forecast horizon Terminal period Economic value added model 2014f 2015f 2016f 2017f 2018f 2019f 2020f NOPAT 1.840 1.952 2.101 2.219 2.342 2.300 2.264 Invested capital, primo year 13.675 14.286 14.801 15.349 15.930 16.536 16.951 WACC 6.7% 6.7% 6.7% 6.7% 6.7% 6.7% 6.7% ROIC. beginning of period 13.5% 13.7% 14.2% 14.5% 14.7% 13.9% 13.4% Spread 6.8% 7.0% 7.5% 7.8% 8.0% 7.2% 6.7% EVA 925 1.000 1.115 1.196 1.280 1.198 1.134 Discount factor 0.94 0.88 0.82 0.77 0.72 0.68 PV of EVA 867 879 918 924 927 813 Invested capital, primo year 13.675 PV of EVA in forecast horizon 5.328 PV of EVA in terminal period 18.471 Estimated enterprise value 37.475 Net interest-bearing debt 7.427 Market value of equity 30.048 Number of shares, mil. 180 DSV stock price pr. share 167,0 Author’s own creation The EVA model utilises identical market value of equity compared to the enterprise value approach. The price per share derived from both methods represents a value of 167,0 DKK, which is slightly below the actually stock price equal to 178,1 DKK per share as of July 1st 2014. However, since this thesis involves annual numbers the stock price of 167,0 needs to be discounted forward with the costs of equity equal to 8.2% compared to the price from July 1st 2014. Furthermore, dividends are incorporated in net interest- bearing debt and equal 7.697 DKKm (dividends equal 270 million). In doing so, it is possible to get an estimated stock price of 174,8 DKK based on the assumptions of this thesis. The price of 174,9 ranges by 2% from the listed price on the market, equal to 178,1. The deviation is minor and may be a cause of the information asymmetry. Compared to other industries, for example healthcare or pharmaceuticals, stock analyst valuations differs more in their interpretations and assumptions due to the uncertain future growth prospects. This indicates DSV’s development as stable and certain in regards to future growth prospects. However, it can be concluded that the price is currently overvalued according to the listed price in the market.

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7.3 Sensitivity Analysis It is suggested by Petersen & Plenborg (2012) that a valuation should always be accompanied by a sensitivity analysis, which investigates the impact of change from core value drivers. As such, change in growth assumptions in the terminal period and the discount factor WACC leads to different stock prices. In other words, the underlying assumptions consist of some uncertainty. Figure 84 shows how the value per share for DSV alters when WACC and the growth rate change. The growth rates on the horizontal axis range from 1% to 4%. The WACC rates range from 4.5% to 10%.

Figure 84: Sensitivity Analysis Terminal growth rate WACC 1,5 2 2,5 3 4 10 66,6 71,1 75,2 82,1 96,7 9 82,1 88,3 93,9 103,8 125,4 8 102,5 111,3 119,4 134,1 168,4 7 135,2 143,4 156,0 179,7 268,5 6,7 151,9 169,2 174,8 219,1 310,4 6 170,4 191,7 213,0 255,6 383,6 5 233,5 272,2 313,8 407,6 813,8 4,5 280,9 336,6 400,3 559,5 1674,2 Author’s own creation The sensitivity analysis verifies that the estimated discount factor (WACC) has a vital influence on the theoretical share price. For example, the estimated share price ranges from 75,2 DKK to 400,3 DKK when the terminal growth rate is held constant equal to 2.5% and WACC is floating from 10 to 4.5%. The terminal growth has a similar influence. For instance, when the estimated WACC are held constant at 6.7 and the growth rates are floating from between 1.5 and 4%, it ranges from 151,9 DKK to 310,4 DKK. Extreme cases are also conducted in Figure 84 with a growth rate of 4 % and a WACC of 4.5%. These two factors obtain a range of differences in the theoretical share price between 280,9 DKK and 1.674,2 DKK. It can be concluded that from the sensitivity analysis that the role of the WACC and the growth factors in determining the theoretical fair value are crucial. The estimation can therefore vary from individual subjectivity and the study applied. For example, WACC consists of many inconsistent variables such as the risk premium (studies are not inconsistent), beta (the risk of the stock varies depending on time period applied), risk free rate (varies depending on period of the valuation) and costs of debt.

7.4 Multiples In section 0.1.7.2 ‘Relative Valuation Approaches’ it was assessed that EV/EBITDA is applied in this case study. The EV/EBITDA multiples are conducted based on forward-looking estimates as suggested by Koller, Goedhart and Wessels (2010). Empirical evidence verifies that forward-looking multiples based on operating profits are more accurate than the historical ones. This implies that EBITDA for 2014 is used as a proxy when building the EV/EBITDA since this gives a more accurate picture for the long-term growth prospects of the firm. The freight forwarding industry is considered as a cyclic industry, meaning that it performs well when the economy is growing and poorly when it stagnating. However, during the historical period analysed,

Page 76 of 127 the DSV has managed to stay on track due to their asset-light strategy. As such, it is assessed to be a reasonable assumption to use forecasts for EBITDA as a proxy for the long-term growth prospect.

Figure 85: DKKm Expected enterprise value EBITDA 2014f EV/EBITDA Kuehne + Nagel 83.347 6.237 13,4 Panalpina 19.164 1.167 16,4 DHL Logistics 262.744 32.721 8,0 Average 12,6 Author’s own creation The forecasts of EBITDA are an average estimate from 19 reputable banks gathered from the Bloomberg terminal. By calculating the average EV/EBITDA ratio for the selected peers, one obtains a multiple of 12.6. This benchmark ratio is below the competitor Kuehne + Nagel, which was assessed to have similar product mix and performance. It seems reasonable to apply a lower multiple for DSV, since Kuehne + Nagel’s ROIC surpasses the DSV’s.

Applying the benchmark multiple to DSV’s forecasted EBITDA of 3.147 million DKK yields an enterprise value of 39.666. In order to obtain the total market value of equity the net interest bearing debt of 7.427 is deducted, leaving 32.239 million DKK. Dividing the estimated market value of equity by the total number of shares outstanding will obtain 179 DKK per share. This price is almost identical to the stock price conducted through the DCF approach. The minor deviation between the two approaches (multiples and DCF) can be explained by the industry; in general it follows the growth from the industry. The DSV valuation was, broadly speaking, based on the assumption that the future product mix will not change significantly during the period since it is already successful and the volume will surpass those of the market with a higher margin than the historical period due to the promising outlook.

Figure 86 shows the sensitivity of the multiple EV/EBITDA ranging between 10 and 14. It shows the DSV share price depending on the size of the multiple. Figure 86 EV/EBITDA DSV Group's Stock Price, DKK 14 204 13 186 12,6 179 11 151 10 134 Author’s own creation

7.5 Conclusion In the literature, analysing the roots of a firm is recommend when allocating investors’ savings due to asymmetric information of the assets value in the firm and incentive problems between the principal and agent. This case study has been an example of how to obtain in-depth knowledge of a firm to reduce this gap and to verify if the firm is valued according to the investor’s opportunity costs of capital. Hence, the research question of this thesis was to assess a theoretical stock price for DSV through a comprehensive valuation approach.

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After offering a background of the freight forwarding industry, their growth strategies were studied. The findings conclude that the commonly used strategies are acquisitions and logistics alliances (horizontal- and vertical alliances). Freight forwarders that concentrate on these two areas may have a distinct advantage since both facilitate the conquering of new market shares. It was shown that DSV has an important track record of acquisitions and that DSV are excellent at redeploying the assets to become more profitable if it suits the business model.

The freight forwarding industry returns on investments are below the overall average from S&P 500 companies characterising this industry as competitive. In the cross-sectional analysis it was shown that DSV have a constant pattern in their development in return on investments (12% on average), profit margin (4% on average) and asset turnover (3% on average) compared to the peers which fluctuate more with a decreasing trend year-to-year. However, DSV’s return on investments perform slightly below one of their main peers Kuehne + Nagel, but their profit margin exceeds all peer firms due to lower fixed costs from staff and other external expenses. It has been emphasised that DSV’s profit margin is higher in the air and sea freight division than in the road freight division.

The volume development in all divisions has surpassed market growth, with the exception of sea freight, which performs closely to market growth movements. The peers’ volume development outperforms that of DSV with expansion in overseas markets. The given transport ratios of revenue, costs and gross profit per unit verify that the development has been stable during the historical period, which indicate no particular change in the product mix or price pressure from the market. In the cross-sectional analysis it is shown that DSV and Kuehne + Nagel pursue a similar product mix since they have somewhat identical costs per unit in air & sea freight. DSV’s gross profit in air & sea freight surpasses its peers, which confirms a strong product mix setup. With regards to road freight estimates of transport ratios shows a stable development, with costs per unit diminishing year-to-year.

The market expectation in the freight forwarding industry shows promising growth prospects compared to the historical development, which shows a slow growth rate. Sea freight is estimated to have the highest compound annual growth rate prospects equal to 3.6% in Europe, 5.6% in the Asia-Pacific region and 3.5% from the United States. Road freight shows a moderate growth increase in Europe with 2.9% in values and 3.0% growth in volume. Airfreight has the lowest growth rate in Europe equal to 1.4% in value and 0.9% in volume.

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The environment framework concluded the future factors, which affects the freight forwarding industry in

Europe. CO2 emissions of ‘Heavy Duty Trucks’ have been statistically shown to pollute more every year, with the consequence that Europe will fail to its 2020 emissions target if no actions are taken. However, the impact was assessed not to harm the DSV Group significantly in the medium term. On the positive side, the internal markets in Europe seem to have become more liberalised with the ambitious 2020 strategy. The economic outlook in Europe looks promising with increase in activity in the labour market slowly stabilising and private and public consumption increasing. DSV’s organic growth rate is more volatile than GDP and the overall growth in the transport service industry. In periods of upturn DSV perform above market rates and in periods of downturn DSV perform worse than market rates.

The findings from previous chapters have supplemented to assess DSV’s strategic situation. Ansoff’s growth matrix shows that DSV pursues a three-fold growth strategy (market penetration, market development and product development), with the former representing the overall contributor to growth. Porter’s generic strategies verified that DSV pursues a costs leadership strategy with lower fixed costs than peers and decreasing direct costs year-to-year.

In the valuation phase the terminal growth was assessed not to surpass the growth of the economy (GDP). The length of the forecast period was determined to be five years, even if the freight forwarding industry is cyclical, and DSV’s stable development in profitability ratios justified the length of the period. A line item forecast approach was applied for revenue growth drivers in air, sea and road freight. Based on previous financial and strategy analyses, each division was forecasted. It was suggested that the airfreight division was forecast with a somewhat similar product mix, but with higher volume growth due to higher market expectations. A similar forecast pattern was conducted for sea freight. Furthermore, it was recommended that road freight was forecasted according to the historical evidence pointing to a slow decrease in revenue per unit and costs per unit, meaning no change in the product mix. However, increase in volume is also expected in road freight. A sales-driven forecast approach was applied on balance sheet items with the exception of financial drivers.

The two valuation approaches ‘the enterprise value’ and ‘economic value added (EVA) frameworks were applied to calculate the share price of DSV Group. The costs of capital were estimated to show an appropriate discount rate consisting of shareholders expectations of the future and the risk taken in buying the stock. Hence, the required rate of equity was estimated using the asset-pricing model CAPM reflecting the risk of the DSV’s stock. Furthermore, the costs of debt were derived from the credit rating model. A stock price of 174,8 DKK was achieved, which is under the value according to the listed price on the market equal to 178,1 DKK as per 01.07.2014.

