Linnaeus University DissertationsNo 360/2019

Katarina Eriksson

Finance and Supply Chain ManagementCoordination of a Dyadic Supply Chain through Application of Option Contracts

linnaeus university press

Lnu.seISBN: 978-91-88898-84-5 (print), 978-91-88898-85-2 (pdf )

Finance and Supply Chain M

anagement

Coordination of a Dyadic Supply Chain through Application of O

ption Contracts K

atarina Eriksson

Finance and Supply Chain Management

Coordination of a Dyadic Supply Chain through Application of Option Contracts

Linnaeus University Dissertations

No 360/2019

FINANCE AND SUPPLY CHAIN

MANAGEMENT

Coordination of a Dyadic Supply Chain through

Application of Option Contracts

KATARINA ERIKSSON

LINNAEUS UNIVERSITY PRESS

Finance and Supply Chain Management: Coordination of a Dyadic Supply Chain through Application of Option Contracts Doctoral Dissertation, Department of Accounting and Logistics, Linnaeus University, Växjö, 2019 ISBN: 978-91-88898-84-5 (print), 978-91-88898-85-2 (pdf) Published by: Linnaeus University Press, 351 95 Växjö Printed by: Holmbergs, 2019

Abstract Eriksson, Katarina (2019). Finance and Supply Chain Management: Coordination of a Dyadic Supply Chain through Application of Option Contracts, Linnaeus University Dissertations No 360/2019, ISBN: 978-91-88898-84-5 (print), 978-91-88898-85-2 (pdf).

The purpose of this dissertation is to study the relationship between dyadic supply chain flexibility and dyadic supply chain profitability.

In today’s global environment, competition is no longer limited to companies but has evolved to supply chains. Supply disruption, lead time uncertainty and stochastic demand can result in costly inefficiencies of up to 40% when companies are trying to coordinate ordering and production. A dyadic supply chain competing in a global economy cannot afford to end up with a 40% smaller share of the pie.

This thesis applies theory and instruments from finance, specifically portfolio theory and real options theory when applying option contracts to create flexibility in a dyadic supply chain.

The methodology applied was to conduct an initial literature review of prior research to establish research gaps (first paper). This resulted in the development of an algorithm (second paper) combining the base stock model and the option mechanism to create flexibility for an OEM and supplier to coordinate ordering and production bilaterally in a multi-period setting. In the third paper the algorithm was applied to a case study using data from two companies, which resulted in the algorithm being tested and validated. Furthermore, option contract theory was integrated with dyadic supply chain practise while Fisher portfolio paradox was addressed.

The dissertation contributes in the following areas: the empirical contribution is evidence of the relationship between dyadic supply chain flexibility and profitability using quantitative data from two companies. The methodological contribution is a method for the objective valuation of dyadic supply chain flexibility and the measurement of profitability, by valuing the option contract. The theoretical contribution is achieved through the integration of portfolio and option theory into SCM while addressing Fisher’s portfolio paradox. The practical contribution is an algorithm that creates flexibility for an OEM and a supplier to coordinate ordering and production bilaterally and maintain its collaborative advantage when competing in a global economy, thus avoiding ending up with a 40% smaller share of the pie. In addition, this dissertation advances this topic in SCM into a quantitatively measurable theory.

Keywords: Quantitative case study, Option contracts, Modelling, Supply chain flexibility, Bilateral coordination, Finance and Supply Chain Management

Acknowledgements I admit being little bit hesitant about entering a PhD programme in a small town (Växjö) after many years as a consultant in finance at PwC Amsterdam. I could not have received a friendlier welcome than the one I got from Christopher von Koch and Magnus Willesson when they invited me to join them in teaching finance, while my fellow PhD student, Ola Nilsson invited me to share his office.

With regard to my PhD studies, I first would like to thank Mirka Kans, Elin Funck and Veronica Ülgen for their opposition to my research proposal. The feedback from Mirka (head opponent) gave me the self-confidence to send my research proposal to “Kellogg” in order to study option contracts in SCM.

Subsequently, this dissertation is formed on the basis of research during and after “Kellogg” and “Erasmus” 2014. I would like to thank the Tom Hedelius Foundation for its generous support that made my exchange visits to Kellogg School of Management Northwestern University Chicago followed by Erasmus University Rotterdam possible.

I would like to express my sincere gratitude to Professor Jan van Mieghem for inviting me as a visiting predoctoral fellow at the Kellogg School of Management, Managerial Economics and Decision Sciences Department, Northwestern University, Chicago, from January to June 2014. The professor is well- known for his theoretical contributions to operations management and his immense knowledge. I cannot express how grateful I am that I was invited and taught the latest “operations management ropes” straight from the “well of knowledge”. The PhD seminars at this faculty guided my research, development of the algorithm and my dissertation up to this day.

My sincere thanks also go to Professor Peter Wakker for inviting me as a visiting doctoral student at the Econometric Institute, Erasmus University Rotterdam, from September to December 2014 to study decisions under uncertainty and who provided me an opportunity to discuss the variables in my algorithm.

Staying at Erasmus University, I also would like to thank Professor Richard Paap at the Econometric Institute, who invited me to study Bayesian Econometrics and of course emeritus professor, Jan van der Meulen at the Economics department for accepting me as a Master’s student in finance in 1994 and for opening the door to Akzo-Nobel (right in the middle of their merger) so that I could get data for my quantitative Master’s thesis. This resulted in my being employed by PwC Corporate Finance Amsterdam and spending 15 wonderful years in the Netherlands.

My time in the Netherlands was enriched by Elisabeth, Lambert, Edwin, Gerrit, Boudewijin, Erwin, Marcel, Ewout, Willecke, “Baggio”, Ilse, Paul, Bart,

Patrick and Roger Liddle - Het was Leuk! I will always have very fond memories of my time in the Netherlands, including Locus Publicus!

The quantitative case study in this dissertation would not have been possible without the terrific support of Volvo CE, namely Leif Nilsson, Peter North and Emanuel Tirenå and Marcus Olsson at the Preferred Supplier AB. I am grateful for their collaboration and skills in the operation of a complex supply chain to assemble a “dumper truck”, from a piece of metal to a complete, giant vehicle. Thanks to Helena Forslund for opening the door to Volvo CE and Håkan Locking for accepting (while on holiday) travelling from Gothenburg to Växjö, on 5 July 2015, (the hottest day that summer) to examine and verify the confidential company data from Volvo CE and the supplier – for two days!

In addition, I would like to thank the many people who put their time and effort into reading my manuscripts at different stages of the dissertation process, including Professor Sven-Olof Collin, Anna Stafsudd and Elin Esperi Hallgren for their opposition in my mid-seminar. Thank you to Professor Kurt Jörnsten, Åsa Gustavsson and Maria Persdotter Isaksson for their opposition in my final seminar.

A PhD journey would not be complete without some headwind and for that I thank Peter Berling, who is as passionate about his research on inventory models as I am about option contracts. Nonetheless, his fierce feedback required me to improve my research, which is what matters.

My special thanks go to Christopher von Koch and Ola Nilsson. This for accepting the formal role as my supervisors and then investing a lot of time and effort in reading my material and providing valuable comments that helped me finish the literature review and kappa in the best manner possible. Moreover, I also thank you for your ongoing support throughout the years - a big, big thank you!

There are no words to express my sincere gratitude to Professor Karin Jonnergård. Amongst colleagues at Linnaeus University and the ELO department, Karin is the rock everybody leans on. In my project she has been more than that, she has been the Northern Star that has guided me in both calm waters and in navigating between Scylla and Charybdis when required. I will always be grateful to Karin and I view her as a role model, as both a leader and a researcher.

I am grateful to my mother, father and sister, who have always supported me in accomplishing this journey and I will always cherish my father for getting me interested in math and his “successful exam bonus” to celebrate exams over a pint of lager at the campus pubs in Örebro and Rotterdam – even if I failed the exams! Last but not least, as Springsteen always said when introducing Clemons, Thank You - Mikael Keskitalo!

I am excited to start a new journey to see where it takes me.

Katarina Eriksson

Växjö, September 2019

1

Contents

1 Introduction ............................................................................................... 5 1.1 Problem discussion ........................................................................... 7

1.1.1 Lack of empirical research with regard to supply chain flexibility ...................................................................................................... 8 1.1.2 Lack of an objective method to measure profitability ................ 10 1.1.3 Lack of a unified theory .............................................................. 11 1.1.4 Traditional solutions to improve supply chain responsiveness ... 12

1.2 Option contracts in theory and in practise ...................................... 13 1.3 The research question ..................................................................... 15 1.4 The purpose of the dissertation ....................................................... 15

1.4.1 Delimitation ................................................................................ 16 1.4.2 Structure ..................................................................................... 16

2 Prior Work ............................................................................................... 17 2.1 Supply chain management .............................................................. 17

2.1.1 The Newsvendor challenge......................................................... 19 2.1.2 Flexibility in the supply chain .................................................... 19 2.1.3 Supply chain contracts ................................................................ 21 2.1.4 The (lack of) empirical evidence ................................................ 22

2.2 Integrating finance and supply chain management ......................... 24 2.2.1 Centralised v. decentralised systems and portfolio theory v.

behavioural finance ..................................................................... 24 2.2.2 Game theory to reach an agreement ........................................... 26 2.2.3 Financial option theory ............................................................... 27 2.2.4 Real option valuation .................................................................. 28 2.2.5 Portfolio theory ........................................................................... 33 2.2.6 Supply chain flexibility via option contracts .............................. 34

3 Research Method ..................................................................................... 37 3.1 Scientific approach and research strategy ....................................... 37 3.2 Phase 1 – Literature review ............................................................ 39

3.2.1 Research methodology ............................................................... 40 3.3 Phase 2 – Development of the algorithm ........................................ 41 3.4 Phase 3 – Quantitative case study ................................................... 42

3.4.1 Selection of dyad companies ...................................................... 43 3.4.2 The research protocol ................................................................. 43 3.4.3 Reliability and validity ............................................................... 43

2

4 Coordination of a Dyadic Supply Chain ................................................. 45 4.1 Map contracts ................................................................................. 46

4.1.1 The wholesale contract ............................................................... 46 4.2 Finance theory ................................................................................ 47

4.2.1 The option mechanism ............................................................... 48 4.3 Quantitative case study – empirical theory testing ......................... 49

4.3.1 Empirical testing of option contract theory ................................ 49 4.3.2 Fisher framework and paradox ................................................... 50 4.3.3 The algorithm validated .............................................................. 51

4.4 Reflections on dyadic cooperation in practise versus theory .......... 51 4.4.1 Contracts in a dyadic supply chain cooperation ......................... 52 4.4.2 Option mechanism versus buy-back contract ............................. 52 4.4.3 Volvo CE and channel power ..................................................... 53 4.4.4 Symmetric information and trust ................................................ 54

5 Summary of the Papers ........................................................................... 55 5.1 Paper I – A theoretical review ........................................................ 56 5.2 Paper II – The algorithm ................................................................. 57 5.3 Paper III – A quantitative case study .............................................. 58

6 Discussions, Contributions and Conclusions .......................................... 61 6.1 Empirical contributions .................................................................. 61 6.2 Measurement method contributions ............................................... 62

6.2.1 Measurement of profitability ...................................................... 62 6.3 Theoretical contributions ................................................................ 63

6.3.1 Middle range theory ................................................................... 64 6.3.2 Fisher portfolio framework and paradox .................................... 66

6.4 Practical contributions .................................................................... 68 6.4.1 Bilateral coordination of ordering and production ..................... 68 6.4.2 Operational hedging ................................................................... 68 6.4.3 Optimise supply chain responsiveness ....................................... 69

6.5 Generalisability of results ............................................................... 69 6.6 Limitations and future research directions ..................................... 69

Appendix ......................................................................................................... 71 Appendix A. Research Protocol January 19th 2015 .................................... 71 Appendix B ................................................................................................. 73 Appendix C ................................................................................................. 74

References ....................................................................................................... 77

3

Compilation of papers

This dissertation is designed as a compilation of three research papers each by a single author. Paper I Eriksson, K. (2019). Application of Option Contracts in Supply Chain Management – A Theoretical Review. In peer-review process Paper II Eriksson, K. (2019). An Option Mechanism to Coordinate a Dyadic Supply chain Bilaterally in a Multi-period Setting. Omega – The International Journal of Management Science, 88, 196-209. Paper III Eriksson, K. (2019). Supply Chain Coordination through Application of Option Contracts – A Quantitative Case Study. In peer-review process

5

1 Introduction

This dissertation addresses a key challenge in supply chain management (SCM), i.e. dyadic supply chain inefficiency. Today, when competition is no longer only between individual companies but also supply chains (Shi and Yu, 2013; Vickery et al., 1999), the traditional challenge of – how much to order and when – becomes considerably more complex. For a company to be competitive it must effectively respond to stochastic demand to limit the adverse effects of the costs of mismatches due to stockouts or overstocks (Biçer et al., 2018; de Treville et al., 2014a; de Treville et al., 2014b). Therefore, when companies are forming supply chains to increase their competitiveness while seeking to optimise supply chain responsiveness (de Treville et al., 2014a; de Treville et al., 2014b; Wang et al., 2014), they must match supply and demand to coordinate production and ordering (Wan and Chen, 2015). This while confronting supply disruption, (Kleindorfer and Saad, 2005; Sodhi et al., 2012), random yield (Cai et al., 2017), lead time uncertainty (de Treville et al., 2004) and stochastic demand (Das and Abdel-Malek, 2003) – challenges that may prove costly. The costly effects of supply chain disruption have been documented and have shown that companies suffering from supply chain disruptions experience approximately 30% lower stock returns than the benchmark, which underscores the importance of having strategies to mitigate costly disruptions (Hendricks and Singhal, 2003, 2005; Hendricks et al., 2009). In addition, Ericsson lost nearly USD 400 million due to its inability to react to its single sourcing risk following the furnace fire in March 2000 in an Albuquerque semiconductor plant owned by Philips NV, leading to the disruption of ASCI chips (Norrman and Jansson, 2004). Furthermore the random yield, i.e. loss in

6

manufacturing, can sometimes be as high as 50% when the quantity produced or received differs from the quantity ordered or expected output (Gavirneni, 2004). Moreover, managing demand uncertainty is a key issue in a dyadic supply chain when it comes to ordering and production. Getting demand forecast wrong can be costly, which is what happened in 2003 when Cisco, Ericsson and Lucent wrongly predicted that the demand for wireless handsets and network equipment would grow rapidly. As a result, American Solectron Corporation increased production capacity and before it was too late to postpone order contracts with nearly 4,000 suppliers it took on a loss of USD 47 billion in inventory (Fu et al., 2017). In the event companies only use the widely applied wholesale contract, this will lead to a lack of flexibility in optimally adjusting ordering and production to respond to supply and demand uncertainty to optimise supply chain responsiveness (de Treville et al., 2014a; de Treville et al., 2014b; Wang et al., 2014). This lack of flexibility may result in inefficiencies, increase the problem of ordering variability, aka bullwhip (Lee et al., 1997) and contribute to ordering that is less than the system-wide optimal quantity due to the effect of double marginalisation (Spengler, 1950). This should not be underestimated. Martínez-de-Albéniz and Simchi-Levi (2009) and Adida and Perakis (2014) found that the loss of total profits due to decentralisation could be 25% of the centralised chain profits, while Perakis and Roels (2007) calculated the loss at up to 40% between an integrated and decentralised supply chain using only a wholesale contract. Thus, when each company seeks to maximise its profits through cooperation it results in a higher retail price, which leads to a lower ordering level and lower combined profit (smaller share of the pie) for the supply chain (Zhao et al., 2010). The need to study flexibility in a wider supply chain context is increasingly being recognised (Krajewski et al., 2005; Schmenner and Tatikonda, 2005; Stevenson and Spring, 2007; Tiwari et al., 2015). According to Kumar et al. (2006), the flexibility of a supply chain is a key and critical issue in the competitive business market and in the foreword to Real Options (Trigeorgis, 1996), Scott Mason writes “Flexibility has value. While this statement is obvious at the conceptual level, it is surprisingly subtle at the applied level. Professional managers have long intuited that both operating flexibility and strategic flexibility ... are important elements in valuation and planning

7

decisions. But precisely how valuable is flexibility, and how can its value be quantified?” (Mason in Trigeorgis 1996). In addition, Lambert and Cooper (2000, p. 65) state “One of the most significant paradigm shifts of modern business management is that individual businesses no longer compete as solely autonomous entities, but rather as supply chains. Business management has entered the era of internetwork competition. …it is now supply chain versus supply chain. In this emerging competitive environment, the ultimate success of the single business will depend on management’s ability to integrate the company’s intricate network of business relationships”. When competition is no longer only local but global and not only between companies but supply chains, research cannot be limited to manufacturing, i.e. intra-firm flexibility and companies winning orders based on low-cost and standardised production (Gerwin, 1993; Jain et al., 2013; Slack, 1987; Upton, 1994). Thus, given the nature of competition, “manufacturing flexibility … is not sufficient to deal with modern firms that are connected through very complex supply chains and operate in very risky and uncertain environments” (Tiwari et al., 2015, p. 768). Subsequently, a key research subject has been to extend the study beyond the individual company to include the supply chain and examine how companies interact with regard to flexibility and performance (Esmaeilikia et al., 2016; Stevenson and Spring, 2009). However, when the study is extended to a dyadic supply chain, we identify some problems.

1.1 Problem discussion Following the idea that “competition is no longer company to company but supply chain to supply chain. A key dimension of supply chain performance is flexibility” (Vickery et al., 1999, p. 16) and increased focus has been directed towards how to achieve supply chain flexibility and relate that to supply chain profitability (Purvis et al., 2014; Tachizawa and Thomsen, 2007). It was the limitations of manufacturing flexibility that prompted researchers to study “components that make an organisation flexible and extends them beyond the organisation's boundaries to other nodes in the supply chain” (Lummus et al., 2003, p. 3). Research went from manufacturing flexibility improving

8

profitability (Bernardes and Hanna, 2009; Gerwin, 1987; Slack, 1987) to product flexibility (Van Mieghem, 1998) to supply chain flexibility (Duclos et al., 2003; Mishra et al., 2014; Sánchez and Pérez, 2005; Stevenson and Spring, 2007, 2009; Tiwari et al., 2015).

1.1.1 Lack of empirical research with regard to supply chain flexibility

One problem in SCM is the relative lack of empirical evidence supporting the benefits attributed to theories (Lambert et al., 2005; Stock et al., 2010). According to Ketchen and Giunipero (2004), researchers argue that the SCM metrics such as speed, quality, cost and flexibility have a major impact on the bottom line. However, the proof of this is simply based on assertion, not empirical evidence. Therefore, there is a need for empirical research providing evidence as to “how and to what extent supply chain activities directly and indirectly shape firm profits and stock price” (Ketchen and Giunipero, 2004, p. 54) Given the importance of supply chain flexibility in practise, authors such as Sánchez and Pérez (2005), call for empirical research: “…contrary to flexibility in manufacturing systems, which has been widely researched, it seems that research on supply chain flexibility has been conspicuous by its absence” (p. 682). This resulted in survey-based empirical studies on the relationship between supply chain flexibility and performance (Avittathur and Swamidass, 2007; Gosling et al., 2010; Merschmann and Thonemann, 2011; Sánchez and Pérez, 2005; Swafford et al., 2006), including supply chain fit and firm performance (Gligor et al., 2015; Wagner et al., 2012). However, the problem or weakness of surveys is evident in Lim (1987), who found that when answering questions about manufacturing flexibility, 11 out of 12 respondents thought of product flexibility. Moreover, Shi and Yu (2013) argue that the response rate in surveys is so low that it is difficult to “perform rigorous statistical analysis and generalize empirical findings” (p. 1287). Furthermore, a key limitation of current surveys on supply chain activities is that responses come from a single respondent subjectively filling in a questionnaire and scoring on a Likert scale rather than from several members in a supply chain (Prajogo et al., 2016). Not surprisingly, some view the current established relationship on supply chain flexibility (integration) and performance as inadequate (Manders et al., 2017; Purvis et al., 2014).

9

The methodological limitations are by no means a new issue. In two comprehensive reviews on empirical research between 2000-2006, Fabbe-Costes and Jahre (2007, 2008) examine the link between supply chain integration and performance. Based on the reviews, they question a number of key issues including how data is collected, examined and verified and how performance is measured. The review (2007) led to the conclusion that there is no empirical evidence on the relationship between integration and performance. In the second review (2008), it was found that less than 2% of 2,017 papers in the initial sample discuss and/or report on empirical studies of relationships between supply chain integration and performance and there appeared to be an almost universal acceptance of the relationship without much proof. Without a clear definition it makes it difficult to provide any solution with regard to what to integrate and any possible benefits of integration. Furthermore, Van der Vaart and Van Donk (2008) reviewed 33 survey-based research articles between 1999-2006 on the relationship between supply chain integration and performance. They find that performance is based on the focal firm (not supply chain) and via subjective measures, and point out their “concerns over methodology” (p. 44). Presutti and Mawhinney (2007) argue for research that focuses on SCM and the financial bottom line of a company or the market value (share price) of a company, which is of interest to practitioners. A broader perspective stops SCM research from being confined and instead applies a more holistic approach through the application of new and multiple research methods. Dooley et al. (2010) and Olson (2010) also ask for more empirical research amongst other things, on how to deal with volatile market supply and demand, which requires close collaboration between finance and SCM. They mean that these topics are highly relevant for practitioners but are hardly tackled in empirical supply chain research. Evidently, empirical research on supply chain integration can be improved. This also has practical relevance as development in this area would probably provide valuable insight into how companies could improve dyadic supply chain effectiveness, resulting in improved supply chain responsiveness (de Treville et al., 2014a; de Treville et al., 2014b; Wang et al., 2014). According to Manders et al. (2017) in future studies, “models have to be tested and evaluated to arrive at well-designed and validated supply chain flexibility measures” (p. 1012).

