enabling one-way leases of temperature controlled ...1257780/fulltext01.pdfindustriell ekonomi och...

IN THE FIELD OF TECHNOLOGYDEGREE PROJECT MECHANICAL ENGINEERINGAND THE MAIN FIELD OF STUDYINDUSTRIAL MANAGEMENT,SECOND CYCLE, 30 CREDITS

, STOCKHOLM SWEDEN 2018

Enabling One-Way Leases of Temperature Controlled Containers: A Heuristic Model

SEBASTIAN FORSBERG

ANTON SVENSSON

KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT


By

Sebastian Forsberg

Anton Svensson

Master of Science Thesis TRITA-ITM-EX 2018:448 KTH Industrial Engineering and Management

Industrial Management SE-100 44 STOCKHOLM

Envägsuthyrning av Temperaturkontrollerade Containrar: En Konceptuell Modell

Av

Sebastian Forsberg

Anton Svensson

Examensarbete TRITA-ITM-EX 2018:448 KTH Industriell Teknik och Management

Industriell Ekonomi och Organisation SE-100 44 STOCKHOLM

Master of Science Thesis TRITA-ITM-EX 2018:448


Forsberg, Sebastian

Svensson, Anton

Approved

2018-05-28 Examinator

Nuur, Cali

Supervisor

Backteman, Richard

Commissioner

Contact person

Abstract There is an asymmetry in demand for transportation means of goods and commodities globally. One industry in which this trend is especially prominent is the pharmaceutical industry, where the European Union is by far the largest net exporter of pharmaceutical products globally, followed by Switzerland. The largest global net importer of pharmaceutical products is America, and given that many pharmaceutical products need to be transported in a cold chain, a niche within the transportation industry has grown – one that focuses primarily on transportation of high-value temperature-sensitive goods. Transportation companies working with circulating assets around the world need to determine how much capacity can be allocated to the sale of one-way trips (which may displace assets within the fleet to places in which they cannot be sold again due to the lack of business) whilst still allowing the sale of round-trip leases to continue. We conducted a case study within Company A that is a cold chain provider for air freight to provide context on how a heuristic model for capacity control should be developed, and incorporated this with learnings from theory within the fields of revenue management and fleet management together with literature in similar business settings. This resulted in a four-step model with unique planning horizons for each level, ranging from strategic perspectives for fleet balancing down to operational aspects of daily allocation and release of containers. We conclude which factors are essential for the context of the case study and showcase how a model can be constructed taking these findings into account. The thesis deals with the issue of capacity control for one-way leases. Other models have used pricing strategies to accomplish similar tasks and this is not included in the proposed model which is a limitation of this study, this is discussed and elaborated on. Furthermore, possible implications for the customer behaviour with the suggested model is discussed. Key-words Container leasing, Pharmaceutical, One-Way Leasing, Rentals, Capacity Control, Revenue Management, Yield Management, Fleet Management

Examensarbete TRITA-ITM-EX 2018:448

Envägsuthyrning av Temperaturkontrollerade Containrar: En Konceptuell Modell

Forsberg, Sebastian

Svensson, Anton

Godkänt

2018-05-28 Examinator

Nuur, Cali

Handledare

Backteman, Richard

Uppdragsgivare

Kontaktperson

Sammanfattning Det finns en global asymmetri i efterfrågan för transport av gods och råvaror. En industri där detta är speciellt framstående är läkemedelsindustrin – där Europa är den största exportören globalt följt av Schweiz. Den största importören av dessa produkter är Nordamerika, och givet att många läkemedelsprodukter behöver transporteras i en kylkedja har en nisch inom transportsektorn vuxit fram – en som fokuserar primärt på transport av temperaturkänsligt gods av högt värde. Transportbolag som jobbar med cirkulerande tillgångar världen över behöver bestämma hur mycket kapacitet som kan allokeras till envägsuthyrningar (vilket kan fördela flottan så att ingen efterfrågan finns för att sälja dessa igen) medan den fortsatta försäljningen av tur-och-retur-uthyrningar fortfarande tillåts. Vi utför en fallstudie inom Företag A, som tillhandahåller tjänster för kylkedjor gjorda för flygfrakt, detta för att skapa förståelse för hur en heuristisk modell för kapacitetskontroll kan utvecklas. Detta vävs samman med lärdomar från teori inom fälten intäktsoptimering och förvaltning och styrning av utrustningsflottor tillsammans med litteratur inom liknande affärskontexter. Detta resulterade i en fyrstegsmodell med unika planeringshorisonter för varje nivå. Modellen sträcker sig från strategiska beslut för balansering av flottan ner till operationella aspekter för daglig allokering och överlåtande av containrar. Vi sammanfattar vilka faktorer som är relevanta inom ramarna för fallstudien och visar hur en modell kan vara uppbyggd där dessa faktorer tas i beaktning. Avhandlingen hanterar problemet kapacitetskontroll för envägsuthyrningar. Andra modeller har använt prissättningsstrategier för att åstadkomma liknande mål och detta har inte inkluderats i den föreslagna modellen vilket är en begränsning i avhandlingen, detta diskuteras och vidareutvecklas. Möjliga implikationer på kundbeteende med den föreslagna modellen diskuteras också.

Nyckelord Containeruthyrning, Läkemedel, Envägsuthyrning, Uthyrning, Intäktsoptimering, Förvaltning, Styrning, Utrustning

Table of Contents 1 Introduction 1

1.1 Background 1 1.2 Problematization 2 1.3 Purpose 3 1.4 Research Questions 3 1.5 Company A 3 1.6 Contribution 4 1.7 Delimitations 4 1.8 Outline 5

2 Method 7 2.1 Research Design 7

2.1.1 Case Study 7 2.1.2 Concept Generation 8

2.2 Data Collection 9 2.2.1 Literature review 9 2.2.2 Interviews 9 2.2.3 Historical Data 11 2.2.4 Ethical Considerations 11

2.3 Research Quality 12 2.3.1 Validity 12 2.3.2 Generalisability 13 2.3.3 Reliability 13 2.3.4 Source Criticism 13

3 Theory 15 3.1 Theoretical Scope 15 3.2 Fleet Management 16

3.2.1 Fleet Planning 17 3.3 Revenue Management 17

4 Literature Review 21 4.1 Car-Sharing and Car Rental Pools 21 4.2 Container Leasing (Sea) 23 4.3 Other Transportation Sectors 24 4.4 Slot Management and Control 25

5 The Case – Company A 27 5.1 Shippers 27

5.2 Forwarders and Airlines 27 5.3 Company A 28

5.3.1 The Order Process 28 5.3.2 Exception-based Policy for Network Trips 29 5.3.3 Fleet Balancing 30

6 Analysis 32 6.1 Enabling One-Way Trips Within Company A 32 6.2 Creating Perishable Assets With Slots 33 6.3 Learnings From Reviewed Literature 34 6.4 Heuristic Four-Step Model for Enabling One-Way Leases Within Company A 37

6.4.1 Balanced Flow of Assets 37 6.4.2 Buffer Status 41 6.4.3 REPO Capabilities 43 6.4.4 Station Capabilities 43 6.4.5 Summarizing the Four Step Model 44

7 Conclusions 46 7.1 Answers to Research Questions 46 7.2 Study Limitations 48

7.2.1 Customer Behaviour 48 7.2.2 Pricing 49 7.2.3 The Heuristic Model 50 7.2.4 Cost Structures 50

7.3 Managerial Implications 51 7.3.1 Implementation 52 7.3.2 Sustainability 53

7.4 Further Work 53 8 References 55 9 Appendix 61

9.1 Appendix 1: General Interview Themes 61

List of Figures Figure 1 Theory Creation 8

Figure 2 Fleet and Revenue Management Intersection 16

Figure 3 Slot control problem of container sea-rail intermodal transport based on R.M. 26

Figure 4 Company A Order Process 29

Figure 5 Flow balance of the EU trucking cluster 38

Figure 6 Cluster balance illustration 39

Figure 7 The imbalances of the European trucking cluster between 2017-06 and 2018-03 40

Figure 8 The two types of slots needed and the corresponding logic of maintaining the fleet balance for each of these 42

Figure 9 A conceptual four-step model for capacity control for a global fleet of circulating assets. 44

Acknowledgements Initially, we would like to express our sincerest gratitude towards KTH Royal Institute of Technology and our supervisor Richard Backteman who has brought us valuable feedback in our writing process. Furthermore, we would like to thank our examinator Cali Nuur as well as peers for your valuable insights and guidance during the seminars. We would like to thank Company A and its employees for being so engaged and helpful with aiding in our project. Without the dedicated and talented staff taking time out of their day to help us with our case study, this would have been impossible. The cornerstone of our case study was our handler at Company A, having spent countless of hours, time and energy in helping to make this a reality. Thank you! Last, but not least, we wish to thank our families and friends for their unconditional support throughout the entire time of our education and while writing this master thesis. Sebastian Forsberg Anton Svensson

Glossary APAC Asia-Pacific

Destination The destination to which goods transported in the container are sent

EMEA Europe, the Middle East and Africa

Network trip Container release and return stations are not equal

Origin The origin from which goods transported in the container are sent

OTIF Number of orders “On Time In Full” according to the original order placed by the shipper/partner. The right number of containers and container types delivered at the right place and time

Release Station The station from which a container is released and the lease starts

REPO Internal short referring to the act of repositioning a container from one station to another

Return Station The station to which a container is returned and lease ends

RFQ Short for “Request for Quotation”

Round Trip Container release and return stations are equal

Shipper The end customer who ultimately uses the container to transport goods from origin to destination

SOL Short for “Start of Lease”

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1 Introduction In this section, the problem formulation, purpose, and research questions to be answered in this thesis will be formulated. Initially, a background chapter briefly introduces the air freight industry and Company A – the studied case company within this thesis. The company is facing challenges with fleet distribution caused by asymmetrical order distribution between different regions in the world. There is a widespread need for additional one-way leases, which is something that the company only offers on an exception basis in a limited amount. This thesis aims to develop a conceptual model to be able to drive sales by setting targets for specific trade lanes and for enabling a spot-market for selling overcapacity within the fleet and working more proactively with repositioning of assets.

1.1 Background Freight companies worldwide are challenged with advanced optimization problems for planning of the fleet and efficient use of internal resources. In 2003, Maritime transport accounted for approximately 74 % of freight transport worldwide, measured in freight movement in tonne-km per year. Air and road transport accounted for approximately 13 % each (Gilbert & Pearl, 2007). Air freight transport is a constantly growing business worldwide, with an average annual growth of 4 % between 2006 - 2016 (World Bank, 2018). Operating in a highly competitive market, airlines strive to find the best solutions for planning cargo distribution and allocation. However, given the widespread distribution and segmentation of different industries across the globe, the transportation needs vary. For instance, the European Union accounts for the greatest part of global exports of agricultural products, iron, steel, and chemicals, while as China is the largest exporter of textiles, clothes, and telecom equipment (World Trade Organisation, 2016). Brazil is a major exporter of soybeans to the European countries (Lee et al., 2012). In addition, the European region is by far the largest net exporter of pharmaceutical products with € 144 billion in exports in 2016, while America is the largest net importer of pharmaceutical products (Eurostat, 2017). However, different commodities require different shipping conditions. Some goods need to be carefully temperature controlled, some goods are perishables that need to be consumed within a specific time frame (putting constraints on shipping time), while such conditions may have little importance for other types of goods. Pharmaceutical products are one example of products that in many cases require a cold chain for the drugs to keep their quality. When considering transportation between continents, there are two primary options available: either maritime shipping or air freight. Out of the two, air freight is per volume the more expensive although faster option (Goel, 2008). This establishes the characteristics of products freighted by air as being relatively expensive and where shipping time is of importance. This can be seen as approximately 35% of the world's total cargo value (Sales, 2016), in comparison to

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the 13% of tonne-km (Gilbert & Perl, 2007), is freighted by air. Within this segment a deciding factor to ship by air is if the product requires a temperature controlled environment. These perishable products, such as fresh produce or pharmaceuticals, warrant a closely monitored environment within the logistics chain as fresh produce can turn bad or expensive pharmaceuticals can go to waste due to improper temperature control (Sales, 2016). These refrigerating standards are regulated by several instances, examples include the IATA TCR1 and the EMA GDP2. Pharmaceutical companies are some of the largest users of air freight in terms of cargo value, as pharmaceutical companies transport goods for over a trillion dollars yearly (IATA, 2018a). In comparison to the total cargo value of air freight which is approximately 6 trillion dollars yearly (IATA, 2018b), this means pharmaceutical products account for more than 16% of the total cargo value of air freight. Pharmaceutical products are commonly freighted by air mainly due to their high value, time constraints, and temperature sensitivity (Sales, 2016; Goel, 2008). Nonetheless, large amounts of pharmaceuticals go to waste due to improper handling, estimations point to numbers as high as 15 - 20 % (Sales, 2016). Thus, keeping pharmaceutical products at the right temperature and protected from the occasionally rough handling within air freight is of high importance (Goel, 2008). This is where Company A has excelled, selling a complete cold chain to be used within air freight. Within the studied case company, the cold chain service is provided through the means of leased temperature controlled containers which keep the goods cooled during transportation. There is an increasing market demand for one-way leases of these assets, which, with the above described phenomenon, would displace the fleet to where there is little demand for the return freight of containers. Thus, if one-way leases are to be allowed, there is a need for demand management – determining how much capacity can be allowed for these trips without creating a deficiency in the fleet. This further warrants the need for an internal perspective of how the fleet is managed currently and how this can be adapted to the emerging customer demand for one-way leases.

