Assessing Supply Chain Resilience Within the Automotive Industry in the Event of a Pandemic

Assessing supply chain resilience within the automotive industry in the event of a pandemic

A multiple case study of the COVID-19 disruption in the Scandinavian and German automotive industry

MASTER THESIS WITHIN: Business Administration NUMBER OF CREDITS: 30 PROGRAMME OF STUDY: International Logistics and Supply Chain Management AUTHOR: Jan Schliebener and Thomas Nickel JÖNKÖPING May 2021

Acknowledgements

This master thesis represents the last step of our two-years advantage at the JIBS in Sweden towards our master’s degree. Despite the challenging times, it was an exciting and interesting avenue to conduct this thesis.

We would like to take this opportunity to express our gratitude to everyone who supported us on our journey. At first, we would like to thank our supervisor, Imoh Antai, for his guidance and assistance. We really appreciate the amount of time he took to provide us with fruitful insights and helpful remarks. He motivated us throughout the whole process, and for sure, his great effort has its positive impact on this thesis.

Further thanks go to our seminar group and fellow students who gave us plenty of feedback and took their time to discuss our study, and guided us in the right direction.

Moreover, we would like to express our deep appreciation to all the respondents of our case companies. Especially during crises times like now, we do not take it for granted that so many experts took their time to provide us with data for our master thesis. Hence, we would like to thank all the experts from the automotive industry for taking part in our interviews and granting us all the information and insights.

Finally, we would like to extend our gratitude to our friends and families, who supported us throughout the whole process of this thesis and provided us with great advice and feedback.

Tack så mycket!

Jan Schliebener & Thomas Nickel

Jönköping, Sweden, May 24, 2021

Master Thesis in Business Administration

Title: Assessing supply chain resilience within the automotive industry in the event of a pandemic Authors: Jan Schliebener and Thomas Nickel Tutor: Imoh Antai Date: 2021-05-24

Key terms: Supply chain management, supply chain resilience, disruption, trajectory, performance, measures, strategies, COVID-19, Automotive, Scandinavia, Sweden, Germany

Abstract

Background: The automotive industry experiences significant challenges such as electric mobility, autonomous cars, smart factories, and ridesharing. Above that, the COVID-19 pandemic did not only affect the global health care system but also caused a disruption that challenged the automotive manufacturing sector and its supply chains. Purpose: The automotive industry was investigated to assess the supply chain resilience during the COVID-19 disruption. Therefore, the supply chain performance along the disruption stages was determined. Also, the usage and value of supply chain resilience measures were explored to characterise the current state of supply chain resilience in the industry. Method: A multiple case study and purposeful sampling were used to gather empirical data. Semi-structured interviews with 21 automotive experts from the Scandinavian and German automotive industry were conducted. A content analysis approach was applied to analyse the primary data. The investigation was supported by a systematic literature review prior to the study and additional company reports as secondary data. Conclusion: The level of supply chain resilience within the automotive industry can be assessed as high. A quick recovery and a corresponding rebound of the production volume can be identified, even though the findings indicate that the COVID-19 disruption also caused a bullwhip effect. To compensate for this effect, agility measures, increased collaboration and information sharing, and risk management were used. Other supply chain resilience measures like technological innovations or sustainability were only used to a limited extent.

Table of Contents

List of Figures ...... v List of Tables ...... v Abbreviations ...... v 1. Introduction ...... 1 1.1 Background ...... 1 1.2 Problem statement ...... 2 1.3 Purpose ...... 3 1.4 Delimitations ...... 5 1.5 Outline ...... 5 2. Theoretical framework ...... 6 2.1 Resource-based view ...... 6 2.2 Automotive supply chains ...... 7 2.2.1 Multi-tier structure ...... 7 2.2.2 Industry-specific characteristics ...... 9 2.3 Supply chain risk management ...... 10 2.4 Supply chain resilience ...... 12 2.4.1 Concepts of supply chain resilience ...... 12 2.4.2 Measures of supply chain resilience ...... 16 2.4.3 Assessment of supply chain resilience ...... 20 2.4.4 Outcomes of supply chain resilience ...... 21 2.5 Conclusion of theoretical study ...... 21 3. Methodology ...... 23 3.1 Research philosophy ...... 23 3.2 Primary data – multiple case study ...... 24 3.2.1 Sampling strategy and process ...... 25 3.2.2 Interview procedure ...... 28 3.2.3 Data analysis procedure ...... 29 3.3 Secondary data ...... 30 3.3.1 Data and reports of case companies ...... 30 3.3.2 Systematic literature review and article sampling ...... 31 3.4 Research quality ...... 32 3.5 Ethical reflection ...... 34 4. Description of empirical findings ...... 35 4.1 Supply chain performance regarding the COVID-19 disruption ...... 35 4.1.1 Preparation for COVID-19 disruption ...... 35 4.1.2 Production stoppages after COVID-19 disruption ...... 36 4.1.3 Restart and recovery after COVID-19 disruption ...... 37 4.1.4 Backlashes after COVID-19 disruption ...... 38 4.2 Supply chain resilience measures regarding the COVID-19 disruption ...... 39 4.2.1 Supply chain (re-) engineering ...... 42 4.2.2 Supply chain collaboration ...... 44 4.2.3 Agility ...... 45 4.2.4 Supply chain risk management culture ...... 47 4.2.5 Technological innovations ...... 48

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4.2.6 Sustainability ...... 49 5. Data analysis and interpretation ...... 51 5.1 Analysis of supply chain performance ...... 51 5.1.1 Supply chain turbulences despite high production volumes ...... 54 5.1.2 Demand-supply gap and bullwhip effect ...... 56 5.2 Supply chain resilience measures ...... 59 5.2.1 Supply chain (re-) engineering ...... 61 5.2.2 Supply chain collaboration ...... 62 5.2.3 Agility ...... 63 5.2.4 Supply chain risk management culture ...... 64 5.2.5 Technological innovations ...... 65 5.2.6 Sustainability ...... 66 6. Conclusion ...... 68 7. Discussion ...... 70 7.1 Managerial and societal implications ...... 70 7.2 Theoretical implications ...... 71 7.3 Limitations ...... 72 7.4 Future research ...... 73 8. Bibliography ...... 74 Appendix ...... i

List of Figures Figure 1: Automotive supply chain tiers ...... 7 Figure 2: Stages of disruption ...... 13 Figure 3: Creating the resilient supply chain ...... 14 Figure 4: Thesis structure ...... 23 Figure 5: Application of holistic multiple case study design ...... 25 Figure 6: Coding scheme ...... 30 Figure 7: COVID-19 disruption impact on supply chain performance .. 52 Figure 8: Causes of SC turbulences despite high production volume .... 54 Figure 9: Demand-supply gap after COVID-19 disruption ...... 57

List of Tables Table 1: Overview of SCRes measures in theory ...... 18 Table 2: Overview of companies in the case study sample ...... 27 Table 3: Overview of SCRes measures in practice ...... 40 Table 4: SCRes measures during varying disruption conditions ...... 60

List of Appendices Appendix 1: SCRes definition matrix ...... i Appendix 2: SCRes concept matrix ...... ii Appendix 3: Interview request ...... iii Appendix 4: Interview guide ...... iv Appendix 5: GDPR Thesis Study Consent Form ...... v

Abbreviations COVID-19 Coronavirus disease 2019 DSM Decision support model EDI Electronic Data Interchange ERP Enterprise-Resource-Planning FKG Fordonskomponentgruppen GPS Global Positioning System JIT Just-in-time KPI Key performance indicators OEM Original Equipment Manufacturer RBV Resource-based view RFID Radio-Frequency Identification SCM Supply Chain Management SCRes Supply Chain Resilience SCRM Supply Chain Risk Management VDA Verband der Automobilindustrie

1. Introduction ______In the following chapter, we introduce the scope of this master thesis. We inform about the impact and challenges of the COVID-19 disruption on global supply chains and automotive supply chains in specific. Based on these challenges, we define the problem and purpose of the thesis and illustrate the delimitations and outline of this study. ______1.1 Background The Coronavirus disease (COVID-19) was first discovered in December 2019 in Wuhan, China (WHO, 2021). It led to a pandemic with worldwide 167 million infections and 3.5 million deaths by the time we conducted this research (Johns Hopkins University, 2021). Moreover, COVID-19 does not only affect the global health care system but also revealed the vulnerability of the manufacturing sector and its supply chains to risks and disruptions (CIPS, 2020; van Hoek, 2020). Wuhan, the centre of the COVID-19 outbreak, is a significant hub of car-component manufacturing that supplies the major global automobile manufacturers with critical parts (LMC, 2020; Belhadi et al., 2021). Consequently, it caused production stoppages globally when Wuhan became a quarantine region in February 2020, even though the virus did not spread in Europe and the United States of America (USA) yet (Hofstätter et al., 2020). The situation worsened in March 2020 when up to 3500 facilities of the automotive and industry sector were unavailable due to their location in quarantine areas within China, South Korea, and Italy (Linton & Vakil, 2020; Statista, 2020).

However, the automotive industry managed to stabilise their supply chains over the rest of the year 2020 and mitigate the impact on the global passenger car market to a decline of 17% of the sold units compared to the previous year (Japan Times, 2020; VDA, 2020; Statista, 2021; Volkswagen AG, 2021). Nevertheless, the recent chip shortage in January 2021 revealed that the disruption is still causing backlashes in the supply chain. The car manufacturers faced an unexpected high consumer demand rebound which could not be fulfilled in the short-term with the current chip supply capacity and led to production stoppages (BBC, 2021; Bloomberg, 2021; DW, 2021; Reuters, 2021).

In conclusion, the COVID-19 disruption will continue to influence the automotive supply chains. The Boston Consulting Group and McKinsey estimate that it will take two years

or more for the global car industry to recover from the pandemic disruption (BCG, 2020; Hofstätter et al., 2020). Nevertheless, the automotive industry was already experiencing significant challenges before the COVID-19 outbreak, such as electric mobility, autonomous cars, smart factories, and ridesharing (Hofstätter et al., 2020). The automotive supply chains are also of interest due to the high global economic significance of the automotive industry (Belhadi et al., 2021). Moreover, the automotive industry played a significant role in prior supply chain research and literature (Womack et al., 1990; Holweg, 2007). Thus, various reasons support an investigation of the automotive supply chains, particularly concerning the COVID-19 disruption, which severely affected the automotive supply chains.

1.2 Problem statement Supply chain disruptions are distinguished between internal (e.g. plant fire) and external disruptions (e.g. economic shock, natural disaster). However, both types decline the operational and financial performance if they are not handled adequately (Hendricks & Singhal, 2005; Hohenstein et al., 2015). The COVID-19 disruption can be aligned with previous unexpected external supply chain disruptions such as terrorist attacks, tsunamis and floods, financial crises, earthquakes, or other diseases (Wagner & Bode, 2006; Stecke & Kumar, 2009; Abeysekara et al., 2019; Notteboom et al., 2021).

The frequency and severeness of these disruptions increased in the last decade due to globalisation, climate change, a fast-changing business environment, and the growing complexity and uncertainty of global supply chain networks (Blackhurst et al., 2005; Craighead et al., 2007; Hohenstein et al., 2015). Therefore, the changing environment requires a change of supply chain capabilities. Supply chains do not only need to be efficient but also resilient to decrease the vulnerability to risks and disruptions (Blackhurst et al., 2005; Christopher & Holweg, 2011; Kamalahmadi & Parast, 2016; Sheffi, 2019). The COVID-19 disruption emphasises the urgency for supply chain resilience (SCRes) since it exposed the increasing dependence on China as the single- country source of supply (Hohenstein et al., 2015; Hofstätter et al., 2020; Linton & Vakil, 2020). Consequently, a McKinsey (2020) survey revealed that 93 per cent of the supply chain executives at leading global companies plan to make their supply chains more resilient.

Following previous researchers, SCRes is embedded in supply chain risk management (SCRM) (Blackhurst et al., 2011; Ivanov et al., 2014; Hohenstein et al., 2015). SCRM is defined as “an inter-organisational collaborative endeavour utilising quantitative and qualitative risk management methodologies to identify, evaluate, mitigate and monitor unexpected macro and micro-level events or conditions, which might adversely impact any part of a supply chain” (Ho et al., 2015, p. 5036). Subsequently, Ponomarov and Holcomb specify SCRes as “the adaptive capability of the supply chain to prepare for unexpected events, respond to disruptions, and recover from them by maintaining continuity of operations at the desired level of connectedness and control over structure and function“ (2009, p.131).

Several scholars already frame the COVID-19 disruption from a SCRes perspective and recommend resilience measures and recovery paths (Dertouzos et al., 2020; Hofstätter et al., 2020; Linton & Vakil, 2020). Also, the first literature reviews in the field of SCRes were conducted to examine a new post-COVID-19 research agenda (Free & Hecimovic, 2020; Queiroz et al., 2020; van Hoek, 2020; Zhu et al., 2020). Above that, other researchers focus on technical or sustainable measures to increase post-COVID-19 SCRes (Ivanov & Dolgui, 2020; Nandi et al., 2020; Salmi et al., 2020; Sarkis, 2020). Additionally, further investigations simulate the long-term disruption impacts on supply chains under the aspect of SCRes (Ivanov, 2020; Singh et al., 2020; Shahed et al., 2021).

However, our literature review revealed two research gaps in the current SCRes literature. First, the research was conducted at the beginning of the pandemic. Hence, more profound insights about the supply chain performance of the automotive industry can be explored after one year of the initial COVID-19 disruption. Second, there is a gap between SCRes research and practice (van Hoek, 2020). The current literature is thin concerning the usage and value of SCRes measures due to the COVID-19 disruption.