10

1.1.2 Lack of an objective method to measure profitability The lack of an objective method to measure dyadic profitability and performance has followed supply chain management (SCM) since the term was introduced into academia (Ellram and Cooper, 1990; Fabbe-Costes and Jahre, 2007). One reason may be that a companies in the chain can affect one another’s performance or another reason might be that researchers simply mix apples and oranges when they are measuring (Van der Vaart and Van Donk, 2008). Nonetheless, this is a key issue and as the saying goes, “you cannot manage what you cannot measure” (e.g. Sink and Tuttle, 1989). Gunasekaran et al. (2001) discuss the lack of a coherent framework and thus provide one for measuring strategic, tactical and operational levels of performance in addressing supplier, delivery, customer service and inventory costs. Chan (2003) acknowledges the attempt by the SCM to build new ‘measures and metrics’, yet claims SCM has too many defects and points out the following in-depth problems. “1. The lack of a balanced approach to integrating financial and non-financial measures. 2. The lack of system thinking, in which a supply chain must be viewed as a whole entity and the measurement system should span the entire supply chain. 3. The loss of the supply chain context” (Chan, 2003, p. 536). Storey et al. (2006) note that SCM performance measurement based on key performance indicators and balanced scorecard does not take into account the fact that the sum of the parts does not equate to the whole. Fabbe-Costes and Jahre (2007) are highly critical to the measurement of performance and ask the thought-provoking question of whether “SCI [Supply Chain Integration] might be the Emperors´ New Suit of business” (p. 835). They point out that performance is measured based on “managers’ perceptions” and that “only one paper” in their study, i.e. Narasimhan and Kim (2002) includes actual data on performance (market share growth and sales growth). Van der Vaart and Van Donk (2008) are equally critical when they claim that SCM seems to suffer from inconsistency in definitions and measurement scales and follow this by stating that “despite our concerns over methodology, the majority of the surveys do report a positive relationship between integration and performance” (p. 53). They argue that results have led to a general belief that a relationship does exist between attitudes and overall performance however, “the question is whether we always understand the nature of this relationship. For instance, it is not that easy to see how a positive attitude towards a supplier results in improved performance by a

11

focal firm when this is measured in terms of profits or market share” (Van der Vaart and Van Donk, 2008, p. 53). Stevenson and Spring (2007) provide a review of how flexibility can be applied across the supply chain, amongst other things to react to uncertainty. In their view, supply chain flexibility cannot be measured until adequately defined. In addition, they state that “future empirical research should approach research design from a network perspective, treating the supply chain as the unit of analysis, in order to develop a more complete understanding of the effects of flexibility across the whole supply chain” (p. 685). Stevenson and Spring (2009) continue to argue for empirical research on supply chain flexibility. They mean that empirical work would help develop a better understanding of this emerging phenomenon, yet also advance the measurements of flexibility and development of analytical models. They acknowledge the problem with regard to long-distance questionnaires relying on the respondents’ interpretations of flexibility in a company or a chain and argue it is futile to try finding a single optimal level of flexibility for a supply chain.

1.1.3 Lack of a unified theory There is no unified theory in SCM (Halldórsson et al., 2007). Based on the situation, one theory can be chosen as the dominant explanatory theory and can then be complemented with one or more other theoretical perspectives (Ellram and Cooper, 2014; Halldórsson et al., 2007, 2015). It may also be possible for SCM researchers to borrow theories from other related fields (Ellram and Cooper, 2014; Stock, 1997) by leaving their disciplinary comfort zone and going beyond the boundaries of their own fields and into other disciplines (Fawcett and Waller, 2011). Stock et al. (2010) identified a number of possible research opportunities in supply chain management including “the use of dyadic research should be of value, especially examining various partnership and alliance relationships” (p. 38) and “the financial implications of technology investment (e.g., ROI, ROA). Because technological advances are occurring continuously, these issues will be of ongoing interest to members of supply chains and hence, should also be to researchers” (p. 39).

12

Costantino and Pellegrino (2010) argue that supply chain research should focus on finance and the real options approach since it could help decision makers reach better informed decisions about supply chain strategies and investments under uncertainty. Similar to financial options, real options evaluate the benefits of managerial flexibility and capture upside potential while limiting downside loss. Sanders and Wagner (2011) argue that the supply chain - finance interface warrant further study and state; the topic “supply chain - finance interface” is multidisciplinary by definition…just as we have borrowed and applied tools from the strategic management and marketing disciplines a few decades ago, practitioners and researchers should borrow tools from finance, such as real options or copula analysis, and apply them to supply chain problems” (Sanders and Wagner, 2011, p. 318).

1.1.4 Traditional solutions to improve supply chain responsiveness

Various approaches are applied in order to improve supply chain responsiveness. Some argue for a tailored supply chain strategy by matching product type (functional or innovative) with supply chain strategy (efficient or responsive), aka the Fisher (1997) framework. Others suggest using supply chain contracts to coordinate supply chain activities.

1.1.4.1 Fisher framework According to scholars, modern investment theory started with the Markowitz (1952) paper on portfolio selection. In his 1997 Harvard Business Review article, Marshall Fisher suggests that the reason supply chain improvement efforts have not produced the expected results in performance, is due to the misalignment of product types with supply chain strategies. Fisher (1997) argues that the problems afflicting many supply chains is a mismatch between the type of product and supply chain and identifies two types of products – those that are primarily functional or innovative – and types of supply chain strategy – an efficient and a responsive. Fisher (1997) proposes that an efficient supply chain provides the best performance for functional products and innovative products perform best when aligned to a market responsive supply chain.

13

1.1.4.2 Supply chain contracts The traditional way to coordinate activities is to use supply chain contracts that specify contract parameters such as quantity, price, quality and deadlines, which fulfil mutual objectives and improve the supplier-buyer relationship (Tsay et al., 1999). According to Arshinder et al. (2008), the objectives of supply chain coordination contracts are the optimisation of total supply chain profits by neither ordering too much nor too little and sharing the demand risk. Nonetheless, while traditional supply contracts can arbitrarily divide profits, coordination is based only on the ordering side, thus ignoring coordination with the production side due to lack of flexibility (Nosoohi and Nookabadi, 2016). The limitation of traditional supply chain contracts resulted in SCM researchers approaching finance to adopt options to create flexibility in order to coordinate ordering and production between the supplier and manufacturer (Birge, 2000; Van Mieghem, 1998). A call (or put) option gives the holder the right – not the obligation – to buy (or sell) an underlying asset by a certain date at a certain price (Hull, 2012). Option contracts originated in derivatives (Black and Scholes, 1973). Myers (1977) coined the term, real options (Dixit and Pindyck, 1994).

1.2 Option contracts in theory and in practise The option contract gives the holder the right to either buy or sell an underlying quantity given known demand. The parameters of this contract outline that the buyer pays the premium to the supplier for reserving stock (before demand is known), following the price the buyer pays when exercising the contract (when demand is known) before or on the contract expiry date. Thus, the key parameters of the contracts are the capacity reservation fee (option premium), the execution fee (exercise price) and expiry date (time) (Zhao et al., 2018). Subsequently, the call (or put) option contract is applied to create upward (or downward) flexibility to coordinate ordering and production under supply, lead time and demand uncertainty and if both are applied, it is called a bidirectional option contract (Zhao et al., 2013). While research in supply chain flexibility is still in its infancy (Manders et al., 2017), option contracts and supply chain flexibility are becoming priorities on the business leaders agenda (Sun, 2013). For example, Hewlett-Packard made 35% of its procurement value through options derivatives (Chen, Hao and Li, 2014), Intel use option contracts (Peng et al., 2012), while Nike use option

14

contracts to hedge future transactions with third-party vendors (Biswas and Avittathur, 2019). Moreover, China Telecom negotiates purchasing prices with its suppliers (Nokia, Motorola, Ericsson, Samsung, Huawei) on a monthly basis followed by offering its retailers the flexibility to obtain products through either a firm order or by exercising call options (Chen and Shen, 2012). As mentioned above, supply disruption, lead time uncertainty and stochastic demand are challenges that are costly and causes inefficiencies when companies in a supply chain are trying to coordinate ordering and production. By only using the wholesale contract, the supply chain has no flexibility to adjust ordering and production to optimise supply chain responsiveness. Today, competition is neither local (but global) nor between companies (but supply chains). Flexibility is a key aspect for a supply chain to be competitive. To limit the efficiency loss, companies are trying to coordinating their activities using contracts. However, while supply chain contracts can arbitrarily divide profits, coordination is focused on the ordering side, thus ignoring the production side. Given that the wholesale contract cannot provide flexibility resulting in substantial loss of efficiency, the limitation of traditional supply chain contracts lead researchers to approach finance to adopt option contracts to create flexibility for supplier and manufacturer. Empirical research in this area is limited to surveys providing perceptual results. A dyadic supply chain competing in a global economy cannot afford to lose its collaborative advantage and end up with a 40% smaller share of the pie, nor measure profitability based on managers’ perceptions. One solution might be option contracts that create flexibility to coordinate ordering and production while also enabling the possibility of measuring profitability and subsequent performance through valuing the option contract. Subsequently, there is increasing interest in examining how option contracts can be applied to coordination of ordering and production, where the objectives of each supply chain partner are aligned to the objectives of the supply chain (Zhao et al., 2016). Given the extensive use of contracts in supply chains, design and application are important to SCM and substantial research has been conducted to investigate the relevant issues over the past few years. Despite the abundance of classical research and given the introduction of instruments derived from finance, new research is needed to

15

broaden the emerging knowledge within the research and business environment. In addition, the rapidly changing and shorter product life cycles combined with the increasing globalisation of the supply chain requires a comprehensive and profound understanding of this important topic (Chiu et al., 2011; Zhao et al., 2016).

1.3 The research question The overall research question is – how to address the challenge of optimising efficiency in a dyadic supply chain and establish a relationship between dyadic supply chain flexibility and dyadic supply chain profitability.

The overall empirical problem is the lack of an objective empirically validated method that optimises efficiency in a dyadic supply chain and establishes a relationship between dyadic supply chain flexibility and dyadic supply chain profitability. This dissertation addresses the overall research question and problem, resulting in the following overall purpose.

1.4 The purpose of the dissertation The main purpose of this dissertation is to study the relationship between dyadic supply chain flexibility and dyadic supply chain profitability. Dyadic supply chain flexibility is defined as the ability of a manufacturer and a supplier to adjust ordering and production to respond to stochastic demand in order to each improve profitability compared to a wholesale contract. The overall purpose is fulfilled in the following three steps. The first step is to conduct a literature review of research on how options contract theory is applied to create flexibility between buyer and seller in supply chain management. The second step is to develop an algorithm that creates flexibility for a buyer and a seller to coordinate ordering and production quantities to respond to stochastic demand to maximise profit for each and coordinate the dyad bilaterally in a multi-period setting. The third and final step is to tie this dissertation together through the application of the algorithm to a case study using quantitative data from two companies to validate the algorithm and empirically test the option contract theory.

16

1.4.1 Delimitation The dissertation has the following delimitations. Firstly, the algorithm is based on call options and ignores the put (Chen and Parlar, 2007) and bidirectional (Zhao et al., 2013) options. Secondly, this study focuses on one OEM and one of its Preferred Suppliers operating with a functional component given stochastic demand. This provided us with an ‘a-ha moment’ when we suddenly understood that in some situations, the Fisher portfolio framework may create a contradiction and we provide a solution to that paradox in this context. However, future research may explore how the algorithm may impact results given an innovative high-tech product and its supply chain. Thirdly, the study data was collected from a single industry. Additional insights could be obtained if future research collects data from different industries and compares the findings.

1.4.2 Structure In addition to this introductory chapter, chapter 2 presents the theory underlining this research that combines SCM and finance option contract theory. Chapter 3 outlines the methodology. Chapter 4 presents the coordination of a dyadic supply chain. Chapter 5 provides a summary of each paper and its contribution. Chapter 6 discuss the findings and overall contributions, including limitations, future research and conclusions.

17

2 Prior Work

This chapter presents prior work related to this dissertation. It begins with the discussion that SCM has no right theory and that to remain relevant as a discipline, the research is urged to apply a multidisciplinary approach (Costantino and Pellegrino, 2010; Sanders and Wagner, 2011). This is followed by manufacturing flexibility (Slack, 1983, 1987; Upton 1994) and flexibility in the supply chain, which is not the same as supply chain flexibility (Manders et al., 2017). This then leads into the discussion of supply chain contracts (Cachon, 2003) and unilateral coordination (Wan and Chen, 2015). The limited empirical studies with regard to supply chain flexibility are then presented before the chapter concludes with how this dissertation adopts theory from finance, including portfolio theory (Fisher, 1997; Markowitz, 1952) and the real options approach (Myers, 1977) to create supply chain flexibility.

2.1 Supply chain management SCM is an interdisciplinary concept that revolves around a cross-functional and integrative approach to the management of activities and flow within and across organisational boundaries (Larson and Halldórsson, 2004). Key aspects of SCM include the design of a supply chain structure and the management of such a structure through interorganisational relationships (Ellram and Cooper, 2014; Halldórsson et al., 2007). The term SCM is credited to consultants (Oliver and Webber, 1982) and was introduced into academia by Ellram and Cooper (1990). The term has helped unite logistics, procurement, operations and distribution under the discipline of SCM (Ellram and Cooper, 2014). However today, nearly 40 years later there is no definitional consensus on supply chain management in academia or among industry practitioners

18

(Ellram and Cooper, 2014), nor is there a unified theory (Halldórsson et al., 2007, 2015). One reason for the lack of a common definition is that academics study part of the supply chain but classify their study and findings under the SCM umbrella (Ellram and Cooper, 2014). Halldórsson et al. (2007, p. 285) discuss the fact that there might not be a “right” theory for the management of supply chains. Instead, they suggest that SCM may borrow concepts from other related fields to equip SCM with the ability to cope with the emerging challenges and opportunities (Ellram and Cooper, 2014; Halldórsson et al., 2007; Klaus, 2009; Stock, 1997). The principal–agent theory, transaction cost analysis, network theory and the resource-based view may be theories that provide valuable insight into how to structure and manage a supply chain successfully (Halldórsson et al., 2007, 2015). Moreover, Halldórsson et al. (2007, 2015) argue that based on the situation, one theory can be chosen as the dominant explanatory theory, which is then complemented by one or more of the other theoretical perspectives. This based on the fact that the practical field of SCM is constantly changing, as the competitiveness of international companies is more and more dependent on the ability to rapidly and efficiently produce and deliver customised products all over the world. At the same time, the value creation takes place outside the boundaries of the individual organisation (Bruce et al., 2004). In SCM there is an ongoing discussion that to remain relevant as a discipline, the research is urged to apply a multidisciplinary approach and that it may benefit from borrowing from others, suggesting this will speed up learning and even establish links to these disciplines (Amundson, 1998; Costantino and Pellegrino, 2010; Sanders and Wagner, 2011; Stock, 1997). The logic is that the complex nature of SCM calls for use of complementary theories (or for professionals, complementary logics) to provide an in-depth understanding of possibilities and actions (Halldórsson et al., 2007; Stock, 1997). The presumption is that SCM borrows from other fields, which entails complementarity (i.e. theories selected must have explanatory relevance to some of the dimensions of SCM) and that this implies theories to capture the multiple nature of SCM (Halldórsson et al., 2007, 2015). Borrowing concepts and logics from other related fields has been key to equipping SCM (and related fields such as logistics, operations management and purchasing) to cope with emerging challenges and opportunities (Ellram and Cooper, 2014; Halldórsson et al., 2007; Klaus, 2009; Meredith, 1998; Stock, 1997). Ellram

19

and Cooper (2014) discuss the fact that over three decades of the use of the term, SCM there has been much ink devoted to the definition and development of the concept and analysis of its use or non-use. They argue that in a manner similar to other disciplines such as economics, there is continual development of theories but little consensus amongst various kinds of economist for example, capitalists versus socialists. The arguments also highlight that within different sides there are the conflicting viewpoints of neoliberalism and Keynesian economics. Their view is that SCM is moving in the direction of friendly disagreement and schools of thought in a manner similar to other disciplines (Ellram and Cooper, 2014).

2.1.1 The Newsvendor challenge When deciding inventory, a distinction must be made between the products – one being non-perishable (no deadline for disposal of inventory) and the other perishable (limited time in inventory). One example of a perishable product is the daily newspaper. This is the kind of product for which the single-period model (and its variations) is designed and is thus referred to as the ‘newsvendor problem’. However, the model being used is just as applicable to other perishable products – from Christmas trees to airline reservations for a particular flight. The newsvendor is about optimising ordering given the overage and underage cost, i.e. the critical fractile. The buyer seeks to limit the cost of unmet demand (underage cost), as well as the cost of excess inventory (overage cost) to maximise profit. In a single period setting, the classic newsvendor problem is to find the product ordering quantity that maximises expected profit under probabilistic demand and the model assumes that if any inventory remains at the end of the period, it will be disposed of (Khouja, 1999; Nahmias, 2009; Rudi and Pyke, 2000).

2.1.2 Flexibility in the supply chain In 1921, the value of flexibility had been already pointed out in decision analysis when Lavington made a connection between variability and the value of flexibility in considering the risk arising from the immobility of invested resources. Stigler (1939) in the theory of the firm defines flexibility as those attributes of cost curves that determine how responsive output decisions are to demand fluctuations. Hart (1940) defined flexibility as a postponement of decisions until more information is obtained as an effective means of dealing with current uncertainty. Marschak and Nelson (1962) suggested flexibility

20

was studied and viewed as a ‘mechanism’ that allows decision makers to take advantage of future information. They meant that greater uncertainty and a greater amount of future information the more important is flexibility. To increase competiveness, companies started applying various forms of manufacturing flexibility. Sethi and Sethi (1990) provide a review on manufacturing flexibility where they show how intra firm flexibility was initially proposed for the manufacturing sector to deal with unexpected changes in production, e.g. equipment breakdowns, variable task times, queuing delays, and re-workings. Olhager and West (2002) acknowledge the value of manufacturing flexibility by suggesting a house of flexibility approach based on the house of quality (Hauser and Clausing, 1988). With the established link that manufacturing flexibility improves profitability, research continued with the application of different forms of flexibility to create flexibility in the supply chain, including volume (Duclos et al., 2003; Vickery et al., 1999), trans-shipment (Barad and Sapir, 2003), postponement (Barad and Sapir, 2003; Saghiri and Barnes, 2016) and sourcing (Narasimhan and Das, 2000). Flexibility in a supply chain has been defined as: a) the degree at which the supply chain adjusts its speed and volume in response to dynamic environments (Duclos et al., 2003; Lummus et al., 2005); b) the ability to adapt to changing business conditions (Gosain and Naim, 2004); c) the ability to rapidly and economically restructure operations (Kumar et al., 2006; Wadhwa et al., 2008); d) responding to customer requirements (Gunasekaran et al., 2001). Esmaeilikia et al. (2016) provide a three-dimensional framework quantifying supply chain flexibility measures for tactical supply chain planning and optimisation based on supply flexibility (e.g. flexibility in procurement), manufacturing flexibility (e.g. flexibility in assembly processes) and distribution (e.g. flexibility in transportation and warehousing processes). Manders et al. (2017) provide a comprehensive review of supply chain flexibility while pointing out that flexibility in the supply chain is not the same as supply chain flexibility. They identified 95 flexibility dimensions that they categorised and mapped into seven business areas: product development, procurement, manufacturing, logistics, marketing, (financial) information and organisation. In addition, Van den Broeke et al. (2018) find that investment in a flexible platform, i.e. a platform not specialised for one specific product segment, can be optimal and reduces investment risk.

21

2.1.3 Supply chain contracts When competition expanded from only individual companies to include supply chains (Shi and Yu, 2013; Vickery et al., 1999) researchers started applying manufacturing flexibility to supply chains and simply called it supply chain flexibility. However, to further optimise the chain they added supply chain contracts (Anupindi and Bassok, 1999; Cachon, 2003; Tsay et al., 1999). According to Wang (2002), a supply chain contract is “a coordination mechanism that provides incentives to all of its members so that the decentralised supply chain behaves nearly or exactly the same as the integrated one” (p. 302). Supply chain contracts try to coordinate activities by specifying all contract parameters such as quantity, price, quality and deadlines under a transfer payment, in order to fulfil mutual objectives and improve the supplier-buyer relationship. Cachon (2003) states three conditions that should be met by a coordinating contract: (1) the set of supply chain optimum decisions should be a pure Nash equilibrium; (2) it should divide the supply chain profits (utilities in general) arbitrarily among the members in the chain; and (3) it should be worth adopting. The first condition addresses the transformation of individuals’ utility functions, while the second indicates that if a contract can divide profit in any manner, it could then be acceptable to all members. Evidently, the third condition is somewhat vague. Similarly, Arshinder et al. (2008), argue that the objectives of coordinating contracts are: a) to optimise the total supply chain profit; b) to limit costs by ordering too much or too little; and c) to share the (demand) risk between the parties. A number of supply chain contracts have been applied across the supply chain to coordinate supply chain activities given quantity and price including buy-back contracts (Basu et al., 2019; Pasternack, 1985), revenue-sharing contracts (Cachon and Lariviere, 2005; Yan and Cao, 2017; Yang et al., 2011), risk-sharing contracts (Gan et al., 2004, 2005), quantity flexibility contracts (Biçer and Hagspiel, 2016; Milner and Rosenblatt, 2002; Tsay et al., 1999), sales rebate contracts (Taylor, 2002), quantity discount contracts (Qi et al., 2004), quality compensation contracts (Lee et al., 2013), advance purchase contracts (Deng and Yano, 2016), inventory subsidising contracts (Chen et al., 2016), gain/loss-sharing contracts (Wang and Webster, 2007), linear margin contracts (Xiao et al., 2015) and cost-sharing contracts (Wang et al., 2011).

22

2.1.4 The (lack of) empirical evidence The premise of an effective supply chain is that if properly managed, its whole value can be greater than the sum of its parts and the optimisation of financial performance along supply chains should be the ultimate goal of any SCM strategy (Shi and Yu, 2013). Following the empirical studies including those of Vickery et al (1999) and Jack and Raturi (2002), the call to study supply chain flexibility in practise increased, e.g. Sánchez and Pérez (2005, p. 682) state: “…contrary to flexibility in manufacturing systems, which has been widely researched, it seems that research on supply chain flexibility has been conspicuous by its absence”. This resulted in additional survey-based empirical studies on the relationship between supply chain flexibility and performance (including Avittathur and Swamidass, 2007; Merschmann and Thonemann, 2011; Sánchez and Pérez, 2005; Swafford et al., 2006) view table 2.1.

Empirical Supply Chain Flexibility Studies (SC = Supply Chain)

Context Objectives Method Findings

Vickery et al. (1999)

Today competition is extended to SC and flexibility is key to performance.

Examine 5 dimensions of flexibility.

Survey a) CEO & Sales >USD 1 million, US furniture industry, b) Sample 65 firms, c) Response rate 20%, d) 7-point Likert scale.

Volume flexibility improves performance (correlated significantly with ROI and return on sales).

Jack and Raturi (2002)

Firms deploy different strategies for creating volume-flexible responses to stochastic demand.

Case study Drivers and sources of volume flexibility Survey Impact of volume flexibility and firm performance.

Case study Interview at 3 firms (capital goods) and Survey "Responses from 750 managers in midwestern USA" via 5-point Likert scale.

Both internal (capacity) and external SC network) sources have a positive impact on a firm’s volume flexibility capability.

Sánchez and Pérez (2005)

"…research on SC flexibility has been conspicuous by its absence" (p. 682).

To explore the relationship between the dimensions of SC flexibility and firm performance

Survey a) Of purchasing managers, b) Sample 126 Spanish automobile suppliers, c) response rate 35.4%, d) 7 point Likert scale.

Aggregate flexibility capabilities (customer - supplier) are positively related to firm performance .

23

Empirical Supply Chain Flexibility Studies (SC = Supply Chain)

Context Objectives Method Findings

Swafford et al. (2006)

Benefits of SC agility is known but no empirical research has shown how to achieve it.