1.2 Problematization There are asymmetries in the distribution and demand for certain transportation methods globally, which constitutes a fleet planning problem for transportation companies. The same phenomenon is observable also within one-way leasing of circular assets, examples including car rentals and container leasing, where the distribution of demand may for instance vary depending on asset type, time of day, location of the asset (availability), and price (Oliveira et al., 2017). In conclusion, we formulate the studied phenomenon as “asymmetrical order distribution with regards to time and location causing challenges in fleet balancing and availability”.

1 Temperature Control Regulations - International Air Transport Association (2018) 2 Good Distribution Practice - European Medicines Agency (2018)

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Businesses which lease equipment, e.g. containers, for one-way leases along with the geographically asymmetrical order distribution phenomenon encounter a problem where the equipment is more often returned at certain locations than others. Over time, this may result in a fleet in which the assets are not located where the demand is.

1.3 Purpose The purpose of this study is to develop a heuristic model for fleet management with the purpose of mitigating issues with geographically asymmetrical order distribution. The study will derive which factors are essential to include to successfully help maintain fleet balance using such a model. Unlike previously made research within the same context, the aim is to create a model that is useful for long-term, strategic fleet planning rather than focusing on the daily dispositioning and/or movement of assets – proactive planning rather than reactive planning. The model should act as a control mechanism to avoid fleet segmentation while enabling additional one-way booking alternatives, with regards to current and future order offerings, fleet balance, and fleet utilization. A case study at Company A will aid in providing context for deciding what dimensions that should be considered in such a model.

1.4 Research Questions The following research questions have been established to help fulfil the purpose of this thesis:

1. Which fleet and revenue management considerations are essential for enabling intercontinental one-way leases in a global business?

2. How can a fleet be managed to allow intercontinental one-way leases whilst not affecting regular business?

These questions will be answered through a blend of contemporary theory and literature regarding one-way leases incorporated with learnings from a case study conducted at Company A, which is introduced below.

1.5 Company A Company A will help form the foundation of this report by being the subject of a case study. By incorporating learnings from the case as well as contemporary theory and literature within fleet and revenue management, our aim is to develop a heuristic model for capacity control for intercontinental one-way leasing. Company A offers a fleet of temperature controlled containers for global lease. The company holds thousands of containers that are distributed over more than 50 stations worldwide. The containers are not allocated to specific stations, but rather moved once requested or needed at another station. The main shippers are large pharmaceutical

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companies shipping high-value goods, and expecting high refrigerating standards to keep goods at the right temperature at all times. The company is facing challenges with fleet distribution caused by asymmetrical order distribution between different regions in the world. One-way trips are allowed in a limited quantity for specific assets on certain trade lanes, but there is a widespread demand for additional one-way leases by customers. This is mainly due to high return logistics costs for customers booking round-trips.

1.6 Contribution This thesis will contribute to research within fleet and revenue management by developing a conceptual model for capacity control within a global fleet of circulating assets. Although the case study was conducted in a niched business, we believe that the findings may prove to be useful within other contexts as well: car rentals, air freight, and maritime container leasing. To our understanding, no previous studies have been made within fleet and/or revenue management for the specific niche of containers used for air freight. Previous literature in related settings have also had a high emphasis on increasing revenue or maximizing profits through optimization of internal processes, some examples are brought forward in the literature review. Instead, we have approached the problem from an outside-in perspective by firstly looking at customer behaviour and then attempting to find ways of satisfying that demand. As such, this thesis will contribute with complementing viewpoints to the existing literature within fleet and revenue management.

1.7 Delimitations For the purpose of analysis, the behaviour of the customers, namely the shippers and/or partners of Company A, is assumed to remain unchanged – meaning their needs are what they are and we will not attempt to change customer demand. Thus, the solution should be centred around fulfilment of customer’s needs rather than altering their behaviour to achieve more efficient internal logistics. The proposed solution will focus on providing service offerings which can increase the value for customers through improved internal logistics within the company which meet the customer needs. Furthermore, the conducted case study will be limited to Company A. Seeing as there are several major differences between industry peers, e.g. fleet and network size, taking all of these into account would diminish the depth which could be achieved within the case study. Thus, the choice was made to focus only on Company A although generalizable conclusions can be drawn on an industry wide perspective. Company A has several types of containers in different air cargo standard sizes which are constructed on different technological solutions. The thesis will only treat one of these container

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types but the authors expect the conclusions drawn from the thesis to be applicable to any other type of container within Company A. Furthermore, Company A has several trucking clusters, the quantitative part of the analysis, with data drawn from the companies’ ERP system, will only consider three clusters of different sizes and on different continents. These are expected to provide sufficient reference points for the other clusters. This report will mainly consider means of capacity control of assets. Within fleet and revenue management, it is common to incorporate pricing decisions to provide a full-flexed solution. However, capacity control is one of the prerequisites of revenue management, which is why we have taken a starting point in that and purposely left out pricing due to time constraints. Looking closer into pricing of assets for revenue management will be discussed and suggested for further work.

1.8 Outline In Table 1, we present the thesis outline to aid the reader in navigating through this thesis. Following this introductory chapter, the chosen methods within this thesis are presented and motivated. A case study approach was chosen in which qualitative interviews laid the empirical foundation together with the following theory and literature review chapters. Fleet and Revenue Management is introduced in the theory chapter. In the literature review, similar business contexts have been examined to aid in the development of a model for capacity control. Next, we present the case company that gave us hands-on experience with the challenges faced within the industry, followed by an analysis chapter where we conclude a four-step model for enabling one-way leases after drawing learnings from previous contexts. Lastly, we answer the research questions and critically evaluate the limitations of this study and suggest further work in the conclusions chapter.

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2 Method In the methodology chapter, the aim is to describe the way the thesis is structured in terms of achieving the purpose. In addition to this, it will describe the ways we acquired and evaluated the empirical material used within the thesis.

2.1 Research Design This chapter will describe how the described problem is made researchable. The research has been centred around a case study performed at Company A. From the case study, the ambition was to draw generalisable conclusions which could aid in the fields of fleet and revenue management, specifically to give a conceptual model of a strategic approach to enable one-way leases for a leasing fleet. In leasing businesses similar to container leasing, e.g. car rentals, fleet and revenue management theory and empirics is scarce (Oliveira et al., 2017). With respect to this, an exploratory research approach (Collis & Hussey, 2014) was chosen in an attempt to discover new aspects within the research area related to container leasing for air freight specifically. The thesis was conducted in an inductive way, meaning the method was to first review literature on similar topics before framing the thesis theoretically. According to Blomkvist and Hallin (2015), it is common an exploratory research method is combined with an inductive approach within social sciences. Within the thesis, qualitative interviews were the primary way of gaining the empirical material needed for analysis, which is common with an inductive approach as it makes conclusions from specific observations in its context into general statements (Stentoft & Halldorsson, 2002).

2.1.1 Case Study We decided to conduct a case study within Company A to understand the dynamics of the air freight container-leasing business to adequately address and answer the research questions. The case study approach is fitting as it allows the exploration and in-depth knowledge of a specific phenomenon within its real-life context (Crowe et al., 2011), meaning the problem described within in the case in the environment of the case. Crowe et al. (2011) further present a general checklist which has been followed, along with common pitfalls of case studies which have been actively sought to be avoided within the thesis. Two of the categories listed are content and method. Three major questions encompass the content; the case being thoroughly defined, major research questions being lucidly defined, and sufficient data sources having been included. The method part includes how the data gathering was conducted and outline in the thesis as well as the triangulation and validation of data and results. They appoint selecting and conceptualising the wrong case as one of the primary pitfalls of a case study approach. This was mitigated by developing in-depth knowledge of theoretical and empirical literature, beyond what is finally used within the thesis, where this knowledge was used to accurately frame the case. This is also closely related to the common pitfall of integration with the theoretical framework, where

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forcefitting is a common issue. Furthermore, bounding and defining the case is of importance, lucidly communicating what is inside and outside of the scope of the study. Blomkvist and Hallin (2015) mention a common problem with case studies being the generalisability of the results, as these can be hard to generalize into broader context, this is also supported by Collis and Hussey (2014). Therefore, the authors consider the implications and ambitions of the thesis in the following chapter.

2.1.2 Concept Generation Considering the lack of theory on fleet and revenue management as it connects to the service offering within logistically circular business such as container leasing, some form of theory creation is necessary to be able to draw constructive conclusions. Stentoft and Halldorsson (2002) suggest four quadrants to classify knowledge creation based on empirical research within logistics. Refer to Figure 1 for a visualization of their model. The quadrants are grouped firstly by the means, referring to the solidity of the theoretical foundation of the research area, where a “solid” theory is normally descriptive in nature and “loose” will be more normative. Secondly, the quadrants relate to the ambition of the research where either theory is tested or new theory is developed. The thesis is positioned in the second quadrant of the model, meaning the generation of new concepts. The theory base within this thesis is loose as almost all theoretical inspirations from both fleet and revenue management are solutions to practical problems (Oliveira et al., 2017), generalisable conclusions are thus scarce and the theory is less descriptive in nature. The same goes for the case study in this thesis, as the conclusions are derived from a case study, generalisability can be questioned (Collis & Hussey, 2014). Furthermore, since there are no previous attempts at solving a similar problem outlined within the case, testing theory is difficult, implicitly rendering the thesis as a theory development rather than a theory test. Therefore, keeping the thesis on a conceptual level is a fitting approach.

Figure 1: Theory creation. A theory creation model by Stentoft and Halldorsson (2002).

There are, however, some difficulties with using this approach which relate to the inherent width of the research area, creating an umbrella for several different fields. This could possibly make

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the thesis subject to “re-labelling” or “title re-engineering”, where new management concepts are introduced for the sake of creating a new concept. (Stentoft & Halldorsson, 2002). To avoid this, the authors have deliberately been careful with creating titles for concepts and rather consistently attempted to use the existing terms within fleet and revenue management, which are two established fields (van Ryzin & Talluri, 2005).

2.2 Data Collection This subchapter describes from where and how data was collected throughout the thesis, including the method used to conduct interviews and ethical considerations with the chosen approach.

2.2.1 Literature review An extensive literature review was conducted encompassing several more theoretical fields than those mentioned within the thesis. This “über-reading” method was used to gain an all-encompassing understanding of the theoretical context of the thesis (Blomkvist & Hallin, 2015). Additionally, it was used to ensure that both the theoretical and empirical framing of the report was aligned and fitting to the case problem. This is mentioned by Crowe et al. (2011) as one of the primary pitfalls of using a case study approach. Thus, it was highly prioritized to ensure that the theoretical framing was correctly accomplished, which was complicated by the lack of research within the context of the case. This means the thesis was highly dependent on the literature review on practical examples of other industries facing similar challenge, e.g. car rentals. This also had other positive effects including gaining a general understanding of the topic as well as detailed insight into areas of interest (Collis & Hussey, 2014). The literature review has been a continuous process throughout the writing of the thesis which has contributed to ensuring that the results and conclusions drawn are both valid and reliable. Primarily only peer reviewed scientific articles have been used, found using search engines such as KTHB Primo and Google Scholar.

2.2.2 Interviews Interviews were used as a qualitative data gathering tool within the study. The conducted interviews were semi-structured, meaning the use of open questions where the questions do not dictate the entire interview but serve as a reference from which the interviewee can elaborate (Collis & Hussey, 2014). Conducted interviews within the study had several different purposes and were structured according to each interview’s purpose respectively. However, some general themes and questions were outlined and asked throughout the interview process. An example of interview structure may be found in Appendix 1. The interviews were conducted in two different settings. In the beginning phase of the study, achieving insight into the working processes and individual challenges of each region and

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department of Company A was considered vital to ensure the correct problematization and purpose of the thesis. The interviews conducted in this phase were less structured and allowed more freedom from the interviewees. To encourage interviewees to share personal opinions which may not be supported internally, these interviews were kept informal, meaning not recorded or transcribed. The second interview setting was used to gather the qualitative material which would form the basis for analysis. These interviews were more structured than previous interviews, with a set agenda and more detailed questions, and were recorded and transcribed. Some of the informal interviews were conducted as workshops with internal stakeholders at the case company to help build a deep understanding of the unique challenges that the case presented and to acquire feedback on our model as it evolved over time. This setting was also used to determine case specific priorities for the analysis. To contextualize the qualitative data gathered (Collis & Hussey, 2014) and to further clarify what the interviewees’ positions were and motivate why the perspectives of these were important, a table stating their position, their department, and date of the interview, was created. The acronyms for the employed interviewees consist of two or three letters and one number respectively. The letters represent the department for which the interviewee works, and the number represents a specific person at that department. The formal interviews conducted which were used as a qualitative data gathering tool for the thesis are listed in Table 2.

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Table 2: The qualitative data gathering interviews

Acronym Job Title Department Date

OPS1 Global Fleet Planner Operations 2018-03-07

OPS2 CCR Team Leader & Logistics Planner SIN Operations 2018-03-14

SM1 Senior VP Sales & Marketing 2018-01-22 2018-03-20

OPS3 Head of Logistics planning & processes Operations 2018-04-16

2018-05-08 OPS4 Head of Operations EMEA Operations 2018-04-20 OPS5 Logistics Planner FRA Operations 2018-04-19 FI1 Head of Pricing & Offering Finance 2018-03-14

OPS7 Head of Global Logistics Operations 2018-04-24

OPS8 Head of Operations Americas Operations 2018-01-16

OPS9 CCR Team Leader FRA Operations 2018-03-20

SUP1 Supplier to Company A – Site Manager Supplier 2018-04-18

2.2.3 Historical Data From Company A, access was given to the ERP system which provided data regarding historical orders, asset movements, and various other case specific parameters. This data was used to aid in designing the model for one-way leasing, where the primary purpose was to provide supporting examples of the logic created. Additionally, it provided insight into and supported the business complexity and facets which have emerged through the qualitative interviews. This way, the historical data could provide another source of objective reliable data to support the gathered empirical data from the qualitative interviews. The data was analysed solely through Microsoft Excel.