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9. Appendix Appendix A: Overview of Figures: Tables and Illustrations

Figure 1: Intermodal process – pp. 14

Figure 2: European mergers and acquisitions activity – Appendix C

Figure 3: Revenue development in road freight – Appendix D

Figure 4: Growth in number of consignments compared to market – Appendix D

Figure 5: Revenue development in air & sea freight – Appendix D

Figure 6: Volume development in tonnes and TEU – Appendix D

Figure 7: Revenue development in solutions – Appendix D

Figure 8: Revenue by region – Appendix D

Figure 9: Return on equity (ROE) – pp. 21

Figure 10: Return on invested capital (ROIC) – pp. 22

Figure 11: ROIC, adjusted for leases – pp. 22

Figure 12: Profit Margin – pp. 23

Figure 13: Asset Turnover (AT) – pp. 23

Figure 14: AT, adjusted for leases – pp. 23

Figure 15: Financial Leverage – pp. 24

Figure 16: Financial Leverage, adjusted for leases – pp. 24

Figure 17: Trend analysis over the period 2010 – 2013 – pp. 25

Figure 18: Common size analysis, percentage of revenue 2013 – pp. 25

Figure 19: Common size analysis, percentage of revenue average from 2010 to 2013 – pp. 26

Figure 20: Common size analysis, percentage of revenue from 2010 - 2013, DSV Group – pp. 26

Figure 21: Profit Margin, air & sea freight – pp. 27

Figure 22: Profit Margin, road freight – pp. 28

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Figure 23: Volume in airfreight from 2010 to 2013 – pp. 29

Figure 24: Volume in sea freight – pp. 30

Figure 25: Airfreight, revenue/tonnes – pp. 31

Figure 26: Airfreight, direct costs/tonnes – pp. 32

Figure 27: Airfreight, gross profit/tonnes – pp. 33

Figure 28: Sea freight, revenue/tonnes – pp. 33

Figure 29: Sea freight, direct costs/tonnes – pp. 33

Figure 30: Sea freight, gross profit/tonnes – pp. 34

Figure 31: Volume total number of consignments – pp. 36

Figure 32: Road freight, revenue/total consignments – pp. 37

Figure 33: Road freight, direct costs/number of consignments – pp. 37

Figure 34: Road freight, gross profit/number of consignments - pp. 37

Figure 35: Market segmentation of transport service industry in Europe – pp. 38

Figure 36: Market segmentation of transport service industry in Asia-Pacific – pp. 38

Figure 37: Market segmentation of transport service industry in United States – Appendix I

Figure 38: European transportation service industry – pp. 39

Figure 39: European road transportation industry – pp. 39

Figure 40: European road transportation industry – pp. 39

Figure 41: Sea freight service industry in Europe – pp. 40

Figure 42: Airfreight service industry in Europe, billion dollars – pp. 41

Figure 43: Airfreight service industry in Europe, volume – pp. 41

Figure 44: Overview of the transportation service industry – pp. 42

Figure 45: CO2 – emission EU-28 and EU-15 – pp. 44

Figure 46: Mt CO2 equivalent – pp. 44

Figure 47: Greenhouse gas emission per industry 2000 – Appendix J

Figure 48: Greenhouse gas emission per industry 2011 – Appendix J

Figure 49: Growth in GDP, industry and DSV correlates – pp. 46

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Figure 50: Private and public consumption: upward trend signals confidence to the system – pp. 46

Figure 51: Exports is expected to growth, EU – pp. 46

Figure 52: Unemployment declines but remains high – pp. 46

Figure 53: ECB interest rates remain record-low – pp. 46

Figure 54: Inflation, (HICP Index all items) EU-28 – Appendix J

Figure 55: Oil prices (USD per barrel) – pp. 47

Figure 56: The PEST-Influence Model – pp. 49

Figure 57: Consignments y/y change pct. – pp. 52

Figure 58: Number of total consignments – pp. 52

Figure 59: Airfreight volume change y/y. – pp. 53

Figure 60: Sea freight volume change y/y. – pp. 53

Figure 61: Airfreight volume, tonnes – pp. 53

Figure 62: Sea freight volume, tonnes – pp. 53

Figure 63: Profit Margins air & sea freight - pp. 55

Figure 64: Profit Margins road freight – pp. 55

Figure 65: Costs per consignment, change y/y. pct. – pp. 55

Figure 66: Costs per consignment, DKK. – pp. 55

Figure 67: Airfreight costs pr. tonne, change y/y – pp. 56

Figure 68: Sea freight costs pr. TEU, change y/y – pp. 56

Figure 69: Airfreight costs pr. tonne (DKK) – pp. 57

Figure 70: Sea freight costs pr. TEU (DKK) – pp. 57

Figure 71: SWOT Analysis – pp. 58

Figure 72: Airfreight forecasts – pp. 62

Figure 73: Sea freight forecasts – pp. 63

Figure 74: Road freight forecasts – pp. 64

Figure 75: Sales – driven approach – pp. 65

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Figure 76: Investment drivers – pp. 67

Figure 77: Budget control: assessment of forecast assumptions – pp. 68

Figure 78: Forecasted ROIC – pp. 68

Figure 79: Forecasted Profit Margin – pp. 68

Figure 80: Forecasted Asset Turnover – pp. 68

Figure 81: Regression betas – pp. 72

Figure 82: Discounted cash flow – pp. 75

Figure 83: Economic value added model – pp. 75

Figure 84: Sensitivity analysis – pp. 76

Figure 85: Multiples – pp. 77

Figure 86: Sensitivity analysis – pp. 77

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Appendix B: Classification of Accounting Items for DSV Group A/S

Minority interests There are different interpretations about how to allocate the minority interests in the analytical balance sheet. Within the framework used in this thesis it is a question of whether minority interests are part of interest bearing debt or part of equity. According to Petersen and Plenborg, there is strong support to treat minority interests as equity capital (Petersen and Plenborg, 2012; Koller, Marc & David 2010).

Cash and cash equivalent Since cash and cash equivalent remain stable over time it is reasonable to place them as excess cash. This argues in favour of classifying cash under interest bearing assets. Firms generally have operating cash to finance upcoming investments.

Assets held for sale (and liabilities related to assets held for sale) Assets that are held for sale and converted into cash within a short time period and are not part of the continuing business should be treated as cash (i.e. part of net financials). In the note from the annual report the assets held for sale related to properties expected to be sold within a 12-month period.

Other securities and receivables The accounting item ‘other receivables’ consists of three items: 1) other securities, 2) deposits, and 3) other receivables. It is debatable whether other securities should be classified under interest bearing assets or as part of the operating assets. In the annual report 2009 note 15 it is mentioned that: “Investments in other securities are classified as available for sale. They mainly relate to unlisted shares and other equity investments”. Since the item is not part of the DVS’s core business, it is treated as a financial item. Other receivables and deposits are interest bearing, arguing that they should be classified under interest-bearing assets in the balance sheet.

Investment in associates Based on the DSV Group’s annual report, associated companies are involved in activities related to the core business. This is therefore included in the invested capital (Petersen and Plenborg, 2012). If it is not part of the core business it should be treated as a financial item.

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Pensions Pensions are typically classified as part of net interest-bearing debt (Petersen and Plenborg, 2012). In situations where pensions are recognised at present value (value in use) and continue as an interest rate until the time they are due, they will be treated as an interest-bearing debt. In the case of DSV this is recognised since operating expenses appears in the income statement. In addition, interest expenses are recognised.

Provisions Similar to pensions, provisions are measured at present value. This suggests that the item should be treated as interest-bearing debt.

Operating Leases (Off Balance Sheet) As discussed, freight forwarders do not own any assets such as trucks, ships, or airplanes. Instead, they are users of leasing contracts (DSV, 2013). Operating leasing is not recorded on the balance sheet, either as an asset or liability. It is recorded as a periodic rental expense associated with the leasing activity in the income statement (Koller, Goedhart and Wessels, 2010). As such, freight forwarders will have unusually low operating profits, since rental expenses consist of implicit interest expenses, and high capital utilization. These two effects counteract each other and as a result boost the return on invested capital (ROIC) (Ibid.). The intention is to provide an accurate picture of the ROIC of the DSV and the peers, regardless of accounting policies. In the period between 2009-2013 DSV’s operating leasing consisted of 23.7% of total assets (K+N 24.6% of total assets, Panalpina do not disclose this).

The adjustment of operating leasing is conducted based on the equation from Koller, Goedhart and Wessels (2010). The value of the leasing agreement is estimated based on the rental expense from the annual reports, cost of debt (Kd) and average asset life. The costs of debt are assumed to be constant between the peer firms; DSV costs of debt are used as a proxy.

푅푒푛푡푎푙 − 퐸푥푝푒푛푠푒푠푡 퐴푠푠푒푡 − 푉푎푙푢푒푡−1 = 1 퐾 + 푑 퐴푠푠푒푡−퐿푖푓푒

Following Koller, Goedhart and Wessels (2010) they suggest following a three-step model. 1) Capitalise the estimated leasing on the balance sheet, with a corresponding adjustment under interest-bearing liabilities. The capitalised value is calculated with the above equation. The costs of debt are estimated by the method suggested by Petersen and Plenborg, 2012. At DSV, the average leasing period used is four and a half years where land and buildings have an average lifetime of 4 years and other operational equipment 5 years (Kuehne + Nagel and Panalpina is assumed to be five years).

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The second level (2) requires adjustment of operating profit by eliminating the implied interest in rental expenses. As such, the implied interest (costs of debt multiplied with rental expenses) is added to the direct costs in the income statement in order to increase the operating profit – NOPAT. The remaining rental expenses are renamed to leasing depreciation, which are the rental expenses minus the implied interest. According to Koller, Marc and David (2010) depreciation it not related to the capital structure and it therefore remains as an operating expense. To assure consistency the implied interest is added back under net financial expenses before tax. The third part of the three-step model are not discussed further.

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Appendix C: Merger and Acquisitions Statistics

Figure 2: European M&A Activity, volume bn. dollars 1.600 1.398 1.400 1.311

1.200 1.118

1.000 840 774 786 800 750 678 646 604 625 600 505 435 379 400

200

0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Source: Author’s own creation, mergerstat.com

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Appendix D: Principal Divisions at DSV Group A/S

Figure 3: Road, DKKm Figure 4: Growth in number of consignments compared 14% with market, change y/y % 10% 24.000 12% 12% 9% 9% 23.000 8% 10% 7% 7% 22.000 8% 8% 6% 21.000 6% 6% 5% 4% 4% 20.000 4% 3% 3% 19.000 2% 1% 2% 2% 2% 0% 0% 18.000 1% 2010 2011 2012 2013 -2% -2% 0% 0% 17.000 2009 2010 2011 2012 2013 -4%

Figure 5: Air - & sea freight, DKKm Figure 6: Tonnes (Air/Dark) and TEUs (Sea/Light)

45% 25.000

40% 39% 900.000 35% 20.000 800.000 30% 700.000 25% 15.000 600.000 500.000 20% 400.000 15% 10.000 300.000 10% 200.000 5% 5% 5.000 100.000 3% 0% 2% 0 -2% 2009 2010 2011 2012 2013 -5% 0 2009 2010 2011 2012 2013

Figure 7: Solution, DKKm Figure 8: Revenue by region 2013, %

8% 5.600 6% 6% Nordics 5.400 9% 4% 3% 3% 7% 2% 2% 5.200 28% Southern Europe 0% -2% 5.000 Other Europe/ -4% EMEA -6% 4.800 Americas -8% 4.600 39% 17% -9% -10% APAC -12% 4.400 2009 2010 2011 2012 2013

Source: Author’s own creation, annual reports DSV

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Appendix E: Reorganised Income Statement, Balance Sheet and Other Leases

Note that currency conversions used in the creation of this thesis have been calculated using annual average exchange rates (income statement) and end date (balance sheet) from the following years 2008, 2009, 2010, 2011, 2012 and 2013.

DSV Group A/S

Income statement DSV Group A/S, DKKm 2008 2009 2010 2011 2012 2013 Revenue 37.435 36.085 42.562 43.710 44.912 45.710 Direct costs 29 27.187 33.242 33.891 34.858 35.705 Gross profit 8.175 8.898 9.320 9.819 10.054 10.005 Other external expenses 1.843 1.988 1.955 2.092 2.116 2.010 Staff costs 3.994 4.671 4.644 4.752 4.864 4.943 EBITDA before special items 2.338 2.239 2.721 2.975 3.074 3.052 Amortisation and depreciation 402 536 519 549 534 500 EBIT before special items 1.936 1.703 2.202 2.426 2.540 2.552 Special Items, net 78 -688 -5 0 -275 -129 Financial costs, net -404 -555 -537 -431 -246 -298 Profit before tax 1.610 460 1.660 1.995 2.019 2.125 Tax on profit for the period 377 269 466 546 589 554 Profit for the period 1.233 191 1.194 1.449 1.430 1.571 Minority interests 6 6 10 9 3 -6 Net profit to parent's shareholders 1.227 185 1.194 1.245 1.284 1.577 Effective tax rate 0,23 0,58 0,28 0,27 0,29 0,26

Analytical Income Statement, DKKm 2008 2009 2010 2011 2012 2013 Revenue 37.435 36.085 42.562 43.710 44.912 45.710 Direct costs 29.260 27.187 33.242 33.891 34.858 35.705 - Implied interest 192 198 195 217 220 227 - Lease depreciation (rental expenses minus implied interest) 937 1.086 1.141 1.074 1.241 1.243 Gross profit 8.346 9.075 9.494 10.013 10.250 10.208 Other external expenses 1.843 1.988 1.955 2.092 2.116 2.010 Staff costs 3.994 4.671 4.644 4.752 4.864 4.943 EBITDA before special items 2.509 2.416 2.895 3.169 3.270 3.255 Amortisation and depreciation 402 536 519 549 534 500 Special Items, net 78 -688 -5 0 -275 -129 EBIT - earnings before interest and tax 2.185 1.192 2.371 2.620 2.461 2.626 Taxes on EBIT 512 697 666 717 718 685 NOPAT 1.673 495 1705 1903 1743 1941 Net financial expenses before tax (implied -753 -732 -648 -466 -525 interest added back) -596 Tax savings on net financial expenses 135 428 200 171 129 131 Net financial expenses after tax -440 -304 -511 -454 -313 -370 Net earnings 1.233 191 1194 1449 1430 1571 Minority interests 6 6 10 9 3 -6 Net profit to parent's shareholders 1.227 185 1.184 1.440 1.427 1577 Effective tax rate 0,23 0,58 0,28 0,27 0,29 0,26