To identify critical factors that decide and influence the agility of an organisation’s SC via a process-based framework.

Survey a) Questionnaire to manufacturing firms, b) Sample of 115 firms & 100 employees, c) Response rate 19%, d) 5-point Likert scale.

Procurement and manufacturing flexibility have significant positive effects on SC agility.

Avittathur and Swamidass (2007)

How do U.S. firms match supplier flexibility in India cooperating with local suppliers where norms are not the same as in the U.S.?

To contribute to understanding of the supply chain practises in India where many U.S. manufacturing plants now operate.

Survey a) Questionnaire to local plant manufacturing managers, b) Sample 26 U.S. manufacturing plants in India (SIC codes 34-38), c) Response rate 38%, d) 5- point Likert scale.

In low-cost markets with high volume, low profit margins, less flexible plants may be suitable. The use of flexible suppliers when plant is not flexible may increase costs.

Merschmann and Thonemann (2011)

Flexibility is costly.

Do companies that match supply chain flexibility to environmental uncertainty perform better than companies that do not?

Interview 32 executives from the consumer goods industry. Survey a) To manufacturing firms with >200 employees (questions from Swafford et al.(2006)), b) Sample of 53 manufacturers in Germany, c) Response rate 15%, d) 5- point Likert scale.

Empirical support for contingency theory in the context of SC design: Companies that match SC flexibility and uncertainty have higher performance than companies that do not!

Table 2.1 Empirical Supply Chain Flexibility Studies

Sánchez and Pérez (2005) also point out the limitation of survey based research, which is that it only produces perceptual results. According to Purvis et al. (2014, p. 102) “The literature on supply chain flexibility is still in its infancy and most of the previous studies of flexibility in the wider context of inter-company collaboration have aimed to build conceptual frameworks and have lacked empirical validation”.

24

According to Prajogo et al. (2016), a key limitation of current surveys on supply chain activity is that responses come from a single respondent rather than members of a supply chain (subjectively filling in a questionnaire and scoring using a Likert scale), which results in a measurement based on manager perception (Fabbe-Costes and Jahre, 2007, 2008).

2.2 Integrating finance and supply chain management

The fact that supply chain contracts can coordinate a dyadic supply chain unilaterally and arbitrarily divide its profit, thus ignores coordination with the production side (Wan and Chen, 2015) led researchers to approach finance, including the Markowitz (1952) portfolio selection, Fisher (1997) framework and real option contracts to create flexibility (Van Mieghem, 1998).

2.2.1 Centralised v. decentralised systems and portfolio theory v. behavioural finance

The centralised system is a term often applied in operations and SCM. It is well-known that the efficiency of a centralised system is superior to that of the decentralised system (Porteus, 2002) because there is only one decision maker deciding the costs that will optimise the chain by avoiding, e.g. the double marginalisation problem (Spengler, 1950). In the supply chain literature, coordination refers to the equivalence of the individual agents who are making decisions that seek to optimise decentralised systems in a manner equal to the centralised supply chain. Thus the centralised system is viewed as the integrated or optimal system and the value of the centralised system is greater than the decentralised system (Cachon, 2003). In a theoretical scenario, a supply chain managed by a central planner able to control all decisions, is referred to as a centralised supply chain and the set of actions that can optimise the performance of the supply chain is called the centralised optimal solution, which results in a coordinated supply chain. However, in practise each company in the supply chain is an independent entity aiming to maximise its own objectives and this is referred to as a decentralised system. In the centralised system, where the central planner decides the order level and pricing, the manufacturer obtains products from the supplier at cost, 𝑐𝑐 . Whereas in the decentralised system, where each company is maximising its own profit under the wholesale contract, the

25

supplier charges a unit price, 𝑤𝑤, (𝑤𝑤 > 𝑐𝑐) . This means that the wholesale contract cannot maximise the performance of the supply chain (note that the supplier requires a unit profit margin (𝑤𝑤 − 𝑐𝑐)). This leads to a lower ordering level and creates inefficiencies in the supply chain (Spengler, 1950; Zhao et al., 2010). Moreover, the problem in reaching the centralised system is also based on the fact that each decision maker is acting in a rational manner. This implies that the actor seeks to maximise its own utility and is able to calculate its optimal decisions. This leads to the maximisation of its utility, given the available information. As a result, each decision maker may not act in a manner that is optimal for the supply chain unless they know that those decisions are also optimal for themselves (Nagarajan and Sošić, 2008). To introduce the issue, let us discuss modern portfolio theory v. behavioural finance. While representing differing schools of thought, they explain investor behaviour. Their different positions may be viewed as modern portfolio theory – being how financial markets would work (in the ideal world) versus behavioural finance – being how financial markets actually work. According to Curtis (2004, p. 21): “Modern portfolio theory represents the best learning we have about how capital markets actually operate, while behavioral finance offers the best insights into how investors actually behave. But markets don’t care what investors think of as risk, and hence idiosyncratic ideas about risk and what to do about it are bound to harm our long-term investment results. On the other hand, Daniel Kahneman, Amos Tversky, and their followers have demonstrated beyond doubt that we all harbor idiosyncratic ideas and that we tend to act on them, regardless of the costs to our economic welfare”. An analogue is the supply chain member who seeks to make decisions that will optimise the value of the chain. However, given that the member runs a decentralised company, decisions are made as independent actors for which incentives may not be in the interests of the chain (Zhao et al., 2010). To support the decision of each member in the chain, the various coordinating contracts have been applied. As contracts have been studied in law, it is known that the contracts applied in SCM take a different approach (Porteus, 2002). In fact, the contracts applied in SCM seek to transform the agent utility

26

functions in a way that the supply chain optimal decisions would also be optimal for the individual. Note that a contract cannot be forced, instead it must be accepted by each party.

2.2.2 Game theory to reach an agreement For each player (manufacturer and supplier) to agree to implement a contract may require a game theory in a given supply chain setting. Game theory was developed by von Neumann and Morgenstern (1945) and argues that economic decisions involving more than one actor (e.g. a buyer and a supplier) take the form of a sequential, strategic game involving anticipation by one actor of the behaviour of the other actor. One well-known game is the Prisoner’s Dilemma, which has been used to show how cooperative behaviour becomes more likely if two actors interact with one another on a repeated basis. During repeated interaction between actors, they get to know each other, build trust and can thus overcome the lack of asymmetric demand information. This is contrary to a one-off interaction under which the model suggests that both actors will behave competitively to try to maximise their individual utility (Nachbar, 1992). The key is designing a contract that could decide the optimal decisions for the chain and show what profit may be the end result and if it would be acceptable to both parties. In the bargaining process, which specifies the contract setup that will lead to an acceptable split of the maximum (expected) supply chain profit, there are two known approaches – the strategic negotiation (Rubinstein, 1982) and the axiomatic negotiation (Nash, 1950) Under strategic negotiation (sequential bargaining), after a contract has been offered by an actor the other actor could offer a new contract (counter-offer), which if not acceptable to the latter, this process will have been proven to reach a mutually acceptable set up that benefits both parties (Rubinstein, 1982). The axiomatic negotiation approach has also been proven to be successful. Here, a contract is proven to be coordinating in the event there is a solution of the underlying problem (Nash, 1950). For a review of game theory in supply chain contracts, see Cachon and Netessine (2006) and for a review of bargaining and negotiation in supply chain, see Nagarajan and Sošić (2008). The underlying premise is that in the integrated SCM approach, actors are rational, self-interested utility maximisers who will cooperate via repeated interactions if they make more money than they would if they do not

27

cooperate. One approach under this idea, is that buyers and suppliers may benefit by trusting each other and share, e.g. demand information and thus signal their commitment to future intentions that would benefit both parties (Özer and Wei, 2006). In addition, Chen and Wang (2015) find in a supply chain consisting of a handset manufacturer and a telecom service operator that companies that have more channel power can gain more profits but according the findings of Luo et al. (2017), a supply chain with a single retailer and two manufacturers with a balanced power relationship amongst supply chain members is the best strategy for improving their respective profit. There is also the Stackelberg Game (von Stackelberg, 1952), in which competition is based on the selection of outputs Q1 and Q2, yet only one can be the be first mover and while that happens, the other company is observing and accordingly chooses Q2 to maximise profit. Nonetheless, company 1 (the leader) knows what company 2 has chosen (based on economic rationality) and therefore pre-emptively expands to lock out and secure larger profit (Amir and Grilo, 1999).

2.2.3 Financial option theory In 1973, Black and Scholes presented their seminal paper in which they derived a mathematical formula, i.e. an analytical solution for a continuous time stochastic process allowing for pricing of call options on shares. In 1979 Cox, Ross and Rubinstein simplified that approach by presenting their binomial option pricing model via a solution for a discrete time stochastic process. The analytical and binomial approaches (providing identical results in the long run) have resulted in an additional capital budgeting method, i.e. real options valuation based on the application of financial option theory to real assets. In theory derivatives, i.e. call and/-or put options provide flexibility for both buyer and seller. When financial call and put options are applied to (real) supply chain relationships, they are often called real options. A financial option is a contract that gives the holder the right (not the obligation) to buy (call) or sell (put) a predefined quantity of an underlying asset at a fixed price (exercise price), either at or before the expiry date of the option (maturity). The holder (buyer) pays a price for this right (option premium). The call (put) option will be exercised only if the value of the underlying asset is greater (lower) than the exercise price, otherwise the option will never be exercised and will expire, worthless (Damodaran, 2012).

28

2.2.4 Real option valuation The real options approach can be traced back to Miller and Modigliani (1961) who identified that the market value of a firm consists of two components, i.e. the net present value (NPV) of cash flows from assets (in place) and present value of growth opportunities. The perception behind the approach was that many types of capital investment have characteristics similar to financial options. Although the options analysis made it possible to value flexibility when investing under uncertainty, it must be pointed out that predictable flexibility can be managed by discounted cash flow (DCF) yet the uncertainty requires options analysis, (Amram and Kulatilaka, 1999). Myers (1977) was first to introduce the term real options with the suggestion that the discretionary investment opportunities of a company could be seen as corporate growth options. A real option can be defined as an opportunity (the right but not the obligation) and flexibility to make future decisions in light of subsequent information. Real options are primarily divided into the following two categories – growth options and flexibility options. Growth options provide management, e.g. with the ability to increase its future business via research and development or by launching a product. Flexibility options provide management with the ability to change plans due to uncertainty, i.e. value the possibility of switching supply contract to be more effective, delay an investment until more information provides a clearer picture of possible outcomes and either reorder supply or return unsold supply based on stochastic product demand. Dixit and Pindyck (1994) examine the problem of irreversibility and the ability to delay an investment under uncertainty using real options. In their book, they examine how real options can be applied when investments are made under uncertainty about future prices and returns and when investment decisions are irreversible. They show that investments with a negative NPV can have a positive strategic value, i.e. when investment is irreversible, NPV then ignores the option of delaying an investment that may be valuable. They demonstrate that an option value may exist to delay the decision to invest and await the arrival of any new information on market conditions. They also show that the various real options models do not inform the level of investment but identify that factors that may affect the decision regarding which investment should be made. According to Hartman-Abel (Abel, 1983; Hartman, 1972) an increase in uncertainty might increase the value of a

29

marginal unit of capital and therefore increase the incentive to invest. This is similar but not the same as Dixit and Pindyck (1994) who maintain that when the volatility increases in the stochastic process that decides the returns from investment, this will then increase the trigger point for decisions to invest. Thus, real options do not provide the optimal level of investment nor optimal returns but uncertainty and increased volatility may provide an option to invest and increase the rate of return before investment is made, but it does not automatically affect the rate of return once (if) the decision to invest is made. Hagspiel (2011) updated the literature on real options and the value of flexibility in flexible versus dedicated manufacturing, while developing a dynamic real options model. She among other study the challenge of technology adoption of a firm that faces uncertainty about both the value and arrival of new technology. Hagspiel (2011) addresses the problem by extending the traditional decision theory models on technology adoption with a model in which technologies do not arrive based on constant Poisson process but that the arrival rate of new technologies can change over time. Rózsa (2016) presents an overview of real options theory in the past two decades and Trigeorgis and Tsekrekos (2018) provide a review of real options and timing of investments in logistics. Craighead et al. (2016) labelled real options theory a general theory and it has proven to be a valuable tool when valuing a number of various uncertain projects, as it can be applied to growth, stage, deferral, scale, switch use and abandonment (Tiwana et al., 2007). A growth option (Tiwana et al., 2007) is present when an investment creates the possibility of future opportunities or allows for the development of future capabilities (Kogut and Kulatilaka, 2001). A stage option allows an investment or project to be completed incrementally (Majd and Pindyck, 1987). A deferral option allows an investment to be postponed to a later point in time (Miller and Folta, 2002). A scale option allows for investments or projects to be expanded (Fichman et al., 2005) while a switch option allows for investments to be redeployed (Trigeorgis, 1993). Finally, an abandonment option is present if the investment or project can be terminated (Haksöz and Seshadri, 2007; Hubbard, 1994). Moreover, real options are also applied to create product flexibility.

30

2.2.4.1 Product flexibility Van Mieghem (1998) studies a firm that has the option to invest in two product-dedicated resources or one flexible resource that can process both products. The paper applies real options to create flexibility when examining the impact of price, cost, demand uncertainty and demand correlations on the investment decisions. He shows, contrary to earlier belief (Fine and Freund, 1990) “that flexible capacity provides no additional value when product demands are perfectly positively correlated” (Van Mieghem, 1998, p. 1071) that flexible capacity is indeed valuable even if demands for both products are perfectly positively correlated. This model was further extended in Van Mieghem and Rudi (2002). Harrison and Van Mieghem (1999) develop the link between operational flexibility and stochastic programmes with recourse, extending the newsvendor model to multivariate demand. Birge (2000) applies the principle of risk-neutral valuation when developing a model that integrates financial risk to solve the capacity investment problem. Birge (2000) shows the relationship between investment in financial instruments and a linear capacity investment can be drawn using an analogue, i.e. the author shows how to create a perfect financial hedge by selling a call option to the firm’s demand above its capacity. Operational hedging is a phrase coined by Huchzermeier and Cohen (1996) who apply real options when switching supply and production to limit risk, given exchange volatility. The interactions between operations and finance are not too extensive, however according to Birge (2015) the Van Mieghem (2003) paper is one exception. With regard to operational hedging, Van Mieghem (2003, p. 296) states “Mitigating risk, or hedging, involves taking counterbalancing actions so that, loosely speaking, the future value varies less over the possible states of nature”. Furthermore, in this paper Van Mieghem decodes the von Neumann–Morgenstern utility (VNMU) approach by showing how the VNMU approach can be used (by each member in a dyadic supply chain) as an approximate solution by using only the mean and variance. To that point, each part (in a supply chain) with a specific utility curve operated under market uncertainty and the assumption of risk neutral probability. Van Mieghem (2003) shows that maximising a utility function with a constant coefficient of risk aversion (or a quadratic concave utility function) is equal to maximising a Mean Variance (MV) performance measure. Instead of trying to model (a closed form solution) a utility curve for a supply manager, it is now

31

instead possible to use MV as an approximation and thus only need the mean and the variance. Bengtsson and Olhager (2002) apply real options to evaluate product flexibility via a case study (ABB) including multiple products, capacity constraints and set-up costs. Flexible production means that a product can be produced with any resource while dedicated production means that a product can only be produced with a single resource. Bengtsson and Olhager (2002) showed that the value of the option is significantly higher if the resource is flexible, i.e. production is not dedicated to a specific product. Furthermore, they showed that the flexibility value of marginal capacity decreases with increasing levels of capacity. In a similar approach, Hagspiel et al. (2016) define the ability to adjust production without cost over time as volume flexibility. They demonstrate that in highly uncertain markets, increased flexibility in production delays the optimal timing of the investment because in such cases, the incentive for the firm to install a large capacity is stronger than the incentive to anticipate the investment due to the increased flexibility. Babich (2006) study supplier pricing decisions with supply disruptions and applies a vulnerable option contract in which both supplier and manufacturer could postpone their decisions given uncertain price and deterministic demand. Here a faster supplier can wait to set its price until the disruption status of the slower supplier is known. Babich (2006) shows that this encourages the manufacturer to order from the slower supplier, as long as prices are moderate because the faster supplier has been used as a backup source. The literature on operational flexibility has also studied various flexibility relationships for example, financial hedging (Chod, Rudi and Van Mieghem, 2010) volume and product (Goyal and Netessine, 2011) and mix, time and volume (Chod, Rudi and Van Mieghem, 2012). Van Mieghem (2007) shows how mismatch cost in supply chain networks can be limited by hedging in terms of resource diversification and flexibility (inventory substitution). Uncertainty resolved over time and operational hedge (flexibility) can create a supply source that results in value being created while risk is further limited. Chod et al. (2012) develop a theoretical model of a two-product firm, that updates its demand forecast continuously while making decisions on capacity, production and pricing and adapting to three types of flexibility - mix, volume

32

and time. Allon and Van Mieghem (2010) show that allocating a portion of demand to a higher cost-reactive supplier versus a lower cost-long lead time supplier was enough to minimise total cost. Van Mieghem and Allon (2015) provide a number of numerical cases on how flexibility enhances supply chain performance.

Jörnsten et al. (2013) apply the option mechanism under discrete demand and show that the mixed contract is preferred to the option-only contract when the manufacturer is risk averse and has a limit as to how much variance it is willing to accept. Boonman, Hagspiel and Kort (2015) study the optimal investment strategies for an incumbent and a potential entrant that can both choose between a product-flexible and dedicated technology in a two products market considered by stochastic demand. While the product-flexible has an advantage under uncertainty, the dedicated allows a firm to commit to production quantities. Notably, they demonstrate that for a not-so-uncertain environment, it is more profitable for the incumbent to invest in dedicated production line as a means of deterring an entrant. De Treville et al. (2014a) developed a real put option model to calculate the value of lead time reduction, comparing a make-to-stock policy with a make-to-order, under the assumption that demand follows a Martingale Method of Forecast Evolution model (Graves et al., 1986; Hausman, 1969; Heath and Jackson, 1994). The model is applied under a newsvendor setting with a single supplier and provides an estimate of how much cheaper a long lead time supplier must be, in order to compensate for the increase in mismatch losses resulting from the longer lead time. The put model “quantify the cost differential required to compensate for supply-risk exposure as lead time increases” (de Treville et al., 2014a, p. 2104). The mismatch cost increases as the lead time reduces, which implies that the value of lead time reduction is bigger when the lead time is relatively short than when it is relatively long. The model was empirically tested in supply chains in de Treville et al. (2014b) and further developed by Biçer et al. (2018) to include jumps, based on the model proposed by Merton (1976).

33

2.2.4.2 Supply via the spot market The introduction of (capacity) options opened the possibility for manufacturers to buy capacity on the spot market (Akella et al., 2001; Golovachkina and Bradley, 2003). Wu, Kleindorfer and Zhang (2002) consider an electricity market with one seller and one or several buyer(s) in which the seller can either sign a long-term contract with one buyer or use the spot market. The paper derives both buyer and seller optimal strategies in a Stackelberg game and shows how to derive option prices based on costs of the system, spot price distribution and the utility of the buyer. Spinler et al. (2003) use a portfolio of option and spot contracts and to have the option exercised they set that price equal to the marginal production costs, which will prevent the uncertain spot price from undercutting the option being applied. The model is extended in Spinler and Huchzermeier (2006). Akella et al. (2001) discuss the fact that prices are often higher than the long-term contract, while Inderfurth and Kelle (2011) show that sourcing via the spot market is flexible yet risky due to market price volatility and uncertainty (Billington et al., 2002; Serel, 2007).

2.2.5 Portfolio theory The (modern) portfolio theory was pioneered by Harry Markowitz in his Portfolio Selection paper published in 1952. The theory shows how risk-averse investors can construct portfolios to optimise or maximise expected return based on a given level of market risk, emphasising that risk is an inherent part of higher reward. According to the theory, it's possible to construct an efficient frontier of optimal portfolios offering the maximum possible expected return for a given level of risk.

2.2.5.1 Fisher’s portfolio framework Fisher’s (1997) portfolio framework, based on Markowitz (1952) is supported in research (Chopra and Meindl, 2007; Collin et al., 2009; Gligor, 2017; Gligor et al., 2015; Wagner et al., 2012) yet there are studies with mixed, inconclusive and even negative results. While Li and O’Brien (2001) confirmed the association between innovative products and responsive manufacturing, they could not support the association between functional products and efficient process (in a mathematical model). Selldin and Olhager (2007) conducted a survey and found the opposite results. Qi et al. (2009) found combinations not included in Fisher’s framework that outperformed Fisher’s typology. In a comprehensive test, Lo and Power (2010) addressed

34

key issues missing in the framework and concluded that “the literature remains unclear as to how the ‘leagile’ system should fit into Fisher´s (1997) model” (p. 142). Furthermore, the limitations in the real business world were evident in the performance of a fashion retailer end-to-end supply chain. Phadnis and Fine, (2017) show that the Fisher framework alone may not be optimal in the supply chain of an apparel firm and that other activities, such as demand forecasting and sourcing are critical performance factors. Moreover, while some studies find that supply chain agility entails a cost premium (Agarwal et al., 2006; Narasimhan et al., 2006), Gligor et al. (2015) find that agility improves efficiency, while Wagner et al. (2012) suggest that aligning supply chain strategies with the requirements of the business environment results in a higher return on assets.

2.2.6 Supply chain flexibility via option contracts The limitation of traditional supply chain contracts led researchers to approach finance to adopt call and put options to create flexibility in order to coordinate ordering and production between the supplier and manufacturer (Birge, 2000; Van Mieghem, 1998). A call (or put) option gives the holder the right (not the obligation) to buy (or sell) an underlying asset by a certain date at a certain price (Hull, 2012). Similarly, the option contract in Figure 2.1 gives the holder the right to either buy or sell an underlying quantity given known demand. The parameters of this contract outline that the buyer pays the premium to the supplier for reserving stock (before demand is known), following the price the buyer pays when exercising the contract (when demand is known), before or on the contract expiry date. Thus, the key parameters of the contracts are the capacity reservation fee (option premium), the execution fee (exercise price) and expiry date (time) (Zhao et al., 2018). Subsequently, the call (or put) option contract is applied to create upward (or downward) flexibility to coordinate ordering and production under supply, lead time and demand uncertainty and if both are applied it is referred to as a bidirectional option contract (Zhao et al., 2013).

35

Figure 2.1 Option contracts applied to address supply, lead time and demand uncertainty.

Numerous companies have adopted option contracts in their ordering practises (Nagali et al., 2008). For example, HewlettPackard made 35% of its procurement value through options derivatives (Chen, Hao and Li, 2014), Intel use option contracts (Peng et al., 2012) while Nike use option contracts to hedge future transactions with third party vendors (Biswas and Avittathur, 2019). China Telecom negotiates purchasing prices with its suppliers (Nokia, Motorola, Ericsson, Samsung, Huawei) on a monthly basis followed by offering its retailers the flexibility to obtain products through either a firm order or by exercising call options (Chen and Shen, 2012).