2.2.4 Ethical Considerations The Swedish Research Council’s four ethical requirements for scientific work were applied throughout the work of this study. Below is a summary of the steps taken to ensure an ethical academic working process:

The information requirement - Prior to the interviews, interviewees employed were informed about the purpose of the study and how the knowledge acquired from the interviews would be used.

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The consent requirement - All the interviewees employed agreed to contribute to this study with their knowledge. Further, they were made aware that participation was voluntary and by no means necessary. The confidentiality requirement - The name of the studied company was censored for confidentiality purposes. Further, interviewed stakeholders were kept anonymous as to not disclose their identities, and the authors signer a non-disclosure agreement with Company A. The good use requirement - The data gathered was used only for the purpose of this study and provided by interviewees who agreed to participate in the study, or digital data sources within the company after being approved from an internal compliance perspective.

2.3 Research Quality Saunders et. al (2009) identify validity, generalisability and reliability as three vital aspects to consider with respect to research quality. Our approach to each of these aspects will be discussed in this chapter joint with an application of source criticism regarding our work.

2.3.1 Validity Validity refers to the extent to which the theory, literature, method, collected data and findings of the study are aligned with the problematization, purpose and research questions. It is about studying the right thing, and using the right tools to do so (Blomkvist & Hallin, 2015; Saunders et al., 2009; Golafshani, 2003). Attention was paid to ensure the validity of qualitative data gathered in this study, considering that the primary way of collecting data was a qualitative approach through semi-structured interviews, even though validity is often high with similar approaches (Collis & Hussey, 2014). One possible deficiency with interviews is the risk of misunderstandings caused by misinterpretations of the questions posed, thus causing the interviewee to provide an answer to another question. The same bias exists conversely; the answers provided by the interviewee may be misinterpreted by the interviewers. To overcome these interview biases, the interview questions were sent to all interviewees well in advance of the interviews. Interviews were recorded where the interviewee deemed it appropriate. Two interviewers were present at each interview - one being in charge of leading the interview, and one being in charge of documenting the interview. A summary of the answers to each question was provided to the interviewee by the interviewers at the end of each interview to ensure that the answers had been interpreted correctly. To further ensure the validity of the research triangulation methods were employed. This means to use two or more theories, data sources or methods for a single phenomenon (Yeasmin & Rahman, 2012). Internally, employees from diverse business units were engaged in the

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interviews to gather manifold perspectives of the problem and input of ways of solving it. Furthermore, this was used alongside with the two theoretical fields of fleet and revenue management. To support parts the qualitative data, historical data was used from ERP system. These combined allowed a thorough triangulation, and in turn ensured the validity of the results.

2.3.2 Generalisability Within a case study limited to a single company, operating in a single industry, the scope is narrowed considerably meaning generalisable results are less outstanding. However, Collis and Hussey (2014) argue that general conclusions are a possibility if key interactions and characteristics of a phenomenon have been captured within the research. Following this, we examined a variety of similar business contexts and their unique challenges, and adopted the learnings made from those contexts to the model, thus making it suitable for other businesses than the case company with only minor tweaks. The Concept Generation has taken a more in-depth view of generalisability and how the authors aim to achieve generalisable results within the thesis.

2.3.3 Reliability The reliability of the gathered qualitative data is dependent on the level of consistency in how similar questions were answered. This means that the generated qualitative data must be repeatable without large discrepancies if the data collection would be repeated (Collis & Hussey, 2014). Availability of interviewees within the thesis was not a problem, meaning several of the interviewees were interviewed more than once. These interviews, as described in the Interviews chapter, ranged from introductory to formal qualitative data gathering sessions which were recorded and transcribed, giving the researchers a chance to assess reliability of the data depending on discrepancies in the answers with regards to the level of formality. Additionally, considering that a majority of the interviewees worked within Company A, establishing an objective view meant conducting interviews in several departments, as these tended to have rather different views on the issue at hand. Thus, the data gathered was further ensured as reliable as data gathered from one department was examined in parallel to that of other departments. This meant that careful phrasing of questions, within the semi-structured approach of interviews, was a key tool in ensuring repeatable data.

2.3.4 Source Criticism The literature employed in theory and literature review chapters have been selected to mainly consist of peer-reviewed academic articles from recognized journals, ensuring high academic validity and reliability. Where the reviewed literature showed a difference of opinion between authors, this has been included to be as transparent as possible. Furthermore, the qualitatively gathered data also must be subject to criticism, as the interviewee’s can be biased towards their department or position (Collis & Hussey, 2014). Thus, interviews were conducted within all

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departments which were directly involved in the case problem, this rendered a more complete picture of the problem allowing the authors to gain their own understanding with less bias. For transparency in the scientific process, the titles and department of each interviewee was listed. In some cases, several qualitative data gathering interviews were conducted where interviewees had ambiguous answers, or the authors deemed another interview was necessary to allow the interviewee to further elaborate, as can be seen in the table of interviews in the Interviews chapter.

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3 Theory This chapter introduces the theoretical framework composed of fleet and revenue management, with two in-depth chapters of the contemporary theory used within these fields, these will be employed to analyse the empirical findings and aid in generating qualitative guidance for the thesis.

3.1 Theoretical Scope Within the thesis, an outside-in perspective has been employed to ensure that the customer needs are put in the first place. An outside-in perspective means starting from the demand of the market rather than optimizing internal processes first and then pushing them to the market (Day & Moorman, 2010). The case warrants both an internal perspective, resolving the operational problem inherently created with selling a one-way trip, as well as a market facing perspective where the demand must be managed to continuously be able to serve customers. Thus, it stands to reason that both operational and customer centric perspectives are needed to establish a theoretical framework which will be all encompassing. Fleet management is the commonly referred to term which handles monitoring, controlling in planning (Goel, 2008) fleets of equipment to meet customer requests as they evolve over time (Powell & Topaloglu, 2005), i.e. an operation centred theory which deals with demand as an external factor that the fleet must cater to. On the other hand, revenue management, or commonly referred to as demand management (van Ryzin & Talluri, 2005) is sales and customer centric in comparison. The two theories are closely related (Oliveira et al., 2017), where they merge is in the fleet planning as revenue management is closely integrated with fleet planning, capacity sizing and decisions where to locate capacity (Talluri & van Ryzin, 2004). Oliveira et al. (2017) argue for the interdependence between these, where the lines between the two fields often appears ambiguous as similar decisions are treated within both fields. An example of the relation between these include revenue management dealing with both empty repositioning, which is an operational issue, and capacity control (Oliveira et al., 2017). Carroll and Grimes (1995) exemplified this with a revenue management system encompassing strategic decisions on fleet planning and deployment. These two theoretical fields create the foundational theoretical framework of the thesis, together providing both of the sought perspectives. Summarized, the principal theoretical fields covered by the report is revenue and fleet management, with the subfield of fleet planning, which are visualized in Figure 2.

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Figure 2: Fleet and Revenue Management Intersection. A visualization of how fleet and revenue

management theoretical scopes intersect. Operations refers to the internal operations department handling the movement of assets and warrants fleet management, while as “Sales” refers to the internal sales department which is more closely related to revenue management. For long-term authenticity of the

solution, these perspectives need to be considered simultaneously.

3.2 Fleet Management Fleet management problems encompass monitoring, controlling and planning (Goel, 2008) fleets of equipment to meet customer demand as it emerges as time progresses (Powell & Topaloglu, 2005). Equipment might be containers which hold freight, rental cars, or business jets. Typically, this equipment provides customers (people or freight) with means of transportation from one place to another (Powell & Topaloglu, 2005). Fleet management deals with questions such as network design, fleet design, fleet utilization, booking requests and scheduling. Furthermore, it embeds decisions within different strategic levels and studied by different research areas, related to clustering, fleet size and composition, fleet distribution, order acceptance & allocation, and pricing (Oliveira et al., 2017). Arguably one of the main challenges which is faced within fleet management is the fact that information emerges over time (Powell & Topaloglu, 2005). Thus, from the decision makers perspective, information is dynamically revealed, meaning that all information is not necessarily known when in the planning stage and may be subject to change as time progresses (Zeimpekis et al., 2007). Adding to the complexity, this is often accompanied with a short time frame to service the customer, making it difficult to wait for the customer demand to be known before acting (Powell & Topaloglu, 2005). Fleet management is a mature field in industries such as passenger airlines and air cargo, rail freight, and maritime shipping (Oliveira et al., 2017). Present theory within the fleet management field is moving beyond merely looking at efficiency in internal logistics processes, such as routing, towards improving the service level offered to customers, agility and responsiveness to varying requirements (Zeimpekis et al., 2007). This research arose from a practical need within

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each of these industries and are more focused on the practical implementation rather than describing underlying theoretical concepts (Oliveira et al., 2017). Reviewing the literature within fleet management lends solutions to each unique industrial context and its inherent challenges rather than underlying theory. There are, however, considerable differences between these and the field of container leasing for air freight which impose specific constraints on the fleet management models and warrants further research. This will be further discussed in the literature review.

3.2.1 Fleet Planning Fleet planning, as a subfield of fleet management, refers specifically to the allocation of assets to be able to meet demand (Teoh & Khoo, 2016). For any industry or company working with the movement of assets, it is important to have some kind of fleet planning strategy in place. It may refer to the distribution of cabs within a city to be able to cover important business areas (Manna & Prestwich, 2014), the allocation of airplanes and usage of airport slots in an attempt to maximize the revenue for a flight (Teoh & Khoo, 2016), or the distribution of empty containers to be able to meet customer demand in an agile manner (Shen & Khoong, 1995). To add to the complexity, additional limiting parameters such as asset types, customer segments, and time and location constraints might be necessary. A central aspect of fleet planning is fleet balancing, which refers to how the fleet is distributed within the network. Fleet balancing issues are commonly applied within several business areas such as the distribution of cabs within a city (Manna & Prestwich, 2014) or the positioning of bicycles in a vehicle sharing system (Gavalas et al., 2015).

3.3 Revenue Management Perishable asset revenue management (PARM), also referred to as yield management (Bodily & Weatherford, 1995), pricing revenue optimization, demand management, demand-chain management (van Ryzin & Talluri, 2005), originated from the airline industry, though it has presently been applied in several other industries, e.g. hotels, and assumes that the service or product becomes worthless after a certain deadline (Vanamalla & Parthasarathy, 2011). According to Berman (2005), yield management is, in short, how a service provider with a fixed capacity can secure higher revenues. This is accomplished through the process of allocating the capacity to the right customer for the right price, thus, maximizing yield or revenue (Kimes, 1989). This is founded in the assumption of the margin cost of providing the service being low and the asset being perishable, stipulating the need to sell all available capacity. As such, a common yield management pricing strategy currently used in airlines is low prices far away from the departure date which increase as the date approaches, only to drop at the last minute to ensure all the capacity is sold.

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In a traditional yield management model, the seller segments the market according to price sensitivity and creates sufficient restrictions between these customer segments to prevent dilution. Following this, a price model is established for each customer segment, based on forecasted demand, and the capacity is allocated (Hellermann, 2006). The yield management model influences the customer purchasing behaviour, customers which are price sensitive tend to purchase their tickets far in advance to ascertain a lower price, thus actual demand is known earlier than without yield management. Persson (2013) showed that other variables than price can influence the purchasing behaviour of customers, using for instance the stock quantity as an incentive. Van Ryzin and Talluri (2005) formulate revenue management as “a firm’s interface with the market, concerning demand management decisions including the methodology and systems required to make them”. Thus, it merges with fleet management (Oliveira et al., 2017) where it is closely integrated with fleet planning, capacity sizing and decisions where to locate capacity (Talluri & van Ryzin, 2004). Revenue management primarily addresses three categories related to demand management (van Ryzin & Talluri, 2005), which are:

Structural - the structure and format in which sales are made. Price - how to price products and services over time and between product categories. Quantity decisions - deals with accept/reject decisions for buying, each segments, products or channels capacity allocation which includes the decisions on when to cut off sales.

They further argue that revenue management strategies are either quantity-based or price-based if they use capacity allocation or prices as the primary tool for demand management. With the example of yield management strategy adopted by airlines, this would constitute a price-based strategy. The segmentation of revenue management is supported by Oliveira et al. (2017), naming revenue management frameworks as functions of either pricing or capacity control. The choice between these is dependent on the business in question and how much influence the business has over either variable. Oliveira et al. (2017) underline the relevance of using integrated approaches for contemporary applications of revenue management, where both price and quantity is used for revenue management. Several contemporary models have been established using integrated approaches, among them Feng and Xiao (2006), where a choice of class is first made and the pricing decision is made post this choice. Furthermore, Maglaras and Meissner (2006) created a formulation for both dynamic pricing and capacity allocation, controlling whether to accept or reject new

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requests. Oliveira et al. (2017) further argue that there is a growing consensus among researchers and practitioners that these two concepts are inevitably linked and cannot be separated. According to Harris and Pinder (1995), Kimes (1989), and Weatherford and Bodily (1992), summarized by Hellermann (2006), certain characteristics of the business conducted are required to successfully implement revenue management. These are as follows:

Perishability - meaning the product or service cannot be stored and sold at a later date. In the case of an airline, a seat on a plane which has departed cannot be sold again. Fixed capacity - the amount of capacity available is difficult to adjust in a short time frame. Low marginal cost - meaning the extra cost incurred for selling one extra unit is low in comparison to adjusting the capacity. Segmentable demand structures - the customers must be segmentable with factors that can differentiate between price-sensitive and price-insensitive customers. An example is segmenting with a time factor as in airlines, where price-insensitive customers book close to a departure date. Advance sales / bookings - seeing as sales made in advance often enable time to be used as a segmentation strategy. Stochastic and fluctuating demand - fluctuating demand implicitly means that having tools to regulate demand is of importance. If demand was fixed and known, the right capacity would be easily achieved by adjusting the available capacity. With a fluctuating demand, capacity utilization is achieved through demand management. Historic sales data and demand forecasting - revenue management approaches are reliant on forecasts as a base for decision making, the company needs historic data on sales to accurately model demand.