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Balance Sheet, DSV Group A/S DKKm 2008 2009 2010 2011 2012 2013 Intangibles 8.436 8.721 8.772 8.683 8.723 8.982 Property, plant & equipment (PPE) 5.093 4.975 4.782 4.503 4.261 3.883 Other receivables 156 105 140 170 153 147 Deferred tax assets 257 379 449 430 409 430 Total non-current assets 13.942 14.180 14.143 13.786 13.546 13.442 Trade receivables 7.356 6.098 7.155 7.112 7.238 7.469 Forwarding in progress 530 513 541 604 629 676 Other receivables 1.299 811 709 849 791 794 Cash and cash equivalents 516 367 363 367 552 707 Assets held for sale 82 211 174 16 38 12 Total current assets 9.783 8.000 8.942 8.948 9.248 9.658 Total assets 23.725 22.180 23.085 22.734 22.794 23.100 Equity and Liabilities 2008 2009 2010 2011 2012 2013 Share capital 190 209 209 190 188 180 Reserves 3.618 5.292 6.340 5.089 5.160 6.038 DSV A/S shareholders' share of equity 3.808 5.501 6.549 5.279 5.348 6.218 Non-controlling interests 49 29 36 30 37 30 Total equity 3.857 5.530 6.585 5.309 5.385 6.248 Deferred tax 429 449 576 527 411 411 Pensions and similar obligations 810 884 871 975 1.078 1.034 Provision 379 562 309 391 418 361 Financial liabilities 7.084 6.637 5.642 6.091 6.190 6.066 Total non-current liabilities 8.702 8.532 7.398 7.984 8.097 7.872 Provisions 288 373 332 215 275 242 Financial liabilities 2.973 620 593 861 923 590 Trade payables 3.855 3.755 4.195 4.350 4.385 4.537 Forwarding in progress 1.361 1.120 1.418 1.283 1.284 1.252 Other payables 2.586 2.233 2.220 2.305 2.248 2.115 Corporation tax 68 0 228 427 197 244 Liabilities relating to assets held for sale 35 17 116 Total current liabilities 11.166 8.118 9.102 9.441 9.312 8.980 Total liabilities 19.868 16.650 16.500 17.425 17.409 16.852 Total equity and liabilities 23.725 22.180 23.085 22.734 22.794 23.100

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Analytical balance sheet, DSV Group A/S 2008 2009 2010 2011 2012 2013 Operating assets Capitalised operating leases 5.807 5.999 5.909 6.582 6.653 6.893 Intangibles 8.436 8.721 8.772 8.683 8.723 8.982 Property, plant & equipment (PPE) 5.093 4.975 4.782 4.503 4.261 3.883 Investments in associates 7 9 19 26 17 Deferred tax assets 257 379 449 430 409 430 Total non-current assets 13.793 14.084 14.022 13.642 13.410 13.295 Total non-current assets, adjusted for operating leases: 19.600 20.083 19.931 20.224 20.063 20.188 Trade receivables 7.356 6.098 7.155 7.112 7.238 7.469 Forwarding in progress 530 513 541 604 629 676 Other receivables 1.299 811 709 849 791 794 Total current assets 9.185 7.422 8.405 8.565 8.658 8.939 Interest bearing assets: Other receivables 110 85 108 133 128 147 Other securities 39 11 13 11 8 Cash and cash equivalent 516 367 363 367 552 707 Assets held for sale 82 211 174 16 38 12 Total interest bearing assets: 747 674 658 527 726 866 Liabilities classified non-interest-bearing debt Deferred tax liabilities 429 449 576 527 411 411 Trade payables 3.855 3.755 4.195 4.350 4.385 4.537 Other payables 2.586 2.233 2.220 2.305 2.248 2.115 Corporation tax 68 0 228 427 197 244 Forwarding in progress 1.361 1.120 1.418 1.283 1.284 1.252 Total 8.299 7.557 8.637 8.892 8.525 8.559 Invested capital (total assets minus liabilities minus interest bearing assets) 14.679 13.949 13.790 13.315 13.543 13.675 Invested capital, adjusted for operating leases 20.486 19.948 19.699 19.897 20.196 20.568 Total equity 3.857 5.530 6.585 5.309 5.385 6.248 Calculated average equity 4.694 6.058 5.947 5.347 5.817 Interest bearing liabilities Capitalised operating leases 5.807 5.999 5.909 6.582 6.653 6.893 Provision (non-current) 379 562 309 391 418 361 Provision (current) 288 373 332 215 275 242 Pensions and similar obligations 810 884 871 975 1.078 1.034 Financial liabilities (current) 2.973 620 593 861 923 590 Financial liabilities (non-current) 7.084 6.637 5.642 6.091 6.190 6.066 Liabilities, assets held for sale 35 17 116 Interest bearing debt 11.569 9.093 7.863 8.533 8.884 8.293 Interest bearing debt, adjusted for operating leases 17.376 15.092 13.772 15.115 15.537 15.186 Other receivables 110 85 108 133 128 147 Other securities 39 11 13 11 8 Cash and cash equivalent 516 367 363 367 552 707 Assets held for sale 82 211 174 16 38 12 Total interest bearing assets: 747 674 658 527 726 866 Net-interest bearing debt (NIBD) 10.822 8.419 7.205 8.006 8.158 7.427 Calculated average net interest bearing debt 9.621 7.812 7.606 8.082 7.793 Net-interest bearing debt (NIBD) 16.629 14.418 13.114 14.588 14.811 14.320 Calculated average net interest bearing debt 15.523 13.766 13.851 14.700 14.565 Invested capital (Equity + NIBD) 14.679 13.949 13.790 13.315 13.543 13.675 Calculated average invested capital 14.314 13.870 13.553 13.429 13.609 Invested capital (Equity + NIBD), adjusted for operating leases 20.486 19.948 19.699 19.897 20.196 20.568 Calculated average invested capital 20.217 19.823 19.798 20.047 20.382

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Identical costs of debt is assumed by the author when conducting the operating leases for the respective firms. However, the new invented debt (leasing) does not have the same risk profile as the rest of the debt on the balance sheet such as debt from credit institutions. It is less risky since operating leases are secured by underlying assets (asset-backed security) e.g. trucks (Koller, Marc & David 2010). The costs of debt are taken from the credit rating assessment. The author has assumed that Kuehne + Nagel and Panalpina have identical credit ratings, and as such the same costs of debt is applied.

Reported Operational leasing, DSV Group 2008 2009 2010 2011 2012 2013 Leasing obligations on land and buildings including terminals and warehouses 0 - 1 year 767 782 791 883 928 1.180 1 - 5 years 1858 1.624 2.105 2.170 2.263 2.712 More than 5 years 1466 1.295 1.133 1.341 1.716 2.023 Total 4.091 3.701 4.029 4.394 4.907 5.915 Leasing obligations on other plant and operating equipment 2008 2009 2010 2011 2012 2013 0 - 1 year 374 352 332 337 381 416 1 - 5 years 442 386 320 370 512 616 More than 5 years 3 1 1 1 9 5 Total 819 739 653 708 902 1.037

Total leasing obligations 4.910 4.440 4.682 5.102 5.809 6.952

Total Assets 23.725 22.180 23.085 22.734 22.794 23.100 Leasing obligations in percentage of total assets 20.7% 20.0% 20.3% 22.4% 25.5% 30.1% Adjusting for operating leasing Recognised in income statement 2008 2009 2010 2011 2012 2013 2014E Operating leasing expenses related to land and buildings 811 943 1024 994 1116 1140 Operating leasing expenses related to other plant and equipment 509 539 507 514 564 558 Total 1.320 1.482 1.531 1.508 1.680 1.698 1759 Growth, y/y. pct. 3.3 -1.5 11.4 1.1 3.6 Capitalise operating leasing 5.807 5.999 5.909 6.582 6.653 6.893 Average leasing period in years 4.5 DSV Group's costs of debt 3.3% Estimations Implied interest: [costs of debt*operating leasing] 192 198 195 217 220 227 Lease depreciation 1.128 1.284 1.336 1.291 1.460 1.471 Total 1.320 1.482 1.531 1.508 1.680 1.698

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Kuehne + Nagel Group

Income statement Kuehne + Nagel Group, DKKm 2009 2010 2011 2012 2013 Net turnover 67.432 83.167 87.655 103.684 106.105 Direct costs 39.854 53.774 55.778 66.777 67.457 Gross profit 27.578 29.393 31.878 36.907 38.648 Other external expenses, net 7.700 7.918 8.377 9.496 9.636 Staff costs 15.715 16.729 18.301 21.839 23.070 EBITDA 4.163 4.953 5.286 5.178 5.942 Amortisation and depreciation 1.369 1.179 1.232 1.344 1.242 EBIT 2.794 3.774 4.054 3.834 4.701 Net financials 75 10 86 67 37 EBT 2.869 3.784 4.140 3.900 4.738 Income tax 654 799 865 921 988 Earnings for the year 2.215 2.985 3.275 2.980 3.749 Effective tax rate 0,23 0,21 0,21 0,24 0,21

Analytical Income Statement of Kuehne + Nagel Group, DKKm 2009 2010 2011 2012 2013 Net turnover 67.432 83.167 87.655 103.684 106.105 Direct costs 39.854 53.774 55.778 66.777 67.457 - Implied interest 362 419 502 507 550 - Lease depreciation (rental expenses - implied interest) 2.258 2.136 2.460 3.036 3.027 Gross profit 27.901 29.768 32.326 37.360 39.140 Other external expenses, net 7.700 7.918 8.377 9.496 9.636 Staff costs 15.715 16.729 18.301 21.839 23.070 EBITDA 4.486 5.328 5.735 5.631 6.433 Amortisation and depreciation 1.369 1.179 1.232 1.344 1.242 EBIT 3.118 4.149 4.502 4.287 5.192 Taxes on EBIT 710 876 940 1.012 1.083 NOPAT 2.407 3.273 3.562 3.275 4.109 Net financial expenses before tax (implied interest added back) -287 -410 -415 -440 -513 Tax savings on net financial expenses -56,6 -77,1 -75,7 -91,2 -94,8 Net financial expenses after tax -192 -288,1 -286,5 -295,1 -359,6 Net earnings 2.215 2.985 3.275 2.980 3.749 Exchange rate (DKKCHF), avg. per year 4,7037 4,9334 5,4048 6,0563 6,1768

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Balance sheet, Kuehne + Nagel Group , CHFm 2009 2010 2011 2012 2013 Property, plant & equipment (PPE) 1.301 1.083 1.146 1.134 1.151 Goodwill 681 590 696 694 688 Other intangibles 273 176 196 141 89 Investments in joint ventures 11 43 39 39 33 Deferred tax assets 190 166 162 195 172 Total non-current assets 2.456 2.058 2.239 2.203 2.133 Prepayments 92 93 97 109 105 Work in progress 224 253 275 306 296 Trade receivables 2.004 2.077 2.278 2.428 2.426 Other receivables 176 129 149 116 107 Income tax receivables 252 34 52 Cash and cash equivalents 981 1331 851 1083 1255 Total current assets 3.477 3.883 3.902 4.076 4.241 Total assets 5.933 5.941 6.141 6.279 6.374 Equity and Liabilities 2009 2010 2011 2012 2013 Share capital 120 120 120 120 120 Reserves 1.693 1.644 1.661 1.791 1.820 Earnings for the year 476 601 601 485 597 Kuehne + Nagel's shareholders' share of equity 2.280 2.365 2.382 2.396 2.537 Non-controlling interests 10 13 23 29 21 Total equity 2.290 2.378 2.405 2.425 2.558 Provisions for pension plans and severance payments 307 284 296 357 340 Deferred tax liabilities 220 173 156 151 136 Finance lease obligations 107 58 43 32 24 Non-current provisions 71 94 97 69 63 Bank liabilities 1 Total non-current liabilities 706 609 592 609 563 Bank and other interest-bearing liabilities 55 49 44 36 21 Trade payables 1.123 1.201 1.285 1.337 1.362 Accrued trade expenses/deferred income 856 877 881 931 936 Income tax liabilities 102 114 106 89 89 Current provisions 87 69 64 68 78 Other liabilities 714 644 764 784 767 Total current liabilities 2.937 2.954 3.144 3.245 3.253 Total liabilities 3.643 3.563 3.736 3.854 3.816 Total equity and liabilities 5.933 5.941 6.141 6.279 6.374