1.3 Purpose This research aims to close the identified research gaps by gathering feedback from the automotive industry to analyse and assess the SCRes concerning the COVID-19 disruption. We conduct a qualitative multiple case study with interviews of automotive supply chain experts from Scandinavia and Germany, focusing on three aspects of SCRes in the automotive industry. First, the COVID-19 disruption has already exposed the

automotive supply chains to varying disruption conditions from December 2019 until March 2021, the date we started to conduct our research. Consequently, we want to analyse and visualise the effects of the disruption on the trajectory of the supply chain performance in the automotive industry. As a result of our literature review, we rely on the eight stages of disruption of Sheffi and Rice (2005) to structure the trajectory. Furthermore, the analysis of the supply chain performance concerning the COVID-19 disruption enables us to assess the SCRes of the automotive industry. Also, this aspect lays the foundation for further analysis of the impact of SCRes measures.

Second, the research problem revealed that the current literature focuses on identifying and recommending possible SCRes measures. However, it is unclear which measures were used in the industry during the varying conditions of the COVID-19 disruption. Hence, we focalise on our interviewed automotive supply chain experts to identify the used SCRes measures to support our assessment. Furthermore, we analyse how these measures were used over the varying disruption conditions. To structure this analysis, we synthesise and categorise SCRes measures in our theoretical study. This framework is based on six categories: supply chain (re-) engineering, collaboration, agility, risk management culture, technological innovation, and sustainability. The first four categories are based on Christopher and Peck (2004). The last two categories are derived from various sources of the literature review to acknowledge the emerging relevance of these topics in the recent SCRes literature (Rajesh, 2018b; Ivanov et al., 2019).

Third and last, we determine the value of the SCRes measures that were used in practice. We assume that some SCRes measures are more valuable and practical for the interviewed supply chain experts than other SCRes measures based on different aspects. The disruption phase, the location, or the supply chain structure of the affected automotive companies might be such possible aspects that influence the suitability of SCRes measures. In line with the second aspect, we rely on the synthesis and categorisation of SCRes measures to structure this research element. In summary, these three aspects of our research goal are explored with the help of the following research questions:

RQ1: How can the trajectory of the COVID-19 disruption be described and visualised regarding the supply chain performance in the automotive industry?

RQ2: How have supply chain resilience measures been used in the automotive industry in the varying disruption conditions of the COVID-19 disruption? RQ3: How valuable were the supply chain resilience measures that had been used in the automotive industry in the varying disruption conditions of the COVID-19 disruption?

1.4 Delimitations This study follows a qualitative approach to assess SCRes beyond the available quantitative data. We want to receive insights and impressions from the supply chain experts of the automotive industry about the COVID-19 disruption since a significant number of assessment attributes of SCRes are also of qualitative characters such as subjective descriptions and linguistic expressions (Rajesh, 2019a). However, we acknowledge the lack of objectivity related to qualitative studies (Easterby-Smith et al., 2018). Therefore, we rely on multiple cases and further triangulation by developing theoretical frameworks prior to the empirical investigation and using additional company data. Furthermore, the research is conducted in Spring 2021 while the COVID-19 pandemic is still present. Since we investigate the COVID-19 disruption that took place in Spring 2020, we assume that it is feasible to describe the supply chain performance and resilience measures concerning this disruption. Last, due to the economic impact and the relevance in supply chain literature, we only focus on the automotive industry (Womack et al., 1990; Holweg, 2007; Belhadi et al., 2021). The investigation of other industries would enable the comparison and increase the validity (Yin, 2017). Nevertheless, it would exceed the limitations of this study.

1.5 Outline Our study is divided into seven chapters. After the introduction in Chapter 1, we lay the theoretical foundation for automotive supply chains, SCRM, and SCRes in Chapter 2. Then, we explain our multiple case study, the purposive sampling, and the content analysis procedure in Chapter 3. Chapter 4 encompasses our research findings of the supply chain performance and the SCRes measures while analysing and interpreting the data in Chapter 5. At last, we provide a conclusion in Chapter 6 and discuss our study in Chapter 7.

2. Theoretical framework ______This chapter establishes the theoretical background of this thesis. First, we introduce the resource-based perspective that we take in our study. Then, we illustrate the essential aspects of automotive supply chains. Third, we present the fundamentals of supply chain risk management that are relevant to our research. Last, we execute our systematic literature review to outline the relevant concepts and measures of supply chain resilience. ______2.1 Resource-based view Concerning the theoretical perspective, we approach our study from a resource-based view (RBV). Within strategic management, identifying the sources of competitive advantage is a major research topic (Porter, 1985; Barney, 1991). Based on the RBV, we assume that competitive advantage is achieved with specific resources and capabilities that a firm owns or could develop. By exploring these resources and capabilities, it can be clarified how firms gain a competitive advantage over other firms (Barney, 1991; Newbert, 2008; Nandi et al., 2020). Resources are defined as the assets that a firm has access to or possesses. It can distinguish between tangible resources like infrastructure or intangible resources such as information sharing (Grössler & Grübner, 2006; Brandon‐ Jones et al., 2014). Moreover, single resources may not lead to a competitive advantage until they are integrated into a bundle of resources to develop capabilities (Newbert, 2007; Sirmon et al., 2008; Brandon‐Jones et al., 2014).

The RBV is helpful to understand and frame how uncertainty can be reduced, and SCRes can be leveraged (Blackhurst et al., 2011; Bode et al., 2011; Ambulkar et al., 2015; Cheng & Lu, 2017). For instance, the buffering and bridging of resources helps to reduce uncertainty and mitigate the disruption impact, while SCRes can be leveraged by resource reconfiguration (Bode et al., 2011; Ambulkar et al., 2015). Also, several scholars relate resources and capabilities to SCRes measures that positively impact SCRes, leading to a competitive advantage (Brandon‐Jones et al., 2014; Dubey et al., 2017; Rajesh, 2019b; Ji et al., 2020). Therefore, we take an RBV to examine when and how supply chain entities create SCRes (Barney, 1991; Cheng & Lu, 2017).

2.2 Automotive supply chains Supply chain management (SCM) as a concept emerged in the literature during the 1980s by Oliver and Weber (1982), who described the connection of logistics with other operational functions (Khojasteh, 2018). This concept gained popularity since globalisation led to more complex supply chain structures. In addition, the time and quality-based competition forced enterprises to organise the flow of materials in and out of the company more efficiently (Mentzer et al., 2001; Ribeiro & Barbosa-Povoa, 2018). SCM is understood as managing the product, information, cash, and demand flow from the first supplier in the supply chain to the final customer (Coyle et al., 2016).

Regarding that a car has up to 6,000 components, the SCM is critical within the automotive industry to ensure the described flow of material, information, and cash to prevent supply shortages and the resulting costly production stoppages (Boysen et al., 2015; Falsafi et al., 2018). Due to this challenging environment, the automotive industry became a reference for other industries, especially SCM (Cox, 1999; Thomé et al., 2014). Since we investigate the SCRes of the automotive industry, our study needs to introduce the necessary context regarding automotive supply chains. Consequently, we present the multi-tier structure and the relevant characteristics that influence the impact of the COVID-19 disruption on the automotive industry.

2.2.1 Multi-tier structure

We illustrate the multi-tier structure of automotive supply chains to enable the characterisation and classification of the automotive companies that are part of our empirical study. Figure 1 simplifies the structure of the automotive supply chain and points out that it consists of several entities that are subsequently described.

Figure 1: Automotive supply chain tiers Source: Adapted from Kim, Chen, & Linderman, 2015

The original equipment manufacturer (OEM) who designs, produces, and brands the car as the end-product has a pivotal role in the automotive supply chain (Carbone & Martino, 2003). Further, the OEM can be described as a supply chain leader who takes a significant role in coordinating and overseeing the supply chain (Mentzer et al., 2001). Also, the OEM has a dominant power relationship with its suppliers, which allows the OEM to enforce innovations and concepts in the upstream supply chain tiers (Cox, 1999).

Next, the first-tier supplier can take the role of a quality buffer, enhancer of productivity, system developer, purchaser, designer, or problem solver to relieve the OEM (Hines, 1998). Doran (2004) further characterises the first-tier suppliers based on a continuum. On the one end, the mature first tier has the capacity and competence to ensure the global supply of modules. Further, the mature first tier has high-quality standards, a global presence and owns critical areas of the modular supply chain. The developing first tier is in the middle of the continuum. These suppliers also have a second-tier business, and they enhance their supplier relationship skills to achieve a better position. At the other end, the fringe first tier is primarily a second-tier supplier who also has a first-tier business but who is only a marginal player regarding the modular supply (Doran, 2004).

Then, the second-tier suppliers are mainly indirect suppliers to the OEMs with few opportunities to supply the OEMs directly. Further, they can be divided into two groups. The first group is the component manufacturer who is specialised in a specific component such as metal stamper or injection moulder. They are responsible for designing and testing the specific component, but not for the superordinate subassembly of the module the component belongs to. The second group is the subassembly manufacturer who produces a subsystem such as steering columns or batteries. Likewise, the subassembly is responsible for the design and production of the subsystem, but not for the whole module (Veloso & Kumar, 2002; Vazquez et al., 2016).

Last, the third tier and raw material supplier are characterised. These suppliers have a more diversified portfolio and supply several industries with their raw materials. Therefore, they are less dependent and focused on the automotive industry than the prior downstream supply chain tiers (Lind et al., 2012). However, this group also includes third-tier suppliers who provide raw materials specifically for the automotive industry (Carbone & Martino, 2003).

2.2.2 Industry-specific characteristics

Based on Thun and Hoenig (2011), two industry-specific supply chain characteristics are considered essential since they contribute to increasing supply chain risks and thereby influence the SCRes of the automotive industry. These characteristics are the growing complexity due to the increasing globalisation and the simultaneous importance of lean and efficient supply chains (Thun & Hoenig, 2011).

Globalisation as the first characteristic encompasses reducing trade barriers and deregulation of commerce that enabled global production networks. The main driver of globalisation is the potential of cost reduction due to lower production costs (Mondragon et al., 2006; Bechmann & Scherk, 2010). Therefore, the automotive supply chains are characterised by highly complex global networks that involve long transportation segments and require a high level of coordination (Kleindorfer & Saad, 2005). Also, a consequence of the length and complexity of the global supply chains is the increased vulnerability to disruptions (Blackhurst et al., 2011).

The second characteristic of the automotive SCM is lean management which Toyota initially introduced to minimise waste and increase efficiency (Womack et al., 1990; Cox, 1999). Toyota’s success encouraged other car manufacturers to copy the system of a leaner, demand-pull, and outsourced production that relies on just-in-time (JIT) deliveries (Cox, 1999; Iskandar et al., 2001; Wagner & Silveira-Camargos, 2012). The JIT concept requires that materials and parts are delivered in the correct quantity when the OEM needs them in production. Lean management is seen as a competitive advantage (chapter 2.1) in the automotive industry improving cost, quality, flexibility, and technology (Scannell et al., 2000; Boysen et al., 2015).

Nevertheless, it requires a high level of information exchange and collaboration. In particular, the information flow is needed in both ways. The upstream information flow contains the order or demand information that enables the improvement of scheduling and inventory control. The downstream information flow involves the order progress information, volumes, delivery dates, and quality. Furthermore, these information flows are also critical to increase overall agility and supply chain performance (Childerhouse et al., & Towill, 2003). Several technologies are recommended to improve the information flows. Iskandar et al. (2001) and Childerhouse et al. (2003) assume the utilisation of

electronic data interchange (EDI) to enable intense information exchange, whereas Mondragon et al. (2006) recommend the usage of web services or RFID (radio-frequency identification) to increase visibility. However, the lean framework, including the JIT system, is still vulnerable to disruptions and can cause worldwide shortages. Hence, the call to increase safety stocks intensifies (Thun et al., 2007; Zhu et al., 2020).

2.3 Supply chain risk management As indicated by the industry-specific characteristics of automotive supply chains (chapter 2.2.2), supply chains are confronted with increasing risks that could negatively impact the firm’s profitability (Kumar et al., 2010). Thus, companies must find strategies to deal with the different risk types (Daultani et al., 2015). Risks within supply chains are defined as “any risks for the information, material and product flows from the original supplier to the delivery of the final product for the end-user” (Jüttner et al., 2003, p. 200). Thereby, supply chain risks expand beyond the boundaries of the single entity, and the boundary covering flow can become a cause of supply chain risk by itself (Jüttner, 2005). Hence, SCRM became an essential part of SCM (Khojasteh, 2018).

SCRM is defined as “an inter-organisational collaborative endeavour utilising quantitative and qualitative risk management methodologies to identify, evaluate, mitigate and monitor unexpected macro and micro-level events or conditions, which might adversely impact any part of a supply chain” (Ho et al., 2015, p. 5036). Based on that definition, it is vital for an enterprise to identify and assess risks besides developing appropriate risk-mitigation strategies (Manuj & Mentzer, 2008; Ho et al., 2015).

First, a risk identification process initiates the SCRM activities since it aims to discover all risks and evaluate their significance (Kern et al., 2012). Thereby, the risk identification process also embraces a classification of the different risk types (Neiger et al., 2009). Supply chain risks are classified as supply risks, demand risks, manufacturing risks, financial risks, information risks, transportation risks, or macro risks (Ho et al., 2015).

Supply risks occur when a company is implementing an outsourcing or offshore-sourcing strategy. As the total costs are supposed to decrease with this strategy, the default risk and the level of uncertainty also increase (Wu et al., 2013). On the other side, demand risks arise due to the difficulty to forecast demand accurately. There is a risk caused by transmission and amplification of order variability throughout a supply chain, the so-

called bullwhip effect (Lee et al., 1997). The risk for the bullwhip effect increases due to the rapidly changing environment and the difficulty to implement accurate forecasts (Kim, 2013).

Manufacturing risks describe adverse events that might occur within a company’s scope, like personnel lacking knowledge and ability to handle new processes (Wu et al., 2006). Financial risks refer to issues that could occur within a firm’s cash flow, such as volatile exchange rates and currency fluctuations (Liu & Nagurney, 2011). Information risks cover the aspect that supply chains are highly interconnected throughout information systems to increase collaboration. With this, threats through viruses, trojan horses, hacking, or simply the accidental destruction of data by employees are considered as risks (Smith et al., 2007).