In contrast with the above characteristics, van Ryzin and Talluri (2005) argue that such criteria are a direct consequence of the history of revenue management, stemming from airlines, and that most contemporary businesses which sell products or services can benefit from revenue management, especially those where managing demand is of tactical importance. The authors further argue that rather than specific traits of the business, there are certain conditions and internal capabilities which can be leveraged for a more favourable revenue management application, these are:

Customer heterogeneity - the less homogenic the customers, the larger is the potential for leveraging the customers’ variations in price sensitivity, preference for products, or purchasing behaviour by segmenting the market.

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Demand variability and uncertainty - the need for sophisticated decision support increases with higher demand variability and uncertainty – making forecasting more difficult. Product inflexibility - means that the need for demand management rises as it is difficult and costly to handle changes in demand. This means that demand management is more beneficial than in businesses where capacity is flexible. Data and information systems infrastructure - required to fully implement revenue management where accurately modelling demand is essential to success. Management culture - is needed to efficiently implement revenue management as it is dependent on management which is trusting and familiar with science and technology.

Revenue management stemmed from a practical need to manage demand, using either capacity or pricing. Thus, most initial models on revenue management were practical models applied within a certain business context, e.g. hotel rooms, car rentals or airlines. In practice, most models used within revenue management are heuristic and not exact optimization models, in part due to practice being ahead of underlying theory. The benefit is that heuristic models are simpler to code, quicker to run and easier to understand whilst being relatively close to the exact optimization models (van Ryzin & Talluri, 2005). There are, however, some common traits which are often used within quantity-based revenue management strategies. Booking limits determine the amount of sales possible for a given class at a given time. Protection levels dictate the capacity which is reserved for each class. Displacement cost is the overarching logic of quantity based strategies, meaning the option to sell capacity should only be done if the revenue from a sale is greater than the value of the capacity to meet the demand, valuing this capacity as its expected opportunity cost (Talluri & van Ryzin, 2004). Price-based revenue management is instead based on the rationale that increasing price reduces sales thus affecting the demand (Oliveira et al., 2017), as such it is argued to be a more profitable way of demand management as it not only limits quantity but also increases the revenue at the same time (van Ryzin & Talluri, 2005).

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4 Literature Review In this section, we present the literature that will provide the foundation of the study. By drawing from empirics in relevant fields, taking the individual differences between the fields into account, the already existing research can contribute to valuable insights for the scope of this study. The knowledge contained in this chapter is case specific and will be extrapolated to contribute within the constraints of our case. We will present some works made regarding contemporary leasing models in similar industries and identify the gaps to which this thesis will contribute. To our understanding, an analysis of fleet management for container leasing for air freight has not yet been conducted in an academic setting. Similar studies have been made within the business of container leasing (focusing rather on different types of containers than those provided by Company A), car rentals, and transportation systems – such as airlines – where seat reservation is being utilised. However, there are several differences which inhibit the direct application of solutions found in these fields. One example is the stakeholder structure, where the end customer (shipper) does not place an order with Company A directly, but through intermediaries such as a forwarder or partner that in turn has an agreement with Company A. Thus, in contrast to many of the empirical foundations used in this chapter, interests of additional stakeholders need to be taken into consideration in the formation of lease agreements.

4.1 Car-Sharing and Car Rental Pools A business with several similarities to that of Company A is car rentals. In comparison to container leasing, car rentals is a fairly well-developed field and both fleet and revenue management have been covered within contemporary research. Car rentals share a similar leasing model as that of Company A, where an asset is booked, leased, returned, inspected (possibly repaired if needed) and then made available for the next lease. Additionally, the time span rented car is similar to the timespan of a leased container from Company A, which is roughly a week. For car rentals, fleet management and planning is a key activity, since a majority of the costs within a car rental business stems from an idle fleet (Oliveira et al., 2017). Activities which are similar to that of Company A include the utilization of empty transfers, or repositioning to meet demand and maintain fleet balance due to one-way rentals. The customer demand within car rentals is stochastic and is often hard to predict, making demand management crucial for efficient use of the fleet. All in all, this makes the car rentals field highly relevant to draw parallels from within our study, with some differences that will be highlighted throughout the chapter. Several studies within the area of car-sharing and car rentals have managed to invent models for optimisation of fleet distribution. Boyacı et al. (2014) studied one-way car-sharing systems in Nice, and developed a Mixed-Integer Linear Programming (MILP) model for planning the

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distribution of charging stations, the number of vehicles to include in the fleet, and a multitude of relocation variables to help decision-makers shape the car-sharing platform. Within the same context, Correia & Antunes (2012) developed a model for the optimization of depot positioning with regards to logistics costs and revenue for one-way car sharing. Rather than focusing on the repositioning of assets between existing stations, their model seeks to find the optimal placement of pickup and drop-off points for a car sharing platform based on trip data within the city of Lisbon (Correia & Antunes, 2012). Barth & Todd (1999) developed and evaluated a simulation model for performance analysis of a multi-station shared vehicle system, taking financial and operational aspects into consideration. Kek et al. (2009) examined the operational aspects of vehicle relocation operations by developing a decision support system which incorporates costs and staffing planning. The model suggested that the number of repositionings could be reduced by roughly 40 %, and staff cost savings of almost 50 % could be achieved. Common for these works is that fleet management and revenue management were incorporated together to different degrees to ensure higher revenues and a better service level to the customers. Oliveira et al. (2017) studied theoretical frameworks and empirical material to consider for the car rental fleet management problem to support further work within the field. The car rental industry is much like the container business that Company A runs – given that one-way rentals is sought-after by customers, and that it presents a logistical issue for the car rental company at hand and not all one-way rentals may be approved. Planning is obstructed in both cases by uncertainties in asset returns. Additionally, both industries are subject to migratory inventory, meaning one-way trips which result in a network imbalance (Carroll & Grimes, 1995). Unlike the case for air freight and air passenger seat reservations, overbooking – which is a typical revenue management technique within those industries (Hellermann et al., 2013) – is not widely utilized within the car-sharing or car rental industry. This could be explained by the highly stochastic and flexible demand for vehicles across stations, as well as the importance of fulfilling customer orders (Oliveira et al. 2017). Within air transport, however, airlines strive to maximize their revenue by overbooking seats and freight space to statistically established levels of non-showing passengers and freight (van Ryzin & Talluri, 2005; Hellerman et al., 2013). While providing useful insight into how fleet management should be designed, there is a lack of global perspective and scale that is crucial for fleet planning at Company A. Moreover, previous studies have mainly taken an inside-out perspective on the phenomenon - meaning that emphasis has been put on optimization of internal logistics and maximizing revenue. The car-rental and the car-sharing industries are also different in the stakeholder structure, where customers usually place their order directly with the rental company and not through an intermediary, which is the case for Company A. Further, car rental customers generally place single rental orders rather than asking for an arbitrary number of assets. One of the measures used within car rentals to increase utilization and order deliverance is the upgrade and downgrade of orders (Oliveira et al., 2014). E.g., if a car from a certain price class is not available at the appointed time for pick-up of

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the car, it is conventional to offer the customer an upgraded offer without additional cost. If no such option is available, the customer may be offered a downgrade at a discounted cost. In both cases, the decision falls upon the customer to accept or refuse the order. Within the car rental industry, capacity is quite flexible due to fleet pooling, where car stations share a fleet of cars and capacity can be adjusted to meet a fluctuating demand, inter-pool movement of assets, where assets move between pools and by controlling the sale and turn-backs of older vehicle (Talluri & van Ryzin, 2004). This creates the capability to adjust the capacity of a single station down to a weekly basis if needed, an example is moving assets to the inner city to meet demand of leisure customers during weekends and repositioning these to the airports for business customers on weekdays (Carroll & Grimes, 1995). Meaning assets can, at a cost, be repositioned to cater to an unpredicted demand. This further complicates the revenue management model as a sale can be made at one station and capacity can be used from a nearby station which repositions an asset. Car rental fleet and revenue management problems are typically addressed over several strategic levels with varying time horizons. These levels range from strategic decisions, such as fleet pooling, taken on a 3-6-month basis to daily operational tasks such as fleet deployment and accept/reject decisions. Decisions taken have some overlap and are linked to each other, even though the time spans can vary (Oliveira et al., 2017).

4.2 Container Leasing (Sea) Businesses which lease containers used for transportation of goods with other modalities than air freight face similar challenges as Company A. The business of container leasing for maritime transportation (container shipping service chain - CSSC) is more well researched in comparison to that of air freight. Within CSSC there are three participants; container rental companies, carriers, and shippers, where the carriers supply the entire shipping service to the shipper. The demand within CSSC is uncertain and places high demands on the shipper’s fleet capacity planning, which in contrast to air freight lease the containers for more than one trip. Thus, the carrier possesses the risk and must avoid the bullwhip effects stemming from over or under-capacity. These are solved through the application of flexible lease agreements with call, put or bidirectional options (Liu et al., 2013). In their research, the authors pose some decision strategies for a container business relating to the design of contracts and agreements with their customers to reduce capacity risk and simultaneously increasing their profit. Shen & Khoong (1995) developed a decision support system for efficient relocation of empty containers, taking on a company-close process (heuristic/conceptual) perspective rather than a mathematical or technical perspective. The decision-support system that is proposed consists of regional planning of container distribution on three levels: terminal (port) level, intra-regional level, and inter-regional level. A simulation of container movements is introduced and a

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minimum cost-perspective is considered, purely based on (forecasted) demands with a planning horizon of three days. Lastly, Chen & Zeng (2010) have formulated a mixed integer non-linear programming problem (MINP) relating to the planning of container shipping using maritime transport, taking into consideration the cost of empty container shipping, available container slots on the ships, and container configurations. An apparent pattern in previous works is the employment of an inside-out perspective - i.e., focusing on optimizing internal processes (Liu et al., 2013) for revenue generation or increased profit margin - rather than having a starting point from the customer’s perspective and how to adjust internal processes accordingly to meet the demand. In comparison to container leasing for air freight, there are some main differences which should be noted before any parallels can be drawn. First and foremost, compared to Company A the lease model is different within maritime shipping, where a carrier leases contracts over a longer time span, planning their capacity well in advance (Liu et al., 2013). This means the carrier must store, reposition and plan the needed capacity of the leased containers themselves, instead of the container rental company. Furthermore, maritime shipping has only three participants involved, whereas air freight can have multiple forwarders and subcontractors as the container first must get hauled from the station storing the containers to get loaded with freight, hauled to the airport and then loaded, freighted, unloaded and hauled to the shippers receiving location and returned the return station. These simple, yet very relevant differences prevent any similar option contracts being used without completely altering the current business model of both the airlines, forwarders and container leasing business.

4.3 Other Transportation Sectors Some parallels can be drawn from other sectors such as air cargo, liner shipping (sea), rail and road freight. These sectors have received more attention in research than that of car rentals (Oliveira et al., 2017) and though they have more differences when compared to container leasing for air freight than aforementioned sectors, there are still lessons to be learned from this research. Revenue management within the air cargo industry has been widely examined in previous works. Kasilingam (1997) investigated the major differences between CRM (Cargo Revenue Management) and PYM (Passenger Yield Management), and highlighted certain development and implementation challenges with CRM. PYM and CRM are linked in the sense that the available cargo space is dependent on the number of expected passengers and the amount of check-in luggage that the plane must carry. Individuals boarding a flight are in general keen to have their luggage shipped with the same flight, while cargo may more easily be re-routed and/or delayed. This entails the first uncertainty for air cargo revenue planning: the amount of available space cannot be established with precision until shortly before the flight departs. Further, contrary to flight seat reservations, where one passenger may use one seat, the shipped cargo

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may come in different shapes in terms of density and volume, meaning that planning must be four-dimensional (width, height, length, and weight) to achieve efficient use of the limited capacity that each plane has (Hellermann, 2006). Goel (2008) classifies managerial fleet management decisions within road freight as being either strategic, tactical or operational. Each decision level is distinguished by the short- or long-term impact they have on the business’s decisions and activities. Strategic decisions usually concern size, type, mix and coverage of the fleet, the time horizon for these decisions usually range from months to several years. Tactical decisions include capacity adjustments in response to demand, pricing and spot market sales, these are usually made on a time horizon from a week to a few years. On the operational level, the concerns are focused on allocation, accept/reject decisions and day-to-day tasks, these are usually made on a daily or weekly basis. Transportation services are commonly priced in three different ways, static pricing refers to the standard listed price of a carrier. Contract pricing refers to fixed prices on a contract basis, reflecting the cost of pickup, delivery and moving the freight. Spot pricing is a price reflecting the current state of the transportation system, usually within a very narrow time horizon. (Goel, 2008). Although road freight may not bear as many similarities with lease contracts as aforementioned areas it is heavily reliant on its fleet and revenue management both in terms of pricing and quantity. As such, it deals with similar decisions on a strategic and tactical level whilst having been researched thoroughly compared to air freight, making it a suitable field to draw parallels from.