Finance Option Contracts

Call Option Contracts

Put Option Contracts

Bidirectional Option Contracts

Applied in supply chains in

response to

Supply uncertainty

Lead time uncertainty

Demand uncertainty

37

3 Research Method

This section discusses the methodology used in this dissertation. Given the overall research questions, it begins by describing the scientific approach and the research strategy followed by a presentation of each phase of the dissertation before discussing validity and reliability.

3.1 Scientific approach and research strategy This research is guided by realism and positivism. Based on the collected data, realists work on examining the data and building knowledge. In addition to realism, positivism is assumed when carrying out variable testing. Positivism assumes that the reality is given objectively and independent of the observer and the instrument applied, and is often used with quantitative methods. Given the aim of building knowledge (Liu et al., 2016; Van de Ven and Johnson, 2006) by solving a practical problem of supply chain inefficiency, the research method used is the applied research. The study uses data taken directly from the real world and applies it to solving problems and developing practical applications, i.e. the algorithm (Eriksson, 2019). Notably, the quantitative research method is aligned with positivism and realism (Håkansson, 2013). This research attempts to establish a relationship between dyadic supply chain flexibility and dyadic supply chain profitability. Given that this research combines Finance and SCM, it has to be acknowledged that while finance has economic theory as its foundation, SCM has no recognised theory on which it rests (Schmenner and Swink, 1998; Halldórsson, 2007; 2015). Subsequently, to know how best to build knowledge (Van de Ven and Johnson, 2006; Liu et al., 2016) to elaborate theory, the first step was to decide which research approach (deductive, inductive and abductive) to apply to gain knowledge.

38

The notion that quantitative research is always confirmatory and deductive or that qualitative research is always exploratory and inductive may not be entirely correct. Qualitative data can be coded quantitatively, e.g. by categorising qualitative research responses for translation into numbers (Creswell, 2005; Trochim, 2006). Nonetheless, the quantitative theorists believe in a single reality that can be measured reliably, while qualitative theorists believe in interpretations via the lens of the researcher. Furthermore, while inductive reasoning give reasons in support of the probable truth, deductive reasoning seeks to provide the guarantee of truth. The basis of inductive reasoning is behaviour while deductive reasoning depends on facts. In inductive reasoning, the argument can be strong or weak as it only describes the likelihood of the inference, while deductive logic is based on the fundamental law of reasoning, i.e. if X then Y. It implies the direct application of available information or facts (Onwuegbuzie and Leech, 2005). In addition, abductive reasoning starts with an incomplete set of observations and proceeds to the likeliest possible explanation. It often involves making an educated guess following observation of a phenomenon for which there is no clear explanation and is often used by doctors when making a diagnosis based on test results (Kovács and Spens, 2005). This study applies the deductive approach as it is based on a large amount of quantitative company data (from Volvo CE and key supplier), where “numbers speak for themselves” and the researcher attempts to be “value-free,” (Onwuegbuzie and Leech, 2005, p. 271). To contribute to theory, this dissertation view of Merton’s (1968) notion of the middle range approach, means theorising through the integration of theory and empirical research. According to Stank et al. (2017) Merton’s middle range theoretical approach has the potential to unify theories of management and is vital to the further advancement of operations and supply chain management because it helps combine the findings and theories of supply chain practise. In Merton’s (1968) view of how to build middle range theories, it is argued that the most promising approach to advance research in the social sciences, is to aim for theories that “lie between the minor but necessary working hypotheses that evolve in the abundance during day-to-day research and the all-inclusive systematic efforts to develop a unified theory that will explain all the observed uniformities of social behavior, social organization, and social change” (Merton, 1968, p. 39).

39

Merton was a sociologist and for sociologists, the notion of theory may at times differ to that of a researcher in Finance and SCM. Merton´s strain theory is one example. In addition, Merton (1968) middle rangeness may not be an absolute but a relative quality (Pinder and Moore, 1979). The interpretation is that the dissertation will contribute to knowledge if the study is defined and limited to using and testing option contract theory in practise that is quantitative company data from two companies and draws conclusions based on facts such as quantitative results in a specific context (Pinder and Moore, 1979). The bridge between empirical research and general theory is an intermediary body of theory that is referred to as middle range theory (Eisenhardt and Bourgeois III, 1988; Weick, 1989). The theoretical contribution is the empirical result from using deductive reasoning to test option contract theory in practise in a manner similar to Ketokivi (2006), who applies a middle range approach to understanding manufacturers’ flexibility strategies within the context of a specific task environment. He notes that “middle range theorizing [is] the appropriate way of developing managerially relevant theories, because application always occurs in a specific context” (Ketokivi 2006, p. 217). This research is divided into three different phases that include a literature review, modelling through the development of an algorithm and deductive reasoning in a quantitative case study. Each phase is described further in the following sections.

3.2 Phase 1 – Literature review The theoretical review plays an important role in the development of knowledge (Fink et al., 2014; Schryen et al., 2015; Schwarz et al., 2007; Webster and Watson, 2002). The reason for conducting a review was “identifying and highlighting knowledge gaps between what we know and what we need to know” (Paré et al., 2015, p. 188). Despite the name change from logistics to SCM, the research is still very much the focus of the inventory model approach of the last four decades i.e. supply fast to minimum cost. The problem is conveyed by Professor Chris Voss, a leading researcher in this field: “In 1984 I published a paper on the “lot size algorithm industry, … the problem was becoming trivial … community has moved on, … [yet]

40

Europe continues to produce lot size papers” (Voss in MacCarthy et al., 2013, p. 945). Moreover, given that the value of flexibility during the last decade has been a “hot topic” in finance via the real options theory approach (to extend the static DCF method), it was a possibility of adding to this field by addressing a number of unanswered questions. This became evident following the literature review, which found that the manufacturing and product flexibility had both been shown to improve profitability (when only companies competed). Now, when competition also includes supply chains it was evident that research on how to create flexibility for more than one company, i.e. supply chain flexibility was yet to be fully explored.

3.2.1 Research methodology SCM has been a melting pot of a number of disciplines, including logistics and transportation, operations management, materials and distribution management, marketing and IT (Giunipero et al., 2008). Furthermore, given the increasing focus on finance applied in SCM in both academic and practitioner literature streams, the review focused on this topic. Subsequently, structured process was employed to identify the relevant literature to review, similar to review studies undertaken by those such as Burgess et al. (2006), Giunipero et al. (2008) and Fayezi et al. (2017). Specifically, a three-step process was adopted for paper selection. In the first step, search was conducted in Scopus and Ebsco using the following combination of keywords and conditions (i.e. all of them joined with Boolean operators): - ‘supply chain*’ and ‘option*’ and ‘contract*’ or - ‘newsvendor*’ and ‘option*’ or - ‘real option*’ and ‘contract*’ and ‘supply chain’ or - ‘supply’ and ‘option* contract*’ used in the title, abstract or keywords in the paper. The first search was conducted in January 2014, the second in June 2018 and the last in December 2018. Results obtained from each database were then compared and cross-checked for duplicates. Similar to Burgess et al. (2006) to control quality in reporting our search was limited to peer-reviewed publications. In addition some papers, including: Black and Litterman (1990, 1992); Markowitz (1952); Van Mieghem (2003); and Von Neumann-Morgenstern (1945) were intentionally included in order to make the review

41

comprehensive, given their contribution to the development of option contract literature. The second step involved screening and giving preference to papers published in A and B category journals (from the ABDC journal quality list from 2018). From this, a total of 239 papers and book chapters were identified. In the third and final step, papers and three book chapters (Cheng et al., 2011; Zhang and Hua, 2012; Zhao et al., 2016) focusing on option contracts, in the context of ordering and production were selected. This reduced the number of papers to 54 (out of 239). As a result, three categories were identified to use option contracts: supply, lead time and demand uncertainty. The review provided an understanding of current research and revealed potential research gaps and amongst other things, that there was no existing option-based model that could create flexibility for a manufacturer and supplier to coordinate ordering and production bilaterally in a multi-period setting and from which it was possible to gain knowledge (Liu et al., 2016; Van de Ven and Johnson, 2006). The review is presented in the first paper and shows current status and a future research agenda.

3.3 Phase 2 – Development of the algorithm The initial work on the development of the algorithm started during a visit to the Kellogg School of Management, Northwestern University (January - July 2014) and attending PhD courses for Professor Jan van Mieghem. The research at this faculty is known for applying derivatives to create flexibility in operations and supply chain management that lead to testing of various stochastic variables, including the reorder point model (R,Q) before it was decided to combine the base stock approach and call options mechanism in the algorithm. To further validate the algorithm, seminars in econometrics for Professor Peter Wakker at Erasmus University followed (September - December 2014). The method used when developing the algorithm can be viewed in a manner similar to the three steps in the Berling (2005, p. 23) mathematical modelling process: 1) Problem formulation – examine the assumptions to make the problem mathematically tractable; 2) Mathematical modelling – describe the formulated problem mathematically; and 3) Validation – validate the results, e.g. through either simulation (second paper) or a case study (third paper). The algorithm was subsequently validated using simulation in Excel, similar to the

42

three steps of Berling (2005) and to what Van Aken, (2005) refers to as alpha testing. The algorithm is presented in the second paper and shows how to create dyadic supply chain flexibility that results in coordination of order and production quantities and improvement in dyadic supply chain profitability in comparison to a wholesale contract – in a multi-period setting.

3.4 Phase 3 – Quantitative case study The quantitative case study validated the algorithm and was conducted at Volvo CE and one key supplier. On 21 August 2014, a first meeting took place at Volvo CE at Braås and at which it was presented how the algorithm may create flexibility in Volvo CE operations. The next meeting was held on 19 January 2015. This time Volvo CE invited the sales manager for one of its suppliers, aka the Preferred Supplier AB and this meeting focused on how to protect company data and maintain confidentiality. It soon became evident that the management of each company had appointed a project manager authorised to trace and supply comprehensive confidential data, including current supply contracts, finance and production cost data from each organisation. This was in order to conduct a quantitative empirical study to examine how the algorithm may create supply chain flexibility that might result in improved profit for each company (compared to current wholesale contract). The project got off the ground immediately as the following 4 months was spent crisscrossing on snowy roads back and forth between each company site collecting data based on the research protocol (appendix A), while guiding each project manager on what data was required. The data was then entered into a spreadsheet and copied to posters that were laid out on the research room floor in the library at Linnaeus University, in order to verify input and output with a handheld calculator. The objectives of the case study are to validate the algorithm, empirically test option contract theory and provide evidence that dyadic SC flexibility improves dyadic SC profitability in comparison to a wholesale contract.

43

3.4.1 Selection of dyad companies Although the study does not seek to generalise the results beyond the immediate empirical study, some criteria were still used to select the dyad companies. First, each company must not be in a monopoly situation. Second, the product life cycle must be functional and long (innovative is often shorter) so as to provide reliable demand data. Third, the company must also have a close cooperation with a supplier with whom it shares key demand information amongst other things. A shortlist of companies were contacted, including Volvo CE and Volvo CE then selected one of its suppliers (providing a key component for a dumper truck) to take part in this quantitative study. The empirical study with (real) quantitative data validates the algorithm and shows ‘how’ it is applied to create dyadic supply chain flexibility to coordinate ordering and production quantities in a multi-period setting – in this case consisting of Volvo CE and one of its suppliers. The research context is presented in the third paper.

3.4.2 The research protocol Stuart et al. (2002) discuss that notion that the case study protocol is key to capturing data for future analysis. The protocol safeguards the data and ensures that the trail of evidence is thoroughly documented. Given that the research protocol is the key instrument in the field testing process, some consideration was given to the preparation and tailoring of the protocol. In order to guide each project manager at Volvo CE and Preferred Supplier AB in staying on course, a research protocol was developed for this assignment that included issues such as unit sales price, unit cost, quantity, option parameters, demand forecasting and lead time. In addition, the protocol also explores optimal order size, timing and optimal production level for the supplier. Thus, the protocol covers data regarding ordering, production and finance (view appendix A).

3.4.3 Reliability and validity To ensure reliability, the research protocol was provided on site via instructions. Through iterations with the Volvo CE commodity manager and sales manager for the supplier during field testing, the protocol was continuously refined (de Treville et al., 2007). This enabled each manager to obtain guidance from the protocol when collecting the quantitative data to ensure the study can be replicated.

44

To ensure construct validity, the data was thoroughly checked by the Volvo CE commodity manager and the sales manager for the Preferred Supplier AB. Each had an interest in getting accurate data provided and examined correctly. Proof of this was when the researcher found that data (sales, order, assembly, delivery and production data between companies) showed a discrepancy. In less than 72 hours, the issue was cleared when it turned out that Volvo CE had experienced a computer glitch, which resulted in one inaccurate data point. The key contribution is that there now exists a quantitative and objective empirical evidence on the relationship between dyadic supply chain flexibility and dyadic supply chain profitability. In addition, the objective measurement method can now take this topic into a quantitatively measurable concept which is a major step in SCM theory and practise.

45

4 Coordination of a Dyadic Supply Chain

This chapter discusses how this dissertation combines finance and SCM to fulfil the purpose of this dissertation. It begins with a brief introduction on the mapping of contracts and the quantitative data that was obtained from Volvo CE and its Preferred Supplier. A discussion then follows on how the wholesale contract, newsvendor model and the integrated system provide a platform for the option mechanism (algorithm) that creates flexibility for Volvo CE and the supplier to coordinate ordering and production, to respond to stochastic demand to maximise the profit for each party. The algorithm is then applied to the case study to validate the algorithm, empirically test option contract theory and the Fisher paradox and fulfil the overall purpose. The final part provides a few reflections on the dyadic cooperation. Based on the theory of ‘Homo Economicus’, each individual member in the supply chain with private information has its own objective and that is to maximise profit. Kahneman and Tversky (1979) have proved that people are in fact not rational human beings, which in this case may cause the supply chain to be inefficient, resulting in an overall lower profit (smaller share of the pie). In a decentralised supply chain, each company makes decisions in its own interests (Zhang, 2011). By using only the wholesale contract, companies lack the flexibility to adjust ordering and production to respond to stochastic demand, which causes inefficiencies and loss of profit (Costantino and Pellegrino, 2010; Stevenson and Spring, 2009). The efficiency loss in a decentralised supply chain when only a wholesale contract is applied can be substantial – in fact up to 40% (Adida and Perakis, 2014; Chen et al., 2000; Martínez-de-Albéniz and Simchi-Levi, 2009; Perakis and Roels, 2007).

46

Therefore, the challenge is to develop an algorithm that enables each manufacturer and supplier to coordinate activities to optimise supply chain responsiveness in order to maximise the size of the pie.

4.1 Map contracts In order to trace required data, the first step was to map existing contracts (Höhn, 2010). This meant working side-by-side with each supply chain manager in each company to examine what data was contained in existing supply chain contracts. In this case study, the map contract part included valuing the current wholesale contract under a newsvendor setting, while collecting data such as unit sales price, unit cost, quantity, demand forecasting and lead time (variability). In addition, optimal order size, timing and optimal production level for the supplier were also explored. The mapping of contracts and collection of data took place between January and June 2015. The research protocol was applied at each site (appendix A).

4.1.1 The wholesale contract In a single period setting, the wholesale contract (the classic newsvendor problem) that provides no flexibility must find the product ordering quantity that maximises the expected profit under probabilistic demand and the model assumes that if any inventory remains at the end of the period, a discount is used to sell it or it is disposed of. In the literature, researchers have followed two approaches to solving the newsvendor problem that give the same results, i.e. either the expected costs of overestimating and underestimating demand are minimised or the expected profit is maximised (Nahmias, 2009; Rudi and Pyke, 2000).

4.1.1.1 The Newsvendor model At the start of the selling season, the retailer needs to determine the optimal order quantity (𝑄𝑄∗) to satisfy customer demand (for notation view appendix B). Customer demand (𝑥𝑥) is assumed to be stochastic and often denoted as a random variable with the probability density function 𝑓𝑓(𝑥𝑥) and the cumulative distribution function 𝐹𝐹(𝑥𝑥). The quantity ordered (𝑄𝑄∗) has a wholesale price per unit of 𝑤𝑤. It is further assumed that the supplier operates with no capacity constraints and zero lead time. Hence, when an order is placed with the supplier at the start of the selling season, the order is immediately satisfied. Sales of the product occur during the whole period at a fixed retail price (𝑝𝑝).

47

If demand during the period is less than the ordered quantity (𝑥𝑥 < 𝑄𝑄) the leftover units (𝑄𝑄 − 𝑥𝑥) are then salvaged by the retailer for a unit salvage value of 𝑣𝑣. However, if the demand is more than the ordered quantity (𝑥𝑥 > 𝑄𝑄) there is then a shortage of units (𝑥𝑥 − 𝑄𝑄), which corresponds to lost sales denoted as 𝑔𝑔. Retailer profits at the end of the period can be formulated as follows:

𝐸𝐸(𝜋𝜋𝑟𝑟) = (𝑝𝑝 − 𝑣𝑣)∫ 𝑥𝑥𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥𝑄𝑄

0 − (𝑤𝑤 − 𝑣𝑣)∫ 𝑄𝑄𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥𝑄𝑄0 + (𝑝𝑝 − 𝑤𝑤 +

𝑔𝑔)∫ 𝑄𝑄𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥∞𝑄𝑄 − (𝑔𝑔)∫ 𝑥𝑥𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥∞

𝑄𝑄 (1) The first and second derivatives show that 𝐸𝐸(𝜋𝜋𝑟𝑟) is concave in 𝑄𝑄 (view appendix C1). The first order condition is sufficient to determine the optimal order policy that maximises the retailer profit function (1) and results in the known critical fractile (critical ratio): 𝐹𝐹(𝑄𝑄∗) = ((𝑝𝑝 − 𝑤𝑤 + 𝑔𝑔) (𝑝𝑝 − 𝑣𝑣 + 𝑔𝑔)⁄ ). Given the retailer’s optimal order quantity, the corresponding expected profit for the supplier accepting this order is: 𝐸𝐸(𝜋𝜋𝑠𝑠) = (𝑤𝑤 − 𝑐𝑐)𝑄𝑄, where 𝑤𝑤 is the wholesale price and 𝑐𝑐 is the unit cost (Khouja, 1999; Nahmias, 2009; Porteus, 2002; Rudi and Pyke, 2000).

4.1.1.2 The integrated system In the centralised system, the manufacturer and the supplier can be viewed as a vertically integrated dyadic supply chain with a single decision-maker that will optimise overall system performance by deciding quantities and pricing. Hence, the objective is to maximise expected profit, which can be formulated as: 𝐸𝐸(𝜋𝜋𝑐𝑐𝑠𝑠) = 𝐸𝐸(𝜋𝜋𝑟𝑟) + 𝐸𝐸(𝜋𝜋𝑠𝑠) = (𝑝𝑝 − 𝑣𝑣)∫ 𝑥𝑥𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥𝑄𝑄

0 − (𝑐𝑐 − 𝑣𝑣)∫ 𝑄𝑄𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥𝑄𝑄0 +

(𝑝𝑝 − 𝑐𝑐 + 𝑔𝑔)∫ 𝑄𝑄𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥∞𝑄𝑄 − (𝑔𝑔)∫ 𝑥𝑥𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥∞

𝑄𝑄 (2) The first and second derivatives show that the 𝐸𝐸(𝜋𝜋𝑐𝑐𝑠𝑠) is concave in 𝑄𝑄 (view appendix C2). The first order condition is sufficient to determine the optimal order policy that maximises the profit function (2) and the critical fractile (critical ratio): 𝐹𝐹(𝑄𝑄𝑐𝑐𝑠𝑠∗ ) = ((𝑝𝑝 − 𝑐𝑐 + 𝑔𝑔) (𝑝𝑝 − 𝑣𝑣 + 𝑔𝑔)⁄ )

4.2 Finance theory In order for Volvo CE and its Preferred Supplier to optimise responsiveness to maximise the size of the pie, meaning get as close as possible to an integrated system, theory from finance is applied. Portfolio theory (Fisher, 1997;

48

Markowitz, 1952) was applied to match the functional bracket component v. innovative with supply chain strategy efficient v. responsive given the stochastic demand (risk). This was followed by acknowledging game theory (Nash, 1950; Rubinstein, 1982; von Neumann and Morgenstern, 1945; von Stackelberg, 1952) and the potential supply chain power that Volvo CE may exert. This is before the option contract mechanism (algorithm) is applied to the data to create flexibility to coordinate the dyadic supply chain bilaterally and to test option contract theory in practise.

4.2.1 The option mechanism The option mechanism has three parameters: option premium (𝑜𝑜), exercise price (𝑒𝑒) and expiry date (time). At the start of the selling season, the retailer determines the optimal number of options �𝑄𝑄𝑜𝑜𝑜𝑜

∗ � to satisfy customer demand. Customer demand (𝑥𝑥) is assumed to be stochastic. The retailer pays the option premium per unit reserved �𝑄𝑄𝑜𝑜𝑜𝑜

∗ � up front. The supplier operates under the assumptions of no capacity constraints and zero lead time. When demand is known, the retailer exercises the options needed before or on the contract expiry date and pays the exercise price. Sales of the product occur during the whole period at a fixed retail price (𝑝𝑝). In the buyer-supplier relationship, the higher the option premium the retailer is willing to pay, the more the supplier is willing to reserve (produce). Yet, the retailer is willing to reserve more if allowed to pay a lower option premium and a higher exercise price (when demand is known), whereas the supplier prefers the opposite. Retailer expected profit can be formulated as follows:

𝐸𝐸(𝜋𝜋𝑜𝑜𝑟𝑟 ) = (𝑝𝑝 − 𝑒𝑒)∫ 𝑥𝑥𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥𝑄𝑄𝑜𝑜𝑜𝑜

0 + (𝑝𝑝 − 𝑒𝑒)∫ 𝑄𝑄𝑜𝑜𝑟𝑟𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥 − 𝑜𝑜𝑄𝑄𝑜𝑜𝑟𝑟∞𝑄𝑄𝑜𝑜𝑜𝑜

(3)

The first and second derivatives show that the 𝐸𝐸(𝜋𝜋𝑜𝑜𝑟𝑟) is concave in 𝑄𝑄𝑜𝑜𝑜𝑜(view appendix C3). The first order condition is sufficient to determine the optimal reservation policy that maximises the retailer’s profit function (3) and results in the known critical fractile: 𝐹𝐹(𝑄𝑄𝑜𝑜𝑟𝑟∗ ) = ((𝑝𝑝 − 𝑜𝑜 − 𝑒𝑒) (𝑝𝑝 − 𝑒𝑒)⁄ ). Given the retailer’s optimal reservation quantity, the corresponding expected profit for the supplier accepting this order is:

𝐸𝐸(𝜋𝜋𝑜𝑜𝑠𝑠 ) = (𝑜𝑜 + 𝑒𝑒 − 𝑐𝑐)𝑄𝑄𝑜𝑜𝑟𝑟 + (𝑒𝑒 − 𝑣𝑣)∫ 𝑥𝑥𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥𝑄𝑄𝑜𝑜𝑜𝑜

0 . Under the newsvendor model, the retailer carries all the demand risk. However, under the option mechanism the retailer and supplier can share

49

demand risk. In addition, the dyad can achieve coordination using the option mechanism by setting the parameters in a manner that gives the retailer the incentive to reserve the same quantity as the integrated dyad. The dyad will then be able to achieve the integrated expected profit (Rudi and Pyke, 2000; Zhao et al., 2010). We derive the option parameters to achieve channel coordination:

𝐹𝐹(𝑄𝑄𝑜𝑜𝑟𝑟∗ ) = �(𝑝𝑝 − 𝑜𝑜 − 𝑒𝑒)

(𝑝𝑝 − 𝑒𝑒) � = �(𝑝𝑝 − 𝑐𝑐 + 𝑔𝑔)(𝑝𝑝 − 𝑣𝑣 + 𝑔𝑔)� = 𝐹𝐹(𝑄𝑄𝑐𝑐𝑠𝑠∗ )

⟹ 𝑒𝑒 = 𝑝𝑝 − 𝑜𝑜 �(𝑝𝑝 − 𝑣𝑣 + 𝑔𝑔)

(𝑐𝑐 − 𝑣𝑣) �

This shows the relationship between the option parameters. Note that the exercise price is negatively correlated to the option premium. Therefore, an increase in the option premium will decrease the exercise price by ((𝑝𝑝 − 𝑠𝑠 + 𝑔𝑔) (𝑐𝑐 − 𝑣𝑣)⁄ ) > 1.