Transportation risks refer to adverse effects on the flow of goods caused by terroristic activities or piracy with potential harm for the entire supply chain (Hishamuddin et al., 2013). Macro risks describe catastrophic events characterised as low probability-high consequence events and unexpectedly disrupt the supply chain flows (Knemeyer et al., 2009). Examples of such macro risks are political crises or natural disasters like hurricanes, droughts, or earthquakes. A pandemic disruption can be classified as a macro risk (Kumar & Havey, 2013).

A second step within SCRM is the risk assessment process, whereby the likelihood and the significance of the potential consequences and losses of an adverse event are evaluated (Brenchley et al., 2003). Herein, a company aims to determine the propagation of the negative effect caused by the risks and measure the impact on the operational supply chain performance (Wu et al., 2007). The risks can either be objectively evaluated based on indicators like financial data or subjectively through assessing the robustness of the relationship between the companies or combining both approaches (Zsidisin et al., 2004). Third and last, SCRM further tries to develop risk mitigation strategies to reduce the negative impacts deriving from the various risk sources (Ceryno et al., 2015). The main objective of SCRM is to establish and maintain a decent level of resilience within supply chains (Grötsch et al., 2013). The principle of SCRes is further elaborated in the next sub- chapter.

2.4 Supply chain resilience A generally accepted definition of SCRes that reaches complete consensus among scholars is not defined yet (Tukamuhabwa et al., 2017; Emenike & Falcone, 2020). Hence, the reviewed articles cite various SCRes definitions from previous studies (Christopher & Peck, 2004; Sheffi & Rice, 2005; ISO22301, 2012; Sahebjamnia et al., 2018; Costa et al., 2018; Um & Han, 2020). Nevertheless, within the reviewed articles, Ponomarov and Holcomb (2009) established the most cited definition regarding the 16 citations within the reviewed literature (appendix 1). Ponomarov and Holcomb (2009) regard a resilient supply chain as “the adaptive capability of the supply chain to prepare for unexpected events, respond to disruptions, and recover from them by maintaining continuity of operations at the desired level of connectedness and control over structure and function“. Our study relies on this definition for two reasons. First, the definition is perceived as relevant in the reviewed literature. Second, it focuses on the term disruption and thereby corresponds to our focus on the COVID-19 disruption to assess the supply chain resilience of the automotive industry.

2.4.1 Concepts of supply chain resilience

In similarity to the SCRes definitions, there is no accepted concept for SCRes in the current literature that highlights the resilience measures, enabling an evaluation of the resilience level (Rajesh, 2020b; van Hoek, 2020). However, several articles rely on prominent frameworks. Two of the most cited frameworks in the reviewed literature are Sheffi and Rice (2005) and Christopher and Peck (2004) (appendix 2). Sheffi and Rice (2005), as the first ones, demonstrate a graphical approach based on the stages of disruption that allows the qualitative characterisation of the system performance and disruption trajectory. They distinguish between eight stages (figure 2).

Figure 2: Stages of disruption Source: Adapted from Sheffi & Rice (2005)

Preparation as the first stage describes that companies can anticipate and thus prepare for disruptions to some extent. The disruptive event that causes the supply chain disruption is the second stage. The third stage is the first response which aims to control the situation and to prevent further damage. Stage four, as the initial impact, covers the damage that the first response could not prevent. The initial impact depends on the magnitude of disruption, available redundancy, and existing SCRes. Also, the supply chain performance starts to decline (Sheffi & Rice, 2005).

However, the disruption eventually reaches stage five, where the supply chain experiences the full impact of the disruption. Firms within the supply chain run out of inventory of critical parts, which leads to a steep decline in supply chain performance. The recovery preparations as stage six start after or in parallel with the first response. If the disruption has been anticipated, the recovery preparations start before the disruption. Typical preparation measures are, for example, the qualification of alternative suppliers, redirection of suppliers’ resources, and the development of alternative transportation routes and modes. The return to normal operating levels is achieved during the recovery as stage seven. Relevant measures are the utilisation of overtime or suppliers’ and customers’ resources. Last, the long-term impact is covered by stage eight. A disruption might cause long-term damages such as a negatively affected supplier or customer relationships which are complicated to recover from. Hence, an extensive period is

assumed to recover from disruption until a supply chain performance level is reached, comparable to the pre-disruption state (Sheffi & Rice, 2005).

The disruption profile framework of Sheffi and Rice (2005) builds the foundation for subsequent, highly cited articles (Tierney & Bruneau, 2007; Cimellaro et al., 2010; Pettit et al., 2013; Tukamuhabwa et al., 2015). Additionally, the literature review revealed that the framework is still relevant in the current literature (Sahebjamnia et al., 2018; Rajesh, 2019a; Emenike & Falcone, 2020; Fan et al., 2020; Polyviou et al., 2020; Rajesh, 2020b; Shekarian & Mellat Parast, 2020).

As the second one, Christopher and Peck (2004) provide a framework that illustrates how to create a resilient supply chain based on categorised measures (figure 3). The framework organises four categories of measures that increase the company’s SCRes.

Figure 3: Creating the resilient supply chain Source: Adapted from Christopher & Peck (2004)

The first category is the (re-)engineering of the supply chain. It describes the measures that are used to design and optimise the supply chain to achieve higher SCRes. Supply chain (re-)engineering involves, for instance, a change of the supply base strategy which requires a change of sourcing decision criteria and a focus on resilience in supplier development. The second category is supply chain collaboration. The category acknowledges that the vulnerability to disruptions is a network-wide issue. Hence, inter-

organisational measures like collaborative planning and information sharing are needed to develop a resilient supply chain (Christopher & Peck, 2004).

The third category, agility, is defined as responding rapidly to unpredictable supply and demand changes. Agility is achieved with visibility and velocity. Visibility is the ability to see the inventory, supply, and demand over the whole supply chain, whereas velocity captures the ability to shorten lead times. The fourth and last category is the creation of SCRM culture. The authors recognise that a management culture change is necessary to ensure that the SCRes measures are adequately implemented. The risk management culture can, for example, be achieved by assigning the responsibility for supply chain risk management on board level and the consideration of risk management in the decision- making process of the companies in the supply chain (Christopher & Peck, 2004).

The framework of Christopher and Peck (2004) is the foundation for various subsequent frameworks that are highly cited (Jüttner & Maklan, 2011; Pettit et al., 2013; Scholten & Schilder, 2015; Tukamuhabwa et al., 2015). For instance, Ponomarov and Holcomb (2009) recognise the groundwork of Christopher and Peck (2004) and develop a more holistic concept that incorporates the logistic capabilities into the SCRes framework. Concerning the reviewed articles, further and more recent frameworks can be traced back to Christopher and Peck (2004), which underlines the framework’s relevance (Abeysekara et al., 2019; Rajesh, 2019a; Rajesh, 2020b).

Although, the literature review also revealed contradicting articles concerning the explicitness of flexibility. Other scholars characterise supply chain flexibility as a separate SCRes strategy (Polyviou et al., 2020; Shekarian & Mellat Parast, 2020; Dubey et al., 2021). They rely on previous research that also singled out flexibility as a resilience driver (Pettit et al., 2013; Hosseini et al., 2019). While Christopher and Peck (2004) state in their definition of SCRes that flexibility is implicit in all four categories.

In sum, we presented two frameworks that follow a different approach to structure the topic of SCRes. However, we pointed out that both frameworks are still relevant even though the related articles were published in 2004 and 2005. Nevertheless, recent trends in the SCRes literature are not covered by the two frameworks. Specifically, scholars established the topics of technological innovation and sustainability as the main trends within the field of SCRes.

Technological innovation encompasses the investigations that identify innovativeness and technology as capabilities to improve SCRes (Golgeci & Ponomarov, 2013; Rajesh, 2017; Ivanov et al., 2019; Parast, 2020; Salmi et al., 2020; Um & Han, 2020). Recent investigations revealed that the COVID-19 pandemic increases the need for technological innovation since the crisis uncovers the vulnerabilities in the supply chain due to the lack of information flow and end-to-end visibility. However, this vulnerability can be tackled by transforming traditional supply chains into digital supply networks (Ivanov & Dolgui, 2020; Queiroz et al., 2020; Rapaccini et al., 2020; Sarkis, 2020; Zhu et al., 2020). Besides, other scholars identify potential measures of technological innovation that improve SCRes, such as 3D printing (Salmi et al., 2020), blockchain technology (Queiroz et al., 2020; Nandi et al., 2020), and data analytics or machine learning (Cavalcante et al., 2019; Dubey et al., 2021).

Furthermore, the literature review revealed that sustainability is also perceived as an enabler of SCRes (Soni et al., 2014; Hosseini et al., 2016; López & Ruiz-Benítez, 2020). Rajesh (2018b) introduces the concept of sustainable-resilient supply networks as networks that combine the benefits of sustainability and resilience. Sarkis (2020) points out that sustainability strategies and practices such as maintained ecosystem services, sustainable localisation, and earning the community’s trust reduce risks and contribute to SCRes. Further, the author concludes that sustainability and resilience are complements that contribute to a post-COVID-19 improvement of the supply chain (Sarkis, 2020). Likewise, Queiroz et al. (2020) recognise that the emerging COVID-19 research agenda involves sustainability and resilience, and they combine the themes under the concept of supply chain viability. Also, other reviewed articles provide specific measures such as green supplier integration or circular economy capabilities that enhance SCRes and sustainability (Bag et al., 2019; Ji et al., 2020; Nandi et al., 2020).

2.4.2 Measures of supply chain resilience

Several reviewed articles provide an overview of the potential measures that increase SCRes (Pettit et al., 2013; Rajesh, 2020a; Um & Han, 2020; Zhu et al., 2020). Lotfi & Saghiri (2018) distinguish in their overview between measures that relate to resilience, leanness, and agility. Also, Ruiz-Benítez et al. (2018) identify 12 resilient supply chain practices and investigate their impact on financial and operational performance. Further,

the literature distinguishes between proactive and reactive SCRes measures (e.g. Tukamuhabwa et al., 2017; Belhadi et al., 2021). Proactive measures are established to prevent the negative impact of supply chain disruptions. In contrast, reactive measures are implemented to recover the supply chain performance to a sufficient level in a sufficient time after the disruption (Dubey et al., 2021). Belhadi et al. (2021) exemplify that a proactive measure is to set up digital connectivity through digital innovations such as blockchain technology or integrated SCRM with a perspective of the whole supply chain. Examples of reactive measures are the creation of redundancy or increased velocity and visibility to respond accurately to a supply chain disruption (Tukamuhabwa et al., 2017).

However, various reviewed articles do not provide an overview of measures. Instead, they select a single or a small number of measures to validate the positive impact of these measures on SCRes. For instance, Polyviou et al. (2020) analyse social capital and a risk- focused culture to build SCRes. At the same time, the overview provided by Rajesh (2017) focuses on the technological measures that increase SCRes. In contrast, Ji et al. (2020) investigate green supplier integration as a measure that increases SCRes and supply chain sustainability.

To provide a structure for this high quantity of measures, we relied on the four categories of the presented framework of Christopher and Peck (2004). The four categories are supply chain (re-) engineering, supply chain collaboration, agility, and creating a SCRM culture. Also, we added technological innovation and sustainability as the fifth and sixth categories to cover the measures that relate to these recent trends. In conclusion, table 1 provides an overview of the relevant SCRes measures based on the reviewed articles.

References References Costa Costa et al., 2018; Cavalcante et al., 2019; Mari et al., 2019; Shin & Park, 2020; Sureeyatanapas 2020; Han, & 2020 Hoek, van 2020a; Rajesh, 2020; al., Um et 2018; al., et Ruiz-Benítez 2016; al., et Hosseini 2015; al., et Thomas 2020; al., 2020a Rajesh, 2020; Parast, Mellat & Esmizadeh et Polyviou 2019a; Rajesh, 2018; al., et Ruiz-Benítez 2017; Seifert, & Lücker 2020a Rajesh, 2020; Parast, Mellat & Shekarian 2020; al., et Singh 2020; al., et Queiroz 2019a; Rajesh, 2019; al., et Singh 2019; Chien, & Fu al.,2021 et Shahed 2020; Krikke, Zhu & 2020; Falcone, & Emenike 2020; al., et Queiroz 2020; al., et Zhu 2017; Seifert, & Lücker 2012; Oyen, Van & Saghafian 2020 Hoek, van 2020b Rajesh, al., 2020; et 2019a; Zhu Rajesh, 2018a; Rajesh, 2020 et al., Queiroz 2018; et al., Sahebjamnia 2018; al., et Ruiz-Benítez et Ruiz-Benítez 2017; et al., Dubey 2014; et al., Brandon‐Jones 2012; Oyen, Van & Saghafian 2020 Han, & Um 2020; Parast, Mellat & Shekarian 2019a; Rajesh, 2018; al., 2019a Rajesh, al., 2020; et Polyviou 2018; al., et Ruiz-Benítez 2020b Rajesh, 2020a; Rajesh, 2020a Rajesh, 2015; al., et Thomas et 2020 al., Zhu 2020a; Rajesh, al.,2019; et Bag 2015; al., et Thomas Measures Measures 1.1 Resilience-oriented Supplier selection selection Supplier Resilience-oriented 1.1 design chain supply Resilience-oriented 1.2 (adaptivity) inventory) (reserve Redundancy 1.3 support decision or forecasting, Simulation, 1.4 (DSM) model Sourcing Diversification/Dual 1.5 review / vulnerability mapping chain Supply 1.6 plan recovery Disaster 1.7 chain) supply (within sharing Information 2.1 planning Collaborative 2.2 Risk hedging 2.3 time) development (product operations Agile 3.1 operations; (parallel flexibility Manufacturing 3.2 planning) assortment dynamic

Category 1. Supply chain (re-) engineering 2. Supply chain collaboration 3. Agility