4.4 Slot Management and Control Traditional slot management is utilised within airport control to grant access to airport infrastructure that is needed for take-off and landing. It is a set of guidelines set by the International Air Transportation Association (IATA) that airlines operating at level 3 airports3 need to comply with. Slot management, as applied by IATA, is implemented as there is a limited amount of resources, insufficient in comparison to the demand (IATA, 2017). This warranted the managerial tool using slots along with the Worldwide Slot Guidelines (WSG) which dictate process of allocation and management of slots. Within this context, a slot is defined by the EC Regulation No 793/2004 of the European Parliament and of the Council as “the permission given by a coordinator to use the “full range of airport infrastructure necessary to operate an air service at a coordinated airport on a specific date and time for the purpose of landing”. This means the total airport capacity is divided into slots of time where the owner of a time slot owns the right to operate within the time slot.

3 Also referred to as “coordinated airport”. A level 3 airport is an airport in which demand exceeds the availability of infrastructure to support that demand. Thus, these airports make use of slots in which an airline must have a slot allocated to it before it can operate on said airport. (IATA, 2005)

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The implementation of slots to manage airport demand has led to several benefits, such as reduced congestion (Brueckner, 2009), increased flight frequency (Mehndiratta & Kiefer, 2002) and to meet fluctuating demand (Fukui, 2009). Additionally, Babic and Kalic (2011) demonstrated that using slots as a control mechanism could increase the number of destinations as well as improve the schedule connectivity. Using slots as a control mechanism thus seems to have been an effective approach for the air industry in meeting customer demands, increasing frequency and ultimately ensuring higher profit (Teoh & Khoo, 2016). Slot control theory is currently used within multiple fields and is not limited to only airports, other examples include seat reservations within airlines in combination with revenue management. In the aforementioned example, a slot would be defined by a seat at a specific route and departure time. Recently, it has also been utilized within container freight in maritime shipping. Teoh and Kooh (2016) used fleet planning, as a function within fleet management, with slot control mechanisms for airlines. Liu and Yang (2013) derived a slot control model based on revenue management for intermodal sea-rail container transport. This model was two staged, where in the first stage slot capacity was allocated to contract customers. In the second stage, slot capacity was allocated to general customers, booking their slots 1-2 weeks in advance to the list price, along with slots for urgent customers. Urgent customers were defined as those booking only days in advance, and that are willing to pay a higher than list price for capacity. Increasing the revenue is thus achieved by setting aside enough slots for urgent customers whilst not having any unsold slots. In essence, this means that slot capacity is sold both as contract sales, and as an intentionally generated spot market for free sales to general and urgent customers, this is visualized by Liu and Yang (2013), in Figure 3 below. The concept of slot management is thus closely linked to quantity-based approaches of revenue management, as the slots create the perishable asset of which revenue management can function.

Figure 3: Slot control problem of container sea-rail intermodal transport based on RM (Liu & Yang,

2013)

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5 The Case – Company A Within this chapter, Company A is presented and the observed phenomenon is framed within the context of Company A. The starting point will be at shipper’s level, evaluating and explaining the needs of Company A’s customers. Further, an in-depth explanation of the current value provision, working processes, challenges and unique considerations of Company A will be presented, this will provide an outside-in view (Day & Moorman, 2010) of the container leasing business operated by Company A and the challenges related to it.

5.1 Shippers Pharmaceutical companies, the end customers of Company A’s service (shippers), produce products which need to be distributed across the world. The goods that they produce are often perishable and require storage and transport in a temperature-controlled environment to keep the desired quality. Additionally, some of the products have time limited life spans in which the products can be used safely (Chung & Kwon, 2016). Due to this time constraint, not all products are suitable for ocean freight and is instead sent via air freight in temperature controlled containers. Further reasons for pharmaceutical companies often choosing air freight over sea freight is the high price-to-weight ratio of the freighted goods – lending the freighting option as a relatively inconsequential cost, or simply not having the time to wait for the next ship to arrive (Goel, 2008). This means that air freight orders often arrive and are cancelled at short notice, as it picks up demand peaks from slower modalities of transport. However, shippers do not contract Company A directly for their transportation needs, the shippers contact either a forwarder, such as DHL, or an airline to book their transportation. In essence, this means that it is the forwarder or airline which places an order for the shipper’s products at Company A.

5.2 Forwarders and Airlines The carriers of the freight, the forwarders or airlines, which receive a transport order from a shipper, place an order with Company A where they choose a release and return station of the leased container along with the origin and destination of the goods. As is often the case, release and return of the asset is the same, meaning the carrier has booked a round-trip which is the default order offered by Company A. For the carrier, this means that the leased container must be returned at the same site where it was released within the lease period, regardless of where and when the goods were loaded and unloaded from the container. This means that the carrier often must ship an empty, non-revenue generating (as the goods have already been emptied), container back to the return station. A value-adding proposition for carriers would be to allow additional one-way routes to reduce the number of empty shipments that they will have to commit to.

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As airlines often have varying capacity in and out of different airports, often favouring their hub, an example is Lufthansa operating out of Frankfurt, it is not always the most economical option to lease a container as close as possible to where the freighted goods are produced (Interview OPS3, 2018). Instead, origin and destination of the goods can be different from the release and return station of the leased container, as transporting the empty container to the return station can be less expensive to a central hub (Interview OPS7, 2018).

5.3 Company A The current working processes and fleet balancing techniques utilized within Company A are described in the following chapters.

5.3.1 The Order Process Once the carriers have placed their order as a request for quotation (RFQ) at Company A, the order is listed and receives a pending status. The order is then set as pending until the asset, a specific container, can be allocated for that specific order. No later than two days prior to the release date of the assets (start of lease, SOL), the order is accepted or rejected and an order confirmation is sent. When containers have been allocated, the ID of the assets provided is sent to the customer, this occurs two days before the release date. Orders placed by carriers vary in size, ranging from a single container to dozens of containers, which can be of different types and sizes. Once the start of lease begins, the lessee has roughly a week of lease time before the lease ends and the asset must be returned to avoid demurrage. Once the asset is returned at the return station it is inspected, if it is damaged it is repaired. Otherwise, it is made available for allocation to new orders. An abridged version of the order process is visualized in Figure 4 below.

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Figure 4: Company A Order Process. An abridged illustration of the order process at Company A.

5.3.2 Exception-based Policy for Network Trips By default, assets may only be booked as round-trips. This entails that in many cases, carriers must return an empty container to the release station after the goods have been delivered to their destination (Interview OPS3, 2018). For certain trade lanes and assets, Company A has introduced the possibility to send assets in one direction (network trips). For instance, certain key customers may be granted an exception for a certain period of time, allowing them to move assets in one direction without having to ship empty containers back to the release station. However, the exception requests that these network trips are based on do not dictate how the orders should be distributed over time, nor the quantity. For instance, a partner may be granted an exception request allowing them to move 100 containers in one direction during a one-year period, but the orders could potentially be distributed only over a small number of weeks during the year, putting high strain on the internal logistics operations. Another example of both quantity and time being unregulated, certain customers have the possibility to move an unlimited number of containers on certain trade lanes where repositionings are commonly needed.

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(Interview OPS7, 2018). Thus, there is a need to develop control mechanisms for increased order predictability, i.e. demand management, and for the possibility of offering additional network trips on several routes.

5.3.3 Fleet Balancing Seeing as demand for temperature controlled air freight of pharmaceuticals is not evenly balanced between continents, containers which are leased for one-way trips, or containers which are leased for a round trip but wrongly returned at a different location than ordered, put the fleet out of balance since there may be less demand for a trip in the opposite direction. For Company A to be able to continuously meet the demand of their customers, maintaining the fleet of containers where they are needed is essential. Solving this balancing issue is done by moving the empty container to a location where demand is higher or more predictable, called a repositioning (REPO) (Interview OPS1, 2018). Company A has divided the operations into three regional areas: Americas4, EMEA5, and APAC6. Within each region, there are clusters in which REPO’s can easily be made. A cluster, in this context, refers to a geographical area in which REPO’s may be made by truck (Interview OPS1, 2018). As trucking is cheaper and more flexible than using air freight, assets which are within the same cluster can essentially be repositioned at any time within the cluster to meet an emerging demand (Interview OPS5, 2018). The balance of assets is a multi-tiered system where on a global level, the fleet is continuously balanced according to the number of containers at each region in relation to received and forecasted orders. This is called a buffer and measured in run-out-time (ROT), meaning the current inventory divided by the demand per day. The buffer is set to handle both peaks in demand and cover for fleet imbalances. The ROT measurement effectively removes the factor of looking at a specific number of containers as this is relatively insignificant in comparison to the balance related to the future demand. Using ROT, a sufficient level to cover demand fluctuations and obtain a targeted service level can be established. On this level, meaning intercontinental repositioning, both air and sea freight is used when needed. However, the relatively long time to ship containers between continents by sea (e.g. between EMEA and the Americas) in relation to the average lead time for an order, has caused problems historically. An example of what these problems were constituted of is the volatility of demand, where a pre-emptive REPO of several containers was ordered via sea freight from EMEA to Americas. Before this shipment arrived, the demand had shifted again, meaning that a new REPO had to be carried out in the opposite direction. Thus, several assets were unavailable for several weeks in opposite directions (Interview OP1, 2018).

4 North and South America 5 Europe, the Middle East and Africa 6 Asia-Pacific

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On the regional level, each region has a regional manager that is responsible for fleet distribution within and between clusters. The fleet is continuously managed by repositioning empty containers to stations where they are needed. The regional planners do not plan according to a forecast of orders but rather limit their time horizon to the currently known order information, this time horizon is 1 week in advance (Interview OPS5, 2018). This is highly manual work, and it is, to our understanding, not based on a predetermined set of rules for repositioning and instead relies heavily on the regional planner’s discretion (Interview OPS5, 2018). To summarize, the distribution of containers is maintained on three levels: regional level, cluster level and at station level. Time horizons used for the planning of each of these levels differ due the difference in REPO time and quantity between levels.

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6 Analysis Within the Analysis chapter, the aim is to answer the research questions which were posed for the thesis. The chapter is introduced using the customer value objective related to Company A’s customers and contemporary theory and literature regarding one-way leases. Next, we present the means of providing this value to customers by defining a ‘slot’ which is the foundation of our proposed solution. With respect to this, key takeaways from previous literature in similar business settings is summarized in chapter 6.3 to help build the heuristic four-step model for intercontinental one-way leasing presented in chapter 6.4.

6.1 Enabling One-Way Trips Within Company A This chapter will be introduced with a discussion of previous literature joint with insights from the interviews conducted in our case study. The aim is to incorporate learnings from literature and qualitative interview data to determine how one-way trips should be made more attainable to meet customer demands within Company A. Due to the imbalance in customer demand for transportation of temperature controlled goods, allowing slots for one-way leases creates a need for demand management to prevent fleet segmentation. Revenue management refers to a wide range of methods and techniques of demand management. Sales format is one of the key considerations within revenue management (van Ryzin & Talluri, 2005) and currently, orders are placed as RFQ’s by the customers for Company A to accept or reject (Interview OPS9, 2018). The customer is provided with options according to their specific set of rules in the RFQ format. Currently, these are managed by granting exceptions for certain customers based on the discretion of individual managers at the company – allowing one-way trips which would normally not be accepted (Interview OPS 3, 2018). Currently, whenever an exception is present in the system, there is no control mechanism limiting the number of assets or the number of orders that can be placed on the trade lane for which the exception is made (Interview OPS 7, 2018). The expectation from the customer is that Company A will deliver on a majority of orders, meaning that regardless of what order they place within their unique set of exceptions, it will almost certainly be fulfilled. Given this, the RFQ system does not feature perishability of the assets which is one of the important cornerstones for revenue management according to Hellermann (2006). Making use of slots is one of the measures that may be employed to create perishability, which is something that Liu & Yang (2013) did in their work regarding sea-rail intermodal transport. Capacity that is fixed or difficult to adjust is a founding assumption for revenue management (Hellermann, 2006; van Ryzin & Talluri, 2005). From this perspective, altering the sales format to selling one-way trips through slots is a valid approach, as it creates a sales format from which demand can be managed, and the slots which are offered to customers can be altered with emerging information regarding customer demand, asset availability, etc. Thus, one-way trips which would offset the fleet to a greater extent than manageable by internal logistics could be cut off easily by limiting

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the number of available slots. The demand will be managed through the number of slots offered to the customers, creating capacity control through the scarcity created with slots as a selling format rather than a RFQ which does not impose any capacity limitations. Leveraging customer heterogeneity is another important aspect of effective revenue management (van Ryzin & Talluri, 2005). By identifying inter-customer differences, the customers may be segmented (Hellermann, 2006), and by meeting the demand of each segment, revenue can grow. Company A currently sells the same offering to both airlines and freight forwarders, i.e. selling round-trips by default (Interview FI1, 2018). For airlines, return logistics can be completed by using their own spare capacity on certain trips. This is not the case for freight forwarders working with Company A (Interview OPS3, 2018) as these typically do not have their own fleet of airplanes, meaning return logistics for an empty container must be purchased by an airline (Interview SM1, 2018). The difference between these is that the airlines face either an opportunity cost for prioritizing return logistics of assets, or, in the case of unutilized capacity being available, only the self-cost price. This is in contrast with the freight forwarders who need to pay an airline for the freight. This means that the one-way trips are highly valued by the freight forwarders and constitutes a customer variation which segmentable. Relating to previous literature regarding car rentals, Company A needs to work more proactively with the repositioning of assets given their global presence and highly stochastic demand. For instance, the model proposed by Kek et al. (2009) is highly reactive and will base repositioning decisions on predetermined thresholds. While this is to some extent possible also within Company A, using only such an approach will not be sufficient given that orders are usually placed with short notice in a timeframe in which it is not possible to reposition assets to suffice that demand. This is further supported by Powell & Topaloglu (2005) who highlight the complexity of having to wait for customer demand to be known before acting when working with narrow service intervals. An option that was considered within the company was to sell over-capacity at a discounted price. However, according to interviewed stakeholders at the company, it has been attempted before and results revealed that it was not possible to send assets on some trade lanes regardless of the pricing (Interview FI1, 2018). Ultimately, it would infer the formation of a spot market for their containers, which was not what the customers demanded (Interview OPS7, 2018; Interview SM1, 2018).