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Analytical balance sheet, Kuehne + Nagel Group DKKm 2009 2010 2011 2012 2013 Operating assets Capitalised operating leases 10.968 12.712 15.206 15.349 16.654 Property, plant & equipment (PPE) 6.526 6.468 7.002 7.008 7.007 Goodwill 3.416 3.524 4.253 4.289 4.188 Other intangibles 1.370 1.051 1.198 871 542 Investments in joint ventures 55 257 238 241 201 Deferred tax assets 953 991 990 1.205 1.047 Total non-current assets 12.321 12.291 13.681 13.615 12.985 Total non-current assets, adjusted for operating leases: 23.288 25.003 28.887 28.964 29.639 Prepayments 462 555 593 674 639 Work in progress 1.124 1.511 1.680 1.891 1.802 Trade receivables 10.053 12.404 13.919 15.006 14.769 Other receivables 883 770 910 717 651 Income tax receivables 1.540 210 317 Total current assets 12.521 15.241 18.643 18.497 18.178 Interest bearing assets: Cash and cash equivalents 4.921 7.949 5.200 6.693 7.640 Total interest bearing assets: 4.921 7.949 5.200 6.693 7.640 Liabilities classified non-interest-bearing debt Deferred tax liabilities 1.104 1.033 953 933 828 Trade payables 5.634 7.173 7.852 8.263 8.291 Accrued trade expenses/deferred income 4.294 5.238 5.383 5.754 5.698 Income tax liabilities 512 681 648 550 542 Other liabilities 3.582 3.846 4.668 4.845 4.669 Total 15.125 17.970 19.504 20.345 20.029 Invested capital (total assets minus liabilities minus interest bearing assets) 9.717 9.561 12.819 11.767 11.134 Invested capital, adjusted for operating leases 20.685 22.273 28.025 27.116 27.788 Total equity 11.488 14.202 14.695 14.987 15.572 Calculated average equity 5.744 12.845 14.449 14.841 15.280 Interest bearing liabilities: Capitalised operating leases 10.968 12.712 15.206 15.349 16.654 Non-current provisions 356 561 593 426 384 Current provisions 436 412 391 420 475 Provisions for pension plans and severance payments 1.540 1.696 1.809 2.206 2.070 Finance lease obligations 537 346 263 198 146 Bank liabilities 5 Bank and other interest-bearing liabilities 276 293 269 222 128 Interest bearing debt: 3.150 3.309 3.324 3.473 3.202 Interest bearing debt, adjusted for operating leases 14.118 16.020 18.530 18.822 19.856 Total interest bearing assets: Cash and cash equivalents 4.921 7.949 5.200 6.693 7.640 Total interest bearing assets: 4.921 7.949 5.200 6.693 7.640 Net-interest bearing debt (NIBD) 9.197 8.071 13.330 12.129 12.216 Calculated average net interest bearing debt -12.2% 65.2% -9.0% 0.7% Net current -1.771 -4.640 -1.876 -3.220 -4.438 Invested capital (Equity + NIBD) 9.717 9.561 12.819 11.767 11.134 Calculated average invested capital 9.639 11.190 12.293 11.451 Invested capital (Equity + NIBD), adjusted for operating leases 20.685 22.273 28.025 27.116 27.788 Calculated average invested capital 21.479 25.149 27.571 27.452

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Reported Operational leasing, Kuehne + Nagel Group DKKm 2008 2009 2010 2011 2012 2013 Leasing obligations on properties and buildings 0 - 1 year 385 334 339 334 351 1-3 years 913 682 637 618 665 More than 3 years 320 258 253 241 249 Total 1.618 1.274 1.229 1.193 1.265 2009 2010 2011 2012 2013 Leasing obligations on operations and office equipment 0 - 1 year 78 71 79 80 84 1-3 years 98 96 112 119 116 More than 3 years 1 3 4 3 1 Total 177 170 195 202 201 Total leasing obligations 9.005 8.624 8.701 8.621 8.925 Total Assets 29.763 35.481 37.523 38.805 38.803 Leasing obligations in percentage of total assets 30.3% 24.3% 23.2% 22.2% 23.0% 24.6% Adjusting for operating leasing Recognised in income statement 2009 2010 2011 2012 2013 2014E Operating leasing expenses 2.620 2.556 2.962 3.543 3.576 3.880 Growth, y/y. pct. -2.5 15.9 19.6 0.9 8.5 Capitalise operating leasing 10.968 12.712 15.206 15.349 16.654 Average leasing period in years 5 Kuehne + Nagel: Costs of debt 3.3% Estimations Implied interest: [costs of debt*operating leasing] 362 419 502 507 550 Lease depreciation 2.258 2.136 2.460 3.036 3.027 Total 2.620 2.556 2.962 3.543 3.576 Exchange rate (DKKCHF) average per year 4,7037 4,9334 5,4048 6,0563 6,1768 Exchange rate (DKKCHF) 31/12 5,0165 5,9722 6,1103 6,1802 6,0877

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Panalpina Group

Income statement, Panalpina Group DKKm 2009 2010 2011 2012 2013 Turnover 28.024 35.344 35.129 40.072 41.741 Direct costs 21.548 28.042 27.146 31.200 32.098 Gross profit 6.476 7.302 7.983 8.873 9.642 Other external expenses, net 1.971 2.602 2.014 2.509 2.719 Staff costs 4.135 4.395 4.823 5.797 5.930 Fines 359 253 EBITDA 375 308 1.146 208 740 EBIT 141 76 942 -240 296 Net financials -75 -46 -30 -27 -77 EBT 65 30 911 -267 219 Income tax 16 158 223 168 147 Earnings for the year 49 -128 689 -435 72 Effective tax rate 0,25 5,25 0,24 0,63 0,67

Analytical Income Statement, Panalpina Group DKKm 2009 2010 2011 2012 2013 Turnover 28.024 35.344 35.129 40.072 41.741 Direct costs 21.548 28.042 27.146 31.200 32.098 - Implied interest 87 95 126 139 160 - Lease depreciation (rental expenses - implied interest) 470 516 544 748 818 Gross profit 6.554 7.387 8.095 8.997 9.785 Other external expenses, net 1.971 2.602 2.014 2.509 2.719 Staff costs 4.135 4.395 4.823 5.797 5.930 Fines 359 253 EBITDA 452 392 1.259 332 883 EBIT 218 161 1.054 -116 439 Taxes on EBIT 35 398 230 151 199 NOPAT 183 -237 824 -267 241 Net financial expenses before tax (implied interest added back) -162 -140 -156 -166 -237 Tax savings on net financial expenses 19 -239 -7 -17 -52 Net financial expenses after tax -134 109 -135 -168 -169 Net earnings 49 -128 689 -435 72 Exchange rate (DKKCHF), avg. pr. year 4,7037 4,9334 5,4048 6,0563 6,1768

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Balance sheet, Panalpina Group, CHFm. 2009 2010 2011 2012 2013 Property, plant & equipment (PPE) 141,3 113,8 113,2 130,2 118,9 Intangibles 71,9 78,1 141,7 134,1 118,1 Investments 37,4 34,8 72,3 31,6 28,3 Derivatives financial instruments 5,6 0,0 0,5 0,0 0,0 Post-employment benefit assets 14,4 10,3 0,0 0,0 19,9 Deferred income tax assets 55,3 65,9 62,3 65,8 65,5 Total non-current assets 325,9 303,0 390,0 361,8 350,7 Other receivables 110,4 98,0 85,0 81,1 102,7 Unbilled forwarding services 83,1 74,7 77,3 85,2 91,2 Trade receivable 856,9 958,1 984,4 1.033,0 1.059,6 Derivatives financial instruments 5,7 20,5 5,0 2,9 2,9 Other current financial assets 10,8 6,1 20,0 0,0 5,5 Cash and cash equivalents 531,8 528,9 573,6 393,1 336,9 Total current assets 1.598,7 1.686,3 1.745,4 1.595,3 1.598,7 Total assets 1.924,6 1.989,2 2.135,3 1.957,1 1.949,5 Equity and Liabilities 2009 2010 2011 2012 2013 Share capital 50,0 50,0 50,0 2,4 2,4 Treasury shares -192,6 -196,0 -197,3 10,0 -3,3 Retained earnings and reserves 856,6 804,3 1.066,1 741,2 698,5 Total equity attributable to present owners Non-controlling interest 7,0 7,9 9,1 9,2 11,7 Total equity 863,6 812,2 927,9 742,8 709,2 Non-current liabilities Borrowings 0,9 0,4 0,2 0,3 0,2 Provisions 66,7 112,6 85,0 73,1 77,6 Post-employment benefit liabilities 39,1 40,7 29,9 59,0 49,7 Deferred income tax liabilities 21,9 20,7 18,7 13,9 16,5 Derivate financial instruments 0,5 Total non-current liabilities 128,6 174,9 133,9 146,2 144,0 Trade payables 519,6 521,2 588,1 572,8 577,2 Other payables 122,8 134,3 144,4 149,5 152,0 Accrued cost of services 159,7 174,8 184,5 200,2 184,5 Borrowings 12,0 9,3 7,3 1,6 3,1 Derivate financial instruments 2,2 5,0 4,6 1,3 1,7 Provisions and other liabilities 103,4 141,1 125,4 124,5 155,4 Current income tax liabilities 12,7 16,4 19,2 18,2 22,4 Total current liabilities 932,5 1.002,1 1.073,5 1.068,1 1.096,2 Total liabilities 1.061,1 1.177,1 1.207,4 1.214,3 1.240,3 Total equity and liabilities 1.924,6 1.989,2 2.135,3 1.957,1 1.949,5

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Analytical balance sheet of Panalpina Group, DKKm 2009 2010 2011 2012 2013 Operating assets Capitalised operating leases 2.623 2.872 3.805 4.198 4.849 Property, plant & equipment (PPE) 709 680 692 805 724 Intangibles 361 466 866 829 719 Post-employment benefit assets 72 62 0 0 121 Deferred income tax assets 278 393 381 407 398 Total non-current assets 1.419 1.601 1.938 2.040 1.962 Total non-current assets, adjusted for operating leases 4.043 4.473 5.744 6.238 6.811 Other receivables 554 585 519 501 625 Unbilled forwarding services 417 446 473 527 555 Trade receivable 4.298 5.722 6.015 6.384 6.450 Total current assets 5.269 6.753 7.007 7.412 7.631 Interest bearing assets: Investments 188 208 442 196 173 Derivatives financial instruments 28 0 3 0 0 Derivatives financial instruments 29 122 31 18 18 Other current financial assets 54 36 122 0 33 Cash and cash equivalents 2.668 3.159 3.505 2.429 2.051 Total interest bearing assets: 2.966 3.526 4.102 2.643 2.275 Liabilities classified non-interest-bearing debt Post-employment benefit liabilities 196 243 183 365 302 Deferred income tax liabilities 110 124 114 86 101 Trade payables 2.607 3.113 3.593 3.540 3.514 Other payables 616 802 882 924 925 Accrued cost of services 801 1.044 1.127 1.237 1.123 Current income tax liabilities 64 98 117 112 136 Total 4.394 5.424 6.018 6.264 6.102 Invested capital (total assets minus liabilities minus interest bearing assets) 2.295 2.931 2.928 3.188 3.491 Invested capital, adjusted for operating leases 4.918 5.803 6.733 7.386 8.340 Total equity 4.332 4.850 5.670 4.590 4.317 Calculated average equity 4.591 5.260 5.130 4.454 Interest bearing liabilities: Capitalised operating leases 2.623 2.872 3.805 4.198 4.849 Borrowings 4 2 1 2 1 Provisions 334 672 520 452 473 Borrowings 60 56 45 10 19 Derivate financial instruments 11 30 28 8 10 Derivate financial instruments 3 Provisions and other liabilities 519 842 766 769 946 Interest bearing debt: 929 1.606 1.360 1.240 1.449 Interest bearing debt, adjusted for operating leases 3.552 4.478 5.166 5.438 6.297 Total interest bearing assets: 2.966 3.526 4.102 2.643 2.275

Net-interest bearing debt (NIBD) -2.037 -1.920 -2.742 -1.403 -826 Calculated average net interest bearing debt Net-interest bearing debt (NIBD) 586 952 1.064 2.795 4.023 Calculated average net interest bearing debt 769 1.008 1.929 3.409 Invested capital (Equity + NIBD) 2.295 2.931 2.928 3.188 3.491 Calculated average invested capital 2.613 2.929 3.058 3.339 Invested capital (Equity + NIBD), adjusted for operating leases 4.918 5.803 6.733 7.386 8.340 Calculated average invested capital 5.360 6.268 7.059 7.863