Table 1: Overview of SCRes measures in theory

References References Brandon‐Jones et al., 2014; Ruiz-Benítez et al., 2018; Rajesh, 2019a; Rajesh, 2018; al., et Ruiz-Benítez 2014; al., et Brandon‐Jones 2020 Han, & Um 2020; al., Zhu et al. 2020; et Yang 2020a; Rajesh, et Queiroz 2020a; Rajesh, 2019; al., et Bag 2018; al., et Ruiz-Benítez 2020 al., Zhu,et 2020; al., 2019 et al., Bag 2018; al., et Ruiz-Benítez 2016; et al., Bühler 2019a Rajesh, 2020 Hoek, van 2019a Rajesh, 2019a Rajesh, 2020 et al., Saglam 2020a; Rajesh 2019a; Rajesh, 2020a Rajesh, 2020 Hoek, van 2020; et al., Nandi 2020; al., et Queiroz 2020; Dolgui, & Ivanov et2020; al., Rappaccini al., 2020; et Zhu et al., 2020 Queiroz 2020; Sarkis, 2020 al., et Salmi 2021 al., et Dubey 2020; Sarkis, 2019; al., et Cavalcante al., Zhu et 2020; 2020; Sarkis, et al. 2020; Nandi 2019; et al., Bag 2020 Hoek, van 2020 Sarkis 2020 et al. Ji

Measures Measures 3.3 Enhance supply chain visibility (of capacity and inventory) and capacity (of visibility chain supply Enhance 3.3 and supply of re-allocation routing, (alternative flexibility Logistics 3.4 demand) system information control of Use 3.5 reduction) time (pipeline Velocity 3.6 management talent Resilience-oriented 4.1 employees among awareness resilience Creating 4.2 management Continuity 4.3 making decision into considerations risk Factor 4.4 employees of Cross-training 4.5 RFID technology, Blockchain 5.1 twin chain supply Digital 5.2 3D printing 5.3 intelligence artificial learning, machine analytics, Data 5.4 remanufacturing) (relocalisation, capabilities economy Circular 6.1 consumer and capacity excess sharing principles: economy Sharing 6.2 integration supplier Green 6.3

Category 3. Agility 4. Supply chain risk management culture 5. Techno- logical Innovation 6. Sustai- nability

Table 1 (continued): Overview of SCRes measures in theory

2.4.3 Assessment of supply chain resilience

Regarding the assessment of SCRes, several articles evaluated the degree of the implementation of the resilience measures. Herein, Ivanov et al. (2019) refer to the proactive and reactive measures that have to be assessed. For proactive measures, the level of inventories or capacity buffers must be evaluated. At the same time, the appropriateness and the number of contingency plans is an indicator for the degree of reactive SCRes measures (Ivanov et al., 2019). Likewise, Hosseini et al. (2016) assess SCRes by using absorptive and adaptive capacities. Absorptive capacities are, for example, the level of physical protection against disruptions caused by natural disasters, the reliability of suppliers by estimating the delay or failure rate, and the amount of backup supplier to secure continuity of production. In contrast, the main adaptive capacities are the level of flexibility within manufacturing and transportation operations (Hosseini et al., 2016).

The reviewed literature also highlights the SCRes assessment based on key performance indicators (KPIs). At first, the SCRes can be measured by the impact on financial KPIs like revenues caused by the disruption (Ivanov & Dolgui, 2020). A further KPI to assess the SCRes is the recovery speed, which describes the time it takes to bring the supply chain back to its previous functionality after a disruption (Emenike & Falcone, 2020; Belhadi et al., 2021). In addition to that aspect, another critical KPI is the determination of continuity levels of operations (Gligor et al., 2019). Further mentioned KPIs that could be deployed to assess the level of SCRes are the customer and operational performance indicators (Ivanov & Dolgui, 2020).

Above that, several articles rely on mathematical indices and models to assess the SCRes level (Pettit et al., 2013; Sahebjamnia et al., 2018). One example is the GResilient index, emerging from a multi-criteria model, which enables the evaluation of greenness and SCRes of automotive manufacturers (Ramezankhani et al., 2018; Mari et al., 2019). Last, the SCRes of suppliers during the supplier selection process is also assessed (Sureeyatanapas et al., 2020). Examples of resilience criteria for the supplier selection process in the reviewed literature are density, complexity, node criticality, responsiveness, and re-engineering capabilities of the supplier (Mari et al., 2019).

2.4.4 Outcomes of supply chain resilience

In sum, within the reviewed literature, the outcomes of increased SCRes are mainly related either to the supply chain performance or the general operational performance of the investigated companies (e.g. Kauppia et al., 2016). Also, companies that react quicker and more efficiently to disruptive events than competitors could access a competitive advantage due to SCRes (Singh et al., 2019). Besides, resilience practices positively impact the sustainability performance of a supply chain since SCRes is considered an approach to develop lasting capabilities (Ji et al., 2020; López & Ruiz-Benítez, 2020).

Regarding the interdependence of agile supply chains and SCRes, Rajesh (2018) mentions that the simultaneous implementation of resilient and agile measures would increase the supply chain performance. Also, Thomas et al. (2015) declare agility as a critical theme to establish manufacturing resilience. Further, Lotfi and Saghiri (2018) mention that agility is a formative element of SCRes, especially within reactive measures.

Concerning the interdependence between lean supply chains and SCRes, Ahmed & Huma (2021) state that strategically lean-oriented companies do not support organisational resilience capabilities. Furthermore, based on previous research, Lotfi and Saghiri (2018) conclude that lean supply chains are more fragile, and their disruption risks increase dramatically. However, the authors also acknowledge that leanness could help to improve the time to recovery performance (Lotfi & Saghiri, 2018). Likewise, Ruiz-Benitez et al. (2018) declare an increased supply chain vulnerability due to leanness because firms reduce their availability to buffer. Nevertheless, lean supply chain practices may boost SCRes practices since the enforcement of lean practices for cost-efficiency reasons also requires implementing SCRes practices due to the increased supply chain vulnerability (Ruiz-Benítez et al., 2018).

2.5 Conclusion of theoretical study Based on our systematic SCRes literature review, we identified two research gaps. First, the COVID-19 supply chain disruption is not well researched regarding that only 16 out of the 85 reviewed articles relate to the COVID-19 crisis. Due to the inherent delay based on the peer review and publication process of journals, the published articles that study the COVID-19 disruption were conducted at the beginning of the pandemic. Therefore, the available literature mainly forecasts and simulates the impact of the disruption on

supply chains under the aspect of SCRes (Ivanov, 2020; Singh et al., 2020; Belhadi et al., 2021; Shahed et al., 2021). Although, one year after the initial COVID-19 outbreak, it is valuable to investigate the actual trajectory of the disruption up to this point. Therefore, Sheffi and Rice (2005) provide a suitable first theory-based research framework to analyse the different disruption phases, the respective supply chain performance, and the role of SCRes in these phases (figure 2).

Second, another gap that the literature review disclosed is the gap between theory and practice (van Hoek, 2020). This gap manifested itself primarily in the examination of SCRes measures. The central aspect of the sampled articles is the recommendation of measures based on a theoretical conceptualisation. For instance, measures like blockchain, circular economy practices (Nandi et al., 2020; Sarkis, 2020), and flexible backup suppliers (Saghafian & Van Oyen, 2012) are explored. Even though studies exist where the authors consult the industry to identify which SCRes measures are used, the linkage to the industry is superficial (Thun & Hoenig, 2011; Dubey et al., 2017; Rajesh, 2017; Rajesh, 2020a; Azadegan & Dooley, 2021).

Hence, we synthesised the SCRes measures from the reviewed literature (table 1) to tackle the lack of qualitative research on the role of SCRes measures in practice as the second research gap. This list of SCRes measures is based on six categories. The first four categories of the supply chain (re-) engineering, collaboration, agility and SCRM culture relate to the presented framework (figure 3) of Christopher and Peck (2004). Several scholars promote the last two categories, technological innovation, and sustainability as research opportunities that should be considered in further investigations related to the COVID-19 disruption (Queiroz et al., 2020; van Hoek, 2020; Sarkis, 2020). Therefore, we use the synthesis of SCRes measures (table 1) as the second theory-based research framework.

3. Methodology ______In this chapter, the multiple case study and the usage of secondary data are explained. The constructionist research philosophy and our purposive sampling strategy are outlined. Afterwards, we illustrate the interview procedure and the subsequent directed content analysis. At last, we describe how research quality and ethics are ensured. ______The research is structured into primary data with an empirical study and secondary data reflecting company data and a theoretical study (figure 4). The empirical study is displayed in chapter 4, whereas the theoretical study is covered in chapter 2. The following sub-chapters explain the study procedure after we introduce our research philosophy.

Figure 4: Thesis structure Source: Own construction

3.1 Research philosophy Awareness about the philosophical assumptions increases the quality of the research (Easterby-Smith et al., 2018). Thereby, we outlined our ontological and epistemological assumptions to illustrate our research philosophy. Concerning this study, we selected relativism as our ontological position. Based on this position, many truths are assumed, which depend on the viewpoint of the observer. Therefore, scientific laws are influenced by the perspective since people are embedded in a context (Putnam, 1987; Easterby-Smith et al., 2018). Furthermore, we followed a constructionist-oriented epistemological approach under our relativist position. The idea of constructionism embodies that objective or external factors do not determine reality, but it is instead a social construction of people (Robson & McCartan, 2016). Hence, we considered that the responses we receive from the interviewed supply chain experts are socially constructed and require

embodied context. The strengths of the constructionism approach are that the value of multiple data sources is acknowledged and enables generalisations beyond the investigated sample. However, the weaknesses are that access to this sample can be difficult and the potential issues regarding discrepant information (Easterby-Smith et al., 2018).

The constructionist position can be further specified as engaged constructionism. An engaged research approach requires close collaboration between academics and practitioners regarding research aims, implementation, and practical implications (Easterby-Smith et al., 2018). Within the field of engaged constructionism, our philosophical position can be described as pragmatism. Pragmatism claims that direct experience is the source of understanding and knowledge (Fendt et al., 2008). This position is pertinent for our research since we also criticised the lack between theory and practice of SCRes measures which we want to tackle. Furthermore, the position emphasises that concrete and abstract have to be balanced as well as reflection and observation (Easterby-Smith et al., 2018). In this manner, we illustrate how we achieve this balance in our empirical study in the following chapter.

3.2 Primary data – multiple case study Constructionist research requires a methodology that uses questions to advance the research, which considers many different perspectives and aims for convergence. The holistic multiple case study approach is coherent with these requirements and the formerly described research philosophy. Further, the multiple case study methods and techniques emphasise the analysis of words, and the methodology relies on triangulation, comparison, and theory generation (Yin, 2017; Easterby-Smith et al., 2018). Above that, a multiple case study enables a descriptive research approach that portrays a situation or phenomenon (Runeson & Höst, 2009; Robson & McCartan, 2016).

Consequently, we investigated the COVID-19 disruption and assessed the SCRes of the automotive industry based on multiple cases (figure 5). The multiple case study allowed us to include a relatively large sample to achieve an overall picture of the impact of the COVID-19 disruption on the automotive industry. Hence, we focused on the cross-case analysation to compare the trajectory of the supply chain performance and the usage and value of SCRes measures concerning different stages of the disruption and different levels

and entities of the supply chain. To assemble a representative sample of the main supply chain tiers, we focused on the OEMs and the first and second supply chain tiers in the automotive supply chain as the case companies (Thomé et al., 2014).

First, we analysed the OEMs, which are naturally a vital supply chain entity in the automotive supply chain since they produce the end-product. Second, we examined the situation at the tier 1 suppliers in the automotive supply chains. The impact and reaction on the COVID-19 disruption and the used SCRes measures might have been different at this entity, which we want to investigate. Third, we also investigated tier 2 suppliers since we believe that these suppliers can also reveal relevant insights concerning the COVID- 19 disruption, its trajectory, and the impact of different SCRes measures (Chapter 2.2.1).

Figure 5: Application of holistic multiple case study design Source: Adapted from Yin, Bateman, & Moore (1983); Yin (2017)

The research design was developed prior to the investigation. Our theoretical study of SCRes provided the theory-based research frameworks for our cases. We synthesised the relevant concepts of our study and the SCRes measures (chapter 2.4). Therefore, we described and analysed the COVID-19 disruption as the single unit of analysis to derive insights about SCRes that can be applied to other disruptions (Stake, 2006; Easterby- Smith et al., 2018). In conclusion, we move from theory to empirical observations, which can be specified as a deductive research procedure (Robson & McCartan, 2016).

3.2.1 Sampling strategy and process

The accumulation of qualitative data requires an appropriate sampling strategy. We referred to purposeful sampling since it is suitable for qualitative studies (Emmel, 2013; Maylor et al., 2016). Also, it allows a selection of instances for cases based on predefined criteria (Emmel, 2013; Easterby-Smith et al., 2018).

We defined four sampling criteria that possible organisations and their expert individuals should meet to be considered for our research. First, we specified the automotive industry by focusing on the Scandinavian and German automotive industries. In both regions, the automotive industry is organised within an association. The Scandinavian Fordonskomponentgruppen (FKG) represents more than 350 member companies from Scandinavian suppliers to the automotive industry (FKG, 2021). In comparison, the German Verband der Automobilindustrie (VDA) constitutes over 600 companies that manufacture cars, trailers, bodies, buses, parts, and accessories. Within the VDA, we prioritised manufacturer group 1, car manufacturers, and manufacturer group 3, which are over 500 parts and accessories manufacturers. Manufacturer group 2 was excluded because it involves special bodies, trailers, and buses that do not correspond to our automotive focus (VDA, 2021a).

Second, the targeted companies were the OEMs and the first or second supply chain tier in the automotive supply chain. Hence, we revised the companies' website within these two associations to exclude the companies that are not producing parts that relate to automotive supply chains. Consequently, we avoided companies that concentrate on automotive engineering, information technology, mobility services, and additional car equipment. We contacted the enterprises via E-Mail or their contact forms on their websites (appendix 3). Thereby, we introduced ourselves, the purpose of our interview request, and our interview guide (appendix 4).