6.2 Creating Perishable Assets With Slots We seek to analyse if the use of slots is an appropriate way of selling services within leasing companies, using Company A to give context to the analysis. Using internal workshops and meeting with key stakeholders, a shared definition of slots was established along with important key outcomes of what a new selling format should provide. From this process of iteration with key stakeholders within the company, a slot was established as being an option for a one-way trip for a single container between two specific trucking clusters. The slot would further be tied

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to a specific type of container, not a specific container. The analysis will be formed alongside these parameters, using the slots as the perishable assets of which the revenue management can function. Considering the above definition of a slot, the terms one-way trips, one-way leases and slots will be used interchangeably. Having previously delimited the thesis from directly looking at pricing approaches to revenue management within the case study, the scope for the analysis is limited to means of capacity control. Capacity control approaches means managing demand through the amount of sales possible rather than increasing price to decrease demand (van Ryzin & Talluri, 2005). This means the analysis will need to determine the possible capacity to offer for one-way trips, starting with which parameters are essential to consider in the context of the case study. From this, a design on determining the available capacity for one-way trips will be suggested, taking the individual challenges from the context of the case into account, where fleet management is essential in ensuring the offered capacity for one-ways does not hinder regular business. The thesis aims to create a model incorporating both service offering improvements and the internal logistics through the use of slots as a format of selling a service. From the initial analysis in the case study, it was concluded – from both the authors and the company perspective – that the use of one-way trips was one of the most prominent value-adding propositions for customers (Interview OPS7, 2018; Interview SM1, 2018; Interview FI1, 2018). This correlated well with the literature reviewed on the topics of slot control and revenue management as slots have previously been used to create the perishable assets of which revenue management can function (Liu & Yang, 2013). The reasoning behind using slots was twofold, firstly as a means of being able to drive sales by offering a set number of slots within a given timeframe, secondly it was to create a spot market for unutilized capacity. It can also be argued that such an approach may incentivize customers to place orders early due to the inherent scarcity of a limited resource (a slot), which in turn increases the lead time for orders and enables Company A to plan fleet distribution more proactively.

6.3 Learnings From Reviewed Literature Based on the learnings from our literature review, we have gathered and summarized the key characteristics and parameters used in similar academic settings for fleet and revenue management related to one-way leases of assets. Below is a summary of a few recurrent categories and related parameters that will help form the foundation of our heuristic model for one-way leasing. Due to the width of the research question, three main themes were identified from the theory and literature review, these are concluded as costs, assets and station factors. To conclude the final model that will be presented in the following chapters, the authors presented the factors which were iteratively decided as important or less important based on workshops with key stakeholders within Company A. This led to the theoretical and empirical material from

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other studies and literature to act as a foundation for the model which was developed within the case study. The three major categories and the parameters associated with these are listed below. Costs Looking at cost aspects has been very prominent in previous research given that many reports have been employed by companies seeking to lower the operational costs for the repositioning of assets. Some examples of cost aspects previously considered are listed below:

- Repositioning costs: costs for the movement of empty assets for fleet balancing (Kek et al., 2009; Correia & Antunes, 2012)

- Opportunity costs: for holding inventory levels or loss of revenue due to protracted transports of empty assets (Kek et al., 2009)

- Station costs: staff and equipment needed to provide assets to the customers. Within air freight, this may also be referred to as ground handling costs. (Boyacı et al., 2014; Kek et al., 2009)

- Asset repair costs: assets are sometimes returned in a damaged state and cannot be used again until they have been repaired, which induces not only a cost for the repair itself, but lost revenue for the time that the asset is unavailable. (Boyacı et al., 2014; Kek et al., 2009)

- Asset depreciation: most circular assets depreciate in value over time as they are used and subject to wear-and-tear. Eventually, assets will have to be replaced. This is especially prominent in the car rental business, where the relative depreciation is high. (Correia & Antunes, 2012)

Assets It is important to have a clear understanding of the distribution and movement of assets to successfully manage a fleet of assets. Below, a summary of some identified aspects covered in previous research is provided:

- Asset distribution: momentary geographical asset distribution needs to be known to enable proactive planning of asset movements by different means (explained below). (Barth & Todd, 1999; Manna & Prestwich, 2014)

- Static repositioning: repositioning in a reactive sense to satisfy momentary needs. (Barth & Todd, 1999; Boyacı et al., 2014)

- Historical predictive repositioning: proactive repositioning based on historical data (Barth & Todd, 1999)

- Exact predictive repositioning: proactive repositioning based on forecasted demand (Barth & Todd, 1999)

Stations The stations at which assets are handled and stored infer capacity limitations mainly regarding staff and inventory space which is crucial to pay respect to for successful fleet management, be it

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a harbour for storing shipping containers or a facility/parking-lot for rental cars. Below is a summary of some considerations from previous literature in similar settings:

- Maximum inventory: each station has limited space for handling of assets which needs to be considered. (Boyacı et al., 2014; Kek et al., 2009)

- Asset movements: there is a limit to how many assets one station may handle per time unit, based on staffing and available space and equipment to handle orders. (Kek et al., 2009)

- Expected demand: the expected demand for assets at a specific station with regards to quantity and asset type. (Oliveira et al., 2014)

- Expected asset returns: the expected number of asset returns at a specific station with regards to quantity and asset type. (Oliveira et al., 2014)

- Damage prediction: estimation of the probability of an asset being returned in damaged condition, which may infer a repair cost and an unavailable asset for the repair period. (Kek et al., 2009)

Overbooking is a commonly used tactic to secure higher revenues from a fixed capacity fleet (Hellermann, 2006). The reason why we decided not to incorporate this in the model was twofold; firstly, due to the ethical reasons with shipping pharmaceuticals which indeed could be questioned with raising the risk of not servicing a client, and furthermore due to the case companies’ KPI of OTIF (On-time-in-full) which is central to the operational department of the case company. As such, further raising the risk of not being able to service a client, e.g. allowing greater imbalances or lower bounds for a buffer status, can be a mentionable approach to further increase revenues although not considered by the authors. The results from the workshop concluded that one of the most pressing variables which needs be included in a model is that of the momentary inventory of a given cluster – which was highlighted as a key factor in enabling proactive planning decisions (Barth & Todd, 1999; Manna & Prestwich, 2014). Additionally, the net expected returns and demand of assets within a cluster (Oliveira et al., 2014) was considered crucial for the case company, this meant looking at the inbound and outbound flow of assets. Furthermore, the ability both proactively reposition assets and reactively reposition assets (Barth & Todd, 1999) within a cluster was an important factor to consider to be enable meeting the demand of orders, as this is a central process employed by Company A. Lastly, taking the specific station capabilities and limitations into account was agreed as an important addition (Boyacı et al., 2014; Kek et al., 2009; Oliveira et al., 2014), ensuring the operational capabilities exist within the given station to meet the capacity determined as available. The resulting model, along with the reasoning behind each of the steps, will be presented in the next chapter.

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6.4 Heuristic Four-Step Model for Enabling One-Way Leases Within Company A In the following, the identified key parameters to consider for a successful development of a heuristic model for one-way leasing will be presented and discussed in the context of Company A. They will form the basis of a conceptual model for slot management. The parameters have been determined by an iterative interview process with key stakeholders at the company, as well as by incorporating knowledge from other empirical settings facing similar issues. Currently, Company A is using three planning levels; global, regional, and station level, where containers are allocated (Interview OPS1, 2018). Incorporating several planning levels into the revenue and fleet management system has been practiced before, one example is the strategic, tactical and operational fleet management utilized within road freight (Goel, 2008). Another example is the three planning levels of inter-regional, intra-regional and terminal level used for a decision support system for container leasing within maritime shipping (Shen & Khoong, 1995). Treating these levels separately allows for differing between decisions which have short-term and long-term impacts (Goel, 2008). These are similar to the planning levels used by Company A, thus using the already established planning levels for fleet and revenue management will suffice while allowing a system sorted from long-term to short-term impact whilst drawing benefit from familiarity of working within these as well as remaining consistent with other processes within the company (Interview OPS3, 2018).

6.4.1 Balanced Flow of Assets Enabling one-way leases has one primary challenge which needs to be addressed, namely that the asset which is sold does not return to its original station. Knowledge about inbound and outbound orders is thus crucial for planning and fleet balancing (Interview OPS1, 2018). However, within the context of the case study, the predictability of order returns is currently low since a majority of orders are not returned within the initially agreed lease period in addition to a large part of the returned containers being damaged and needing extensive repair before the next lease (Interview SM1, 2018; Interview OPS5, 2018; Interview OPS2, 2018). This may partly be mitigated by converting round-trips to one-way trips or increasing the share of one-way trips, since historical data revealed that the predictability of order-returns for one-way trips was considerably higher. The inbound and outbound orders to and from each cluster will be used to evaluate the flow of assets within the fleet at station level, cluster level, and regional level. Assets which are returned within the same cluster as they were released from do not cause this imbalance as the assets can easily be repositioned to the original station (Interview OPS5, 2018; Interview OPS2, 2018), this rationalizes only looking at trips which are between clusters. Within the case of Company A, the assets are not owned or assigned to specific stations, the station merely holds the assets for the next trip (Interview OPS5, 2018; Interview OPS2, 2018).

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This means that as long as there are new assets coming in to the cluster, assets which were leased as one-ways does not cause an imbalance in the positioning of the assets for that specific cluster (Interview OPS5, 2018; Interview OPS1, 2018). Thus, the first step to enable one-way services is establishing that there is a balance of flows, namely that the number of inbound and outbound assets need to be balanced in the long run. Regular business, i.e. round-trips, do not affect the balance of the fleet since the asset is returned at the same location once the lease has ended. Hence, there are only two activities which affect the fleet balance currently, namely one-way trips between two clusters – which are currently exception based – and REPO’s (Interview OPS7, 2018). To establish that there indeed is a balance between inbound and outbound orders in the current situation, inbound orders and REPO’s were aggregated and plotted using a running 30-day average, whilst doing the same with outbound orders and REPO’s. From the results it is visible that the demand is highly seasonal (considering the 30-day running average), and in certain periods of times there are either larger inbound or outbound flows from this specific cluster, but over time there is still a balance. The results for the EU trucking cluster are visualized in Figure 5 below.

Figure 5: Flow balance of the EU trucking cluster. A 30-day moving average of orders and REPO’s of both inbound (Sum of In) assets and outbound (Sum of Out) assets is visualized. It can be observed that

the flows are balanced over time. The scales have been redacted given that they are irrelevant for the purpose of demonstrating a seasonal pattern in the historical asset movements.

We use an analogy to simplistically model a cluster of stations, which is that of a kitchen sink. In a sink there is inbound flow, in our case inbound one-way trips and REPO’s, and outbound flow through the drain, in our case outbound one-way trips and REPO’s. Additionally, if the flows are somewhat balanced, there is a level of water within the sink, the number of assets within the

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cluster. Going forward, this analogy will be used to explain the functioning of a cluster within the context of the case study. Figure 6 below is a visualization of the analogy.

Figure 6: Cluster balance illustration. Clusters may be illustrated as water tanks with an inlet and an outlet, corresponding to inbound- and outbound assets. The water level in the tank illustrates the aggregated station buffer level within the cluster. Seasonal patterns in asset movements could be observed from historical data. As such, within the model, there is a need for an allowance of how much the inbound and outbound flows may vary before affecting regular business. Establishing this is difficult within a business as dynamic as Company A, where the need for freight is unpredictable (Interview OPS1, 2018). Company A has experienced a stable and high service level during the recent past (Interview OPS5, 2018; Interview OPS7, 2018). Company A therefore asserts that the imbalances which were handled during this period are imbalances which can be handled also in the future (Interview OPS3, 2018). We define imbalances as the net difference between inbound orders and REPO’s and outbound orders and REPO’s over a given timespan, e.g. a week. Seeing as weeks do not function independently and following weeks aggregate into a larger deficiency, looking at a single weekly peak does not lend as much information without the flows of previous weeks. Using a running average takes the previous time periods into account which gives a better understanding of the net imbalance caused by a single week. Within the case, we used a 30-day running average to look at imbalance of flows as this was the time it takes to re-establish balance using the slowest modality of freight (Sea) used to carry out REPO’s (Interview OPS1, 2018; Interview OPS5, 2018). Additionally, it complements the current planning levels used by Company A, as it takes a less granular perspective than the current global fleet planning process (Interview OPS3, 2018). The imbalance of flows for the given time span within the cluster of EU is visualized in Figure 7 below. A positive imbalance corresponds to a week in which more assets have returned to the cluster than assets that have been released from the cluster, and

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conversely a negative imbalance corresponds to a week in which more assets have been released from the cluster than assets that have been returned to the cluster. The net weekly imbalance is also displayed, showing the need for using a running average as consecutive weeks aggregate into a large imbalance.