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Reported Operational leasing, Kuehne + Nagel Group DKKm 2009 2010 2011 2012 2013 2014E Recognised in income statement Operating leasing expenses 556.4 611 669 887 978 1130 Growth. y/y. pct. 9.8 9.5 32.5 10.3 15.5

Capitalise operating leasing 2.623 2.872 3.805 4.198 4.849

Average leasing period in years (assume five years as this is consistent with K+N and DSV) 5 Panalpina: Costs of debt (assume same credit rating) 3.3%

Exchange rate (DKKCHF) average per year 4,7037 4,9334 5,4048 6,0563 6,1768 Exchange rate (DKKCHF) 31/12 5,0165 5,9722 6,1103 6,1802 6,0877

Estimations Implied interest: [costs of debt*operating leasing] 87 95 126 139 160 Lease depreciation 470 516 544 748 818 Total 556 611 669 887 978

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Appendix F: Financial Ratios

Elling and Sørensen (2005) argue that the balance sheet offers a single day picture whereas the income statement reflects the entire year. To ensure consensus of the ratios, average values of balance sheet items are suggested. All preceding ratios are calculated in accordance with Petersen and Plenborg (2012):

DSV Group Financial Ratios 2010 2011 2012 2013 ROIC: [NOPAT / Invested capital, average] * 100 11.4% 13.0% 11.9% 13.2% Profit Margin: [NOPAT / Revenue] 3.7% 4.0% 3.6% 3.9% Asset Turnover: [Revenue / Invested capital. average] * 100 3,1 3,2 3,3 3,4

Check: (PM*ATO)-ROIC 0,0 0,0 0,0 0,0 ROE: [Net earnings after tax / BV Equity, average] 19.7 24.4 26.7 27.0 Financial ratios after capitalised operating leases 2010 2011 2012 2013 ROIC: [NOPAT / Invested capital, average] * 100 8.7% 9.7% 8.8% 9.6% Profit Margin: [NOPAT / Revenue] * 100 4.0% 4.4% 3.9% 4.3% Asset Turnover: [Revenue / Invested capital, average] 2,1 2,2 2,2 2,2

Check: (PM*ATO)-ROIC 0,0 0,0 0,0 0,0 ROE: [Net earnings after tax / BV Equity, average] 19.7 24.4 26.7 27.0

Kuehne + Nagel Group Financial Ratios 2010 2011 2012 2013 ROIC: [NOPAT / Invested capital, average] * 100 30.9% 28.7% 23.8% 32.5% Profit Margin: [NOPAT / Net turnover] * 100 3.6% 3.7% 2.8% 3.5% Asset Turnover: [Net Turnover / Invested capital, average] 8,6 7,8 8,4 9,3

Check: (PM*ATO)-ROIC 0,0 0,0 0,0 0,0 ROE: [Net earnings after tax / BV Equity, average] 23.2 22,7 20,1 24,5 Financial ratios after capitalised operating leases 2010 2011 2012 2013

ROIC: [NOPAT / Invested capital, average] * 100 15.4% 14.3% 12.0% 15.1% Profit Margin: [NOPAT / Net turnover] * 100 4.0% 4.1% 3.2% 3.9% Asset Turnover: [Net Turnover / Invested capital. average] 3,9 3,5 3,8 3,9

Check: (PM*ATO)-ROIC 0,0 0,0 0,0 0,0 ROE: [Net earnings after tax / BV Equity, average] 23.2 22.7 20.1 24.5

Panalpina Group Financial Ratios 2010 2011 2012 2013 ROIC: [NOPAT / Invested capital, average] * 100 -12.3% 24.3% -12.8% 2.9% Profit Margin: [NOPAT / Net turnover] * 100 -0.9% 2.0% -1.0% 0.2% Asset Turnover: [Net turnover / Invested capital, average] 13,5 12,0 13,1 12,5

Check: (PM*ATO)-ROIC 0,0 0,0 0,0 0,0 ROE: [Net earnings after tax / BV Equity, average] -2.8% 13.1% -8.5% 1.6% Financial ratios after capitalized operating leases 2010 2011 2012 2013 ROIC: [NOPAT / Invested capital, average] * 100 -4.2% 13.4% -3.6% 3.3% Profit Margin: [NOPAT / Net turnover] * 100 -0.6% 2.4% -0.6% 0.6% Asset Turnover: [Net Turnover / Invested capital, average] 6,6 5,6 5,7 5,3

Check: (PM*ATO)-ROIC 0,0 0,0 0,0 0,0 ROE: [Net earnings after tax / BV Equity, average] -2.8 13.1 -8.5 1.6

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Appendix G: Common Size Analysis and Trend Analysis

Common-size analysis of DSV's revenue and operating expenses Avg. 2010 - Items in percentage of revenue 2009 2010 2011 2012 2013 2013 Revenue 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Direct costs 75.3% 78.1% 77.5% 77.6% 78.1% 77.8% Gross profit 24.7% 21.9% 22.5% 22.4% 21.9% 22.2%

Other external expenses 5.5% 4.6% 4.8% 4.7% 4.4% 4.6% Staff costs 12.9% 10.9% 10.9% 10.8% 10.8% 10.9% EBITDA before special items 6.2% 6.4% 6.8% 6.8% 6.7% 6.7% 0.0% Amortisation and depreciation 1.5% 1.2% 1.3% 1.2% 1.1% 1.2% Special Items. net -1.9% 0.0% 0.0% -0.6% -0.3% -0.2%

EBIT - earnings before interest and tax 2.8% 5.2% 5.6% 5.0% 5.3% 5.3% Taxes on EBIT 1.6% 1.4% 1.5% 1.5% 1.4% 1.5% NOPAT 1.2% 3.7% 4.0% 3.6% 3.9% 3.8%

Net financial expenses before tax -1.5% -1.3% -1.0% -0.5% -0.7% -0.9% Tax shield 0.9% 0.4% 0.3% 0.2% 0.2% 0.2% Net earnings 0.4% 2.6% 3.2% 3.1% 3.4% 3.1%

Common-size analysis of Kuehne + Nagel's revenue and operating expenses Avg. 2010 - Items in percentage of revenue 2009 2010 2011 2012 2013 2013 Revenue 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Direct costs 59.1% 64.7% 63.6% 64.4% 63.6% 64.1% Gross profit 40.9% 35.3% 36.4% 35.6% 36.4% 35.9% 0.0% Other external expenses 11.4% 9.5% 9.6% 9.2% 9.1% 9.3% Staff costs 23.3% 20.1% 20.9% 21.1% 21.7% 20.9% EBITDA 6.2% 6.0% 6.0% 5.0% 5.6% 5.6% 0.0% Amortisation and depreciation 2.0% 1.4% 1.4% 1.3% 1.2% 1.3% EBIT 4.1% 4.5% 4.6% 3.7% 4.4% 4.3% Taxes on EBIT 0.0% NOPAT 3.2% 3.6% 3.7% 2.8% 3.5% 3.4% 0.0% Net financial expenses before tax 0.11% 0.01% 0.10% 0.06% 0.03% 0.1% Tax shield 0.03% 0.00% 0.02% 0.02% 0.01% 0.0% Net earnings 3.3% 3.6% 3.7% 2.9% 3.5% 3.4%

Common-size analysis of Panalpina's revenue and operating expenses Avg. 2010 - Items in percentage of revenue 2009 2010 2011 2012 2013 2013 Revenue 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Direct costs 76.9% 79.3% 77.3% 77.9% 76.9% 77.8% Gross profit 23.1% 20.7% 22.7% 22.1% 23.1% 22.2% 0.0% Other external expenses 7.03% 7.36% 5.73% 6.26% 6.51% 6.5% Staff costs 14.76% 12.44% 13.73% 14.47% 14.21% 13.7% EBITDA 1.3% 0.9% 3.3% 0.5% 1.8% 1.6% 0.0% Amortisation and depreciation 1% 0% 3% -1% 1% 0.8% EBIT -0.27% -0.13% -0.09% -0.07% -0.19% -0.1% Taxes on EBIT 0.12% 1.12% 0.65% 0.38% 0.48% 0.7% NOPAT 0.38% -0.91% 2.03% -0.97% 0.23% 0.1% 0.0% Net financial expenses before tax -0.3% -0.1% -0.1% -0.1% -0.2% -0.1% Tax shield 0.1% -0.7% 0.0% 0.0% -0.1% -0.2% Net earnings 0.2% -0.4% 2.0% -1.1% 0.2% 0.2%

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Trend Analysis DSV 2009 2010 2011 2012 2013 Revenue 100 118 121 124 127 Direct costs 100 122 125 128 131 Gross profit 100 105 110 113 112 Other external expenses 100 98 105 106 101 Staff costs 100 99 102 104 106 EBITDA 100 122 133 137 137 EBIT 100 129 142 149 150 NOPAT 100 375 418 381 425 Net earnings 100 625 759 749 823

Trend Analysis Kuehne + Nagel 2009 2010 2011 2012 2013 Revenue 100 123 130 154 157 Direct costs 100 135 140 168 169 Gross profit 100 107 116 134 140 Other external expenses 100 103 109 123 125 Staff costs 100 106 116 139 147 EBITDA 100 119 127 124 143 EBIT 100 135 145 137 168 NOPAT 100 138 149 136 172 Net earnings 100 135 148 134 169

Trend Analysis Panalpina 2009 2010 2011 2012 2013 Revenue 100 126 125 143 149 Direct costs 100 130 126 145 149 Gross profit 100 113 123 137 149 Other external expenses 100 132 102 127 138 Staff costs 100 106 117 140 143 EBITDA 100 82 306 55 197 EBIT 100 54 669 -170 211 NOPAT 100 -304 672 -369 92 Net earnings 100 -261 1402 -886 147

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Appendix H: Estimation of Market Consignment in Road Freight

2009 2010 2011 2012 E2013 Transportation Service bn. dollars 538,1 594,9 607,4 600,4 600 Volume bn freight tonne kilometers 1905,1 1993,9 1987,3 1961,2 1986,7 Revenue ($) / freight tonne kilometers (FKT) 1.51 1.68 1.64 1.77 1.70 - Change per cent -11.0% 2.5% -8.3% 4.3% DKKUSD 5,3524 5,6268 5,3562 5,7916 5,6157 Earnings pr. consignment (Average for EU) 12.552 13.939 13.592 14.721 14.081 Sources Market Line and European Commission

Assumptions: Average distance km. - Country average 610.5 Average tonnes per consignment - Country average 13.6

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Appendix I: Forecasts Transport Service Industry

Figure 45: Market Segmentation of Transport Service Industry in USA, bn. dollars

53,1 37,9 Road freight 83,8 Sea freight

Rail freight

797,1 Air freight Source: Own Making, Market line

Outlook: Overall Transportation

European Transportation Service Industry 2009 - 2018 2009 2010 2011 2012 2013 E2014 E2015 E2016 E2017 E2018 Billion dollars 538,1 594,9 607,4 600,4 600 616,6 638,5 660,4 684,1 657,2 - Growth, pct. 10.6% 2.1% -1.1% -0.1% 2.8% 3.6% 3.4% 3.6% -3.9% GDP change y/y, pct. -4.4% 2.0% 1.7% -0.3% 0.2% 1.6% 1.8% 1.9% 1.9% 1.9% Source: Market line and IMF statistics

Outlook: Road Transportation Road Transportation Service Industry 2008 - 2018 2008 2009 2010 2011 2012 E2013 E2014 E2015 E2016 E2017 Billion dollars 511,4 401,9 450,6 452,6 445,3 446 458 475,7 493,1 512,7 - Growth, pct. -21.40% 12.1% 4.0% -1.60% 0.10% 2.70% 3.90% 3.60% 4.00% Source: Market line

Road Transportation Service Industry 2008 - 2018 2008 2009 2010 2011 2012 E2013 E2014 E2015 E2016 E2017 Volume bn. freight tonne kilometres 2116,6 1905,1 1993,9 1987,3 1961,2 1986,7 2043,5 2112,1 2184,8 2269,1 - Change, pct. -10.0% 4.7% -0.3% -1.3% 1.3% 2.9% 3.4% 3.4% 3.9% Source: Market line

Outlook: Sea Transportation

Sea Transportation Europe 2009 - 2018 2009 2010 2011 2012 2013 E2014 E2015 E2016 E2017 E2018 Billion dollars 66,6 71,8 75,5 74,3 74,4 77,1 79,7 82,7 85,7 89,0 - Change, pct. 7.9% 5.1% -1.6% 0.2% 3.6% 3.3% 3.6% 3.8% 3.9% Source: Market line