Third, we required that the role of the interviewees was related to automotive supply chain management. Since not every company has a SCM department, we also accepted managing directors, purchasing, sales staff, or industry analysts if they were confident to answer our questions based on our written request and interview guide. Fourth, the interviewee had to compare the situation of the automotive supply chains before and after the COVID-19 disruption. Hence, we assured that the interviewees were in the same position over the last two years and demonstrated adequate years of experience.

As a result, we obtained 18 interviews with participants that met our sampling criteria. Above that, our study is supported and expanded by interviews with a pure third supply chain tier supplier and two industry analysts who work for the automotive associations and related institutions to include their macro-perspective on the industry. In table 2, we have displayed the anonymised information of the 21 participants and companies in total.

Experience 6 years 3 years 19 years 35 years 31 years 23 years 2 years 15 years 20 years 25 years 26 years 34 years 12 years 11 years 21 years 10 years 16 years 30 years 35 years 22 years 20 years Interviewee’ s s role Interviewee’ SC manager (JIT) SC manager (ramp-up) Logistics network planner Value stream leader Warehouse manager SCM of Head SC manager SCM of Head Plant logistics manager CEO SCM of Head General logisticsmanager Logistics manager Plant manager businessDirector development of Head logistics managerSales Automotive- Managing director SC manager Industry analyst Industry analyst Supply Chain Tier OEM OEM OEM Tier 1, 2, 3 Tier 1, 2, 3 Tier 1, 2, 3 Tier 1, 2 Tier 1, 2 Tier 1, 2 Tier 1, 2 Tier 1, 2 Tier 1, 2 Tier 1, 2 Tier 1 Tier 1 Tier 2 Tier 2 Tier 2 Tier 3 - - Company’ s s automotive products Company’ manufacturingCar manufacturingCar Contractual manufacturingcar Chemicals, tapes, other materials Chemicals (Adhesives, coatings, etc.) Chassis, steering knuckles, crankshafts doors, Vehicle bodyseats and Radars, cameras Injection moulding, ventilation system Pressing sheet welding and metal Interior (Plastic components, harness Cable componentsand (JIT) (corrosionWaxes protection) (secondSeats seating row) Suspension Connecting parts, direct screw tubes Steel for engines and chassis Coatings Chemicals (Coating) Services Financial for Automotive Automotive association

1 4 6 7 9 11 13 14 16 18 19 21

I I 2 I 3 I I 5 I I I 8 I I 10 I I 12 I I I 15 I I 17 I I I 20 I Number

Table 2: Overview of companies in the case study sample

As the table shows, we aimed for a broad spectrum of experts from different supply chain tiers and diverse professional backgrounds and experiences. Thereby, we attempted different perceptions of the topic and assured a qualitative outcome regarding the collected data.

3.2.2 Interview procedure

In line with our ontology and epistemology, we relied on semi-structured interviews, which enable more flexibility than fully structured interviews and allow the investigator to understand and explore the explanations and meanings of the experts (Saunders et al., 2009; Robson & McCartan, 2016; Easterby-Smith et al., 2018). This flexibility was also needed regarding the diversity of backgrounds and expertise of our interviewees. Furthermore, in coherence with our research purpose, the objective of a semi-structured interview is to receive descriptive and explanatory responses (Runeson & Höst, 2009).

The interview was divided into three parts (appendix 4). First, the purpose of the study was introduced. Then the interviewees were asked to specify their role and responsibility within the company, their working experience, and how their job contributed to the supply chain reaction to the COVID-19 disruption.

Second, based on the first theory-based research framework (figure 2), we discussed the supply chain performance trajectory concerning the COVID-19 disruption and asked the participants how they would describe the development. More specifically, we requested that the interviewees describe the availability of material, lead times, and production volumes for the period affected by the disruption. Furthermore, we used the illustration of the disruption stages of Sheffi and Rice (2005) to discuss whether the trajectory applies to the experienced COVID-19 disruption or where the illustrated trajectory diverges from the experienced trajectory.

The third part relates to the SCRes measures within the different stages of the pandemic based on our second theory-based research framework, the categorisation of SCRes measures (table 1). The six categories are supply chain (re-) engineering, collaboration, agility, risk management culture, technological innovations, and sustainability. To stick to our qualitative approach, we asked which and how measures were used within the different categories during the different phases of the disruption and which value the measures had from the interviewee's standpoint.

The interview was supported by a PowerPoint presentation that illustrated the stages of disruption and the categorisation matrix to enhance the understanding of the theoretical concepts and facilitate the discussions. Both researchers attended every interview, and the interviews were either conducted in English or German. After the interview session

and a debriefing, the interviews were transcribed. Subsequently, the interviews conducted in German were translated into English with the web-based software DeepL. The interview length varied between 34 and 96 minutes. In total, 21 interviews with a summated length of 18 hours were conducted from March until May 2021.

3.2.3 Data analysis procedure

In line with our constructionist multiple case study, we decided to follow the content analysis approach to investigate our collected data. Content analysis is defined as a technique that aims to evaluate data within a particular context given the meanings a specific group ascribes to them (Krippendorff, 2018). Since our study is based on a priori design, we utilised the content analysis approach as a deductive method to be coherent (Neuendorf, 2017).

To specify, the coding scheme relied on a directed content analysis which requires predetermined codes and categories that are systematically searched for in the collected data (Hsieh & Shannon, 2005; Easterby-Smith et al., 2018). Figure 6 illustrates that we derived our codes from the established SCRes frameworks (chapter 2.4.2). On the one hand, we relied on the eight stages of disruption (Sheffi and Rice, 2005) to outline the supply chain performance trajectory. Hence, the codes encompassed the different disruption stages. On the other hand, we used our synthesis of SCRes measures as codes to structure how the SCRes measures were used and valued during the pandemic. We conducted the coding with the qualitative research computer program ATLAS.ti. The software enabled us to scan the transcripts and tag the relevant parts with the codes illustrated in figure 6.

Above that, we complemented the directed content analysis with a summative content analysis approach that aims to understand content by identification and quantification. The quantification attempts to explore the usage of the content, which goes beyond a quantitative focus on counting (Hsieh & Shannon, 2005). We applied the summative content analysis approach to objectify the usage of SCRes measures. First, we counted the mentions of SCRes measures in the interviews to summarise our findings (table 3). Second, we also quantified which SCRes measures were used for each disruption stage (table 4). The quantifications were used to explore the usage and value of the SCRes measures to respond to the COVID-19 disruption.

Figure 6: Coding scheme Source: Adapted from Easterby-Smith et al., 2018

3.3 Secondary data Our secondary database is twofold. At first, to reflect the findings gained through our interviews, we reviewed corporate data such as business reports or statements on the companies’ websites. Further, we used our theoretical study as the second set of secondary data. Following, we will also describe our approach to article sampling.

3.3.1 Data and reports of case companies

Secondary data sources include business reports, websites, or newspaper articles to provide further information regarding a specific company, supplier, or product (Easterby- Smith et al., 2018). These data sources enable a researcher to compare different datasets or gain further in-depth insights (Irwin, 2013). Examining a variety of data sources helps to create triangulation, which increases the credibility of a qualitative study (Guba & Lincoln, 1982). We utilised this form of secondary data by reviewing business reports

from the year 2020 of Scandinavian and German automotive OEMs and their leading suppliers. We further reviewed additional presentations and reports that interviewees provided in some cases. By reviewing those datasets, we aimed to manifest and expand the results derived from our primary data.

3.3.2 Systematic literature review and article sampling

Integrative or traditional literature reviews are common concepts to sum up representative literature on a research topic in an integrated way to generate new frameworks and perspectives on the topic (Torraco, 2005). Therefore, we relied on the integrative literature review in chapters 2.1, 2.2, and 2.3 to introduce general aspects of supply chain management and provide the context for our study of SCRes in the automotive industry. We selected, evaluated, and summarised the literature based on our reflection (Jesson et al., 2011; Easterby-Smith et al., 2018). As a part of our reflection, we considered the number of citations and the contribution to the overall literature. Hence, we regarded the selected sources as the most relevant background information for our research.

In contrast, we applied a systematic approach for the supply chain resilience chapter (2.4). A systematic literature review is characterised by a restriction to peer-reviewed articles and searches in the leading bibliographic databases. Further, the article sampling is based on explicit criteria, and each filtering decision is documented. The systematic approach aims to identify, evaluate, and synthesise the relevant studies on a given topic (Easterby- Smith et al., 2018). We used the systematic literature review to give an overview of definitions and concepts of SCRes that can be found in the literature. As a result of this, we developed the theoretical frameworks for our research purpose. Second, we synthesised the measures and outcomes of SCRes that are considered adequate and therefore contribute to a competitive advantage. As an outcome of our systematic literature review, we critically evaluated the previously done research in the field of SCRes. Thereby, we also identified research gaps in the literature, which built the foundation for our empirical investigation.

Concerning the article sampling, we built our systematic literature review (2.4) on the online database Web of Science. We searched on February 9th, 2021, for relevant academic literature based on a set of keywords. At first, we selected the keyword “supply chain” with the filter “Topic” AND the keyword “resilience”, also with the filter “Topic”.

This search request led to a total amount of 1405 articles. Consequently, to further narrow the number of articles, we focused our search scope on the manufacturing sector to which the automotive sector belongs. Therefore, we added the keyword “manufacturing” and the filter “Topic” to our search request, which led to 181 articles. However, to find all relevant articles for our research, we also added the keyword “COVID” as we wanted to avoid missing recent literature investigating the pandemic connected to SCRes. Having “manufacturing” OR “COVID” as a third search row resulted in 250 articles.

As a second step, to ensure the quality of the articles, we further restricted our literature review to articles from journals that have an impact factor above 1.5 since these journals are considered reputable international journals (Easterby-Smith et al., 2018). Consequently, 78 articles were excluded due to their impact factor below 1.5, including conference papers. Last, we transferred the remaining 173 articles into the reference management and knowledge organisation software Citavi and investigated the abstracts, introductions, and conclusions to identify which articles appropriately relate to the topic of SCRes. The examination of the abstracts, introductions, and conclusions revealed that several articles related to resilience from a medical, agricultural, or computer science perspective rather than to SCRes. Therefore, we had to exclude these 88 articles. The remaining 85 articles built the basis of the systematic literature review.

3.4 Research quality Since our research follows an engaged constructionism philosophy, a dependency on the experiences and knowledge of the experts is created. To warrant research quality, we follow Guba and Lincoln (1982), who defined four criteria that lead to a trustworthy qualitative study when being appropriately implemented. Those four criteria are credibility, transferability, dependability, and confirmability (Guba & Lincoln, 1982).

First, the credibility criterion deals with how compatible the results of qualitative research are (Merriam, 1998). To enhance the credibility of our research, we used the triangulation approach, which is defined as the usage of different kinds of actions or perspectives to build up confidence in the exactness of observations (Patton, 1990; Easterby-Smith et al., 2018). We enable triangulation by using primary and secondary data and comparing opinions from the various experts we interviewed. Above that, tactics were considered, such as emphasising the researcher’s independent status or clarifying the possibility of

withdrawal for the interviewee to establish credibility (Shenton, 2004). We included those tactic recommendations in the GDPR consent form that the interview participants signed. Furthermore, we guaranteed anonymous treatment in our information sheet, which protected participants from eventual negative repercussions (appendix 5). With this, we wanted to create an atmosphere in which the interviewees could openly discuss their opinions and perspectives to enhance our research’s credibility.

Second, transferability describes the extent to which the results of a study can be employed for other contexts or settings. The assessment of the extent of transferability lies within the researcher’s responsibility conducting the generalising (Shenton, 2004). Further, Guba and Lincoln (1982) mention that transferability is possible to a certain degree if an accurate description of the research contexts and central assumptions are available. We ensured transferability by providing an in-depth description of our research design and describing in detail the criteria used for our purposive sampling approach.

Third, dependability describes the expectation that if the research work were repeated as described, in the same context, with similar methods and participants, the obtained results would be similar (Shenton, 2004; Saunders et al., 2009). We aimed to avoid dependability concerns by sampling data from companies that signalled their willingness to participate in this research project and share information with us beforehand. Further, we tried to reduce the participant bias by creating a pleasant interview atmosphere by complying with the interview agenda to the interviewee’s schedule. Also, we explained our questions through the assistance of PowerPoint slides, and we ensured that the participants had time to develop their answers. Since we guaranteed anonymity beforehand of each interview, the respondents could speak their minds without worries of any negative consequences from third parties.

Fourth and last, confirmability refers to the quality criteria of ensuring objectivity. Herein, the researcher must take steps to help assure that the research results are the outcomes of the ideas and experiences of the informants, rather than the opinions and preferences of the researcher (Shenton, 2004). One of our approaches to ensure confirmability was to clearly describe how we generated, coded, and analysed the sampled data throughout the study. We further created confirmability by utilising comprehensive citations to clarify how we obtained our findings and interpretations.

3.5 Ethical reflection Ethical principles refer to the appropriateness of the researcher’s behaviour concerning the right of those who become part of the research or are affected by it (Easterby-Smith et al., 2018). Therefore, research ethics are associated with the formulation and specification of a research topic, the access and collection of data, the storage and analysis of data, and the write up of research results in a morally and responsibly way (Saunders et al., 2009). To assure an ethical approach throughout our study, we follow the principles of Bell & Bryman (2007), which are: 1) ensure that no harm affect research participants, 2) respect the privacy and dignity of research participants, 3) ensure the fully informed consent and confidentiality, 4) protection of anonymity, 5) avoiding deception, 6) the requirement to declare any affiliation, 7) warrant honesty and transparency, 8) create reciprocity, and 9) avoid misrepresentation (p. 71).