4.3 Quantitative case study – empirical theory testing

The study uses deductive reasoning to test the general theories in an empirical context and use the results to provide new knowledge (Liu et al., 2016; Van de Ven and Johnson, 2006) that elaborates existing theory in a specific or limited context, i.e. middle range theory. Middle range theory is an intermediary body of theory between general theory and empirical research (Eisenhardt and Bourgeois III, 1988; Stank et al., 2017).

4.3.1 Empirical testing of option contract theory Option contract theory is tested empirically by feeding the algorithm (Eriksson, 2019) with quantitative data from both companies to test whether option contract theory holds in practise, i.e. provide evidence that option contracts create flexibility that improves dyadic supply chain profitability. Based on the results it can be concluded that: a) option contracts create flexibility that enables Volvo CE and its Preferred Supplier to coordinate ordering and production to respond to stochastic demand; and b) that bilateral coordination improves profitability for both companies compared to only a wholesale contract.

50

4.3.2 Fisher framework and paradox Fisher (1997, p. 106) argues that functional products are “the staples” that satisfy the consumer’s “basic needs” and since those needs change little over time, such products have predictable demand and long life cycles. Furthermore, since functional products are satisfying the basic needs shared by consumers, there is little variety and customisation in product offerings, the entry barriers to market are assumed to be low and as a consequence, profit margins are low. When it comes to innovative products, Fisher (1997, p. 106) discuss that innovation may enable companies to limit competition and earn higher profit margins, at least for a period, “The very newness of innovative products makes demand for them unpredictable”. Moreover, the lifecycle of an innovative product is assumed to be relatively short because as competitive imitation occurs, the profit margins earned by the first mover are eroded and the company is forced to further product innovation to recapture its competitive advantage. Consequently, the key argument of the Fisher (1997) framework is that management decides its supply chain strategy efficient/lean or market responsive/agile based on the characteristic of the product i.e. functional or innovative and the extent to which demand is predictable, which in turn decides the supply chain strategy. During our cooperation we noticed that Volvo CE finds itself in a paradox in terms of the Fisher framework. The bracket (53 kilos) component is defined as functional based on its long product life cycle (>5 years) and small number of variants (Fisher, 1997; Wagner et al., 2012). The bracket is tailored and provided to Volvo CE by the supplier and plays a key role in the integration of the dumper truck. If Volvo CE management and the management of its Preferred Supplier decide to follow Fisher’s core argument, they would then be focusing on a cost efficient (lean) supply chain given it has a functional component (bracket) in a functional product (dumper truck). However, while the bracket component is functional with an assumed predictable demand, it plays just one part among thousands of other components (provided by hundreds of other suppliers) in the complex project of assembling a dumper truck. The bracket component must be fitted into place in a specific assembly window at the Volvo site (after tailor development by the supplier) to enable the next component to be fitted. As a result, the assembly of a complex dumper truck with functional

51

components may in fact require a responsive supply chain. In addition, the demand for the truck is far from predictable as it is stochastic. Should Volvo CE and its Preferred Supplier invest money in a flexible (responsive) supply chain (given the stochastic demand and the unpredictable assembly window) or limit cost and focus on a lean supply chain given that components are functional? This creates a paradox for Volvo CE. Our study provides a solution. This study challenges the current question of which product (functional or innovative) decides which SCM approach should be adopted: lean, agile or leagile. Instead, it asks: what level of flexibility is required given stochastic demand? Because demand is stochastic it begins here. Thus, instead of a supply chain manager investing time and money figuring out which lean, agile or leagile solution to adopt based on a product that is viewed as either functional or innovative, the solution is to assume that all demand is stochastic irrespective of the product. Assuming stochastic demand, the key factor is the level of flexibility that can be agreed upon when demand is known. This study provides a solution that enables each company’s supply chain manager, to apply the algorithm to coordinate ordering and production given demand. Subsequently, the current question of whether a specific strategy should be adopted, is replaced with a solution that enables each supply chain manager to act based on demand.

4.3.3 The algorithm validated According to Berling (2005), the way to validate an algorithm is either to run a simulation or apply a case study. In this research both of these methods were applied. The algorithm was also presented and complemented by international experts in the field at the Supply Chain and Finance 5th Annual Symposium on 9 June 2015 at Linnaeus University.

4.4 Reflections on dyadic cooperation in practise versus theory

The algorithm has not yet been implemented at Volvo CE. Nevertheless, this study shows the potential of applying option contracts in supply chain practise. Below are some reflections on why the algorithm was developed instead of picking an off-the-shelf two tariff contract and how management of Volvo CE and the Preferred Supplier expressed they would cooperate when

52

applying the algorithm to coordinate ordering and production to stay competitive in the business.

4.4.1 Contracts in a dyadic supply chain cooperation The main objective of (research on) coordinating contracts is to get a group of rational agents viewed as a nexus-of-contracts to interact with each other according to predefined rules, in order to optimise a supply chain (Arshinder et al., 2008; Wang and Parlar, 1994). Although contracts have been studied in law, economics and marketing disciplines, their study in SCM takes a rather different approach: “What distinguishes SCM contract analysis may be its focus on operational details, requiring more explicit modeling of materials flows and complicating factors such as uncertainty in the supply or demand of products, forecasting and the possibility of revising those forecasts, constrained production capacity, and penalties for overtime and expediting” (Tsay et al., 1999, p. 302). According to Cachon and Lariviere (2001), there are two classes of compliance regime: voluntary and forced. If a contract can coordinate settings for a specific supply chain under a voluntary-compliance regime, it could coordinate under the forced-compliance regime as well, however the opposite might not be the case. Another possibility is to work solely with a relational contract (Taylor and Plambeck, 2007; Van Mieghem 1999). A relational contract refers to the fact that the enforcement of the contract comes from the value of the future relationship instead of direct legal enforcement. This agreement is based on trust and the assumption that each party will honour the agreement because of the future value of cooperation.

4.4.2 Option mechanism versus buy-back contract The supply chain contracting literature has a number of two-part tariff contracts that share demand risk between both parties. Two-part tariffs can achieve a distribution of risk in the supply chain and thus, they align incentives to maximise supply chain profit. Two-part tariffs require two parameters – an initial fee per unit contracted upfront when ordering inventory and a second fee when demand is known (Tsay et al., 1999). Cachon (2003) describes an option contract simply as a combination of buy-back and quantity flexibility contracts, thus a valid question is, why develop an option mechanism instead of applying a buy-back contract off-the-shelf. In

53

the buy-back contract (Pasternack, 1985) the supplier accepts return of unsold inventory at the end of the sales period. For this the supplier will pay the retailer a buy-back fee for each unit returned. To avoid arbitrage, the buy-back fee must be smaller than or equal to the sum of wholesale price and transportation costs. The buy-back contract shares inventory risk as the supplier increases its share of demand risk. Pasternack (1985) shows that supply chain coordination is possible using a buy-back contract with a given fixed price and full return rights to partial credit. However, there are a number of key issues with the buy-back contract (compared to the option mechanism) that motivated the development of an optimal solution for Volvo CE and its Preferred Supplier. The buy-back contract causes the following problems: a) requires the supplier to monitor costs on the manufacturer side; b) may not be applied to a non-substitutional product; c) requires the buyer to be rational since it can return stock (costly for the supplier); d) requires additional transportation costs for the manufacturer, sending stock back to the supplier; and e) given that buy-back allows for the right to return stock, it may also contribute to the costly bullwhip effect. The major disadvantage of a buy-back contract is the shipping back and forth of inventory. These costs reduce supply chain profit, as transportation fees are lost for the supply chain. Following the description posed by Cachon (2003), Wang et al. (2015) made a comparison between the option contract and the buy-back contract and concluded that the option contract outperform the buy-back contract in all conditions. Wang et al. (2015) find that the higher profit margin for the option contract primarily results from savings in transportation costs. Given that the bracket component is tailored for Volvo CE and there are costs of shipping inventory back and forth incurred by each company, the buy-back was never considered in this study. Instead, the option mechanism was developed, not only to avoid the cost of shipping back and forth but also for bilateral coordination. Furthermore, the option mechanism may also be used for hedging, which is not possible with the buy-back - at least not for short-term production since all quantities must be shipped before the sales period.

4.4.3 Volvo CE and channel power In game theory each player seeks to maximise their value by taking into account what the other player might do given that the decisions of each player affect each other’s values (Nash, 1950; Rubinstein, 1982; von Stackelberg, 1952). Given the strength of Volvo CE in the dyadic supply chain, it could be

54

argued that it exerts its channel power to influence the supplier to reduce the wholesale price. Forcing the supplier to reduce the wholesale price would shift a higher share of channel profit to Volvo CE without reducing dyadic profit. During this study, Volvo CE show no intention of exerting its channel power. On the contrary, at one point Volvo CE conveyed that it would allow the supplier to keep the existing contract, although a competitive supplier could submit a proposal with a lower price. This was somewhat surprising, yet Volvo CE provided its justification for this: competitive suppliers often provide competitive pricing in order to get a foot in the door with a price that cannot be sustainable in the long run. As a result Volvo CE argues it is better to keep the supplier that provides quality at competitive pricing (though somewhat higher). Notably, in a study of power structure in a two echelon supply chain with a manufacturer and a retailer, Chen, Wang and Chan (2017) concluded that the dominant member should not exploit its power over the other in order to achieve sustainability goals.

4.4.4 Symmetric information and trust Volvo CE and supplier were cooperating based on information symmetry meaning that Volvo CE shares demand data and the supplier shares cost information related to the component. Similar to above, some may argue why would Volvo CE not inflate demand forecast to have the supplier build costly inventory. The answer may be seen in a recent extensive analysis of supply chain projects. This extensive examination over sixty project implementations between multinational organisations in various industries, confirms why Volvo CE shares its information with the supplier. In the study by Brinkhoff et al. (2015) it was found that the success of projects depends on the degree of trust between parties. In fact, the degree of trust was shown to be a stronger predictor of success than the degree of asymmetric power/dependence between the firms. In addition, with over 17,000 suppliers across the globe, Boeing commented, “it has sometimes been a job to persuade all these suppliers to invest enough to meet future demand.” The company learned that an effective way to do so is to build more trust in the supply chain and be more open to sharing information with suppliers (The Economist, 2012, p. 65).

55

5 Summary of the Papers

This section provides a short summary of each paper, describing its contribution to the research aim. This dissertation is a compilation of three research papers, each by a single author. The dissertation is developed in a way in which each paper follows on from the other. Thus the silver thread throughout this dissertation can be traced as follows: The first paper is a review of how options contracts are applied in SCM. The review identifies research gaps with regard to how the option mechanism creates flexibility in a dyadic supply chain for a manufacturer and supplier to coordinate ordering and production quantities unilaterally and in one period. In addition, the review found that the measurement of profitability in empirical research is limited to manager’s perception only. Given that the identification of research gaps is fundamental to building knowledge to elaborate theory, this resulted in the development of an algorithm that could address the identified research gaps, which resulted in the second research paper. The second paper is a developed algorithm that creates flexibility for an Original Equipment Manufacturer (OEM) and supplier to coordinate ordering and production bilaterally in a multi-period setting. In addition, the algorithm provides an objective way to measure profitability by valuing the option contract and can thus be viewed as a measurement instrument, which in turn allows for further progress within the field of SCM. Having developed an algorithm that creates flexibility between two companies (contrary to flexibility inside a company) and a way in which to objectively

56

measure profitability, the final step was to tie the dissertation together by validating the algorithm and testing theory in an empirical quantitative case study. Subsequently, in the third paper, the developed algorithm was applied to a quantitative empirical study resulting in the algorithm being tested and validated. N.B. The initial review of option contracts applied in SCM to establish research gaps was started in 2014. This was followed by development of the algorithm and then its validation using quantitative data from the OEM and supplier. In June and December 2018 the review was updated, resulting in the algorithm being published ahead of both the review and the case study.

5.1 Paper I – A theoretical review Eriksson, K. (2019). Application of Option Contracts in Supply Chain Management – A Theoretical Review. In peer-review process Purpose – The purpose of this review is to study the application of option contracts applied in SCM and to identify potential areas for future research. Methodology/Approach – A structured review technique based on a three‐stage refinement process, including review of keywords, title, abstract, and conclusion were used to identify 54 peer‐reviewed articles focusing on option contracts applied in supply chains. Findings – This review finds that option contracts create flexibility to coordinate ordering and production unilaterally, which improves profitability compared to a wholesale contract. In addition, the review finds that profitability measurement is limited to manager’s perception only. Moreover, the paper identifies a number of areas of future research that will help academics gain a better understanding of the interface between finance and SCM and set the agenda for future research in this direction. Research Implications – Given that identification of research gaps is fundamental in the development of knowledge to elaborate theory, this review contributes by identifying research gaps on the new option mechanism – advent in supply chain management. Practical Implications – Given the amount of money that is lost due to supply chain inefficiency, there is a practical contribution for managers knowing how the option mechanism works in a supply chain setting that

57

creates flexibility to coordinate production and ordering given stochastic demand. Originality/Value – This paper is (to the best of my knowledge) the first review that encompasses supply chain option contracts. While suggesting numerous research gaps it also provides a research agenda for dyadic supply chain flexibility research. Keywords: Theoretical review, Option contracts, Flexibility, Supply Chain Management, Newsvendor

5.2 Paper II – The algorithm Eriksson, K. (2019). An Option Mechanism to Coordinate a Dyadic Supply chain Bilaterally in a Multi-period Setting. Omega – The International Journal of Management Science, 88, 196-209. Purpose –The purpose of this paper is to present an algorithm that combines the base stock model and the option mechanism in order to create flexibility for an original equipment manufacturer (OEM) and supplier to coordinate ordering and production quantities to respond to stochastic demand to maximise each profits and coordinate the dyad bilaterally in a multi-period setting. Methodology/Approach – The methodology used in this paper can be described as econometric modelling under which an algorithm is developed to address research gaps. Findings – This paper extends current work by allowing a manufacturer with make-to-order production to carry over inventory and derive the optimal order-up-to level for each period to maximise profits. In the event the manufacturer cannot meet demand, it can backorder supply for a proportional backorder cost. Under the option contract, the supplier can simultaneously plan production to maximise its own profit. As a result, ordering and production quantities are optimised and the dyad coordinated bilaterally in a multi-period setting. Research Implications – The developed algorithm provides researchers with a method to (objectively) measure dyadic supply chain profitability.

58

Practical Implications – The developed algorithm provides managers with an algorithm to coordinate ordering and production to optimise supply chain responsiveness and increase their competitiveness. Originality/Value – This paper provides an option-based algorithm that creates flexibility for an Original Equipment Manufacturer (OEM) and supplier to coordinate ordering and production quantities to respond to stochastic demand, maximise the profits for each and coordinate the dyad bilaterally in a multi-period setting. Furthermore, the algorithm provides researchers with the possibility of studying the Fisher portfolio framework from a dyadic supply chain position. Keywords: Options, Finance, Dyadic flexibility, Supply chain coordination, Fisher portfolio framework

5.3 Paper III – A quantitative case study Eriksson, K. (2019). Supply Chain Coordination through Application of Option Contracts – A Quantitative Case Study. In peer-review process Purpose – The purpose of this case study is to apply option contracts in supply chain practise to provide empirical evidence that dyadic supply chain flexibility improves dyadic supply chain profitability for one or both companies compared to a wholesale contract by addressing the following questions: (i) How do option contracts create dyadic supply chain flexibility? (ii) Why does dyadic supply chain flexibility coordinate ordering and production bilaterally? Methodology/Approach – The present case study applies an algorithm that combines the base stock model and the option mechanism using data from a multinational Original Equipment Manufacturer and its Preferred Supplier to create flexibility. This is done to address the problem of how to coordinate ordering and production bilaterally in a multi-period setting, in order for each company to maximise profit and share demand risk in comparison to only a wholesale contract. The data collection took place between January and June 2015. To ensure the research design with regard to reliability and validity, a comprehensive research protocol (appendix A) was developed and applied. Findings – The findings show that dyadic supply chain flexibility improves dyadic supply chain profitability compared to a wholesale contract.

59

Research Implications – Providing empirical support for the option contract theoretical association between the relationship between dyadic supply chain flexibility and dyadic supply chain profitability the paper advances evidence for the importance of dyadic supply chain flexibility. In addition, it substantially benefits researchers with its ability to value dyadic supply chain flexibility and measure profitability. Practical Implications – The practical contributions of this study include validation of the algorithm that will enable management to create flexibility to coordinate production and ordering between two companies, in order to share (demand) risk and improve profit for both companies compared to a wholesale contract. This may have a significant value, given that the efficiency loss in a dyadic supply chain when only a wholesale contract is applied can be substantial (up to 40% per year). Originality/Value – While testing and validating an algorithm for identifying and creating dyadic supply chain flexibility and for objectively measuring and valuing dyadic supply chain profitability, the paper advances this topic in SCM to a quantitatively measurable theory. In addition, similar to how manufacturing flexibility (two decades ago) was shown to improve firm performance, empirical evidence (through objective measurements) now exists that dyadic supply chain flexibility improves dyadic supply chain profitability (a topic that has been debated at least since 1999). Keywords: Dyadic supply chain flexibility, Option contracts, Case study, Fisher’s portfolio framework

61

6 Discussions, Contributions and Conclusions

This chapter concludes the dissertation and discusses how the purpose was fulfilled by presenting the key research findings for this dissertation. It begins with the empirical, measurement method, theoretical and practical contributions and follows with the applicability of the results and limitations before concluding with suggestions for future research.

6.1 Empirical contributions The empirical contributions of this dissertation are 1) providing quantitative evidence of the relationship of dyadic supply chain flexibility and dyadic supply chain profitability; and 2) the application of quantitative data from two companies when exploring and examining the concept of dyadic supply chain flexibility between two companies. The importance to empirically study supply chain flexibility and profitability was called for by Sánchez and Perez (2005), which resulted in survey-based studies (Avittathur and Swamidass, 2007; Gosling et al., 2010; Merschmann and Thonemann, 2011; Swafford et al., 2006). Nonetheless, there are a number of weaknesses associated with surveys, not least when measuring profitability between two companies in a dyadic supply chain (Lim, 1987; Prajogo et al., 2016; Shi and Yu, 2013; Soni and Kodali, 2012; Stevenson and Spring, 2009). The empirical contributions of this dissertation are results based on quantitative data, (finance, production and ordering) collected from both companies via an on-site research protocol by each company project manager.

62

There is no subjective judgement as to how much a variable may or may not cause an effect resulting in an economic performance. Instead, the quantitative data has been fed into the algorithm resulting in flexibility and profitability being measured and valued objectively. This empirical contribution is a response to Sánchez and Pérez (2005) who state: “Future research might also develop objective measures of supply chain flexibility since a possible limitation of the current study is its reliance on perceptual data” (p. 697). In addition, based on a review of supply chain flexibility from 1990-2013, Tiwari et al. (2015) state that research in supply chain flexibility and its impact of relationships on business functions are “In the infancy stage and need considerable attention”(p. 783). As a result, this dissertation contributes by addressing the lack of empirical evidence supporting the benefits attributed to theories (Jouni et al., 2011; Kisperska-Moron and Swierczek, 2011; Lambert et al., 2005; Naslund and Williamsson, 2010; Stock et al., 2010). Having empirical evidence on the key relationship (established) between dyadic supply chain flexibility and dyadic supply chain profitability will benefit researchers, as it is now possible to identify flexibility between members in the dyadic supply chain and the interaction between them with regard to flexibility and performance, i.e. profitability (Esmaeilikia et al., 2016; Stevenson and Spring, 2009).

6.2 Measurement method contributions 6.2.1 Measurement of profitability The lack of an objective method for measuring profitability has followed SCM since the term was introduced into academia (Ellram and Cooper, 1990), which is a significant flaw with regard to the objective measurement of (supply chain) profitability (Fabbe-Costes and Jahre, 2007). Today, SCM performance is measured via a Likert scale based on a long-distance questionnaire, which relies on the individual respondent’s interpretation of amongst other things, flexibility and performance (Fabbe-Costes and Jahre, 2007; Prajogo et al., 2016). Note that this subjective measurement problem is by no means limited to flexibility, as it is applied across research including when research seeks to establish the relationship between lean and agile to firm performance (Gligor et al., 2015; Wagner et al., 2012).

63

As a result, some view the current established relationship between supply chain flexibility (integration) and performance as inadequate (Chan, 2003; Gunasekaran et al., 2001; Purvis et al., 2014; Sink and Tuttle, 1989; Stevenson and Spring, 2007, 2009; Tiwari et al., 2015; Van der Vaart and Van Donk 2008). In addition, Fabbe-Costes and Jahre (2007) concluded that there is no empirical evidence on the relationship between integration and performance, while pointing out that performance is measured based on “managers’ perceptions” (p. 844). In a quantitative case study using real data from ABB, Bengtsson and Olhager (2002) establish the relationship between product flexibility and profitability by measuring the value of the option. In a similar manner this dissertation provide results by valuing the option and the wholesale contract, thus providing an objective measurement method. The dissertation measures the effect and economic value of each variable. Being able to measure and value each variable provides researchers with the possibility of examining the effect of variables when evaluating (economic) performance.