Sea Transportation Asia-Pacific 2009 - 2018 Billion dollars 2009 2010 2011 2012 2013 E2014 E2015 E2016 E2017 E2018 Sea freight 182,4 199,1 208,7 215,5 226,0 237,8 250,6 264,8 280,3 297,4 - Change, pct. 9.2% 4.8% 3.3% 4.9% 5.3% 5.3% 5.7% 5.9% 6.1% Source: Market line

Sea Transportation United States 2009 - 2018 2009 2010 2011 2012 2013 E2014 E2015 E2016 E2017 E2018 Billion dollars 46,9 50,6 51,9 50,9 53,1 54,6 56,5 58,5 60,7 63 - Change, pct. 7.8% 2.6% -2.0% 4.4% 2.7% 3.4% 3.7% 3.7% 3.8% Source: Market line

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Outlook: Air Transportation

Air Transportation Europe 2009 - 2018 2009 2010 2011 2012 2013 E2014 E2015 E2016 E2017 E2018 Billon dollars 30,1 28,8 31,3 30,8 30,5 30,7 31,4 32,1 32 32,6 - Change, pct. -4.1% 8.8% -1.7% -1.0% 0.8% 2.3% 2.0% -0.2% 1.8% Source: Market line

Air Transportation Europe 2009 - 2018 200 9 2010 2011 2012 2013 E2014 E2015 E2016 E2017 E2018 Volume bn. freight tonne kilometres 35,5 34,6 36,1 35,3 35,4 35,2 35,9 36,5 36,4 37,0 - Change, pct. -2.6% 4.2% -2.2% 0.4% -0.8% 2.2% 1.7% -0.3% 1.6% Source: Market line

Air Transportation Asia-Pacific 2009 - 2018 2009 2010 2011 2012 2013 E2014 E2015 E2016 E2017 E2018 Billion Dollars 52,3 53,8 55,7 55,2 56,2 57,9 58,8 59 60,3 61,3 - Change, pct. 2.8% 3.6% -0.9% 1.8% 3.0% 1.5% 0.4% 2.1% 1.7% Source: Market line

Air Transportation Asia-Pacific 2009 - 2018 2009 2010 2011 2012 2013 E2014 E2015 E2016 E2017 E2018 Volume bn. freight tonne kilometres 81,1 83,5 82,4 81,4 83,2 84,0 85,0 85,3 86,8 87,8 - Change, pct. 3.0% -1.3% -1.2% 2.3% 0.9% 1.2% 0.3% 1.9% 1.1% Source: Market line

Air Transportation United States 2009 - 2018 2009 2010 2011 2012 2013 E2014 E2015 E2016 E2017 E2018 Billion dollars 36,2 37,2 40 39,2 37,9 39,2 40 38,6 38,6 38,9 - Change, pct. 2.9% 7.4% -2.0% -3.3% 3.6% 1.9% -3.4% 0.0% 0.8% Source: Market line

Air Transportation United States 2009 - 2018 2009 2010 2011 2012 2013 E2014 E2015 E2016 E2017 E2018 Volume bn. freight tonne kilometres 88,2 104,7 104,1 100,5 97,4 101,1 103,2 99,9 100,1 101,2 - Change, pct. 18.7% -0.6% -3.5% -3.1% 3.8% 2.1% -3.2% 0.2% 1.0% Source: Market line

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Appendix J:

CO2 Emission Divided into Industry

Figure 47: Greenhouse gas emissions per Figure 48: Greenhouse gas emissions per industry, 2000 industry, 2011

Agriculture, Agriculture, forestry and forestry and Mining and fishing fishing quarrying Mining and 12% 12% 2% quarrying Households Households 1% 18% Manufacturi 18% ng Manufacturi Other 23% Other ng services, services, 20% water supply water and supply and construction construction Electricity, 11% Electricity, 12% gas, steam gas, steam and air Transport and air Transport conditioning 8% conditioning 10% supply supply 27% 26%

Note: Road transportation is the only industry segment which has not diminished. The percentages from the above figures do not reveal this. For example, ‘electricity, gas, steam and air conditioning’ and ‘other services, water supply and construction’ have increased in percentage, but not in absolute numbers. The reason for the percentage increase is due to the relatively larger decrease of others, which is why their share of the total has increased in percentage.

Figure 54: Inflation, (HICP Index all items) EU-28

4 3,5

3

2,5

2 1,5 1 0,5

0

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Appendix K: Ansoff’s Growth Model and Porter Generic Strategies

Source of competitive advantage

Low cost Differentiation

Cost Industry-wide Differentiation Leadership

Single Focus Segment

Products

Existing New

Ex Product Market Penetration Development stin g Mar kets

Market Ne Diversification w Development

Source: Author’s own creation, inspiration from strategi i vindervirksomheder (2013)

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Appendix L: Forecast Model

Line items approach:

Historical period Explicit forecasting period (forecast horizon) Terminal period Avg. Airfreight (DKKm) 2010 2011 2012 2013 '10-13 E2014 E2015 E2016 E2017 E2018 E2019 E2020 Revenue 8.180 8.336 8.234 8.198 8.237 8.387 8.580 8.778 8.981 9.188 9.306 9.417 - Change y/y pct. 35.6% 1.9% -1.2% -0.4% 9.0% 2.3% 2.3% 2.3% 2.3% 2.3% 1.3% 1.2% Direct costs 6.465 6.534 6.373 6.399 6.443 6.514 6.631 6.750 6.871 6.995 7.142 7.285 - Change y/y pct. 42.7% 1.1% -2.5% 0.4% 10.4% 1.8% 1.8% 1.8% 1.8% 1.8% 2.1% 2.0% Gross profit 1.715 1.802 1.861 1.799 1.794 1.873 1.950 2.028 2.110 2.193 2.165 2.132 - Change y/y pct. 14.2% 5.1% 3.3% -3.3% 4.8% 4.1% 4.1% 4.0% 4.0% 4.0% -1.3% -1.5% Check Direct costs as a % of Revenue 78% 77% 78% 78% 78% 77% 77% 77% 76% 77% 77% Gross profit as a % of Revenue 22% 23% 22% 22% 22% 23% 23% 23% 24% 23% 23% Chapter two - financial analysis Volume, Tonnes 248.797 262.362 259.057 259.365 257.395 264.552 269.843 275.240 280.745 286.360 291.514 296.762 - Change y/y pct. 29.2% 5.5% -1.3% 0.1% 8.4% 2.0% 2.0% 2.0% 2.0% 2.0% 1.8% 1.8% Revenue/Tonnes 32.878 31.773 31.785 31.608 32.011 31.703 31.798 31.893 31.989 32.085 31.925 31.733 - Change y/y pct. 4.9% -3.4% 0.0% -0.6% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% -0.5% -0.6% Direct costs/Tonnes 25.985 24.905 24.601 24.672 25.041 24.622 24.573 24.524 24.475 24.426 24.499 24.548 - Change y/y pct. 10.4% -4.2% -1.2% 0.3% 1.3% -0.2% -0.2% -0.2% -0.2% -0.2% 0.3% 0.2% Gross profit/Tonnes 6.893 6.868 7.184 6.936 6.970 7.080 7.225 7.369 7.514 7.659 7.425 7.185 - Change y/y pct. -11.7% -0.4% 4.6% -3.4% -2.7% 2.1% 2.0% 2.0% 2.0% 1.9% -3.1% -3.2% Chapter three - market analysis Market growth: - Asia Pacific 2.8% 3.6% -0.9% 1.8% 1.8% 3.0% 1.5% 0.4% 2.1% 1.7% 1.7% - Europe -4.1% 8.8% -1.7% -1.0% 0.5% 0.8% 2.3% 2.0% -0.2% 1.8% 1.3% - United States 2.9% 7.4% -2.0% -3.3% 1.3% 3.6% 1.9% -3.4% 0.0% 0.8% 0.6% Market growth, volume: - Asia Pacific 3.0% -1.3% -1.2% 2.3% 0.7% 0.9% 1.2% 0.3% 1.9% 1.1% 1.1% - Europe -2.6% 4.2% -2.2% 0.4% 0.0% -0.8% 2.2% 1.7% -0.3% 1.6% 0.9% - United States 18.7% -0.6% -3.5% -3.1% 2.9% 3.8% 2.1% -3.2% 0.2% 1.0% 0.8% Chapter four - environmental analysis GDP Change y/y 2.0% 1.7% -0.3% 1.2% 0.9% 1.6% 1.8% 1.9% 1.9% 1.9% 1.8% Historical period Explicit forecasting period (forecast horizon) Terminal period Avg. Sea freight (DKKm) 2010 2011 2012 2013 '10-13 E2014 E2015 E2016 E2017 E2018 E2019 E2020 Revenue 11.224 10.590 11.622 11.997 11.358 12.467 12.967 13.501 14.070 14.677 15.029 15.389 - Change y/y pct. 41.0% -5.6% 9.7% 3.2% 12.1% 3.9% 4.0% 4.1% 4.2% 4.3% 2.4% 2.4% Direct costs 9.144 8.302 9.214 9.498 9.040 9.811 10.144 10.498 10.875 11.277 11.593 11.931 - Change y/y pct. 51.4% -9.2% 11.0% 3.1% 14.1% 3.3% 3.4% 3.5% 3.6% 3.7% 2.8% 2.9% Gross profit 2.080 2.288 2.408 2.499 2.319 2.656 2.823 3.003 3.195 3.400 3.436 3.459 - Change y/y pct. 8.2% 10.0% 5.2% 3.8% 6.8% 6.3% 6.3% 6.4% 6.4% 6.4% 1.0% 0.7% Check Direct costs as a % of Revenue 81% 78% 79% 79% 80% 79% 78% 78% 77% 77% 77% Gross profit as a % of Revenue 19% 22% 21% 21% 20% 21% 22% 22% 23% 23% 23% Chapter two - financial analysis Volume TEU 707.193 727.861 725.806 772.142 733.251 799.167 827.937 858.571 891.196 925.953 949.102 972.829 - Change y/y pct. 18.9% 2.9% -0.3% 6.4% 7.0% 3.5% 3.6% 3.7% 3.8% 3.9% 2.5% 2.5% Revenue/TEU 15.871 14.549 16.013 15.537 15.493 15.599 15.662 15.724 15.787 15.851 15.835 15.819 - Change y/y pct. 18.5% -8.3% 10.1% -3.0% 4.3% 0.4% 0.4% 0.4% 0.4% 0.4% -0.1% -0.1% Direct costs/TEU 12.930 11.406 12.693 12.301 12.333 12.276 12.252 12.227 12.203 12.178 12.215 12.264 - Change y/y pct. 27.3% -11.8% 11.3% -3.1% 5.9% -0.2% -0.2% -0.2% -0.2% -0.2% 0.3% 0.4% Gross profit/TEU 2.941 3.143 3.318 3.236 3.160 3.323 3.410 3.497 3.585 3.672 3.620 3.555 - Change y/y pct. -9.0% 6.9% 5.5% -2.4% 0.2% 2.7% 2.6% 2.6% 2.5% 2.4% -1.4% -1.8% Chapter three - market analysis Market growth: - Asia Pacific 9.2% 4.8% 3.3% 4.9% 5.6% 5.3% 5.3% 5.7% 5.9% 6.1% 5.7% - Europe 7.9% 5.1% -1.6% 0.2% 2.9% 3.6% 3.3% 3.6% 3.8% 3.9% 3.6% - United States 7.8% 2.6% -2.0% 4.4% 3.2% 2.7% 3.4% 3.7% 3.7% 3.8% 3.5% Chapter four - environmental analysis GDP Change y/y 2.0% 1.7% -0.3% 1.2% 0.9% 1.6% 1.8% 1.9% 1.9% 1.9% Historical period Explicit forecasting period (forecast horizon) Terminal period Avg. Road freight (DKKm) 2010 2011 2012 2013 '10-13 E2014 E2015 E2016 E2017 E2018 E2019 E2020 Revenue 21.103 22.641 22.654 23.116 22.379 23.969 24.852 25.769 26.771 27.811 28.673 29.561 - Change y/y pct. 8.7% 7.3% 0.1% 2.0% 4.5% 3.7% 3.7% 3.7% 3.9% 3.9% 3.1% 3.1% Direct costs 16.998 18.361 18.308 18.817 18.121 19.531 20.271 21.040 21.880 22.753 23.434 24.111