Figure 7: The imbalances of the European trucking cluster between 2017-06 and 2018-03, each value represents the imbalance of one aggregated week. The scales have been redacted given that they are

irrelevant for the purpose of showing the tolerance of imbalances in historical asset movements. For the purpose of reference, the scale and values can be multiplied by any fixed integer to provide a better

reference value.

The scale in Figure 7 has been redacted given that it is irrelevant for the purpose of showing the tolerance of imbalances in historical asset movements, the important thing to note is these values as they compare to the peak imbalance of a single week in isolation, e.g., a negative imbalance of 0,89 can be allowable if previous and consecutive weeks cause less of an imbalance to the running average. Using this 30-day running average, it was established that the EU cluster could handle weeks of up to 0,43 as an inbound running average imbalance whilst the outbound running average imbalance was about 0,38. These figures are only allowable for a certain time period, e.g. 2-3 weeks.

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6.4.2 Buffer Status The “buffer status”, also referred to as “run out time”, is an important index currently utilized internally at Company A solely for the management of global fleet planning (Interview OPS1, 2018). The buffer status is a way of tracking the amount of current inventory (stock of assets) as it relates to the demand. It is founded in historical data, knowledge about current fleet balance, and knowledge about upcoming orders. Data from different sources is aggregated and translated into this parameter, which ultimately is an indication of the number of days that a cluster will be able to suffice demand with regards to a given service level, an awaited number of containers that are returned damaged (approximately one third), a desired safety stock, and a “working inventory” needed to handle the daily in- and outbound flows of containers (Interview OPS1, 2018). From a company perspective, buffer status is an already established and proven successful way to measure the amount of inventory within a cluster (Interview OPS1, 2018), this KPI will be central in the model enabling one-way leases. Using the analogy as described above, the amount of water within the sink represents the amount of available assets within a given trucking cluster, meaning the aggregated buffer status of a given cluster. One-way trips can only be offered in the case of the buffer status remaining within the acceptable upper and lower bounds of the cluster, meaning regular, round-trip, business can still be offered without a decrease in service level. Using upper and lower bounds does, however, create the need for both outgoing (release) and ingoing (return) slots, as the only way to avoid approaching either bound is by manipulating inbound and outbound flows. It is plain to see that there are two ways of balancing for each bound, when approaching the upper bound one can either decrease the inbound flow or increase the outbound flow. Correspondingly, when approaching the lower bound one can either decrease the outbound flow or increase the inbound flow. Both methods are achievable within Company A, decreasing either flow is simply done by offering less slots for one-way trips and doing less REPO’s from the given cluster (Interview OPS7, 2018; Interview OPS3, 2018). There are however some difficulties with increasing flows as the customer demand is difficult to increase due to the aforementioned imbalance in flow of goods between continents (Interview FI1, 2018; Interview SM1, 2018; Interview OPS3, 2018), thus, the only realistic way of increasing flows is through the use of REPO’s, which is a costly option (Interview OPS7, 2018; Interview OPS3, 2018). Both logics of avoiding the upper and lower bounds are important, it is essential to note that the cost of increasing flows are vastly greater than decreasing them (comparing a revenue and profit generating trip to a cost-incurring trip) (Interview OPS7, 2018; Interview OPS3, 2018), and should only be done when it is of strategic importance. We prefer defaulting to the option of decreasing inbound slots when nearing the upper bound and decreasing outbound slots when nearing the lower bound. Inarguably, this means that for a one-way trip to be offered on a given trade lane there needs to be both an outbound slot available from the release cluster, and an inbound slot in the return cluster. The amount of either must be manipulated.

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This effectively creates a spot market for one-way trips using unutilized capacity, akin to the last-minute sales of capacity within road freight (Goel, 2008), where these can be offered “safely” without impeding regular business. To drive sales towards customers, there needs to be a tangible way of selling one-way trips rather than the “if we have the capacity”-approach, this needs to have the format of being able to offer X number of trips over Y timespan with a specific release and return cluster (Interview SM1, 2018; Interview OPS7, 2018). To accomplish such a logic, there needs to be a way to alter the number of slots that can be offered on specific trade lanes, meaning one clusters need to be manipulated as to have outbound slots available and the receiving cluster needs to have inbound slots available, regardless of the current state of the asset flow and balance. Thus, there needs to be two types of slots available, one which sets a target for the number of slots available and can be utilized when deemed strategically important. “Dynamic slots” will be used for fleet balancing by selling overcapacity. “Target slots” will be used to be able to meet customer demands on specific trade lanes. The operations and sales departments within Company A may then establish target levels for the static slots on specific trade lanes to enable the possibility to send assets in that direction. The number of available slots for both slot types should be determined by the current buffer level within the cluster, this is visualized in Figure 8.

Figure 8: The two types of slots needed and the corresponding logic of maintaining the fleet balance for

each of these. For dynamic slots, a high buffer level should decrease the number of available inbound slots while as a low buffer level should decrease the number of available outbound slots. For static slots, a high buffer level should increase the number of outbound slots while a low buffer level should increase the number of inbound slots. The resulting shortage in the opposite direction should be compensated through an increase in REPO’s (Interview OPS7, 2018). Due to the aforementioned inherently higher cost of implementing such a logic, we suggest having these two functions separated, meaning defaulting to the cost saving strategy but enabling the possibility of a strategic decision to offer a given number of trips when it is of strategic importance.

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6.4.3 REPO Capabilities The amount of assets which can be released from a given station is dependent on more than just the amount of assets at the station at the current time. The primary tool used for ensuring the fleet is positioned correctly related to demand, both on a regional and global level, is REPO’s (Interview OPS4, 2018). On the regional level, each stations’ needs are assessed primarily by the current stock of assets and if this is insufficient, all assets at stations within the cluster which have unutilized capacity are subject to repositioning to meet the demand (Interview OPS5, 2018). Due to short lead times, this is hard to achieve on a global level but large imbalances between continents are handled. The time required to reposition capacity to where it is needed is relatively predictable in the vast majority of Company A’s business areas (Interview OPS5, 2018; Interview OPS8, 2018), excluding clusters in APAC due to the inherent geographical challenges (Interview OPS2, 2018). To summarize, REPO capabilities are essential to assess when deciding on the amount of capacity available for one-way trips. The aforementioned factors, asset flow balance and buffer status, cover the most crucial factors of enabling one-way trips. There are, however, some additions which need to be made to function within the case of Company A. It is important to note that the selling of one-way trips between trucking clusters only works in theory, in the end, there is a station that must release each of the assets within an order, and a station that must handle the returning assets (Interview OPS5, 2018; Interview OPS9, 2018). This prompts the same need as any other order; there must be an asset on the release station to meet the demand. There is already a well-functioning process within Company A for achieving this regional planning. It will constitute one important part of our suggested solution, namely working reactively with repositioning to emerging information within the clusters (Interview OPS4, 2018; Interview OPS5, 2018). In short, this means that the future orders should be planned with the available assets within a cluster, and not only within a specific station. These REPO capabilities, meaning the possibility of moving assets between stations to meet demand within a given time frame, need to be considered when determining the amount of one-way trip slots to offer, as a station which is low on assets currently can have assets repositioned to meet demand from nearby stations within the cluster (Interview OPS2, 2018; Interview OPS5, 2018).

6.4.4 Station Capabilities Each station used within Company A is unique, some are subcontracted and others are owned by the company (Interview OPS7, 2018). This means that each station has a specific set of capabilities, e.g. repair capabilities for a certain type of container (Interview SUP1, 2018). Additionally, there is a limit for maximum storage and how many assets the station has resources to handle during one day (Interview SUP1, 2018), affecting both possible REPO’s and sales. The most prominent capabilities which need to be taken into account when offering an increased number of one-way trips are; inventory capacity per container type, maximum number of inbound and outbound containers per day and/or container type, and station repair capabilities

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per asset type. These parameters both affect the number of inbound slots which can be offered to a specific station and the number of outbound slots, these are only relevant over a short time span as the situation is very dynamic for each station (Interview SUP1, 2018).

6.4.5 Summarizing the Four Step Model To summarize, four planning levels with unique planning horizons enable a holistic model for capacity control within a global fleet of assets for one-way leasing. Refer to Figure 9 for a visualization and brief explanation of each of the planning levels incorporated in our model for capacity control.

Figure 9: A conceptual four-step model for capacity control for a global fleet of circulating assets.

Firstly, asset flow balance needs to be ensured in the long term as to not displace the fleet. A balanced flow of assets must be attained within each business region to ensure long-term feasibility of the solution. In a shorter time perspective (four weeks to a couple of months), an imbalance in the asset flows is allowed to be able to offer one-way leases of assets. However, an imbalance is only allowed within the boundaries of a “buffer status” – which is an indication of how long a specific region may supply the expected upcoming demand with respect to an imbalance buffer to cover for unforeseen orders. This buffer also needs to account for the probability of an asset that is returned in damaged condition and needs repair before it can be leased again. Thirdly – in a time perspective ranging from one week to one day – the ability to

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reposition assets within a region needs to be considered to be able to meet unforeseen demand from a specific station by supporting with assets from nearby stations. Lastly, the limitations of each and every station need to be taken into consideration to ensure that all assets promised to customers can actually be delivered.

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7 Conclusions Within the conclusions chapter, the research questions will be answered, results from the analysis will criticized and critically self-reviewed in order to provide readers with an understanding of the limitations, shortcomings and further research warranted from the thesis. Furthermore, the managerial implications of the results and conclusions drawn will be described from the authors point of view. Furthermore, suggested

7.1 Answers to Research Questions Here, we will summarize the answers to our research questions based on the findings from the analysis which were based on literature in previous business settings incorporated with the learnings from a case study within Company A.

1. Which fleet and revenue management considerations are essential for enabling intercontinental one-way leases in a global business?

Referring to the theory and literature employed in this study, we have concluded the major considerations for being able to enable one-way leases in a global context. First and foremost, it was common that three different planning levels were incorporated and interplaying to enable a holistic view of the fleet. This was further supported by the existing planning levels within the case company, which were designed in a similar way: global, regional, and station planning. As previous studies have mainly been carried out in a business-centred context, cost and revenue aspects have been central and mainly covered the following costs:

- Repositioning costs - Opportunity costs - Station costs - Asset repair costs - Asset depreciation

Further, extracted from businesses that work with circulating assets, the following considerations have been the most prominent regarding the assets:

- Asset distribution - Static repositioning - Historical predictive repositioning - Exact predictive repositioning

Lastly, the stations physically working with the daily displacement of assets infer constraints related to staff, storage space, and workload:

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- Maximum inventory - Asset movements - Expected demand - Expected asset returns - Damage prediction

2. How can a fleet be managed to allow intercontinental one-way leases whilst not

affecting regular business? Our analysis revealed that a model which aims to allow intercontinental one-way leases can be constructed in a hierarchical manner, starting from a long-term perspective with strategic decisions regarding fleet balancing and maintaining sufficient inbound and outbound flows of assets as to satisfy both regular business while creating a spot market for one-way leases. This model needs to incorporate the different time horizons used for each planning level in a funnelling approach, starting from a bird’s-eye view of the business on a strategic level with a longer time horizon whilst keeping the short time perspectives in mind – such as the day-to-day releases of assets. In this manner, the strategic thinking can still be possible without interference with day-to-day operational tasks, as they create sufficient boundaries from one another. Concluding from theory and previous literature in similar business settings as well as qualitative case study data, we have come up with a heuristic four-step model for enabling one-way leases on a global scale:

● Firstly, there needs to be a balance in ingoing and outgoing assets of each region in which the business is operating over the long term. This is to ensure that individual handling stations do not get clogged with or completely depleted of assets, and that an adequate inventory level is held at each region.

● Secondly, the buffer status is used as an indication of needed capacity to ensure the availability of containers for regular business. Keeping the buffer status within the acceptable and established limits will let regular business continue whilst allowing slots when there is unutilized capacity.

● Thirdly, the REPO capabilities within a given cluster ensure that the cluster can provide for the station which is intended to release the assets.

● Lastly, taking the specific station capabilities into account, day-to-day operational aspects can be incorporated. This includes the considerations established from the first research question: maximum inventory, asset movements, expected demand, expected asset returns, and damage prediction.

The model incorporates a customer-centric perspective by allowing to sell guaranteed slots on specific trade lanes by setting targets and working proactively with repositioning to meet the corresponding demand.

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7.2 Study Limitations Within this chapter, the major limitations of the research conducted within the thesis are discussed. Firstly, the limitations as a direct consequence of our delimitations within the study will be presented. After that, model-specific constraints and weaknesses related to the case study will be brought forward. A general issue that might raise questions regarding this study is the generalizability of the findings. Arguably, statistical generalizability can never be achieved in a case study given that a case study per definition regards a specific business setting or problem with unique prerequisites (Blomkvist & Hallin, 2015). As was the case in this study, the case company encompassed specific constraints that needed to be considered to be able to develop a model for capacity control. In theory, however, the issue is the same for any company working with intercontinental one-way leases of assets. However, some degree of analytical generalizability (Blomkvist & Hallin, 2015) can be achieved given that the model is highly influenced by businesses that in essence are very similar to Company A, thus requiring no or minor tweaks to be applicable also within those business settings.