6.3 Theoretical contributions It has been suggested that researchers in SCM may borrow theories from other related fields (Ellram and Cooper, 2014; Halldórsson et al., 2007, 2015; Stock, 1997) by leaving their disciplinary comfort zone and going beyond the boundaries of their own fields and into other disciplines (Fawcett and Waller, 2011). By combining finance option contract theory with stochastic inventory models, this dissertation provides that multidisciplinary approach that Sanders and Wagner (2011) argue is key for SCM to remain relevant as a discipline. It also acknowledges the call for SCM to adapt the options approach to evaluate the benefits of flexibility (Costantino and Pellegrino, 2010). The aim is to contribute to theory via “knowledge production” (Van de Ven and Johnson, 2006, p. 808) with which general theories including portfolio (Fisher, 1997; Markowitz, 1952) and real options (Myers, 1977) are elaborated via middle range theory. However, there is little guidance for building middle range theory in the supply chain context but it provides theoretical insight that can be applied to an empirical context (Carter, 2011; Craighead et al., 2016).

64

Specifically, middle range theory refers to testing option contracts and the Fisher portfolio framework in an empirical context and using the results to consolidate knowledge on each topic in a limited dyadic supply chain context.

6.3.1 Middle range theory The underlying general theory is the real options theory (Craighead et al., 2016; Myers, 1977) from which the small-scale theory (Halldórsson et al., 2015), i.e. contract options theory (Van Mieghem, 1998) is applied to a SCM context. This is similar to Ketokivi (2006) applying what Merton (1968, p. 51) discusses as developing “stepping stones in the middle distance”, i.e. using limited yet quantitative confidential data to develop middle range theory (Bourgeois, 1979). The middle range theory (MRT) on which this study provides new stepping stones is supply chain flexibility. Broadie et al. (2011) argue that by their nature, general theories are broad in scope and more abstract and therefore, there is an inherent difficulty with the interface between general theory and empirical research. This dissertation is not seeking to explain everything about a general subject, (e.g., how markets function or how to manage markets) nor does it aim to generate new theory, only testing an existing theory on a subset of phenomena relevant to a particular context. Following Craighead et al. (2016) in labelling real options theory as a general theory, the results provide new knowledge that elaborates the theory in a limited context, i.e., middle range theory. The options contract theory is tested empirically by feeding the algorithm (Eriksson, 2019) with quantitative data from both companies to test whether the theory holds in practise, that is provides empirical evidence that options contracts create supply chain flexibility that improves supply chain profitability. According to Stank et al. (2017) general theorising seeks to conduct research in new areas to extend the generalisability of a theory cross domains, while MRT seeks to consolidate knowledge regarding how, why and when variables related to a phenomenon of interest generate outcomes within a specific domain (Pawson and Tilley, 1997). The goal of applying middle range theory is to produce knowledge (Liu et al., 2016; Van de Ven and Johnson, 2006) by empirically testing option contract

65

theory in a limited context, i.e. supply chain flexibility. A theoretical contribution is not about making or saving money but seeking the theoretical understanding of it (Holmström et al., 2009). The drawback of using one case study (lower generalisability) is acknowledged yet the goal is insight into a specific context and not a statistical generalisation (Ketokivi, 2006). Stuart et al. (2002) outline that the case method is often chosen to identify an effect and not to represent all similar situations, in reference to a study by Van Maanen (1988). Moreover, a single case study at the Kawasaki plant managed to refute the dismissed (at that time) just-in-time production applied in the US by non-Japanese staff (Schonberger, 1982). In addition, a single case study at Honda examined the centre console assembly based on the fact that the “cup holder … is one of the five key features that affect a consumer´s purchase decision” (Choi and Hong, 2002, p. 472), which ultimately provided broader knowledge about structural patterns of value chains (Ketokivi and Choi, 2014). Similarly, a single case study at Volvo CE and one of its key suppliers is adopted to provide a broader knowledge of the potential through the application of option contracts in dyadic supply chain practise using functional components (Busse et al., 2017). If the algorithm would be implemented it could significantly improve profitability in the dyadic supply chain by creating flexibility for companies to coordinate ordering and production bilaterally. The Supplier can apply the algorithm to decide the optimal production quantity that maximises its profit, while the manufacturer can design the option contract to guarantee stock in order to optimise responsiveness to demand to maximise profit. Therefore, an additional contribution of this study is “in the interaction between model development and its impact on decision making in a typical application” (Biçer et al., 2018, p. 19). The empirical results based on deductive reasoning, contribute to theory in related areas including supply chain practise view, supply chain flexibility and the Fisher portfolio framework. Carter et al. (2017) suggest extending the practise-based view (PBV) (Bromiley and Rau, 2016) to inter-organisational practises. Given that the

66

PBV is limited to one company, while SCM extends to a supply chain of companies, it is suggested the PBV be extended to the interorganisational level of analysis, both in terms of practise and performance. The results in this dissertation can be viewed as a direct response to Carter et al. (2017), who define relational performance as mutually generated benefits by two organisations that cannot be generated by either of those organisations alone, including profit maximisation in the supply chain (Cheung et al., 2011). Three decades ago, it was proven that manufacturing (intra-firm) flexibility improves profitability (Slack, 1983; Upton, 1994), followed by Van Mieghem (1998) using real options to create product flexibility to improve profitability and Bengtsson and Olhager (2002) providing empirical evidence of that relationship. This dissertation provides empirical evidence that option contracts can extend the theory of manufacturing and product flexibility to supply chain flexibility.

6.3.2 Fisher portfolio framework and paradox This study adds to the Fisher (1997) portfolio framework in two ways: a) it addresses the Fisher paradox, followed by b) elevating it to a higher dimension – supply chains. If Volvo CE and its Preferred Supplier were to decide to follow Fisher’s core argument, the companies would then be asked to spend money in a responsive (agile) supply chain given the stochastic demand. But given that there is a functional component involved (bracket) in a functional product (dumper truck), the companies would supposedly focus on cost efficiency (lean) – which is a paradox. Interestingly, de Treville et al. (2014, p. 344) state, “This exploration aided in fleshing out the middle ground described by Fisher (1997) concerning products that appear to be functional yet generate high mismatch costs. As lead times increase, higher volatility exposure may increase left-over inventory enough to dramatically reduce residual value, causing a previously functional product to experience the mismatches expected from an innovative product”. Our argument is similar but not the same. In their case, the functional product experiences mismatch costs, which are expected with an innovative product

67

based on long lead time and of course, the long lead time is itself based on an ordering choice. Volvo CE has no choice. The dumper truck is made of thousands of functional components from various suppliers that must be assembled in a narrow window to avoid costly production interruptions, which turns the deterministic assembly setting into a stochastic setting. This study allows management to adjust to Fisher’s supply chain framework. However, instead of management investing time and money figuring out how to fit into the Fisher framework, the algorithm allows Volvo CE and its Preferred Supplier to apply a responsive (flexible) supply chain, given a functional component (bracket) in a functional product (dumper truck). By assuming stochastic demand (which it is) the key factor is the level of flexibility. As a result, the deciding factor for how to optimise supply chain responsiveness is no longer based on the component being functional or innovative – instead it is the level of flexibility that can be agreed upon when the demand the product generates is known. As a result, the algorithm enables the supply chain manager at each company to act based on demand, while using the algorithm to coordinate ordering and production bilaterally to maximise profit. The Fisher portfolio framework and related models provide useful guidance for management given a certain manufacturing context, in other words a fairly high-volume manufacturing supply chain in which there is a repeated production process. However, in a specific inter-organisational context given a functional component (bracket) that plays a key part in a functional product (dumper truck), the current core argument creates a contradiction that is addressed by this study and algorithm (Eriksson, 2019). The study elaborates the general portfolio theory through the addition of an extra level in the small scale Fisher portfolio framework theory applied in dyadic SCM. Mahdavi and Olsen (2017) recently pointed out that there is a lack of modelling work in the field of Fisher’s framework. Notably, an added contribution of this dissertation is the evidence that the algorithm enables the Fisher (1997) framework to be elevated to a higher dimension – supply chains (Wang et al., 2014). This occurs when it matches the functional component

68

(bracket) to a responsive (flexible) strategy for the benefit of the dyadic supply chain, i.e. Volvo CE and Preferred Supplier AB.

6.4 Practical contributions Based on the results, there are also practical contributions that can be made including the following:

6.4.1 Bilateral coordination of ordering and production There is value in flexibility (Trigeorgis, 1996). The algorithm creates flexibility for a manufacturer and a supplier to coordinate ordering and production bilaterally. The supplier can apply the algorithm under the option contract to decide the optimal production quantity that will maximise its profit, while the manufacturer can design the option contract to guarantee inventory to respond to stochastic demand to maximise profit. The effect of bilateral coordination is a dyadic supply chain avoid ending up with a 40% smaller share of the pie and instead keeping its collaborative advantage when competing in a global economy.

6.4.2 Operational hedging This dissertation contributes by providing SCM with an algorithm that can be applied as an operational hedging tool against demand risk. When the manufacturer places the order real demand is not known, thus demand risk is present. The manufacturer places the order based on a forecast (in order for the supplier to prepare capacity for production). When the manufacturer pays the option premium (for the flexibility) to increase the ordering quantity to meet stochastic demand it also buys a demand risk hedge. This is because the option gives the manufacturer the right (not the obligation) to buy the reserved quantity. Subsequently, if real demand is lower than forecast, the manufacturer may not exercise all its options (buys ordered quantity) and avoids being stuck with costly inventory. Similarly, the algorithm benefits the supplier. This is because if the manufacturer cancels orders due to low demand, the supplier is compensated via the option premium for taking on extra cost (material, staff, etc.) to create capacity (production) to provide flexibility for the manufacturer (Van Mieghem, 2003).

69

6.4.3 Optimise supply chain responsiveness According to Wang et al. (2014) a company either chooses whether to be responsive or efficient when determining its operations strategy. Instead of a company manager being in a position to decide between a cost-benefit analysis (Wang et al., 2014) or how best to achieve dynamic lead time reduction, “enough to permit production to order” (de Treville et al., 2014a, p. 2115), the algorithm allows ordering and production decisions to be made given stochastic demand, which significantly reduces the risk of mismatch costs due to stockouts or overstocks.

6.5 Generalisability of results Although this study was limited to a specific industry, i.e. heavy construction equipment and a functional component with a long life cycle that is not substitutable, there is no reason why the algorithm and results would not be applicable to other similar industries. The algorithm was validated beyond Berling (2005) mathematical modelling method in three steps and in accordance with what Van Aken, (2005) refers to as alpha testing. The algorithm was put through a field test (Van Aken et al., 2016) to empirically validate its possible contributions in order to establish the quality of research design, i.e. reliability and validity. Furthermore, to ensure reliability a comprehensive research protocol was developed and used on-site via instructions. To ensure construct validity, the data was thoroughly checked by both the Volvo CE commodity manager and sales manager for the Preferred Supplier. Given that the empirical study was based on quantitative research, this provides objective results and thus the subjectivity of the study that caused “perceptual results” in traditional research is not a limitation in this dissertation.

6.6 Limitations and future research directions Although the algorithm and study coordinates ordering and production bilaterally extended to a multi-period setting, there are limitations. The study uses the real company data to simulate the effect of the algorithm being implemented and consequently no actual implementation has taken place. Future research may explore the effects of the option mechanism being implemented. In addition, the study focuses on one single industry, i.e. the heavy construction equipment industry in Sweden. Furthermore, the study

70

examines the cooperation between one manufacturing company, one key supplier and one key (functional) component. As a result, the study neither generalises the results to industries, e.g. the food industry with perishable goods or the innovative high-tech product supply chain. Additional insights could be obtained if future research collects data from different industries and compares the findings. Nevertheless, focusing on one single industry could be perceived as advantageous as it allows for in-depth analysis (Dubois and Gadde, 2002, 2014). Furthermore, as both the manufacturer and the supplier are supposed to be risk-neutral, decision-making behaviour under loss aversion is applicable (Chen et al., 2014) and finally while the algorithm applies call options, future research may include put or bidirectional options (Nosoohi and Nookabadi, 2016). Nevertheless, having the algorithm tested and validated provides several avenues for future research. One avenue could be options with dynamic exercise prices. For example, this may enable various production and transportation costs given various order quantities during different time periods. Another avenue might be the application of options on options – also known as compound options in finance. Applying compound options in SCM may provide opportunities amongst other things, such as the ability to make multiple decisions regarding how and when to deploy resources, e.g., expand or contract, production capacity within a manufacturing and distribution network. Future research may expand the current dyadic supply chain to a triadic. In addition, the first paper identifies a number of areas of future research that may help academics gain a better understanding of the interface between finance and SCM and set the agenda for future research in this direction.

71

Appendix

Appendix A. Research Protocol January 19th 2015

A.1. Volvo CE quantitative data

1. Unit demand data 2. Unit ordering data 3. Unit selling price 4. Unit purchase cost 5. Unit salvage value 6. Unit shortage cost 7. Unit holding cost 8. Unit back order cost 9. Unit production (assembly) cost

A.2. Preferred Supplier quantitative data 10. Unit ordering data 11. Unit wholesale price 12. Unit (marginal) production cost 13. Unit salvage value 14. Unit shortage cost 15. Unit holding cost 16. Unit back order cost 17. Unit purchase cost (raw material) 18. Set-up cost The issues below were discussed while the quantitative data was collected, in order for the author to “fully” understand the dyadic supply chain business, product (dumper truck) and component (bracket).

A.3. Volvo CE 19. How many weeks to assemble a dumper

truck? 20. How many components in a dumper truck?

21. How many dumper trucks do you sell per week?

22. What is the length of time from order to delivery?

23. What is the selling price for a dumper truck? 24. What is the production cost? 25. What is your profit margin (I guess

confidential)? 26. How (long) is the product life cycle? 27. Main competitors and market share? 28. Your distribution network in Sweden v.

abroad? 29. How many production sites in Sweden v.

abroad? 30. How many suppliers in Sweden v. abroad? 31. Define Supplier status? 32. How do you compete against CAT and

Komatsu (price, quality, service) given “high cost” in Sweden?

A.4. The Preferred Supplier AB

33. Why do you have a Preferred Supplier status?

34. Why are you taking part in this study? 35. What do you expect to come out of this

study? 36. What is the function of a bracket? 37. More than one bracket on a dumper truck? 38. How many stages to build a bracket? 39. What is the weight of a bracket? 40. How much of your sales is Volvo CE? 41. How dependent are you on Volvo CE given

ABB?

72

A.5. Map current supply chain contracts

The map contract data is based on Höhn (2010)

42. What issues are covered in the current contract?

43. Contract length & termination of contract 44. Unit wholesale price (fixed or dynamic) 45. Minimum purchase (quantity) given time

horizon 46. Decision rights over key variables price v.

quantity 47. Any buy-back clause, quantity discount 48. Production capacity & lead time for delivery 49. Service level & quality level 50. What information is (not) shared?

A.6. Other 51. Demand for dumper truck easy/difficult to

forecast? 52. Demand forecast sharing with Supplier? 53. How do you calculate demand forecast

(variability)? 54. How costly is a production stop due to no

supply? 55. How do you calculate cost of capital (COC)? 56. Which COC was applied to inventory v.

investments?

57. How do you calculate (unit) backorder cost 58. How (often) does ordering take place? 59. How much do you order? 60. Who is your metal supplier? 61. Safety stock & critical stock? 62. How long is delivery lead time (variability)? 63. How long is manufacturing lead time? 64. How do you reduce delivery lead time? 65. How do you reduce manufacturing lead

time? 66. Is the bracket produced MTS or MTO? 67. Criteria for selecting a (preferred) supplier? 68. Do you apply an option contract in your SC

business? 69. How do you calculate option contract

variables? 70. Which inventory model (R,Q S,s, other) do

you use? 71. What are the costs of shortage (money,

goodwill)? 72. Who carries the cost for shortages? 73. Who carries the cost for overage? 74. How is (wholesale) pricing agreed? 75. On what basis can pricing be renegotiated? 76. Being a supplier to other firms? 77. Number of white/blue collar workers

31/12/2014?

Bracket 53 kg

73

Appendix B Notation: 𝑥𝑥 Stochastic demand 𝑓𝑓(𝑥𝑥) Probability density function (Pdf) 𝐹𝐹(𝑥𝑥) Cumulative distribution function (Cdf) 𝑝𝑝 Sales price 𝑐𝑐 Unit production cost 𝑤𝑤 Wholesale price 𝑣𝑣 Unit salvage value 𝑔𝑔 Unit shortage penalty cost (goodwill) 𝑜𝑜 Option premium per unit reserved 𝑒𝑒 Exercise price per unit exercised 𝑄𝑄 Optimal order quantity 𝑄𝑄𝑐𝑐𝑠𝑠 Optimal order quantity centralised system 𝑄𝑄𝑜𝑜𝑟𝑟 Optimal order quantity with option contract 𝜋𝜋𝑟𝑟 Retailer expected profit of the decentralised system 𝜋𝜋𝑠𝑠 Supplier expected profit of the decentralised system 𝜋𝜋𝑐𝑐𝑠𝑠 Dyad expected profit of the centralised system 𝜋𝜋𝑜𝑜𝑠𝑠 Supplier expected profit with option contract 𝜋𝜋𝑜𝑜𝑟𝑟 Retailer expected profit with option contract

74

Appendix C C1. Proof of 𝐹𝐹(𝑄𝑄∗) 𝐸𝐸(𝜋𝜋𝑟𝑟) = (𝑝𝑝 − 𝑣𝑣)∫ 𝑥𝑥𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥𝑄𝑄

0 − (𝑤𝑤 − 𝑣𝑣)∫ 𝑄𝑄𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥𝑄𝑄0 + (𝑝𝑝 − 𝑤𝑤 +

𝑔𝑔)∫ 𝑄𝑄𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥∞𝑄𝑄 − (𝑔𝑔)∫ 𝑥𝑥𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥∞

𝑄𝑄

𝐸𝐸�𝜋𝜋𝑟𝑟(𝑄𝑄)� = (𝑝𝑝 − 𝑤𝑤 + 𝑔𝑔)𝑄𝑄 − (𝑝𝑝 − 𝑣𝑣 + 𝑔𝑔)∫ 𝑥𝑥𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥𝑄𝑄0

Using the first order condition: 𝑑𝑑𝐸𝐸�𝜋𝜋𝑜𝑜(𝑄𝑄)�

𝑑𝑑𝑄𝑄= (𝑝𝑝 − 𝑤𝑤 + 𝑔𝑔) − (𝑝𝑝 − 𝑣𝑣 + 𝑔𝑔)𝐹𝐹(𝑄𝑄)

and the second derivative: 𝑑𝑑2𝐸𝐸�𝜋𝜋𝑜𝑜(𝑄𝑄)�

𝑑𝑑𝑄𝑄2 = −(𝑝𝑝 − 𝑣𝑣 + 𝑔𝑔)𝑓𝑓(𝑄𝑄) < 0

As the second derivative is negative, the first derivative is sufficient and the critical fractile is: 𝐹𝐹(𝑄𝑄∗) = ((𝑝𝑝 − 𝑤𝑤 + 𝑔𝑔) (𝑝𝑝 − 𝑣𝑣 + 𝑔𝑔)⁄ ) and the optimal order quantity is: 𝑄𝑄∗ = 𝐹𝐹−1((𝑝𝑝 − 𝑤𝑤 + 𝑔𝑔) (𝑝𝑝 − 𝑣𝑣 + 𝑔𝑔)⁄ ) C2. Proof of 𝐹𝐹(𝑄𝑄𝑐𝑐𝑠𝑠∗ ) 𝐸𝐸(𝜋𝜋𝑐𝑐𝑠𝑠) = 𝐸𝐸(𝜋𝜋𝑟𝑟) + 𝐸𝐸(𝜋𝜋𝑠𝑠) = (𝑝𝑝 − 𝑣𝑣)∫ 𝑥𝑥𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥𝑄𝑄

0 − (𝑐𝑐 − 𝑣𝑣)∫ 𝑄𝑄𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥𝑄𝑄0 +

(𝑝𝑝 − 𝑐𝑐 + 𝑔𝑔)∫ 𝑄𝑄𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥∞𝑄𝑄 − (𝑔𝑔)∫ 𝑥𝑥𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥∞

𝑄𝑄

𝐸𝐸�𝜋𝜋𝑐𝑐𝑠𝑠(𝑄𝑄)� = (𝑝𝑝 − 𝑐𝑐 + 𝑔𝑔)𝑄𝑄 − (𝑝𝑝 − 𝑣𝑣 + 𝑔𝑔)∫ 𝑥𝑥𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥𝑄𝑄0

Using the first order condition: 𝑑𝑑𝐸𝐸�𝜋𝜋𝑜𝑜(𝑄𝑄)�

𝑑𝑑𝑄𝑄= (𝑝𝑝 − 𝑐𝑐 + 𝑔𝑔) − (𝑝𝑝 − 𝑣𝑣 + 𝑔𝑔)𝐹𝐹(𝑄𝑄)

and the second derivative: 𝑑𝑑2𝐸𝐸�𝜋𝜋𝑜𝑜(𝑄𝑄)�

𝑑𝑑𝑄𝑄2 = −(𝑝𝑝 − 𝑣𝑣 + 𝑔𝑔)𝑓𝑓(𝑄𝑄) < 0

As the second derivative is negative, the first derivative is sufficient and the critical fractile is: 𝐹𝐹(𝑄𝑄𝑐𝑐𝑠𝑠∗ ) = ((𝑝𝑝 − 𝑐𝑐 + 𝑔𝑔) (𝑝𝑝 − 𝑣𝑣 + 𝑔𝑔)⁄ ) and the optimal order quantity is: 𝑄𝑄𝑐𝑐𝑠𝑠

∗ = 𝐹𝐹−1((𝑝𝑝 − 𝑐𝑐 + 𝑔𝑔) (𝑝𝑝 − 𝑣𝑣 + 𝑔𝑔)⁄ )

75

C3. Proof of 𝐹𝐹(𝑄𝑄𝑜𝑜𝑟𝑟∗ ) 𝐸𝐸(𝜋𝜋𝑜𝑜𝑟𝑟 ) = (𝑝𝑝 − 𝑒𝑒)∫ 𝑥𝑥𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥𝑄𝑄𝑜𝑜𝑜𝑜

0 + (𝑝𝑝 − 𝑒𝑒)∫ 𝑄𝑄𝑜𝑜𝑟𝑟𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥 − 𝑜𝑜𝑄𝑄𝑜𝑜𝑟𝑟∞𝑄𝑄𝑜𝑜𝑜𝑜

𝐸𝐸�𝜋𝜋𝑜𝑜𝑟𝑟(𝑄𝑄𝑜𝑜𝑟𝑟)� = (𝑝𝑝 − 𝑜𝑜 − 𝑒𝑒)𝑄𝑄𝑜𝑜𝑟𝑟 − (𝑝𝑝 − 𝑒𝑒)∫ 𝑥𝑥𝑓𝑓(𝑥𝑥)𝑑𝑑𝑥𝑥𝑄𝑄0

Using the first order condition: 𝑑𝑑𝐸𝐸�𝜋𝜋𝑜𝑜𝑜𝑜�𝑄𝑄𝑜𝑜𝑜𝑜��

𝑑𝑑𝑄𝑄𝑜𝑜𝑜𝑜= (𝑝𝑝 − 𝑜𝑜 − 𝑒𝑒) − (𝑝𝑝 − 𝑒𝑒)𝐹𝐹�𝑄𝑄𝑜𝑜𝑜𝑜�

and the second derivative: 𝑑𝑑2𝐸𝐸�𝜋𝜋𝑜𝑜�𝑄𝑄𝑜𝑜𝑜𝑜��

𝑑𝑑𝑄𝑄𝑜𝑜𝑜𝑜2 = −(𝑝𝑝 − 𝑒𝑒)𝑓𝑓�𝑄𝑄𝑜𝑜𝑜𝑜� < 0

As the second derivative is negative, the first derivative is sufficient and the critical fractile is: 𝐹𝐹(𝑄𝑄𝑜𝑜𝑟𝑟∗ ) = ((𝑝𝑝 − 𝑜𝑜 − 𝑒𝑒) (𝑝𝑝 − 𝑒𝑒)⁄ ) and the optimal order quantity is: 𝑄𝑄𝑜𝑜𝑜𝑜

∗ = 𝐹𝐹−1((𝑝𝑝 − 𝑜𝑜 − 𝑒𝑒) (𝑝𝑝 − 𝑒𝑒)⁄ ).