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- Change y/y pct. 11% 8% 0% 3% 5.3% 3.8% 3.8% 3.8% 4.0% 4.0% 3.0% 2.9% Gross profit 4.105 4.280 4.346 4.299 4.258 4.438 4.581 4.729 4.891 5.058 5.239 5.449 - Change y/y pct. 1.1% 4.3% 1.5% -1.1% 1.5% 3.2% 3.2% 3.2% 3.4% 3.4% 3.6% 4.0% Check Direct costs as a % of Revenue 81% 81% 81% 81% 81.0% 81% 82% 82% 82% 82% 82% Gross profit as a % of Revenue 19% 19% 19% 19% 19.0% 19% 18% 18% 18% 18% 18% Chapter two - financial analysis Volume 1.731.731 1.873.733 1.892.470 1.968.169 1.866.526 2.046.896 2.128.772 2.213.923 2.306.907 2.403.798 2.480.719 2.560.102 - Change y/y pct. 12.0% 8.2% 1.0% 4.0% 6.3% 4.0% 4.0% 4.0% 4.2% 4.2% 3.0% 3.2% Revenue/consignment 12.186 12.083 11.971 11.745 11.996 11.710 11.675 11.640 11.605 11.570 11.558 11.547 - Change y/y pct. -0.8% -0.9% -1.9% -1.2% -0.3% -0.3% -0.3% -0.3% -0.3% -0.1% -0.1% Direct costs/consignment 9.816 9.799 9.674 9.561 9.712 9.542 9.522 9.503 9.484 9.465 9.447 9.418 - Change y/y pct. -1.1% -0.2% -1.3% -1.2% -0.9% -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% -0.3% Gross 2.370 2.284 2.296 2.184 profit/consignment 2.284 2.168 2.152 2.136 2.120 2.104 2.112 2.129 - Change y/y pct. -10.8% -3.8% 0.5% -5.1% -4.8% -0.7% -0.7% -0.7% -0.7% -0.7% 0.3% 0.8% Chapter three - market analysis Market growth: - Europe 2.0% 1.7% -0.3% 0.2% 0.9% 2.7% 3.9% 3.6% 4.0% n.a. 2.8% Market growth, volume: - Europe 4.7% -0.3% -1.3% 1.3% 1.1% 2.9% 3.4% 3.4% 3.9% n.a. 3.6% Chapter four - environmental analysis GDP Change y/y 2.0% 1.7% -0.3% 1.2% 0.9% 1.6% 1.8% 1.9% 1.9% 1.9%

Historical period Explicit forecasting period (forecast horizon) Terminal period Average Solutions (DKKm) 2010 2011 2012 2013 '10-13 E2014 E2015 E2016 E2017 E2018 E2019 E2020 Revenue 4.861 5.009 5.181 5.469 5.130 5.688 5.915 6.181 6.460 6.750 6.885 7.023 - Change y/y pct. 1.8% 3.0% 3.4% 5.6% 3.5% 4.0% 4.0% 4.0% 4.0% 4.0% 2.0% 2.0% Direct costs 3.401 3.526 3.743 4.060 3.683 4.263 4.476 4.700 4.935 5.182 5.337 5.497 - Change y/y pct. 2.4% 3.7% 6.2% 8.5% 5.2% 5.0% 5.0% 5.0% 5.0% 5.0% 3.0% 3.0% Gross profit 1.460 1.483 1.438 1.409 1.448 1.425 1.439 1.482 1.525 1.569 1.548 1.526 - Change y/y pct. 0.5% 1.6% -3.0% -2.0% -0.7% 1.1% 1.0% 2.9% 2.9% 2.9% -1.3% -1.4% Check Direct costs as a % of Revenue 70% 70% 72% 74% 72% 75% 76% 76% 76% 77% 78% Gross profit as a % of Revenue 30% 30% 28% 26% 28% 25% 24% 24% 24% 23% 22%

Historical period Explicit forecasting period (forecast horizon) Terminal period Avg. DSV Group A/S 2010 2011 2012 2013 '10-13 E2014 E2015 E2016 E2017 E2018 E2019 E2020 Revenue 45.368 46.576 47.691 48.780 47.104 50.510 52.315 54.229 56.281 58.426 59.893 61.390 - Change y/y pct. 17.9% 2.7% 2.4% 2.3% 6.3% 3.5% 3.6% 3.7% 3.8% 3.8% 2.5% 2.5% Direct costs 36.008 36.723 37.638 38.774 37.286 40.118 41.522 42.988 44.561 46.206 47.506 48.824 - Change y/y pct. 22.3% 2.0% 2.5% 3.0% 7.4% 3.5% 3.5% 3.5% 3.7% 3.7% 2.8% 2.8% Gross profit 9.360 9.853 10.053 10.006 9.818 10.392 10.793 11.242 11.720 12.221 12.387 12.566 - Change y/y pct. 4.7% 5.3% 2.0% -0.5% 2.9% 3.9% 3.9% 4.2% 4.3% 4.3% 1.4% 1.4% Check Direct costs as a % of Revenue 79% 79% 79% 79% 79% 79% 79% 79% 79% 79% 80% Gross profit as a % of Revenue 21% 21% 21% 21% 21% 21% 21% 21% 21% 21% 20% Chapter four - environmental analysis GDP Change y/y 2.0% 1.7% -0.3% 1.2% 0.9% 1.6% 1.8% 1.9% 1.9% 1.9%

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Sales Driven Approach:

Forecast assumptions Historical period Explicit forecasting period Terminal Period 2010 2011 2012 2013 Avg. '10-13 E2014 E2015 E2016 E2017 E2018 E2019 E2020 Growth drivers (lime-item approach) Organic growth 14.6% 2.1% 0.5% 0.0% 4.3% Revenue growth 17.9% 2.7% 2.7% 1.8% 6.3% 3.5% 3.6% 3.7% 3.8% 3.8% 2.5% 2.5% - Road freight 8.7% 7.3% 0.1% 2.0% 4.5% 3.7% 3.7% 3.7% 3.9% 3.9% 3.1% 3.1% - Airfreight 35.6% 1.9% -1.2% -0.4% 9.0% 2.3% 2.3% 2.3% 2.3% 2.3% 1.3% 1.2% - Sea freight 41.0% -5.6% 9.7% 3.2% 12.1% 3.9% 4.0% 4.1% 4.2% 4.3% 2.4% 2.4% Cost drivers (change. y/y. pct.) Direct costs 22.3% 2.0% 2.5% 3.0% 7.4% 3.5% 3.5% 3.5% 3.7% 3.7% 2.8% 2.8% - Road freight 10.8% 8.0% -0.3% 2.8% 5.3% 3.8% 3.8% 3.8% 4.0% 4.0% 3.0% 2.9% - Airfreight 42.7% 1.1% -2.5% 0.4% 10.4% 1.8% 1.8% 1.8% 1.8% 1.8% 2.1% 2.0% - Sea freight 51.4% -9.2% 11.0% 3.1% 14.1% 3.3% 3.4% 3.5% 3.6% 3.7% 2.8% 2.9% Cost drivers (margins. sales- driven approach) Cost of goods sold as percentage of revenue 78.1% 77.5% 77.6% 78.1% 77.8% 78.1% 78.0% 77.9% 77.8% 77.7% 77.9% 78.2% Other external expenses as a percentage of revenue 4.6% 4.8% 4.7% 4.4% 4.6% 4.6% 4.6% 4.6% 4.6% 4.6% 4.6% 4.6% Staff costs as a percentage of revenue 10.9% 10.9% 10.8% 10.8% 10.9% 10.7% 10.7% 10.7% 10.7% 10.7% 10.7% 10.7% EBITDA-margin 6.4% 6.8% 6.8% 6.7% 6.7% 6.6% 6.7% 6.8% 6.9% 7.0% 6.8% 6.5% Special items 0.0% 0.0% -0.6% -0.3% -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% -0.2% Amortisation and depreciations as a percentage of revenue 1.5% 1.2% 1.3% 1.2% 1.3% 1.3% 1.3% 1.3% 1.3% 1.3% 1.3% 1.3% EBIT as a percentage of revenue 5.2% 5.6% 5.0% 5.3% 5.3% 5.1% 5.2% 5.3% 5.4% 5.5% 5.3% 5.0% Net financial expenses percentage of revenue 1.3% 1.0% 0.5% 0.7% 0.9% 0.7% 0.6% 0.6% 0.6% 0.6% 0.6% 0.6% Efficient tax rate 28.1% 27.4% 29.2% 26.1% 27.7% 24.5% 23.5% 22.0% 22.0% 22.0% 22.0% 22.0% Depreciation as % of intangible and tangible assets 3.83% 4.16% 4.11% 3.89% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% Investment drivers Intangible and tangible assets as percentage of revenue 32% 30% 29% 28% 29.8% 28.0% 28.0% 28.0% 28.0% 28.0% 28.0% 28.0% - Tangible assets as percentage of revenue 11% 10% 9% 8% 9.9% 8.0% 8.0% 8.0% 8.0% 8.0% 8.0% 8.0% - Intangible assets as percentage of revenue 21% 20% 19% 20% 19.9% 20.0% 20.0% 20.0% 20.0% 20.0% 20.0% 20,0% Other non-current assets as a percent of revenue 0.9% 1.1% 1.0% 0.9% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% - Deferred taxes 0.9% 1.0% 1.0% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9%

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- Investments in associates 0.0% 0.1% 0.0% 0.0% 0.04% 0.04% 0.04% 0.04% 0.04% 0.04% 0.04% 0.04% Non - current assets as a percent of revenue 32.8% 31.2% 29.9% 29.1% 30.7% 29.0% 29.0% 29.0% 29.0% 29.0% 29.0% 29.0% - Trade receivables 16.8% 16.3% 16.1% 16.3% 17.0% 17.0% 17.0% 17.0% 17.0% 17.0% 17.0% 17.0% - Forwarding in progess 1.3% 1.4% 1.4% 1.5% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% - Other receivables 1.7% 1.9% 1.8% 1.7% 2.1% 2.1% 2.1% 2.1% 2.1% 2.1% 2.1% 2.1% Current - assets 19.7% 19.6% 19.3% 19.6% 19.5% 20.5% 20.5% 20.5% 20.5% 20.5% 20.5% 20.5% - Trade- and other payables 15.1% 15.2% 14.8% 14.6% 14.9% 14.9% 14.9% 14.9% 14.9% 14.9% 14.9% 14.9% - Forwarding in process. liabilities 3.3% 2.9% 2.9% 2.7% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% - Deferred tax liabilities 1.4% 1.2% 0.9% 0.9% 1.1% 1.1% 1.1% 1.1% 1.1% 1.1% 1.1% 1.1% - Corporate tax 0.5% 1.0% 0.4% 0.5% 0.6% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% Net working capital as a percentage of revenue 0.55% 0.30% 1.24% 1.77% 1.0% 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% 2.2% Financial drivers Net interest bearing debt as percentage of invested capital 60.4% 52.2% 60.1% 60.2% 58.2% 58.0% 58.0% 58.0% 58.0% 58.0% 58.0% 58.0% Net financial expenses as a percentage of NIBD 7.5% 5.4% 3.0% 4.0% 5.0% 5.0% 5.0% 5,0% 5,0% 5,0% 5,0% 5,0%

Forecasting: Income Statement

Forecast assumptions Historical period Explicit forecasting period (forecast Terminal Period Analytical Income Statement (DKKm) 2009 2010 2011 2012 2013 E2014 E2015 E2016 E2017 E2018 E2019 E2020 Revenue 36.085 42.562 43.710 44.912 45.710 47.331 49.023 50.816 52.739 54.749 56.124 57.526 Direct costs 27.187 33.242 33.891 34.858 35.705 36.943 38.235 39.585 41.034 42.549 43.746 44.960 Gross profit 8.898 9.320 9.819 10.054 10.005 10.388 10.787 11.231 11.705 12.201 12.378 12.566

Other external expenses 1.988 1.955 2.092 2.116 2.010 2.177 2.255 2.338 2.426 2.518 2.582 2.646 Staff costs 4.671 4.644 4.752 4.864 4.943 5.064 5.245 5.437 5.643 5.858 6.005 6.155 EBITDA (excluding special items in forecast) 2.239 2.721 2.975 3.074 3.052 3.147 3.287 3.456 3.636 3.824 3.791 3.765

Amortisation and depreciation 536 519 549 534 500 615 637 661 686 712 730 748 Special Items, net -688 -5 0 -275 -129 -95 -98 -102 -105 -109 -112 -115

EBIT - earnings before interest and tax 1.015 2.197 2.426 2.265 2.423 2.437 2.552 2.694 2.845 3.003 2.949 2.902 Taxes on EBIT 594 617 664 661 632 597 600 593 626 661 649 638 NOPAT 421 1.580 1.762 1.604 1.791 1.840 1.952 2.101 2.219 2.342 2.300 2.264

Net financial expenses before tax -555 -537 -431 -246 -298 -312 -300 -311 -322 -335 -347 -356 Tax savings on net financial expenses (tax shield) 325 151 118 72 78 77 70 68 71 74 76 78 Net financial expenses after tax -230 -386 -313 -174 -220 -236 -229 -242 -251 -261 -271 -278