Those ethical principles were considered throughout our whole thesis. During our interviews, we were guaranteeing the anonymity of the participant’s name, position, and organisation and clarifying the usage of the conducted data by sending the GDPR consent form to each interview partner (appendix 5). Each participant signed the consent form before the interview, in which we explicitly guaranteed anonymity, confidentiality, the possibility of withdrawal, and data access after the interview. We further clarified the purpose and background of our study when reaching out to possible respondents. To assure social distancing during the current COVID19-pandemic, we conducted all interviews online via Microsoft Teams. Therein, all participants accepted a recording of the interview conversation to make transcribing possible. To further assure confidentially and build trust, all mentioned persons or companies were held anonymously within the transcripts.

Furthermore, we used pseudonyms for each interview participant when citing our empirical data throughout our study (e.g. “I 1”). All recorded audios and the transcripts were stored securely and immediately deleted once the research process was over. Further, we warrant that we carry out this master thesis research for academic purposes only and have no other interests at stake.

4. Description of empirical findings ______The following chapter summarises the main empirical findings based on the conducted interviews. The first section describes and visualises the supply chain performance trajectory of the COVID-19 disruption in the automotive industry. The second section outlines the SCRes measures concerning the COVID-19 disruption. ______4.1 Supply chain performance regarding the COVID-19 disruption

The interviewee’s descriptions of the supply chain performance trajectory are summed up in four sub-chapters. First, we focus on the pre-disruption stage from January 2020 to March 2020. Second, the production stoppage, starting at the end of March 2020 until the end of April 2020, is described. Third, we describe the restart and recovery from May 2020 to December 2020. Fourth and last, we reflect on the first quarter of 2021.

4.1.1 Preparation for COVID-19 disruption

During the pre-disruption stages, the OEMs’ focus was securing inventory and maintaining the production if possible (I 2). In February and March, they started increasing the stock levels of their components that are not produced in proximity to the production plants in Central Europe as they recognised the progressing virus spread in Europe (I 1). Hence, the lockdowns in China and Italy caused the first challenges in material transportation, but the availability of components was still relatively good (I 3).

Likewise, some suppliers were already affected by lockdowns in China and Italy (I 7, I 8, I 9, I 12). These suppliers already had material constraints to solve to prevent the stop of production lines on the OEM level (I 8). The demand of other suppliers, who were not affected in February and March, increased in the weeks before the disruption since the OEMs raised the orders to heighten their inventory (I 1). Thereby, the orders indicated the opposite, and there were no disruptions that we could see (I 17). Therefore, it was unlikely that the suppliers would contrast to the customer’s demand and risk a stop of the customers’ production, resulting in enormous compensation costs (I 4). I 14 summarises the situation of these suppliers:

“The situation was at the beginning of last year, January and February, that there was a repeated request from our customers on increasing their

demand. And we were trying to follow that, and although the situation was getting already worse in Italy.” (I 14)

4.1.2 Production stoppages after COVID-19 disruption

The OEMs ran out of certain components at the end of March 2020 and had to stop our production (I 1). Another reason for the OEMs to halt production was the contract restrictions (I 2) and the concern that simply employees would no longer be able to come to the factory, and we would not be able to produce for lack of employees (I 3). I 2 pointed out the interests of the OEMs before the stoppage:

“It was more a question of prioritising, of looking at where we could tear down first, how we could push it out as far as possible and, above all, how we could control it. In other words, we know exactly that we are going to tear down in week 35, which means that we will close our work in weeks 36 to 40 and not say okay, we will close in week 36 and then realise at the beginning of week 30, something is not working here.”

Nevertheless, the actual decision to stop the production lines was at short notice. Hence, the OEMs were flooded with other components that were already in the delivery process. Above that, they had to cancel further orders. Although, the OEMs also used the shutdown to fill up the inventory of the scarce components (I 1, I 2, I 3). The length of the production stoppage at the OEM level varied between 4-8 weeks (I 3, I 14, I 17).

Depending on their supply chain tier, the suppliers were informed about the production stoppage and the refusal to receive new supply either from the OEM directly or from their superordinate tier 1 supplier (I 6, I 12). Generally, the suppliers received the information at short notice between one and seven days in advance (I 6, I 7, I 17, I 18). In a meeting with an OEM in March after the problems in Italy were already very clearly visible, I 6 asked whether the production would continue, which the OEM confirmed. I 6 inevitably had to continue working, but they were informed about the production stoppages one week later. The suppliers then had to coordinate the reception, rerouting, and refusal of incoming and outgoing goods with lots of hands-on operations (I 17). The suppliers like I 10 and I 11 could not cancel every order considering the long lead times of components that were already underway:

“What was massive for us at the beginning of the first two months was not so much that we now had too little material, but quite the opposite. The material was pushed in. Especially when you are now being supplied from Asia, the containers were unstoppable. But our customers, the OEMs and then the tier ones, said no, I do not need to supply anymore. We are closed. So, this was not a shortage of material. It was a surplus of material in the first place.” (I 11)

However, the suppliers that were already affected by the lockdowns in China and Italy could outbalance their supply deficit, and the situation eased for them (I 8, I 12). Furthermore, there was uncertainty at every supply chain level whether and how fast the economy would recover. Consequently, some companies prepared for a conservative recovery scenario in the weeks after the production stoppage and reduced their fixed costs to minimise the losses. These firms reduced their number of employees, prepared to decrease their stock levels over the weeks, and made new forecasts that resulted in smaller raw material orders for the rest of the year (I 8, I 17, I 18).

4.1.3 Restart and recovery after COVID-19 disruption

The OEMs started the production on a low level and increased the production volume steadily (I 2). Due to the uncertainty, the OEMs started to produce the ordered cars in the pipeline until the economic situation became more evident, as I 1 explains:

“You can see that especially during the first wave of Corona, the demand dropped massively, so there were hardly any orders for vehicles. But we still had a lot in the pipeline, so we made up for these vehicles first.”

To produce these cars, the OEMs first checked the availability of the suppliers and their components. Also, protection measures had to be installed to ensure the employees’ health (I 3). The OEMs were producing at full capacity again around July and August (I 1). Furthermore, they reached their capacity limits at the end of the year 2020 (I 3). Nevertheless, the capacity was also restricted at some level by the availability of components such as semiconductors. I 21 explained that the orders of semiconductors were reduced during the disruption. Consequently, the capacities of the semiconductor

industry drifted towards the thriving consumer electronics industry, which could not be reversed without delay due to the long lead times (I 13, I 20, I 21).

The suppliers followed the approach of the OEMs. The first-tier suppliers were informed that the production would restart at a specific date, and the information was forwarded to the upstream suppliers. At the supplier level, a steady increase in demand over the summer was recognised (I 10, I 11, I 18). Then, the demand of the OEMs exceeded the plans of the suppliers. In general, the suppliers expressed their surprise concerning the quick recovery and the correspondingly high demand from the OEMs (I 8, I 17, I 20, I 21):

“We were taken by surprise. We slowly stopped reordering and used up our stock. But when it was used up, it just went in the other direction again, and that is why we had these problems.” (I 19)

Especially the companies that followed a conservative forecast for the second half of 2020 were overwhelmed by the demand of the OEMs:

“And we made a new forecast and used it with a different way to get the market situation to understand that. And it seems that we underestimated the demand then because it was raised very fast then during the Q3 and Q4 especially on some products.” (I 8)

This resulted in the first backlashes after the disruption, and several components needed an allocation process on the OEM level. Despite the disturbances, the suppliers managed to fulfil the demand, and most of the suppliers reached production levels above the volume they had at the pre-COVID-19 disruption stage (I 7, I 13).

4.1.4 Backlashes after COVID-19 disruption

The first quarter of 2021 involved further backlashes. The OEMs manufactured on similar production levels as before the disruption, but the availability of electrical components that include semiconductors was still tense (I 2). Hence, the semiconductor shortage, described as one of the biggest problems at the beginning of 2021 (I 1), also led to a short production stoppage for some OEMs (I 4). Consequently, the OEMs rescheduled their production to manufacture the car models where the scarce components were still

available (I 6, I 19). Also, they monitor impacts such as the shortages in the raw material sector of steel and plastic granulates and the limited air and sea freight capacities (I 3).

Comparable to the second half of 2020, the first and second-tier suppliers adjusted to the rising demand of the OEMs, and the production volume also returned to a normal level (I 7, I 13, I 17). Above that, several suppliers even report a growing demand, especially towards the tier 2 and tier 3 level (I 5, I 10, I 12, I 16). I 4, as a supplier involved on tier 1, 2, and 3 levels, recognises normal growth based on actual market demand. Whereas I 19 as a pure third-tier supplier, cannot understand where all these parts are going that we are selling at the moment. As a result, the suppliers that were involved in the allocation process during the second half of 2020 are still affected by backlogs that lead to understocking and under-supply of finished parts, of semi-finished parts in all plants worldwide, and also in the raw material area as I 6 specifies (I 8, I 16, I 19). Despite the high demand, the suppliers also experienced massive fluctuations (I 6). On the one hand, they were affected by the described semiconductor, steel, and plastic shortages. On the other hand, the suppliers had additional freight capacity issues, packaging shortages, and further virus outbreaks (I 5, I 7, I 19). I 14 summarises the backlashes that the suppliers experience at the beginning of 2021:

“Since the end of last year to the first quarter of this year and it continues with the availability of material, with the price of the material going up, with the hiccups in the supply chain but also at our customers. Because our customers are also having all kinds of problems, some with COVID, some related to a lack of semiconductors, some related to a lack of other parts, because the supply chain is at the moment under a huge strain and stress.”

4.2 Supply chain resilience measures regarding the COVID-19 disruption As the second part of our findings, the following table 3 illustrates the SCRes measures used to respond to the COVID-19 disruption. The list of measures relates to the synthesis of SCRes measures in table 1. Based on our content analysis approach, we outline which SCRes measures each interviewee’s case company used. The industry analysts I 20 and I 21 relate to the measures they are aware of that they were used in the industry. Further, the following sub-chapters encompass the number of interviewees that refer to each measure and their remarks.

X X X I21

X X I20

X X X X X X X I19

X X X I18

X X I17

X X X X X X X X X I16

X X X X X I15

X X X X X X X I14

X X X X X X I13

X X X X X X X X I12

X X X X I11

X X X I10

X X X X X X X I9

X X X X X X I8

X X X X X X X I7

X X X X X X I6

X X X X X X X I5

X X X X X X X I4

X X X X X X X X X X X X I3

X X X X X X X X X X X X X I2

X X X X X X X X X I1 I1

Measures Interview 1.1 Resilience-oriented Supplier Resilience-oriented 1.1 selection supply chain Resilience-oriented 1.2 design inventory) (reserve Redundancy 1.3 or forecasting, Simulation, 1.4 (DSM) model support decision Sourcing Diversification/Dual 1.5 / mapping chain Supply 1.6 review vulnerability plan recovery Disaster 1.7 sharing Information 2.1 planning Collaborative 2.2 Risk hedging 2.3 (product operations Agile 3.1 time) development flexibility Manufacturing 3.2 visibility chain supply Enhance 3.3 Logistics flexibility 3.4 system information control of Use 3.5 reduction) time (pipeline Velocity 3.6

Table 3: Overview of SCRes measures in practice Source: Own construction

X X X I21 I21

X I20 I20

X I19 I19

X X I18 I18

X X X X X X I17 I17

X X I16 I16

X I15 I15

X X X X X I14 I14

X X X I13 I13

X X I12 I12

X X X X I11 I11

X X I10 I10

X X X X I9 I9

X X X X I8 I8

X X X I7 I7

X X X X X I6 I6

X I5 I5

X X X X I4 I4

X X X X X I3 I3

X X X X X I2 I2

X X X X I1 I1

Measures Interview . Rslec-retd talent Resilience-oriented 4.1 management awareness resilience Creating 4.2 employees among management Continuity 4.3 into Factor considerations risk 4.4 making decision employees of Cross-training 4.5 RFID technology, Blockchain 5.1 twin chain supply Digital 5.2 machine 3D printing 5.3 analytics, Data 5.4 intelligence artificial learning, capabilities economy Circular 6.1 remanufacturing) (relocalisation, and principles: economy Sharing 6.2 capacity excess sharing consumer integration supplier Green 6.3

Table 3 (continued): Overview of SCRes measures in practice Source: Own construction

4.2.1 Supply chain (re-) engineering

Table 3 displays seven resilience measures that relate to supply chain (re-) engineering. First, the resilience-oriented supplier selection (1.1) was mentioned two times (table 3), and it is used independently of the COVID-19 disruption. I 3 described that then we also have risk management behind it, which also consists of financial risk management, where there is also a financial evaluation and a risk management evaluation for every listed service provider.

Second, the resilience-oriented supply chain design (1.2) was mentioned eight times (table 3) and is focused on increasing the own production capabilities. What we are building up is our own production because that is simply our tendency, where we want to go (I 16). But during the ongoing COVID-19 pandemic, changes were rarely made because you are not allowed to change anything if you have a product that has been qualified and validated in terms of all the tests that have to be done (I 15).

Third, the measure of stock level enlargement (1.3) could be found on every supply chain level as a reaction to the COVID-19 disruption and was mentioned by ten interviewees (table 3). I 1 reported that we still have higher stocks than before the pandemic. However, there are limitations to the increase of inventory. First, the companies are restricted by their limited storage space (I 1, I 5). Second, the stock increase is linked to tied-up capital and increased costs (I 7, I 17). Third, not every supplier could increase their inventory due to previous shortages (I 8, I 16). Nevertheless, increased safety stocks were recognised as advantageous on all supply chain tiers. I 6 summarises the value of increased stock and highlights the role of finished goods:

“It definitely helps us to balance risks there and to deal with them. So, from my point of view, the most fundamental issue in terms of resilience is how to measure stocks of finished goods, how expensive are finished goods compared to a non-supply.”

Fourth, especially suppliers perceived forecasts and simulations (1.4) as relevant measures which were named seven times (table 3) as displayed by I 11:

“We have realised earlier that long-term planning is extremely helpful in tackling such dents, such downturns, and we work in the automotive industry, where we get from 90-95% of our customers a preview of at least six months. Some even go to 12 months, so that is even more popular.”