77

References

Abel, A. B. (1983). Optimal investment under uncertainty. The American Economic Review, 73, 228.

Adida, E. & Perakis, G. (2014). The effect of supplier capacity on the supply chain profit. Annals of Operations Research, 223, 1-52.

Agarwal, A., Shankar, R. & Tiwari, M. (2006). Modeling the metrics of lean, agile and leagile supply chain: An anp-based approach. European Journal of Operational Research, 173, 211-225.

Akella, R., Araman, V. F. & Kleinknecht, J. (2001). B2b markets: Procurement and supplier risk management in e-business. In: Geunes, H. E. R. J., Pardalos, P. M. & Romeijn, H. E. (eds.) Supply chain management: Models, applications, and research directions. Dordrecht: Kluwer Academic Publishers.

Allon, G. & Van Mieghem, J. A. (2010). Global dual sourcing: Tailored base-surge allocation to near-and offshore production. Management Science, 56, 110-124.

Amir, R. & Grilo, I. (1999). Stackelberg versus cournot equilibrium. Games and Economic Behavior, 26, 1-21.

Amram, M. & Kulatilaka, N. (1999). Real options: Managing strategic investment in an uncertain world.

Amundson, S. D. (1998). Relationships between theory-driven empirical research in operations management and other disciplines. Journal of Operations Management, 16, 341-359.

Anupindi, R. & Bassok, Y. (1999). Supply contracts with quantity commitments and stochastic demand. In: Tayur, S., Ganeshan, R. & Magazine, M. (eds.) Quantitative models for supply chain management. Netherlands, Kluwer Academic Publishers.

Arshinder, Kanda, A. & Deshmukh, S. G. (2008). Supply chain coordination: Perspectives, empirical studies and research directions. International Journal of Production Economics, 115, 316-335.

Avittathur, B. & Swamidass, P. (2007). Matching plant flexibility and supplier flexibility: Lessons from small suppliers of u.S. Manufacturing plants in india. Journal of Operations Management, 25, 717-735.

Babich, V. (2006). Vulnerable options in supply chains: Effects of supplier competition. Naval Research Logistics, 53, 656-673.

Barad, M. & Sapir, D. E. (2003). Flexibility in logistic systems—modeling and performance evaluation. International Journal of Production Economics, 85, 155-170.

78

Basu, P., Liu, Q. & Stallaert, J. (2019). Supply chain management using put option contracts with information asymmetry. International Journal of Production Research, 57, 1772-1796.

Bengtsson, J. & Olhager, J. (2002). Valuation of product-mix flexibility using real options. International Journal of Production Economics, 78, 13-28.

Berling, P. (2005). On determination of inventory cost parameters. Theses, Lund : Univ., 2005 (Lund : Media-tr.).

Bernardes, E. S. & Hanna, M. D. (2009). A theoretical review of flexibility, agility and responsiveness in the operations management literature: Toward a conceptual definition of customer responsiveness. International Journal of Operations & Production Management, 29, 30-53.

Biçer, I. & Hagspiel, V. (2016). Valuing quantity flexibility under supply chain disintermediation risk. International Journal of Production Economics, 180, 1-15.

Biçer, I., Hagspiel, V. & De Treville, S. (2018). Valuing supply-chain responsiveness under demand jumps. Journal of Operations Management, 61, 46-67.

Billington, C., Johnson, B. & Triantis, A. (2002). A real options perspective on supply chain management in high technology. Journal of Applied Corporate Finance, 15, 32-43.

Birge, J. R. (2000). Option methods for incorporating risk into linear capacity planning models. Manufacturing & Service Operations Management, 2, 19-31.

Birge, J. R. (2015). Om forum—operations and finance interactions. Manufacturing & Service Operations Management, 17, 4-15.

Biswas, I. & Avittathur, B. (2019). Channel coordination using options contract under simultaneous price and inventory competition. Transportation Research Part E: Logistics and Transportation Review, 123, 45-60.

Black, F. & Litterman, R. (1990). Asset allocation: Combining investor views with market equilibrium. Goldman, Sachs & Co.

Black, F. & Litterman, R. (1992). Global portfolio optimization. Financial analysts journal, 48, 28-43.

Black, F. & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81, 637-654.

Boonman, H. J., Hagspiel, V. & Kort, P. M. (2015). Dedicated vs product flexible production technology: Strategic capacity investment choice. European Journal of Operational Research, 244, 141-152.

Bourgeois, L. J. (1979). Toward a method of middle-range theorizing. Academy of Management Review, 4, 443-447.

Brinkhoff, A., Özer, Ö. & Sargut, G. (2015). All you need is trust? An examination of inter‐organizational supply chain projects. Production and Operations Management, 24, 181-200.

Bromiley, P. & Rau, D. (2016). Operations management and the resource based view: Another view. Journal of Operations Management, 41, 95-106.

Brodie, R. J., Saren, M. & Pels, J. (2011). Theorizing about the service dominant logic: The bridging role of middle range theory. Marketing theory, 11, 75-91.

Bruce, M., Daly, L. & Towers, N. (2004). Lean or agile: A solution for supply chain management in the textiles and clothing industry? International Journal of Operations & Production Management, 24, 151-170.

79

Burgess, K., Singh, P. J. & Koroglu, R. (2006). Supply chain management: A structured literature review and implications for future research. International Journal of Operations & Production Management, 26, 703-729.

Cachon, G. P. (2003). Supply chain coordination with contracts In: De Kok, A. G. & Graves, S. C. (eds.) Handbooks in operations research and management science. Amsterdam, The Netherlands.

Cachon, G. P. & Lariviere, M. A. (2001). Contracting to assure supply: How to share demand forecasts in a supply chain. Management Science, 629.

Cachon, G. P. & Lariviere, M. A. (2005). Supply chain coordination with revenue-sharing contracts: Strengths and limitations. Management Science, 51, 30-44.

Cachon, G. P. & Netessine, S. (2006). Game theory in supply chain analysis. Models, methods, and applications for innovative decision making.

Cai, J., Zhong, M., Shang, J. & Huang, W. (2017). Coordinating vmi supply chain under yield uncertainty: Option contract, subsidy contract, and replenishment tactic. International Journal of Production Economics, 185, 196-210.

Carter, C. R. (2011). A call for theory: The maturation of the supply chain management discipline. Journal of Supply Chain Management, 47, 3-7.

Carter, C. R., Kosmol, T. & Kaufmann, L. (2017). Toward a supply chain practice view. Journal of Supply Chain Management, 53, 114-122.

Chan, F. T. S. (2003). Performance measurement in a supply chain. International Journal of Advanced Manufacturing Technology, 21, 534-548.

Chen, F., Drezner, Z., Ryan, J. K. & Simchi-Levi, D. (2000). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management Science, 46, 436-443.

Chen, F. & Parlar, M. (2007). Value of a put option to the risk-averse newsvendor. IIE Transactions, 39, 481-500.

Chen, S., Lee, H. & Moinzadeh, K. (2016). Supply chain coordination with multiple shipments: The optimal inventory subsidizing contracts. Operations Research, 64, 1320-1337.

Chen, X., Hao, G. & Li, L. (2014). Channel coordination with a loss-averse retailer and option contracts. International Journal of Production Economics, 150, 52-57.

Chen, X. & Shen, Z.-J. (2012). An analysis of a supply chain with options contracts and service requirements. IIE Transactions, 44, 805-819.

Chen, X. & Wang, X. (2015). Free or bundled: Channel selection decisions under different power structures. Omega - The International Journal of Management Science, 53, 11-20.

Chen, X., Wang, X. & Chan, H. K. (2017). Manufacturer and retailer coordination for environmental and economic competitiveness: A power perspective. Transportation Research Part E: Logistics and Transportation Review, 97, 268-281.

Cheng, F., Ettl, M., Lin, G. Y., Tonner, M. & Yao, D. D. (2011). Designing flexible supply chain contracts with options. In: Kempf, K. G., Keskinocak, P. & Uzsoy, R. (eds.) Planning production and inventories in the extended enterprise. Springer New York.

Cheung, M. S., Myers, M. B. & Mentzer, J. T. (2011). The value of relational learning in global buyer‐supplier exchanges: A dyadic perspective and test of the pie‐sharing premise. Strategic Management Journal, 32, 1061-1082.

Chiu, C.-H., Choi, T.-M. & Tang, C. S. (2011). Price, rebate, and returns supply contracts for coordinating supply chains with price-dependent demands. Production and Operations Management, 20, 81-91.

80

Chod, J., Rudi, N. & Van Mieghem, J. A. (2010). Operational flexibility and financial hedging: Complements or substitutes? Management Science, 56, 1030-1045.

Chod, J., Rudi, N. & Van Mieghem, J. A. (2012). Mix, time and volume flexibility: Valuation and corporate diversification. Review of Business and Economic Literature, 57, 262-283.

Choi, T. Y. & Hong, Y. (2002). Unveiling the structure of supply networks: Case studies in honda, acura, and daimlerchrysler. Journal of Operations Management, 20, 469-493.

Chopra, S. & Meindl, P. (2007). Supply chain management. Strategy, planning & operation. Das summa summarum des management, 265-275.

Collin, J., Eloranta, E. & Holmström, J. (2009). How to design the right supply chains for your customers. Supply Chain Management: An International Journal, 14, 411-417.

Costantino, N. & Pellegrino, R. (2010). Choosing between single and multiple sourcing based on supplier default risk: A real options approach. Journal of Purchasing and Supply Management, 16, 27-40.

Cox, J. C., Ross, S. A. & Rubinstein, M. (1979). Option pricing: A simplified approach. Journal of Financial Economics, 7, 229-263.

Craighead, C. W., Ketchen Jr, D. J. & Cheng, L. (2016). “Goldilocks” theorizing in supply chain research: Balancing scientific and practical utility via middle-range theory. Transportation Journal, 55, 241-257.

Creswell, J. W. (2005). Educational research: Planning, conducting, and evaluating quantitative and qualitative research, Upper Saddle River, N.J. , Pearson/Merrill Prentice Hall.

Curtis, G. (2004). Modern portfolio theory and behavioral finance. The Journal of Wealth Management, 7, 16-22.

Damodaran, A. (2012). Investment valuation: Tools and techniques for determining the value of any asset., New York, Wiley.

Das, S. K. & Abdel-Malek, L. (2003). Modeling the flexibility of order quantities and lead-times in supply chains. International Journal of Production Economics, 85, 171-181.

De Treville, S., Bendahan, S. & Vanderhaeghe, A. (2007). Manufacturing flexibility and performance: Bridging the gap between theory and practice. International Journal of Flexible Manufacturing Systems, 19, 334-357.

De Treville, S., Biçer, I., Chavez-Demoulin, V., Hagspiel, V., Schürhoff, N., Tasserit, C. & Wager, S. (2014b). Valuing lead time. Journal of Operations Management, 32, 337-346.

De Treville, S., Schürhoff, N., Trigeorgis, L. & Avanzi, B. (2014a). Optimal sourcing and lead-time reduction under evolutionary demand risk. Production and Operations Management, 23, 2103-2117.

De Treville, S., Shapiro, R. D. & Hameri, A.-P. (2004). From supply chain to demand chain: The role of lead time reduction in improving demand chain performance. Journal of Operations Management, 21, 613-627.

Deng, S. & Yano, C. A. (2016). Designing supply contracts considering profit targets and risk. Production and Operations Management, 25, 1292-1307.

Dixit, A. K. & Pindyck, R. S. (1994). Investment under uncertainty, Princeton, New Jersey, Princeton University Press.

Dooley, K. J., Yan, T., Mohan, S. & Gopalakrishnan, M. (2010). Inventory management and the bullwhip effect during the 2007-2009 recession: Evidence from the manufacturing sector. Journal of Supply Chain Management, 46, 12-18.

Dubois, A. & Gadde, L.-E. R. (2002). Systematic combining - an abductive approach to case research. Journal of Business Research, 55, 553-560.

81

Dubois, A. & Gadde, L.-E. (2014). “Systematic combining”—a decade later. Journal of Business Research, 67, 1277-1284.

Duclos, L. K., Vokurka, R. J. & Lummus, R. R. (2003). A conceptual model of supply chain flexibility. Industrial Management & Data Systems, 103, 446-456.

Economist. (2012). Faster, faster, faster402, 65-65 Eisenhardt, K. M. & Bourgeois III, L. J. (1988). Politics of strategic decision making in

high-velocity environments: Toward a midrange theory. Academy of Management Journal, 31, 737-770.

Ellram, L. M. & Cooper, M. C. (1990). Supply chain management, partnership, and the shipper-third party relationship. The International Journal of Logistics Management, 1, 1-10.

Ellram, L. M. & Cooper, M. C. (2014). Supply chain management: It's all about the journey, not the destination. Journal of Supply Chain Management, 50, 8-20.

Eriksson, K. (2019). An option mechanism to coordinate a dyadic supply chain bilaterally in a multi-period setting. Omega - The International Journal of Management Science, 88, 196-209.

Esmaeilikia, M., Fahimnia, B., Sarkis, J., Govindan, K., Kumar, A. & Mo, J. (2016). Tactical supply chain planning models with inherent flexibility: Definition and review. Annals of Operations Research, 244, 407-427.

Fabbe-Costes, N. & Jahre, M. (2007). Supply chain integration improves performance: The emperor's new suit? International Journal of Physical Distribution & Logistics Management, 37, 835-855.

Fabbe-Costes, N. & Jahre, M. (2008). Supply chain integration and performance: A review of the evidence. International Journal of Logistics Management, 19, 130-154.

Fawcett, S. E. & Waller, M. A. (2011). Making sense out of chaos: Why theory is relevant to supply chain research. Journal of Business Logistics, 32, 1-5.

Fayezi, S., Zutshi, A. & O'loughlin, A. (2017). Understanding and development of supply chain agility and flexibility: A structured literature review. International Journal of Management Reviews, 19, 379-407.

Fichman, R. G., Keil, M. & Tiwana, A. (2005). Beyond valuation:“Options thinking” in it project management. California Management Review, 47, 74-96.

Fine, C. H. & Freund, R. M. (1990). Optimal investment in product-flexible manufacturing capacity. Management Science, 36, 449-466.

Fink, A., Kliewer, N., Mattfeld, D., Mönch, L., Rothlauf, F., Schryen, G., Suhl, L. & Voß, S. (2014). Model-based decision support in manufacturing and service networks. Business & Information Systems Engineering, 6, 17-24.

Fisher, M. L. (1997). What is the right supply chain for your product? Harvard Business Review, 75, 105-116.

Fu, X., Dong, M. & Han, G. (2017). Coordinating a trust-embedded two-tier supply chain by options with multiple transaction periods. International Journal of Production Research, 55, 2068-2082.

Gan, X., Sethi, S. P. & Yan, H. (2004). Coordination of supply chains with risk‐averse agents. Production and Operations Management, 13, 135-149.

Gan, X., Sethi, S. P. & Yan, H. (2005). Channel coordination with a risk‐neutral supplier and a downside‐risk‐averse retailer. Production and Operations Management, 14, 80-89.

Gavirneni, S. (2004). Supply chain management at a chip tester manufacturer. The practice of supply chain management: Where theory and application converge. Springer.

82

Gerwin, D. (1987). An agenda for research on the flexibility of manufacturing processes. International Journal of Operations & Production Management, 7, 38-49.

Gerwin, D. (1993). Manufacturing flexibility: A strategic perspective. Management Science, 39, 395-410.

Giunipero, L. C., Hooker, R. E., Joseph‐Matthews, S., Yoon, T. E. & Brudvig, S. (2008). A decade of scm literature: Past, present and future implications. Journal of Supply Chain Management, 44, 66-86.

Gligor, D. M. (2017). Re‐examining supply chain fit: An assessment of moderating factors. Journal of Business Logistics, 38, 253-265.

Gligor, D. M., Esmark, C. L. & Holcomb, M. C. (2015). Performance outcomes of supply chain agility: When should you be agile? Journal of Operations Management, 33, 71-82.

Golovachkina, N. & Bradley, J. (2003). Supplier-manufacturer relationships under forced compliance contracts. Manufacturing & Service Operations Management, 5, 67.

Gosain, S., Malhotra, A. & El Sawy, O. A. (2004). Coordinating for flexibility in e-business supply chains. Journal of Management Information Systems, 21, 7-45.

Gosling, J., Purvis, L. & Naim, M. M. (2010). Supply chain flexibility as a determinant of supplier selection. International Journal of Production Economics, 128, 11-21.

Goyal, M. & Netessine, S. (2011). Volume flexibility, product flexibility, or both: The role of demand correlation and product substitution. Manufacturing & Service Operations Management, 13, 180-193.

Graves, S. C., Meal, H. C., Dasu, S. & Qui, Y. (1986). Two-stage production planning in a dynamic environment. In: Axter, S., Schneeweiss, C. & Silver, E. (eds.) Multi-stage production planning and inventory control. Springer.

Gunasekaran, A., Patel, C. & Tirtiroglu, E. (2001). Performance measures and metrics in a supply chain environment. International Journal of Operations & Production Management, 21, 71-87.

Hagspiel, V. (2011). Flexibility in technology choice: A real options approach. PhD, CentER, Tilburg University.

Hagspiel, V., Huisman, K. J. & Kort, P. M. (2016). Volume flexibility and capacity investment under demand uncertainty. International Journal of Production Economics, 178, 95-108.

Haksöz, Ç. & Seshadri, S. (2007). Supply chain operations in the presence of a spot market: A review with discussion. Journal of the Operational Research Society, 58, 1412-1429.

Halldórsson, Á., Hsuan, J. & Kotzab, H. (2015). Complementary theories to supply chain management revisited–from borrowing theories to theorizing. Supply Chain Management: An International Journal, 20, 574-586.

Halldórsson, Á., Kotzab, H., Mikkola, J. H. & Skjøtt-Larsen, T. (2007). Complementary theories to supply chain management. Supply Chain Management: An International Journal, 12, 284-296International Journal, 20, 574-586.

Harrison, J. M. & Van Mieghem, J. A. (1999). Multi-resource investment strategies: Operational hedging under demand uncertainty. European Journal of Operational Research, 113, 17-29.

Hart, A. G. (1940). Anticipations, uncertainty, and dynamic planning, AM Kelley New York

Hartman, R. (1972). The effects of price and cost uncertainty on investment. Journal of Economic theory, 5, 258-266.

Hauser, J. R. & Clausing, D. (1988). The house of quality, Harvard Business Review. Harvard Business School Publication Corp.

83

Hausman, W. H. (1969). Sequential decision problems: A model to exploit existing forecasters. Management Science, 16, B-93-B-111.

Heath, D. C. & Jackson, P. L. (1994). Modeling the evolution of demand forecasts ith application to safety stock analysis in production/distribution systems. IIE Transactions, 26, 17-30.

Hendricks, K. B. & Singhal, V. R. (2003). The effect of supply chain glitches on shareholder wealth. Journal of Operations Management, 21, 501-522.

Hendricks, K. B. & Singhal, V. R. (2005). Association between supply chain glitches and operating performance. Management Science, 51, 695-711.

Hendricks, K. B., Singhal, V. R. & Zhang, R. (2009). The effect of operational slack, diversification, and vertical relatedness on the stock market reaction to supply chain disruptions. Journal of Operations Management, 27, 233-246.

Holmström, J., Ketokivi, M. & Hameri, A. P. (2009). Bridging practice and theory: A design science approach. Decision Sciences, 40, 65-87.

Hubbard, R. G. (1994). Investment under uncertainty: Keeping one's options open. Journal of Economic Literature, 32, 1816-1831.

Huchzermeier, A. & Cohen, M. A. (1996). Valuing operational flexibility under exchange rate risk. Operations Research, 44, 100-113.

Hull, John C. (2012). Options, futures, and other derivatives, Harlow, Essex, Pearson Education.

Håkansson, A. (2013). Published. Portal of research methods and methodologies for research projects and degree projects. Proceedings of the International Conference on Frontiers in Education: Computer Science and Computer Engineering (FECS). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 67-73.

Höhn, M. I. (2010). Relational supply contracts : Optimal concessions in return policies for continuous quality improvements, Berlin Springer.

Inderfurth, K. & Kelle, P. (2011). Capacity reservation under spot market price uncertainty. International Journal of Production Economics, 133, 272-279.

Jack, E. P. & Raturi, A. (2002). Sources of volume flexibility and their impact on performance. Journal of Operations Management, 20, 519-548.

Jain, A., Jain, P., Chan, F. T. & Singh, S. (2013). A review on manufacturing flexibility. International Journal of Production Research, 51, 5946-5970.

Jouni, P., Huiskonen, J. & Pirttilä, T. (2011). Improving global spare parts distribution chain performance through part categorization: A case study. International Journal of Production Economics, 133, 164-171.

Jörnsten, K., Lise Nonås, S., Sandal, L. & Ubøe, J. (2013). Mixed contracts for the newsvendor problem with real options and discrete demand. Omega - The International Journal of Management Science, 41, 809-819.

Kahneman, D. & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263-291.

Ketchen, J. D. J. & Giunipero, L. C. (2004). The intersection of strategic management and supply chain management. Industrial Marketing Management, 33, 51-56.

Ketokivi, M. (2006). Elaborating the contingency theory of organizations: The case of manufacturing flexibility strategies. Production and Operations Management, 15, 215-228.

Ketokivi, M. & Choi, T. (2014). Renaissance of case research as a scientific method. Journal of Operations Management, 32, 232-240.