Net earnings 191 1.194 1.449 1.430 1.571 1.604 1.722 1.859 1.968 2.081 2.029 1.986

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Forecasting: Balance Sheet

Forecast assumptions Historical period Explicit forecasting period Terminal Period Balance sheet (DKKm) 2008 2009 2010 2011 2012 2013 E2014 E2015 E2016 E2017 E2018 E2019 E2020 Intangible and tangible (PPE) assets 13.696 13.554 13.186 12.984 12.865 13.253 13.726 14.229 14.767 15.330 15.715 16.107 Net working capital 253 236 129 559 810 1.033 1.074 1.121 1.163 1.207 1.236 1.267 Invested capital 13.949 13.790 13.315 13.543 13.675 14.286 14.801 15.349 15.930 16.536 16.951 17.374

Equity, begin 3.857 5.530 6.585 5.309 5.385 6.248 6.000 6.216 6.447 6.690 6.945 7.119 Net earnings 191 1.194 1.449 1.430 1.571 1.604 1.722 1.859 1.968 2.081 2.029 1.986 Other comprehensive income 49 176 -195 -143 16 Total comprehensive income 240 1.370 1.254 1.287 1.587 Transactions with owners (share buyback and dividends) 1.433 -315 -2.530 -1.211 -724 - Dividends (incl. minorities) -52 -110 -190 -235 - Share buyback and sale -297 -2.418 -1.084 -538 - Others 34 -2 63 49 Equity, end 5.530 6.585 5.309 5.385 6.248 6.000 6.216 6.447 6.690 6.945 7.119 7.297

Net interest-bearing debt (NIBD) 10.822 8.419 7.205 8.006 8.158 7.427 8.286 8.584 8.903 9.239 9.591 9.832 10.077 Invested capital (Equity and NIBD) 13.949 13.790 13.315 13.543 13.675 14.286 14.801 15.349 15.930 16.536 16.951 17.374

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Forecasting: Cash Flow Statement and Net Working Capital

Forecast assumptions Historical period Explicit forecasting period Terminal Period Cash flow statement (DKKm) 2008 2009 2010 2011 2012 2013 E2014 E2015 E2016 E2017 E2018 E2019 E2020 + NOPAT 421 1.580 1.762 1.604 1.791 1.840 1.952 2.101 2.219 2.342 2.300 2.264 + Depreciation and amortisation 536 519 549 534 500 615 637 661 686 712 730 748 Net working capital (NWC) + Trade receivables (non interest-bearing) 7.356 6.098 7.155 7.112 7.238 7.469 8.046 8.334 8.639 8.966 9.307 9.541 9.779 + Forwarding in process, assets 530 513 541 604 629 676 663 686 711 738 766 786 805 + Other receivables 1.299 811 709 849 791 794 994 1.029 1.067 1.108 1.150 1.179 1.208 + Investments in associates 7 9 19 26 17 0 13 13 13 13 13 13 13 + Deferred tax assets 257 379 449 430 409 430 426 441 457 475 493 505 518 - Trade payables and other payables 6.441 5.988 6.415 6.655 6.633 6.652 7.052 7.304 7.572 7.858 8.158 8.362 8.571 - Forwarding in process, liabilities 1.361 1.120 1.418 1.283 1.284 1.252 1.420 1.471 1.524 1.582 1.642 1.684 1.726 - Deferred tax liabilities 429 449 576 527 411 411 521 539 559 580 602 617 633 - Corporate tax 68 0 228 427 197 244 116 115 112 116 120 123 127 NWC 1.150 253 236 129 559 810 1.033 1.074 1.121 1.163 1.207 1.236 1.267 Change in NWC 897 17 107 -430 -251 -223 -41 -46 -42 -44 -30 -31

Investments, Intangiable and tangiable assets 13.529 13.696 13.554 13.186 12.984 12.865 13.253 13.726 14.229 14.767 15.330 15.715 16.107 - Investments, depreciations 536 519 549 534 500 615 637 661 686 712 730 748 Change in Investments -703 -377 -181 -332 -381 -1.003 -1.111 -1.163 -1.224 -1.275 -1.114 -1.141 = Free cash flow to the firm (FCFF) 1.151 1.950 2.557 2.003 2.154 1.229 1.437 1.553 1.639 1.735 1.885 1.840 Change in net interest-bearing debt (NIBD) -2.403 -1.214 801 152 -731 859 299 318 336 352 241 245 Net financial expenses after tax -555 -537 -431 -246 -298 -236 -229 -242 -251 -261 -271 -278 Tax shield 325 151 118 72 78 77 70 68 71 74 76 78 Free cash flow to equity (FCFE) -1.482 350 1.443 1.677 1.203 211 979 1.060 1.122 1.196 1.450 1.396

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Forecast assumptions Historical period Explicit forecasting period Terminal Period Net working capital 2008 2009 2010 2011 2012 2013 Average E2014 E2015 E2016 E2017 E2018 E2019 E2020 + Trade receivables (non interest-bearing) 7.356 6.098 7.155 7.112 7.238 7.469 7071 8046 8334 8639 8966 9307 9541 9779 - Percentages of revenue 19.7% 16.9% 16.8% 16.3% 16.1% 16.3% 17% 17.0% 17.0% 17.0% 17.0% 17.0% 17.0% 17.0% + Forwarding in process. assets 530 513 541 604 629 676 582 663 686 711 738 766 786 805 - Percentages of revenue 1.4% 1.4% 1.3% 1.4% 1.4% 1.5% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% + Other receivables 1.299 811 709 849 791 794 876 994 1029 1067 1108 1150 1179 1208 - Percentages of revenue 3.5% 2.2% 1.7% 1.9% 1.8% 1.7% 2.1% 2.1% 2.1% 2.1% 2.1% 2.1% 2.1% 2.1% + Investments in associates 7 9 19 26 17 0 13 13 13 13 13 13 13 13 - Percentages of revenue 0.02% 0.02% 0.04% 0.06% 0.04% 0.00% 0.03% 0.03% 0.03% 0.03% 0.02% 0.02% 0.02% 0.02% + Deferred tax assets 257 379 449 430 409 430 392 426 441 457 475 493 505 518 - Percentages of revenue 0.7% 1.1% 1.1% 1.0% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% - Trade- and other payables 6.441 5.988 6.415 6.655 6.633 6.652 6464 7052 7304 7572 7858 8158 8362 8571 - Percentages of revenue 17.2% 16.6% 15.1% 15.2% 14.8% 14.6% 14.9% 14.9% 14.9% 14.9% 14.9% 14.9% 14.9% 14.9% - Forwarding in process. liabilities 1.361 1.120 1.418 1.283 1.284 1.252 1286 1420 1471 1524 1582 1642 1684 1726 - Percentages of revenue 3.6% 3.1% 3.3% 2.9% 2.9% 2.7% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% - Deferred tax liabilities 429 449 576 527 411 411 467 521 539 559 580 602 617 633 - Percentages of revenue 1.1% 1.2% 1.4% 1.2% 0.9% 0.9% 1.09% 1.1% 1.1% 1.1% 1.1% 1.1% 1.1% 1.1% - Corporate tax 68 0 228 427 197 244 194 116 115 112 116 120 123 127 - Percentages of revenue 0.18% 0.00% 0.54% 0.98% 0.44% 0.53% 0.6% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% Total net working capital (NWC) 1.150 253 236 129 559 810 523 1.033 1.074 1.121 1.163 1.207 1.236 1.267 Change in NWC 897 17 107 -430 -251 -223 -41 -88 -42 -86 -30 -61

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Appendix M: Forecast of Dividends and Share Buyback

2010 2011 2012 2013 E2014 E2015 E2016 E2017 E2018 E2019 E2020 Shares outstanding, ult. 210,4 190,7 184 179 159,5 148,5 147,5 138,5 135,5 135,5 135,5 Dividends 105 190 235 270 290 300 320 330 345 350 360 Earnings 1.194 1.449 1.430 1.571 1.604 1.722 1.859 1.968 2.081 2.029 1.986 Earnings per share 5,7 7,3 7,8 8,9 10,0 11,0 12,0 13,0 14,0 15,1 16,1 Dividends per share 0,5 1,0 1,3 1,5 1,8 2,0 2,2 2,4 2,5 2,6 2,7 Payout ratio 0,088 0,136 0,164 0,169 0,182 0,184 0,181 0,183 0,181 0,172 0,165

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Appendix N: Regression Analysis

Regression: Panalpina

40,0 y = 1,1349x + 0,5633 30,0 R² = 0,2523 20,0

10,0 0,0 -15,0 -10,0 -5,0 0,0 5,0 10,0 15,0

Return Panalpina Return -10,0

-20,0 -30,0

-40,0

Return MSCI World

Regression: Kuehne + Nagel 35,0 y = 0,7489x + 0,3031 30,0 R² = 0,2731 25,0 20,0 15,0 10,0 5,0 0,0 -15,0 -10,0 -5,0 -5,0 0,0 5,0 10,0 15,0

Return Nagel Return Kuehne + -10,0 -15,0 -20,0

Return MSCI World

Regression: DHL

25,0 20,0 y = 1,1341x + 0,4849 R² = 0,5865 15,0 10,0 5,0 0,0

-15,0 -10,0 -5,0 -5,0 0,0 5,0 10,0 15,0 Return Return DHL -10,0 -15,0 -20,0 -25,0 Return MSCI World

Page 123 of 127 Regression: DSV

70,0 y = 1,4085x + 0,556 60,0 R² = 0,4184 50,0 40,0

30,0 20,0 10,0 0,0 -15,0 -10,0 -5,0 0,0 5,0 10,0 15,0 Return Return DSV -10,0 -20,0 -30,0

Return MSCI

Page 124 of 127 Appendix O: Credit Analysis

US industrial long-term debt Three years median AAA AA A BBB BB B CCC EBIT interest cover 21.4 10.1 6.1 3.7 2.1 0.8 0.1 EBITDA interest cover 26.4 12.9 9.1 5.8 3.4 1.8 1.3 FCF/total debt, per cent 84.2 25.2 15 8.5 2.6 -3.2 -12.9 FFO/total debt, per cent 128.8 55.4 43.2 30.8 18.8 7.8 1.6 Return on capital, per cent 34.9 21.7 19.4 13.6 11.6 6.6 1 Operating income/revenue, per cent 27 22.1 18.6 15.4 15.9 11.9 11.9 Long-term debt/capital, per cent 13.3 28.2 33.9 42.5 57.2 69.7 68.8 Total debt/capital, per cent 22.9 37.7 42.5 48.2 62.6 74.8 87.7

US Treasury, 10 year AAA AA A BBB BB B 3.38 per cent 1.9 2.4 3.6 4.7 11.2 13.1 3.38 per cent 0.6 0.7 0.8 1.3 2.6 3.2 Average 1.3 1.6 2.2 3.0 6.9 8.2 Source: Petersen and Plenborg (2012 pp. 291)

Rating 2010 2011 2012 2013 Average EBIT / Net financial expenses 4.1 5.6 9.2 8.1 6.8 EBITDA / Net financial expenses 5.1 6.9 12.5 10.2 8.7 Free cash flow / total liabilities 27.1% 31.9% 24.6% 29.0% 28% EBIT / Invested capital 15.9% 18.2% 16.7% 17.7% 17% Total liabilities / Total assets 71.5% 76.6% 76.4% 73.0% 74%

Rating 2010 2011 2012 2013 Average EBIT / Net financial expenses BBB A AA AA A EBITDA / Net financial expenses BBB BBB AA A A Free cash flow / total liabilities AA AA AA AA AA EBIT / Invested capital BBB A A A A Total liabilities / Total assets B B B B B Total rating BB B A A A 3.8 2.6 1.6 1.8 2 AA = 1 A = 2 B = 3 BB = 4 BBB = 5

2010 2011 2012 2013 The weighted average effective interest rate (loans and credit institutions) 2.3% 1.9% 1.5% 2.2% Average 2.0% Credit spread according to S&P and Moody’s as of May 2,2 Credit spread according to Petersen and Plenborg 2,2 Credit spread according to DSV 2,0 Average 2,1

Page 125 of 127 Appendix P: WACC

Capital structure 2009 2010 2011 2012 2013 Closing price, end year 94 123,3 130 145,7 178,1 Number of shares (million), ultimo. 209 207 186 178 180

Market Capitalisation million DKK 19.618 25.462 24.134 25.944 32.058 +Net interest bearing debt 8.419 7.205 8.006 8.158 7.427

Enterprise Value 28.037 32.667 32.140 34.102 39.485

E/V 0,70 0,78 0,75 0,76 0,81 D/V 0,30 0,22 0,25 0,24 0,19

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