Also, planning meetings to create those long-term forecasts and simulations were mentioned by suppliers like I 6, who described:

“What has helped us a bit are standardised, weekly meetings […] where the planning department sits together, partly supported by key accountants and they discuss all customer developments that affect the respective planners with a lead time of 5 months.”

Fifth, interviewees acknowledge the potential of diversification and dual sourcing (1.5) to increase SCRes, as six interviewees referred to this measure (table 3). I 19 as a supply chain tier 3 declared that they could then validate a second supplier for material very quickly, which is also very difficult in our business. Within the direct suppliers on the supply chain tier 1 level, the supplier base was reviewed so that for purchasing or material procurement, the thought has come up that maybe we should have a backup supplier for us. That has already changed. (I 7).

Sixth, the mapping of the supply chains (1.6) was found seven times within the sampled data (table 3). For example, the supply chain tier 1 and 2 respondent I 7 described:

“What helped a lot was to understand where the suppliers are located. First of all, it was important to identify which supplier is located in which province and which province is closed at the moment, or which border we are crossing at the moment. That was very important, also to think about, okay, how can we set up possible routes?”

Seventh and last, the creation of disaster recovery plans (1.7) was mentioned by three interviewees (table 3). All three OEM respondents mentioned reactive plans for the case that their JIT deliveries are delayed. For instance, I 3 relies on a tracking system based on GPS (Global Positioning System) to monitor their JIT transports.

4.2.2 Supply chain collaboration

In general, an intensified collaboration and communication were recognised among all tiers of the automotive supply chains. Several interviewees acknowledged the closer coordination (I 1) and the higher frequency of meetings upstream and downstream (I 8) than before the COVID-19 disruption. To further distinguish the category of supply chain collaboration, three measures were investigated.

First, the sharing of information (2.1) was described to play an essential role since data sharing and knowledge sharing are elementary (I 6). Sixteen interviewees referred to this measure (table 3). The different supply chain tiers are connected via EDI systems to a high degree:

“So, we have EDI communication with our suppliers, which is beyond 96/97/98%, depending on the projects, […] in this reaction it helped us to control better what was coming up or what was no longer coming up, so what could be cancelled.” (I 3)

Even though the EDI systems were generally helpful, the systems were impacted by a certain amount of uncertainty because we did not know when the production would start again and in what quantities we would be able to produce as described by I 1. As a result, the systems were not updated and maintained, which led to call-off orders after the COVID-19 disruption (I 6, I 14).

Second, the collaborative planning (2.2) was named by 15 interviewees. The interviewees referred to the exchange between OEM and supplier based on the demand forecast provided by the OEM with a horizon of 6-12 months (I 1, I 2, I 11, I 17). Especially in the start-up logistics of new models, the collaboration intensified because in contrast to simply sending out information on a shutdown, we also proactively asked the suppliers (I 2). However, most of the suppliers are still the recipients of the news because it is a very reactive world in the automotive industry (I 6). Also, the collaborative planning was impacted by uncertainties because in the situation we are in today, we can have new information in 1-2 hours later (I 8). Still, the aspect of collaboration was described as a helpful aspect as summarised by I 9:

“These long-term calls or medium-term calls, you could orientate yourself on how the start-up curve is shaping up again, how we should come back, how we should align ourselves […], that was helpful.

Third, risk hedging (2.3) or risk sharing over the supply chain was mentioned in three responses (table 3). It was explained that the OEMs have the dominant role in all relationships. However, discussions between the OEMs and their suppliers were still possible to solve the risks related to capital cost due to high inventory or the production stoppages (I 7, I 11). Furthermore, the risk-sharing was also helpful between the suppliers in their supply chains who collaboratively solved the issues (I 9, I 12).

4.2.3 Agility

We investigated six agility measures. The shortening of product development time (3.1) as the first considered measure was only mentioned twice (table 3). The automotive industry is characterised by fixed product life cycles with certified components (I 12). Consequently, the product development was not applied as a SCRes measure.

Second, the manufacturing flexibility (3.2) was named by 13 interviewees (table 3). On the OEM level, manufacturing flexibility was described to play an important role. I 1 stated that they are restricted in the manufacturing of different models on one assembly line. However, they have partly exploited these restrictions to be able to build the vehicles where we have all the parts available (I 1). At the supplier level, two approaches could be found. On the one hand, suppliers such as I 11 mention that their enterprise does not tend to be a production company that is oriented to high flexibility. On the other hand, companies emphasise their flexibility and ability to switch between certain products of different customers within the manufacturing process, which they used primarily during the uncertain restart (I 5). Furthermore, there is also an example of manufacturing flexibility over multiple supply chain entities as described by I 2:

“We have also worked out methods of how we can, for example, update certain software here in our factory, also by inviting suppliers, in other words by, let us say, bringing a part of the supply chain to our factory at an early stage and then saying, okay, we do not risk anything again on the transport […], but we bring the parts directly to our factory and then say,

okay, maybe two people have to come to our factory to rework or flash the parts.”

Third, the supply chain visibility (3.3) was pointed out in six interviews (table 3). It was outlined as helpful on the OEM level to track the supplies enabling a reaction if any shortages occur. I 1 specified that as suppliers are obliged to report their stocks to us daily, so that we have a very good overview and our dispatchers are divided into suppliers, and they check the suppliers’ stocks every day and intervene accordingly, blocking vehicles if parts are not expected to be delivered.

Fourth, logistics flexibility (3.4) was described by eleven responses (table 3). The switch to air freight was specified as the primary measure within logistics flexibility and thereby secured the production after the COVID-19 disruption (I 1, I 3, I 15). Other switches in the modes of transportation were point-to-point connections and small shipping companies to use private ports (I 6) for sea freight, Sprinters, or standard mega-trailers with multiple driver changes (I 12) for road transport. Moreover, the switch to rail transport was also used by some suppliers (I 7, I 19). Besides the switch of transport modes, another aspect of logistics flexibility was the creation of additional transport capacities before the production stoppage, as I 2 illustrates:

“We are not the only OEM that produces any parts in China, especially in Wuhan or the surrounding area. We have to make sure that we are not the last ones to book any transport options, ship, plane, etc. when it comes to bottlenecks. Yes, then it went relatively quickly that we had these bottlenecks and then used the transport capacities that had fortunately been created beforehand.”

Fifth, the use of control information systems (3.5) was mentioned in eleven interviews (table 3) and can be found on all levels. The OEMs regard it as the daily business of our dispatchers to check the suppliers’ stocks every day and intervene accordingly if deliveries are not on time (I 1). Similarly, tier 1 and 2 suppliers point out backlog lists (I 11) used as an information system to identify potential problems (I 12, I 13). One of the more prominent suppliers who operate on tier 1, 2, and 3 stated that they have a

centralised control tower that monitors and deals with backlogs and delivery issues for all supply chains in the company (I 4).

Sixth, the velocity or pipeline reduction (3.6), which was named 12 times (table 3), relates to the described actions as a part of logistics flexibility. The pipeline reduction was described as one of the primary measures during the recovery to secure the production and was mainly achieved by using air freight (e.g. I 1, I 3, I 15). I 6 stated that he has never flown so much material in the past ten years as in the last four months. Especially the suppliers that experience a shortage, such as the semiconductor suppliers, rely on the increase of velocity, as I 16 underlines:

“All we do or can do at the moment is really to shorten delivery times.”

4.2.4 Supply chain risk management culture

Five measures were investigated in the SCRM culture category. However, the resilience- oriented talent management (4.1) as the first considered measure was not touched upon by the interviewees (table 3). Second, resilience awareness (4.2) was mentioned in 12 interviews (table 3). The primary expression of that measure over the whole automotive supply chains was the board-level involvement. On the OEM level, the importance of restoring the production capacities and a working supply chain was recognised on the top management level (I 1). Also, on the tier 1 level, I 9 said that the managing directors or the top management were also involved in the controls and directly on site. Last, I 17, on supply chain tier 2, described that the board and the top management have been really involved in the whole procedure and the process […]. So, that has been working well under the circumstances.

Third, continuity management (4.3) was named 13 times. It is mainly related to the set- up of task forces. The OEMs used task forces to deal with the COVID-19 disruption and its aftermath, as the semiconductor shortage. As an example, I 1 mentioned:

“We have various task forces dealing with the issue of Corona, especially in the area of supply security, but also for the semiconductor issue where we are trying to counteract with additional capacities.”

The data shows a split picture on the supplier level. Some suppliers used task forces, while others deployed their normal processes to cope with the situation. For example, I 11 mentioned that it was handled through the standard division of labour. For the establishment of supply chain task forces, I 12 stated there were task forces as this could no longer be solved with normal management. Further, escalation management systems were used as a part of continuity management to recognise if certain deliveries are at risk (I 12).

Fourth, several approaches were described how risk considerations were factored into decision making (4.4) as 13 interviewees referred to it (table 3). For instance, I 17 mentioned that they reviewed agreements saying that if we cannot deliver on time, then that will cost us a lot of money? And of course, if we had such agreements, those would also be prioritised to find new ways and new suppliers. Apart from that, they also assessed the risks of customers going bankrupt. Also, I 14 declared that they recognised dependency on certain suppliers as risky and are now working on at first, breaking the monopoly of the big ones and the Corona pandemic, which was, let us say, strengthening the decision. Furthermore, it was seen as an advantage to have the suppliers close by to reduce cross-borders risks (I 20).

Fifth and last, the cross-training of employees was only mentioned by three interviewees (table 3). Some suppliers used it during the stages of low production and short-time working to access the total capacity of the remaining production employees (I 8, I9).

4.2.5 Technological innovations

The reviewed data implicated that most automotive firms used technologies exemplary for the planning processes or communication. As mentioned in chapter 4.2.2, many companies mainly work with EDI and ERP (Enterprise-Resource-Planning) systems for planning processes and information sharing. Regarding communication, the importance of technologies like Microsoft Teams increased due to social distance obligations during the COVID-19 pandemic (I 3, I 11, I 15). Nevertheless, the interviewees primarily relied on existing technologies and technological innovation due to the COVID-19 disruption rarely took place (I 6, I 7, I 12, I 15). The four defined supply chain measures of this category illustrate the low relevance.

First, RFID or blockchains (5.1) were named during three interviews (table 3). However, they were rarely used in the automotive industry to track and trace the flow of materials along the supply chain (I 1, I 2). Instead, I 3 explained that they rely on standard tracking tools based on GPS to react if, for instance, a JIT delivery stuck in traffic or due to border controls. I 1 described a likewise tracking tool as they are trying to implement the digital transmission of delivery stocks, especially with our JIT suppliers. We have already done this with the first components, which means that the planner can access the stocks in the warehouse live and can see whether he has to intervene here or not.

Second, the usage of a digital supply chain twin (5.2) was not reported (table 3). I 1 and I 6 described specific initiatives where they digitalise and map the movement of material. However, these initiatives were already started before the COVID-19 disruption. Third, 3D printing (5.3) was similarly not mentioned by any case company (table 3). Fourth and last, the usage of big data analytical tools (5.4) can be recognised to a limited extent within the sampled data since it was pointed out by four interviewees (table 3). I 3 illustrated that we have what we call value-added analytics, where we analyse all the data that is collected in a big data application, where we can simply recognise patterns where there are deviations of a structural nature so that we can then react accordingly in the future planning. However, this tool was also implemented before the COVID-19 disruption.

4.2.6 Sustainability

Last, we investigated three measures that relate to the sustainability category. First, 13 responses named the circular economy capabilities (6.1) as a relevant SCRes measure (table 3). The central aspect of this measure was the (re-) localisation of suppliers due to cost, sustainability, and risk considerations. For example, I 14 on the supply chain tier- one described that they were always having our key suppliers close by, and this is what keeps on doing. We already moved some supplies from China to Turkey because it was closer and easier to handle. Moreover, on the OEM level, I 2 summarised:

“Yes, relocalisation is definitely the keyword, and I have noticed that they have consciously said that they want to reduce the size of the supply chain, even if at first glance it is more cost-intensive to produce somewhere else, but that there are no border crossings. These are also issues that later have to be covered by expensive special processes such as air freight or special

transports. And if you do not have to do that in the first place, the whole thing will probably be a bit cheaper at the end of the day.”

Second, aspects related to the sharing economy principles (6.2) were only mentioned in two interviews (table 3). One of them was I 11, who received requests from other suppliers within their network in the plastics processing industry whether they could spare some tonnes of plastic granulate. Besides, sharing suppliers or customers with other suppliers on the same level was not considered as a SCRes measure. Third and last, the integration of green suppliers (6.3) was named only four times (table 3). Nevertheless, the topic’s relevance was acknowledged as comparable to the circular economy capabilities aspect (I 10, I 12, I 17).

5. Data analysis and interpretation ______The purpose of this chapter is to analyse and interpret our empirical data. We are discussing the findings from our interviews regarding our theoretical framework derived from the literature. The findings of this discussion will pave the way to answer our research questions. ______

In general, the empirical study confirms our assumption that the COVID-19 disruption is a special case of disruption that justifies an investigation (chapter 3.2). The supply chain experts reported on suppliers that filed for the contractual force majeure clause. The suppliers could not supply anymore due to the COVID-19 disruption, which they classified as an extraordinary event to be freed from their contractual obligations to supply (I 12, I 14, I 19). Further, the interviewees referred to the financial crisis 2008-2009 to emphasise the scope of the COVID-19 disruption. They clarified that the financial crisis had a slower and more drawn-out impact, and it had fewer collateral damages on the sea and air freight access, ports, and border controls (I 2, I 5, I 17). Therefore, several interviewees stated that they had not experienced a similar disruption (I 6, I 7, I 11):

“And the COVID pandemic, with the issues that were in there, with the fast- moving nature and the collateral damage, I have not experienced in my professional life so far in the past 25 years.” (I 6)

We rely on our theoretical study to structure the analysis. First, the structure of the supply chain performance analysis in chapter 5.1 is based on the eight disruption stages of Sheffi and Rice (2005). Second, the disruption stages are combined with the synthesised overview of SCRes measures (table 1) to interpret in chapter 5.2 how the measures were used and valued during the varying disruption conditions.