84

Khouja, M. (1999). The single-period (news-vendor) problem: Literature review and suggestions for future research. Omega - The International Journal of Management Science, 27, 537-553.

Kisperska-Moron, D. & Swierczek, A. (2011). The selected determinants of manufacturing postponement within supply chain context: An international study. International Journal of Production Economics, 133, 192-200.

Klaus, P. (2009). Logistics research: A 50 years’ march of ideas. Logistics Research, 1, 53-65.

Kleindorfer, P. R. & Saad, G. H. (2005). Managing disruption risks in supply chains. Production and Operations Management, 14, 53-68.

Kogut, B. & Kulatilaka, N. (2001). Capabilities as real options. Organization Science, 12, 744-758.

Kovács, G. & Spens, K. M. (2005). Abductive reasoning in logistics research. International Journal of Physical Distribution & Logistics Management, 35, 132-144.

Krajewski, L., Wei, J. C. & Tang, L.-L. (2005). Responding to schedule changes in build-to-order supply chains. Journal of Operations Management, 23, 452-469.

Kumar, V., Fantazy, K. A., Kumar, U. & Boyle, T. A. (2006). Implementation and management framework for supply chain flexibility. Journal of Enterprise Information Management, 19, 303-319.

Lambert, D. M. & Cooper, M. C. (2000). Issues in supply chain management. Industrial Marketing Management, 29, 65-83.

Lambert, D. M., García‐Dastugue, S. J. & Croxton, K. L. (2005). An evaluation of process‐oriented supply chain management frameworks. Journal of Business Logistics, 26, 25-51.

Larson, P. D. & Halldórsson, Á. (2004). Logistics versus supply chain management: An international survey. International Journal of Logistics: Research and Applications, 7, 17-31.

Lee, H. L., Padmanabhan, V. & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management Science, 43, 546-558.

Lee, C. H., Rhee, B.-D. & Cheng, T. (2013). Quality uncertainty and quality-compensation contract for supply chain coordination. European Journal of Operational Research, 228, 582-591.

Li, D. & O’Brien, C. (2001). A quantitative analysis of relationships between product types and supply chain strategies. International Journal of Production Economics, 73, 29-39.

Lim, S. H. (1987). Flexible manufacturing systems and manufacturing flexibility in the united kingdom. International Journal of Operations & Production Management, 7, 44-54.

Liu, X., Wu, Y.-C. J. & Goh, M. (2016). Collaborative academic–industry scm research and knowledge building. International Journal of Logistics Research and Applications, 19, 19-40.

Lo, S. M. & Power, D. (2010). An empirical investigation of the relationship between product nature and supply chain strategy. Supply Chain Management: An International Journal, 15, 139-153.

Lummus, R. R., Duclos, L. K. & Vokurka, R. J. (2003). Supply chain flexibility : Building a new model. Global Journal of Flexible Systems Management (Global Institute of Flexible Systems Management), 4, 1-13.

Lummus, R. R., Vokurka, R. J. & Duclos, L. K. (2005). Delphi study on supply chain flexibility. International Journal of Production Research, 43, 2687-2708.

85

Luo, Z., Chen, X., Chen, J. & Wang, X. (2017). Optimal pricing policies for differentiated brands under different supply chain power structures. European Journal of Operational Research, 259, 437-451.

MacCarthy, B. L., Lewis, M., Voss, C. & Narasimhan, R. (2013). The same old methodologies? Perspectives on om research in the post-lean age. International Journal of Operations & Production Management, 33, 934-956.

Mahdavi, M. & Olsen, T. L. (2017). Designing effective supply chains in strategic alignment with demand characteristics and market requirements. Foundations and Trends® in Technology, Information and Operations Management, 10, 89-208.

Majd, S. & Pindyck, R. S. (1987). Time to build, option value, and investment decisions. Journal of Financial Economics, 18, 7-27.

Manders, J. H. M., Caniëls, M. C. J. & Ghijsen, P. W. T. (2017). Supply chain flexibility: A systematic literature review and identification of directions for future research. The International Journal of Logistics Management, 28, 964-1026.

Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7, 77-92. Marschak, T. & Nelson, R. (1962). Flexibility, uncertainty, and economic theory.

Metroeconomica, 14, 42-58. Martínez-De-Albéniz, V. & Simchi-Levi, D. (2009). Competition in the supply option

market. Operations Research, 57, 1082-1097. Mason-Jones, R., Naylor, B. & Towill, D. R. (2000). Engineering the leagile supply chain.

International Journal of Agile Management Systems, 2, 54-61. Meredith, J. (1998). Editorial report for journal of operations management. Journal of

Operations Management, 16, 123-126. Merschmann, U. & Thonemann, U. W. (2011). Supply chain flexibility, uncertainty and

firm performance: An empirical analysis of german manufacturing firms. International Journal of Production Economics, 130, 43-53.

Merton, R. C. (1976). Option pricing when underlying stock returns are discontinuous. Journal of Financial Economics, 3, 125-144.

Merton, R. K. (1968). Social theory and social structure, New York, Free press. Miller, K. D. & Folta, T. B. (2002). Option value and entry timing. Strategic Management

Journal, 23, 655-665. Miller, M., H. & Modigliani, F. (1961). Dividend policy, growth, and the valuation of

shares. The Journal of Business, 34, 411-433. Milner, J. M. & Rosenblatt, M. J. (2002). Flexible supply contracts for short life-cycle

goods: The buyer's perspective. Naval Research Logistics (NRL), 49, 25-45. Mishra, R., Pundir, A. K. & Ganapathy, L. (2014). Manufacturing flexibility research: A

review of literature and agenda for future research. Global Journal of Flexible Systems Management, 15, 101-112.

Myers, S. C. (1977). Determinants of corporate borrowing. Journal of Financial Economics, 5, 147-175.

Nachbar, J. H. (1992). Evolution in the finitely repeated prisoner's dilemma. Journal of Economic Behavior & Organization, 19, 307-326.

Nagali, V., Hwang, J., Sanghera, D., Gaskins, M., Pridgen, M., Thurston, T., Mackenroth, P., Branvold, D., Scholler, P. & Shoemaker, G. (2008). Procurement risk management (prm) at hewlett-packard company. Interfaces, 38, 51-60.

Nagarajan, M. & Sošić, G. (2008). Game-theoretic analysis of cooperation among supply chain agents: Review and extensions. European Journal of Operational Research, 187, 719-745.

86

Nahmias, S. (2009). Production and operations analysis, Boston, Mass., McGraw-Hill. Narasimhan, R. & Das, A. (2000). An empirical examination of sourcing's role in

developing manufacturing flexibilities. International Journal of Production Research, 38, 875-893.

Narasimhan, R. & Kim, S. W. (2002). Effect of supply chain integration on the relationship between diversification and performance: Evidence from japanese and korean firms. Journal of Operations Management, 20, 303-323.

Narasimhan, R., Swink, M. & Kim, S. W. (2006). Disentangling leanness and agility: An empirical investigation. Journal of Operations Management, 24, 440-457.

Nash, J. F. (1950). The bargaining problem. Econometrica: Journal of the Econometric Society, 18, 155-162.

Naslund, D. & Williamson, S. (2010). What is management in supply chain management?-a critical review of definitions, frameworks and terminology. Journal of Management Policy and Practice, 11, 11-28.

Naylor, J. B., Naim, M. M. & Berry, D. (1999). Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain. International Journal of Production Economics, 62, 107-118.

Norrman, A. & Jansson, U. (2004). Ericsson's proactive supply chain risk management approach after a serious sub-supplier accident. International Journal of Physical Distribution & Logistics Management, 34, 434-456.

Nosoohi, I. & Nookabadi, A. S. (2016). Outsource planning through option contracts with demand and cost uncertainty. European Journal of Operational Research, 250, 131-142.

Olhager, J. & West, B. M. (2002). The house of flexibility: Using the qfd approach to deploy manufacturing flexibility. International Journal of Operations & Production Management, 22, 50-79.

Oliver, R. & Webber, M. (1982). Supply chain management: Logistics catches up with strategy. In: Christopher, M. L. (ed.) Logistics: The strategic London: Chapman & Hall.

Olson, E. G. (2010). Supply chain opportunity in an uncertain economic recovery. Supply Chain Management, 15, 488-492.

Onwuegbuzie, A. J. & Leech, N. L. (2005). On becoming a pragmatic researcher: The importance of combining quantitative and qualitative research methodologies. International Journal of Social Research Methodology, 8, 375-387.

Özer, Ö. & Wei, W. (2006). Strategic commitments for an optimal capacity decision under asymmetric forecast information. Management Science, 52, 1238-1257.

Paré, G., Trudel, M.-C., Jaana, M. & Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information & Management, 52, 183-199.

Pasternack, B. A. (1985). Optimal pricing and return policies for perishable commodities. Marketing Science, 4, 166.

Pawson, R. & Tilley, N. (1997). An introduction to scientific realist evaluation. Evaluation for the 21st century: A handbook. Thousand Oaks, CA, US: Sage Publications, Inc.

Peng, C., Erhun, F., Hertzler, E. F., Kempf, K. G. J. M. & Management, S. O. (2012). Capacity planning in the semiconductor industry: Dual-mode procurement with options. 14, 170-185.

Perakis, G. & Roels, G. (2007). The price of anarchy in supply chains: Quantifying the efficiency of price-only contracts. Management Science, 53, 1249-1268.

Pinder, C. C. & Moore, L. F. (1980). Middle range theory and the study of organizations Boston, Mass.

87

Porteus, E. L. (2002). Foundations of stochastic inventory theory, Stanford University Press.

Prajogo, D. I., Oke, A., Olhager, J., Brown, S. & Brown, S. (2016). Supply chain processes: Linking supply logistics integration, supply performance, lean processes and competitive performance. International Journal of Operations & Production Management, 36, 220-238.

Presutti Jr, W. D. & Mawhinney, J. R. (2007). The supply chain - finance link. Supply Chain Management Review, September, 32-38.

Purvis, L., Gosling, J. & Naim, M. M. (2014). The development of a lean, agile and leagile supply network taxonomy based on differing types of flexibility. International Journal of Production Economics, 151, 100-111.

Qi, X., Bard, J. F. & Yu, G. (2004). Supply chain coordination with demand disruptions. Omega - The International Journal of Management Science, 32, 301-312.

Qi, Y., Boyer, K. K. & Zhao, X. (2009). Supply chain strategy, product characteristics, and performance impact: Evidence from chinese manufacturers. Decision Sciences, 40, 667-695.

Rózsa, A. (2016). Development of real option theory in the last 20 years. Annals of the Academy of Oradea, Economic Science Series, 25, 698-709.

Rubinstein, A. (1982). Perfect equilibrium in a bargaining model. Econometrica: Journal of the Econometric Society, 97-109.

Rudi, N. & Pyke, D. (2000). Teaching supply chain concepts with the newsboy model. Supply chain management: innovations for education, 2, 170-180.

Saghiri, S. S. & Barnes, S. J. (2016). Supplier flexibility and postponement implementation: An empirical analysis. International Journal of Production Economics, 173, 170-183.

Sánchez, A. M. & Pérez, M. P. (2005). Supply chain flexibility and firm performance: A conceptual model and empirical study in the automotive industry. International Journal of Operations & Production Management, 25, 681-700.

Sanders, N. R. & Wagner, S. M. (2011). Multidisciplinary and multimethod research for addressing contemporary supply chain challenges. Journal of Business Logistics, 32, 317-323.

Schmenner, R. W. & Swink, M. L. (1998). On theory in operations management. Journal of Operations Management, 17, 97-113.

Schmenner, R. W. & Tatikonda, M. V. (2005). Manufacturing process flexibility revisited. International Journal of Operations & Production Management, 25, 1183-1189.

Schonberger, R. (1982). Japanese manufacturing techniques: Nine hidden lessons in simplicity, Simon and Schuster.

Schryen, G., Wagner, G. & Benlian, A. (2015). Theory of knowledge for literature reviews: An epistemological model, taxonomy and empirical analysis of is literature.

Schwarz, A., Mehta, M., Johnson, N. & Chin, W. W. (2007). Understanding frameworks and reviews: A commentary to assist us in moving our field forward by analyzing our past. ACM SIGMIS Database, 38, 29-50.

Selldin, E. & Olhager, J. (2007). Linking products with supply chains: Testing fisher's model. Supply Chain Management: An International Journal, 12, 42-51.

Serel, D. A. (2007). Capacity reservation under supply uncertainty. Computers & Operations Research, 34, 1192-1220.

Sethi, A. K. & Sethi, S. P. (1990). Flexibility in manufacturing: A survey. International Journal of Flexible Manufacturing Systems, 2, 289-328.

88

Shi, M. & Yu, W. (2013). Supply chain management and financial performance: Literature review and future directions. International Journal of Operations & Production Management, 33, 1283-1317.

Sink, D. S. & Tuttle, T. C. (1989). Planning and measurement in your organization of the future, Norcross, Ga., Industrial Engineering and Management Press.

Slack, N. (1983). Flexibility as a manufacturing objective. International Journal of Operations & Production Management, 3, 4-13.

Slack, N. (1987). The flexibility of manufacturing systems. International Journal of Operations & Production Management, 7, 35-45.

Sodhi, M. S., Son, B. G. & Tang, C. S. (2012). Researchers' perspectives on supply chain risk management. Production and Operations Management, 21, 1-13.

Soni, G. & Kodali, R. (2012). A critical review of empirical research methodology in supply chain management. Journal of Manufacturing Technology Management, 23, 753-779.

Spengler, J. J. (1950). Vertical integration and antitrust policy. The Journal of Political Economy, 58, 347-352.

Spinler, S. & Huchzermeier, A. (2006). The valuation of options on capacity with cost and demand uncertainty. European Journal of Operational Research, 171, 915-934.

Spinler, S., Huchzermeier, A. & Kleindorfer, P. (2003). Risk hedging via options contracts for physical delivery. OR Spectrum, 25, 379-395.

Stank, T. P., Pellathy, D. A., In, J., Mollenkopf, D. A. & Bell, J. E. (2017). New frontiers in logistics research: Theorizing at the middle range. Journal of Business Logistics, 38, 6-17.

Stevenson, M. & Spring, M. (2007). Flexibility from a supply chain perspective: Definition and review. International Journal of Operations & Production Management, 27, 685-713.

Stevenson, M. & Spring, M. (2009). Supply chain flexibility: An inter-firm empirical study. International Journal of Operations & Production Management, 29, 946-971.

Stigler, G. (1939). Production and distribution in the short run. The Journal of Political Economy, 305-327.

Stock, J. R. (1997). Applying theories from other disciplines to logistics. International Journal of Physical Distribution & Logistics Management, 27, 515-539.

Stock, J. R. & Boyer, S. L. (2009). Developing a consensus definition of supply chain management: A qualitative study. International Journal of Physical Distribution & Logistics Management, 39, 690-711.

Stock, J. R., Boyer, S. L. & Harmon, T. (2010). Research opportunities in supply chain management. Journal of the Academy of Marketing Science, 38, 32-41.

Storey, J., Emberson, C., Godsell, J. & Harrison, A. (2006). Supply chain management: Theory, practice and future challenges. International Journal of Operations & Production Management, 26, 754-774.

Stuart, I., Mccutcheon, D., Handfield, R., Mclachlin, R. & Samson, D. (2002). Effective case research in operations management: A process perspective. Journal of Operations Management, 20, 419-433.

Sun, A. (2013). How a flexible supply chain delivers value. available at: www.industryweek.com.

Swafford, P. M., Ghosh, S. & Murthy, N. (2006). The antecedents of supply chain agility of a firm: Scale development and model testing. Journal of Operations Management, 24, 170-188.

89

Tachizawa, E. M. & Thomsen, C. G. (2007). Drivers and sources of supply flexibility: An exploratory study. International Journal of Operations & Production Management, 27, 1115-1136.

Taylor, T. A. (2002). Supply chain coordination under channel rebates with sales effort effects. Management Science, 48, 992-1007.

Taylor, T. A. & Plambeck, E. L. (2007). Supply chain relationships and contracts: The impact of repeated interaction on capacity investment and procurement. Management Science, 1577.

Tiwana, A., Wang, J., Keil, M. & Ahluwalia, P. (2007). The bounded rationality bias in managerial valuation of real options: Theory and evidence from it projects. Decision Sciences, 38, 157-181.

Tiwari, A. K., Tiwari, A. & Samuel, C. (2015). Supply chain flexibility: A comprehensive review. Management Research Review, 38, 767-792.

Trigeorgis, L. (1993). The nature of option interactions and the valuation of investments with multiple real options. Journal of Financial and Quantitative Analysis, 28, 1-20.

Trigeorgis, L. (1996). Real options: Managerial flexibility and strategy in resource allocation, Cambridge, Mass., MIT Press.

Trigeorgis, L. & Tsekrekos, A. E. (2018). Real options in operations research: A review. European Journal of Operational Research, 270, 1-24.

Trochim, W. M. (2006). The research methods knowledge base, 2nd edition. Retrieved from http://www.socialresearchmethods.net/kb

Tsay, A. A., Nahmias, S. & Agrawal, N. (1999). Modeling supply chain contracts: A review. In: Tayur, S., Ganeshan, R. & Magazine, M. J. (eds.) Quantitative models for supply chain management b2 - quantitative models for supply chain management. Norwell, MA.: Kluwer Academic Publishers.

Upton, D. M. (1994). The management of manufacturing flexibility. California Management Review, 36, 72-89.

Wadhwa, S., Saxena, A. & Chan, F. (2008). Framework for flexibility in dynamic supply chain management. International Journal of Production Research, 46, 1373-1404.

Wagner, S. M., Grosse-Ruyken, P. T. & Erhun, F. (2012). The link between supply chain fit and financial performance of the firm. Journal of Operations Management, 30, 340-353.

Van Aken, J. E. (2005). Management research as a design science: Articulating the research products of mode 2 knowledge production in management. British journal of management, 16, 19-36.

Van Aken, J. E., Chandrasekaran, A. & Halman, J. (2016). Conducting and publishing design science research: Inaugural essay of the design science department of the journal of operations management. Journal of Operations Management, 47, 1-8.

Van de Ven, A. H. & Johnson, P. E. (2006). Knowledge for theory and practice. Academy of Management Review, 31, 802-821.

Van den Broeke, M. M., Boute, R. N. & Van Mieghem, J. A. (2018). Platform flexibility strategies: R&D investment versus production customization tradeoff. European Journal of Operational Research, 270, 475-486.

Van der Vaart, T. & Van Donk, D. P. (2008). A critical review of survey-based research in supply chain integration. International Journal of Production Economics, 111, 42-55.

Van Mieghem, J. A. (1998). Investment strategies for flexible resources. Management Science, 44, 1071-1078.

Van Mieghem, J. A. (1999). Coordinating investment, production, and subcontracting. Management Science, 45, 954-971.

90

Van Mieghem, J. A. (2003). Commissioned paper: Capacity management, investment, and hedging: Review and recent developments. Manufacturing & Service Operations Management, 5, 269-302.

Van Mieghem, J. A. (2007). Risk mitigation in newsvendor networks: Resource diversification, flexibility, sharing, and hedging. Management Science, 53, 1269-1288.

Van Mieghem, J. A. & Allon, G. (2015). Operations strategy : Principles and practice, Belmont, Mass, Dynamic Ideas.

Van Mieghem, J. A. & Rudi, N. (2002). Newsvendor networks: Inventory management and capacity investment with discretionary activities. Manufacturing & Service Operations Management, 4, 313-335.

Wan, N. & Chen, X. (2015). Bilateral coordination strategy of supply chain with bidirectional option contracts under inflation. Mathematical Problems in Engineering, 2015.

Wang, C. X. (2002). A general framework of supply chain contract models. Supply Chain Management: An International Journal, 7, 302-310.

Wang, C. X. & Webster, S. (2007). Channel coordination for a supply chain with a risk-neutral manufacturer and a loss-averse retailer. Decision Sciences, 38, 361-389.

Wang, Q. & Parlar, M. (1994). A three-person game theory model arising in stochastic inventory control theory. European Journal of Operational Research, 76, 83-97.

Wang, S.-D., Zhou, Y.-W., Min, J. & Zhong, Y.-G. (2011). Coordination of cooperative advertising models in a one-manufacturer two-retailer supply chain system. Computers & Industrial Engineering, 61, 1053-1071.

Wang, T., Thomas, D. J. & Rudi, N. (2014). The effect of competition on the efficient–responsive choice. Production and Operations Management, 23, 829-846.

Wang, X., Li, F., Liang, L., Huang, Z. & Ashley, A. (2015). Pre-purchasing with option contract and coordination in a relief supply chain. International Journal of Production Economics, 167, 170-176.

Webster, J. & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a. MIS quarterly, 26, 13-23.

Weick, K. E. (1989). Theory construction as disciplined imagination. Academy of Management Review, 14, 516-531.

Vickery, S. N., Calantone, R. & Dröge, C. (1999). Supply chain flexibility: An empirical study. Journal of Supply Chain Management, 35, 16-24.

Von Neumann, J. & Morgenstern, O. (1945). Theory of games and economic behavior, Princeton University Press.

Von Stackelberg, H. (1952). The theory of the market economy, Oxford, Oxford University Press.

Wu, D. J., Kleindorfer, P. R. & Zhang, J. E. (2002). Optimal bidding and contracting strategies for capital-intensive goods. European Journal of Operational Research, 137, 657-676.

Xiao, T., Choi, T.-M. & Cheng, T. (2015). Optimal variety and pricing decisions of a supply chain with economies of scope. IEEE transactions on engineering management, 62, 411-420.

Yan, R. & Cao, Z. (2017). Product returns, asymmetric information, and firm performance. International Journal of Production Economics, 185, 211-222.

Yang, D., Choi, T.-M., Xiao, T. & Cheng, T. (2011). Coordinating a two-supplier and one-retailer supply chain with forecast updating. Automatica, 47, 1317-1329.

91

Zhang, F. (2011). Supply chain coordination. In: Cochran, J. J., Cox, L. A., Keskinocak, J. P., Kharoufeh, J. P. & Smith, J. C. (eds.) Wiley encyclopedia of operations research and management science. Wiley.

Zhang, B. & Hua, Z. (2012). A portfolio approach to multi-product newsvendor problem with budget constraint. Handbook of newsvendor problems. Springer.

Zhao, Y., Choi, T.-M., Cheng, T. & Wang, S. (2018). Supply option contracts with spot market and demand information updating. European Journal of Operational Research, 266, 1062-1071.

Zhao, Y., Ma, L., Xie, G. & Cheng, T. C. E. (2013). Coordination of supply chains with bidirectional option contracts. European Journal of Operational Research, 229, 375-381.

Zhao, Y., Meng, X., Wang, S. & Cheng, T. E. (2016). Contract analysis and design for supply chains with stochastic demand, Springer.

Zhao, Y., Wang, S., Cheng, T. C. E., Yang, X. & Huang, Z. (2010). Coordination of supply chains by option contracts: A cooperative game theory approach. European Journal of Operational Research, 207, 668-675.