7.2.1 Customer Behaviour It was assumed that customer behaviour will remain unchanged and that the solution should be centred around fulfilment of customer’s needs rather than altering their behaviour to achieve more efficient internal logistics for any company using the model. Our belief is, however, that that the introduction of a capacity management model within a company will most certainly change customer behaviour by incentivizing lead time since slots will be a limited quantity service offering. This is supported by Liu & van Ryzin (2008) in their work regarding inducing early purchases through strategic capacity rationing. Thus, this constitutes a considerable limitation of this study. Further, Persson (2013) studied reduction of customer related costs by influencing customer behaviour, acknowledging that certain customer behaviours could drive customer related costs, diminishing the sales margin. This behaviour was observed also within the case company, where orders were placed with short notice that the company tried to conform with, which required a high level of demand forecast planning and high safety buffers to keep the service level high. It should be noted, however, that influencing the customer behaviour does not necessarily have to be at the cost of customer satisfaction of quality of service, instead it can aim to achieve positive effects to both parties – referred to as an improvement in service productivity by Grönroos & Ojasalo (2004) – which was the true within the case study in this report. In conclusion, the assumption that the demand and behaviour will remain unchanged is a limitation of the thesis and further research is warranted to accurately described how customer behaviour may change with the introduction of capacity control mechanisms.

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7.2.2 Pricing The thesis was theoretically framed within fleet and revenue management, these fields being interrelated in such a manner that the theoretical framing could provide context for both internal logistical issues and the customer perspective of revenue management. One of the customer perspectives within revenue management is the aspect of pricing the services provided, with the assumption that demand can be managed by increasing the price, thus effectively lowering the customer demand. Conversely, a reduction in the price may increase the customer demand. The pricing aspect of revenue management was delimited from the analysis, but this does not mean that the pricing aspect is irrelevant in a general sense or for Company A. Our view is that pricing strategies could act to complement the capacity control mechanism with the sole purpose of creating greater revenues – rather than directly used to affect demand. An example of such a strategy of using an integrated approach is allowing the capacity control mechanism to dictate the number of slots offered to customers whilst a pricing revenue management model can generate greater revenues. Greater revenues could be achieved with a dynamic pricing model that raises prices when the capacity control model creates asset scarcity or by segmenting the customers according to their time sensitivity to justify a different price. In that case, the price would increase the closer to the release date that an asset it booked. These higher prices compared to the current contract or list prices used by Company A would then increase the revenue of a sale. An important distinction here is that capacity is never lost, rather the only perishable asset is the lost day of sale (Netessine & Shumsky, 2002) – which within the case study is relatively cheap in comparison to the revenue of a sale, but the opportunity cost of a lost sale is large. Contemporary research within similar business and with similar theoretical framings have concluded that integrated approaches between price and capacity-control is indeed a favourable approach (Oliveira et al., 2017). Pricing schemes which segment customers according to time-sensitivity could provide other valuable effects for Company A as well, where the current booking lead time from the customers is generally short (Interview OPS1, 2018; Interview SM1, 2018). Increasing this lead time could enable reductions in repositioning costs by allowing for more proactive planning, as well as an increase in OTIF numbers given the increased possibility to move assets to satisfy orders with the additional time frame. Additionally, allowing discounted prices could help reach market segments of more price sensitive customers (Berman, 2005), where the current service offered by Company A currently may have been too expensive. This has been practiced in a slot control model which was based on revenue management for intermodal sea-rail container transport, where urgent customers would pay a higher than list price for capacity (Liu & Yang, 2013). The use of slots to control capacity and using different pricing schemes means the use of integrated approaches. Another simile would be that of the spot pricing used within road freight for last minute sales, this is used to sell all available capacity – which is less relevant to company A as the capacity is not lost, selling all available capacity at lower than list price is less interesting for

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the case company due to the relatively low cost of having low container utilization (Interview OPS3, 2018).

7.2.3 The Heuristic Model The selection of variables to include in the model was based on findings in literature, theory and on the empirical material gathered from the case study. Some of these are highly specific to case study of Company A such as values and time horizons which were chosen to correspond to important time frames currently used within the company. These aspects are as such only applicable to the particular case of Company A and all time frames used or integer values seen within the case study should be reconsidered when trying to draw conclusions to other cases. Furthermore, the model developed from the case in no way encompass all variables which could be used to derive a model for capacity control, rather this should be viewed as an example of the reasoning behind a model adapted to aid in solving a specific case. Further examples of how the derived model for enabling one-way leases is case specific is how the model was adapted to the specific planning methods used for each planning level. Meaning the use of buffer status, with run out time as the primary variable, as a measure to establish the availability of assets within a given cluster. The authors thus recommend readers to draw knowledge from the thesis to review current working process within the given case and attempt to work with methods which are already proven within the context of the case. The suggested model from the analysis took a little to no risk approach, meaning there is in theory an asset for each sale, which was the intended approach by the authors and has been practiced in other fields such as car rentals (Oliveira et al., 2017). However, it should be noted that increasing the risk of not servicing a client can drastically increase revenues - as have been noted with overbooking for seat reservations (Hellermann et al, 2013). The proposed model does not in any way optimize towards the lowest allowable service level of company nor does it incorporate any substantial risk of not servicing clients.

7.2.4 Cost Structures A founding assumption of capacity control methods in revenue management is that the cost of providing the service rendered is less than the revenue generated by the sale (van Ryzin & Talluri, 2005). This is a limitation within the study as there is no way of establishing that the revenue generated by the sale is greater than the cost as a price was delimited from the case. Comparing the cost is an essential way of establishing how much capacity should be allocated for service, an example would be the use of target slots within the case – where REPO’s are forced to a location to provide one-way slots. In such an example, the comparison of the costs of the repositionings to the revenue of the sale is an essential feature missing from the model. Some costs which can be included in a capacity control model are suggested here: container holding costs (idle cost, value depreciation), leasing costs (customer prices), costs for repositioning

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between each station in the network, and opportunity costs (lost revenue because of a non-available asset, for instance because of a lengthy repositioning of a container). In essence, the cost structures are needed to form the basis of decision making within the slot management capabilities, e.g. repositioning containers to remote locations to offer slots does not make any business sense if the cost of selling the capacity at this location outweighs the revenue for the lease.

7.3 Managerial Implications As stated by van Ryzin & Talluri (2005), management culture is one business condition that is important for a company to successfully adopt a revenue management technique. Given the highly complex nature of revenue management, they argue that management within a company seeking to adopt revenue management practices need to be familiar with science and technology to fully understand the benefits and trust in the usage of such an approach. Thus, it will be important to make sure that there is a joint understanding of the benefits and goals of a revenue management technique to be able to pursue it. Within the thesis, we assumed customer demands remain unchanged, meaning that the current demand for round-trip leases would remain equal. Thus, the need for assets to meet this demand was prioritized, as is visible in the model as it was adapted to not directly impact this part of the business. However, with the introduction of one-way leases as a service for all customers, rather than being exception based, there is a need for an analysis of how customers would perceive this and if it would affect the customer demand. There are three ways the one-way leases would be sold through, either from completely new customer segments, e.g. a freight forwarder who can now ship pharmaceuticals with one-way leases as there is no expensive return logistics to handle. Realistically, there is also the possibility that customers currently leasing round trips would start leasing one-ways instead. This scenario would affect the current business. To avoid this, the authors would as a solution consider charging a premium for the one-way leases, this would effectively raise revenues for one-way leases while still maintaining an incentive for customers with capabilities for return logistics to continue purchasing round-trip leases. The third option is the introduction of the spot market leading to the conversion of a future repositioning into a one-way lease. This would logically be immensely valuable for the case company as it would turn a cost into a revenue generating trip. As such, trips which would constitute a conversion of a REPO to a one-way lease are very important and from the authors perspective could possibly be priced in such a way to reach all customer segments to further increase the possibility of incurring such a customer demand. The return of assets with one-way leases has historically proven to be more predictable than round-trip leases (Interview OPS5, 2018), which would improve accuracy of plans using inbound assets as a variable with the extended use of one-way leases. However, there are other factors which could be affected with this development as well. Demurrage is a fine charged for the

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delayed return of assets, e.g. an asset which was returned later than planned. Fines, such as the one mentioned above, are in essence setting a price for the service rendered, in this case the handling of a late return (Gneezy & Rustichini, 2000). With the increased accuracy of returns, the revenue stemming from demurrage would consequently decrease, the revenue between these two need to be compared.

7.3.1 Implementation The authors view is that the derived model for capacity control is possible to implement within global leasing businesses, although it may still benefit from the aforementioned pricing addition. The timeframes determined within the case study needs to be considered for each unique application – as there can be unique differences between different business contexts. Considering the possible implications of a model such as the one proposed within the thesis, the implementation would have to be iterative and start from a select couple of routes in a small scale. Once the concept has been proven, scaling can begin. The authors based the analysis on a single type of container and analysed only three clusters – these could possibly be the founding clusters where an implementation could be possible. A suggested way of implementing the model is also by starting to use static slots within these – where the slots can be manipulated by hand, and using the proposed model as a decision support system, where the calculated capacity for slots is used as a comparison value. This approach would allow the critical review of the system before implementation – being able to efficiently enable both one-way leases and evaluating the model concurrently. Klein and Knight (2005) outline six key factors which shape the process and outcomes of innovation implementations. These begin with the implementation policies and practices which are present within a company, e.g. education and training to use the innovation but also technical assistance. Also included is the incentives offered by the organisation for innovation use. Secondly, the organisational climate, e.g. the shared willingness and reception of new technology innovations. This goes along and is highly related with the third factor – managerial support for new technology, the presence of managers who understand the importance and need for the innovation. The fourth factor is the is financial support the innovation receives. This entails the financial means to allow thorough implementation practices with training and relaxing performance requirements during the implementation and learning phase. Fifth is the learning orientation of the organisation, e.g. the need for organisational support for learning, education for employees and the knowledge creation and retention of the organisation. This also means that employees are encouraged to experiment and take risks to achieve better practices and efficiency in an innovation which is likely riddled with errors, bugs and improvements in the first stages of implementation. Managerial patience is the sixth factor outlined which refers to the need for managers to realize the need for allowing short-term loss of efficiency and productivity to achieve a successful implementation. Allowing the organisation the time to work out the initial problems of an innovation which ultimately can provide long-term success (Klein & Knight,

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2005). The authors would suggest establishing these policies internally to increase the chances of successfully implementing the model.

7.3.2 Sustainability Sustainability is often studied with consideration to three “sustainability pillars”: social, economic and environmental sustainability. These may also be referred to as the three P’s: people, profit, and planet (Boström, 2012). In a nutshell, the terminology is used to describe a development that meets the needs of today without compromising the ability for future generations to meet their needs (Moldan et al., 2012). The social sustainability (people) pillar refers to an organisation’s ability of generating trustworthiness and loyalty in society. Economic sustainability (profit) refers to an organisation’s ability to generate return on investments to its stakeholders in a sustainable manner through efficient resource use. Lastly, the environmental (planet) pillar refers to the ecological context of sustainability work – for instance, reduction in the emission of greenhouse gases (Jitmaneeroj, 2016). Consolidating all three aspects, Wu et al. (2017) provide a definition for sustainable development in a business context as “development that meets the needs for a firm’s direct and indirect stakeholders without compromising its ability to meet the needs of future stakeholders”. The first (people) pillar of sustainability is within the context of the case study benefited by further offering freight forwarders services for cold-chain transportations. This ensures more capabilities for the pharmaceutical industry to distribute their goods throughout the world – thereby, in the long run, contributing to an improvement in the availability of pharmaceutical products globally. Referring to the second (economic) pillar, the economic benefits of using the model presented in this thesis applies to both the asset provider and the lessee. The asset provider may reduce the number of movements of empty assets by converting them to paid leases instead. By creating a market for non-utilized assets based on regional balance levels, new businesses opportunities may also arise. Further, within the context of the case study, given an increased predictability in asset returns, more proactive repositioning of the assets within the fleet could be enabled which will lead to further cost savings for the asset provider. Lastly, referring to the environmental (planet) pillar of sustainability, an increased predictability in asset returns allows for more proactive repositioning of asset. That way, recurrent transports of containers by ship may be enabled instead of using air freight, consequently reducing the environmental impact of repositioning assets.

7.4 Further Work Drawing from the main limitation of this study – not incorporating pricing strategies into the revenue management practices – entails the first suggestion for further work based on this study. While providing a useful starting point to means of capacity control, our view is that pricing is

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essential to consider for long-term feasibility of a revenue management strategy within a business. Allowing one-way trips does not implicitly mean that all one-way leases sold will be to new customers. There will inevitably exist a conversion from round-trips to one-way leases by allowing additional one-way leases within a fleet. Converting existing business (round-trips) to one-way leases is highly valuable for customers but may infer aggregated logistics cost for the company through repositioning of assets, which warrants further work to determine means of maintaining revenue (e.g. through a change in the price model) because of this. Case studies from other leasing fields are also warranted in terms of deepening the understanding of fleet and revenue management and its empirical content for lease or rental businesses. The literature review noted similar experiences within a diverse set of leasing businesses, deepening the fields within other case contexts allows for the development of a more full-flexed model for capacity control. It would also enable more generalizable conclusions, furthering the theory within both fleet and revenue management.

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9 Appendix

9.1 Appendix 1: General Interview Themes This template constitutes the base of which the interviews with company representatives within the case study were built.

● General theme 1: What is your position within the company and what are your primary responsibilities and assignments?

● General theme 2: Given that an outside-in perspective is warranted from the company, what do the customers demand?

● General theme 3: What is your perception of the current one-way lease process? ● General theme 4: What is the internal logistics perspective on the current one-way lease

process? ● General theme 5: We present our current understanding of the situation and acquire

feedback and thoughts on our conceptual model.

www.kth.se

enabling one-way leases of temperature controlled ...1257780/fulltext01.pdfindustriell ekonomi och...

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