5.1 Analysis of supply chain performance

The following chapter answers the first research question that asks for a description and visualisation of the supply chain performance trajectory in the automotive industry during the COVID-19 pandemic. The responses of the supply chain experts (chapter 4.1) are structured by applying the disruption trajectory from Sheffi and Rice (2005) to the

COVID-19 disruption (figure 7). The COVID-19 disruption demonstrates all eight stages of the theoretical model, which enhances the framework’s relevance.

Figure 7: COVID-19 disruption impact on supply chain performance Source: Own construction, adapted from (Sheffi & Rice, 2005)

The findings revealed small preparation efforts of the automotive industry specifically for the disruptive event, the initial COVID-19 outbreak in China. Instead, the automotive industry relied on its risk management and information systems. However, the OEMs increased their inventory as a first response, leading to higher demand at the supplier level. The initial impact struck the first suppliers in the quarantine regions of China and Italy and stopped their production in February and March. Subsequently, OEMs also experienced the propagation of the disruption, known as the ripple effect (Ivanov et al., 2014; Ivanov, 2020), regarding the first shortages and a declining production volume and supply chain performance.

The time of full impact relates to the production stoppage from the end of March until the end of April 2020 initiated by the OEMs due to lack of components and increasing security and health concerns over the production employees. Therefore, the supply chain performance collapsed for this period. The parallel preparation for recovery was not aligned. Consequently, the conservative recovery planning with a stepwise decrease of the high inventories and reduced long-term orders of some suppliers contributed to further turbulences in the subsequent recovery stage.

The OEMs also initiated the recovery process, and the automotive supply chains proceeded steadily and fast. Thus, the supply chain performance also rose steadily during the recovery stage from May until December 2020. However, the unaligned preparation for recovery and semiconductor, steel, plastic, and other shortages contribute to supply chain turbulences that confine the supply chain performance at the last stage in the first quarter of 2021.

Nevertheless, based on the quick recovery of production volumes and overall supply chain performance, it can be stated that the automotive industry has, in general, a high level of SCRes. Moreover, the OEMs such as I 2 recognise a high SCRes along the supply chain:

“So I think we have definitely noticed that we can now trust ourselves with many issues in terms of problem-solving that we probably would not have trusted ourselves with a year or a year and a half ago and that the suppliers are simply much, much more flexible than they were two years ago.”

Furthermore, the responses revealed that the COVID-19 disruption trajectory is more compressed than the theoretical illustration of Sheffi and Rice (2005, figure 2), as indicated by figure 7. This reflects the described speed of recovery in the automotive production and supply chain performance (I 1, I 17). Also, it can be specified that the COVID-19 disruption in the automotive industry followed a V-shaped recovery regarding production volume and turnover (I 12, I 14, I 18). Regarding that Belhadi et al. (2021), as one of the sources from the literature review, developed a U-shaped and V-shaped recovery scenario, it can be validated that the V-shaped scenario became a reality. This interpretation is in line with several sources that display a quick V-shaped recovery based on the production figures (ACEA, 2020, VDA, 2021b).

Even though it can be verified that the automotive industry has high SCRes, several aspects can be analysed and interpreted beyond the high production volume and the illustrated trajectory. In particular, the described backlashes and the costly usage of air freight to secure production during the recovery phase and at the beginning of 2021 can be identified over the automotive supply chain. Hence, the following two sub-chapters acknowledge these supply chain turbulences. The first sub-chapter outlines the cause and

effects of the ongoing supply chain turbulences and backlashes. The second sub-chapter highlights the bullwhip effect and the resulting gap between demand and supply as the main issue for unrecovered suppliers.

5.1.1 Supply chain turbulences despite high production volumes

Even though the automotive supply chains recovered regarding the overall production volume (figure 7), the supply chain performance is still affected by a gap in material availability and lead times. Therefore, OEMs and suppliers could only secure the supply of the production line through special measures (I 5, I 6, I 12, I 16). Figure 8 highlights the leading causes of the supply chain turbulences in an Ishikawa diagram.

Figure 8: Causes of SC turbulences despite high production volume Source: Own construction

The main driver for the high production volumes is the high market demand. The automotive industry underestimated the end customers’ demand for cars during the COVID-19 pandemic. Consequently, the forecasts and orders planned during the disruption and the preparation for recovery were eventually exceeded by the actual demand. One reason is the fast containment of the virus outbreak and the subsequent quick recovery in China (I 8, I 12, I 15, I 20). Another reason is the economic stimulus packages such as the short-time compensation in Sweden or Germany (I 3, I 6, I 14, I 18, I 19) or the German purchase grant for electric vehicles (I 4, I 12, I 21). Thus, the high market demand pushed the fast recovery that contributed to the supply chain turbulences.

On the other side, the COVID-19 disruption caused several limitations that restrict the supply chain performance of the automobile industry. The described semiconductor shortage (chapter 4.1.3) as one of the causes was also recognised by other sources (CLEPA, 2021a; Vakil & Linton, 2021). Comparably, the steel supply is also affected (CLEPA, 2021b). During the disruption and recovery preparation stages, the cancellation of orders accelerated redistribution of steel supply towards Asia and especially China who utilised the free steel capacity after their quick recovery (I 11). Consequently, the automotive suppliers deal with less supply and increased steel prices due to the higher demand (I 6). Regarding the supply chain performance, the longer lead times and volatility are the primary disturbance, as I 17 reveals:

“If we had before the pandemic, maybe six weeks of delivery time from the mills, now afterwards we had maybe 8 to 12 weeks, and that has increased over time. So now we are actually up to between four and six months. And we are not even sure that we will get it on time if they say four months. It could be five and a half. So we have no idea.”

Likewise, the raw material suppliers of plastic were also overwhelmed by the volatility. Besides, the plastic industry was affected by the destruction of chemical plants due to the deep freeze and power crisis in Texas in February 2021. Thereby, the capacity for plastic granulate was further decreased, which worsened the plastic supply situation and increased the prices (I 12, I 14, I 19).

Another cause for the supply chain disturbances is the lack of freight capacity. The shipping companies also tried to minimise their losses during the pandemic and decreased their freight capacities. Fewer passenger flights led to decreased air cargo capacity, increased air freight rates, and complicated accessibility (I 19). Additionally, the sea freight rates in the first quarter of 2021 were twice as high as before the COVID-19 disruption. Furthermore, transportation on time on the agreed ship was not guaranteed due to the scarce shipping space. Above that, port congestions such as at the USA west coast affected the shipment schedules and accessibility of continents. This congestion also disrupted the container loop, which led to a lack of empty freight containers in Europe. I 7 summarises the freight disturbances in the following statement:

“This means that once a week, I am actually looking for containers somewhere in Europe, which my forwarder then has to fetch from somewhere so that we can load our goods in the hope that they will be on the next ship. And of course, the shipping companies let you pay a lot for this limited shipping space.”

The shortage of raw materials also affects the packaging of the components. Pallets, as well as cartons, are scarce (I 5, I 9). As the icing on the cake described I 12, the temporary closure of the Suez Canal end of March 2021 which led to further delays and turbulences within the automotive supply chains (I 4, I 5, I 6, I 13). In conclusion, the COVID-19 disruption caused ongoing backlogs and volatility that still affect the supply chain performance of the European automotive industry, as other authors also recognise (CLEPA, 2021c). Even though the overall recovery was fast, and the production volumes are high again, there are suppliers in the automotive supply chains that have not completed their recovery, as I 12 summarises:

“And ultimately, we are now confronted with the fact that this recovery phase has not yet been completed, but at the same time, the OEMs are, of course, pushing and trying to drive up output on a global basis. This is, of course, a rather shaky story, and we are now noticing the effects of the whole thing daily.”

5.1.2 Demand-supply gap and bullwhip effect

Based on the described supply chain turbulences, the following sub-chapter focuses on the supply chain performance of the unrecovered suppliers. Even though the overall performance of the automotive supply chain has recovered, we elaborate on the lack of material and the volatile lead times to provide a comprehensive investigation of the first research question. The chosen illustration of the supply chain performance trajectory (figure 7) demonstrates the overall high SCRes and the correlative capability to recover quickly regarding the overall level of production. However, the framework of Sheffi and Rice (2005) has two main limitations. First, the framework enforces the illustration of one perspective. Naturally, this leads to the visualisation of the overall perspective of the OEMs at the end of the automotive chain. This approach is adequate to display the overall supply chain performance during the pandemic. Nevertheless, the difficulties of the

suppliers are not exposed properly. The backlashes during the first quarter of 2021 are highlighted in figure 7, but the described efforts to secure the production during the recovery are not visible in the trajectory.

As the second limitation, the figure reduces supply chain performance to one aspect: the overall production volume of the automotive supply chains. Since we define SCRes as the capability of a supply chain to prepare, respond, and recover from disruptions by maintaining continuity of operations at the desired level (Ponomarov & Holcomb, 2009), the production volume can be perceived as the primary expression of this capability. Nevertheless, the singular trajectory does not enable us to visualise the gap between the actual demand and the possible supply that led to the raw material shortages and volatile lead times (chapter 5.1.1.). For instance, the automotive companies still received components during the production stoppage because the long-haul freight traffic could not be cancelled on short notice (chapter 4.1.2). This indicates a certain level of supply chain performance, whereas figure 7 implies that the supply chain performance collapsed at that stage. In conclusion, an enlargement of the portrayal of the supply chain performance would be the visualisation of the demand-supply gap in the automotive supply chains after the COVID-19 disruption, as illustrated in figure 9.

Figure 9: Demand-supply gap after COVID-19 disruption Source: Adapted from Lee et al. (1997)

Figure 9 contrasts supply and demand with a focus on the difficulties on the supplier level. The figure follows the same timeline as figure 7. Thereby, it points out several issues that were described by the suppliers (e.g. I 6). First, a small initial gap between supply and demand was created when the OEMs increased their demand to anticipate the upcoming disruption even though the first suppliers already experienced shortages. Second, the main gap was formed during the COVID-19 disruption when all automotive production was stopped abruptly, and the demand of the OEMs collapsed while the suppliers received further deliveries that led to high inventories. Third, the fast recovery caused a switch because the demand of the OEMs increased sharply, whereas the inventories of the suppliers decreased steadily due to the described difficulties and led to a state of understocking. Fourth, the OEMs experienced a production stoppage at the beginning of 2021 because of the semiconductor shortage indicated by the bend in the demand curve. That enabled a slight overall improvement of the supplier inventories even though the affected suppliers are still battling the understocking.

The fluctuations, the substantial switch from a stage of low demand to a stage of high demand, and the continuous backlogs on upstream supply chain tier levels are indicators of the bullwhip effect (chapter 2.3). The bullwhip effect is caused by a rapidly changing environment (Kim, 2013) which applies to the COVID-19 disruption with its abrupt stoppage and the fast recovery. Another factor contributing to the bullwhip effect is the difficulty of implementing accurate forecasts (Kim, 2013), which the COVID-19 disruption can also claim. During the downturn stages, the future demand situation was still under high uncertainty, which caused some suppliers to follow misleadingly conservative planning and forecast for the rest of the year. The distorted demand signals provided by the OEMs were also counterproductive and guided some suppliers into backlogs and understocking. Especially on the second and third-tier level, the responses of the suppliers indicate record-high demand even though it is reported that the OEMs and most of the tier 1 suppliers are on normal production level again:

“We now have a very high order backlog. We still deliver what we produce immediately.” (I 19)

These issues are indicators that aspects of the bullwhip effect, such as demand signal processing, order batching, or rationing and multiple inventories (Lee et al., 1997;

Holweg, 2005), contributed to the supply chain turbulences and a de-coupled demand on the upstream supply chain tier levels.

5.2 Supply chain resilience measures Subsequently, we illustrate our analysis and interpretation of how the SCRes measures were used and valued during the varying COVID-19 disruption conditions to mitigate the impact of the disruption on the supply chain performance. Thereby we target the research questions 2 and 3. The analysis is summarised in table 4. On the one hand, table 4 displays the synthesised SCRes measures, which builds on the interviewees' responses (table 3). On the other hand, the table's columns relate to the disruption stages of Sheffi and Rice (2005). Based on our summative content analysis approach (chapter 3.2.3), table 4 quantifies the usage of SCRes measures for each disruption stage of the COVID-19 disruption. One positive response of an interviewee can relate to several disruption stages, so the number of mentions of a SCRes measure can exceed the total number of 21 interviews.

As a result, we can explore and outline which SCRes measures contributed to the high level of SCRes of the automotive industry that we assessed in chapter 5.1. Further, we specify which SCRes measures were used during which COVID-19 disruption stages to emphasise the value of the SCRes measures. Above that, the overview in table 4 is the foundation for the elaboration on the different SCRes categories and the corresponding measures in the sub-chapters.

In general, the empirical data revealed that the OEMs used more SCRes measures than most suppliers (table 3). Regarding the fast recovery on the OEM level and the difficulties of some suppliers, it can be concluded that the usage of specific SCRes measures increased the SCRes and mitigated the COVID-19 disruption impact. Especially the usage of agility measures can be highlighted, which compensated the extended lead times and lack of material and thereby sustained the production. This conclusion is in line with Ruiz- Benítez et al. (2018), who also recognised a lower implication in developing resilient strategies of especially tier 2 and 3 suppliers.

Stages of disruption

Supply chain resilience measures Preparation Preparation and impact Initial response first for Preparation recovery Recovery impact Long-term 1.1 Resilience-oriented Supplier selection 1 1 3 1.2 Resilience-oriented supply chain design (adaptivity) 2 1 1 2 5 1.3 Redundancy (reserve inventory) 4 6 6 1 6 1.4 Simulation, forecasting, or decision support model 2 3 6 3 1.5 Diversification/Dual Sourcing 2 3 3 1.6 Supply chain mapping/vulnerability review 1 3 4 1 1.7 Disaster recovery plan 3 2.1 Information sharing (within supply chain) 3 6 9 7 3 2.2. Collaborative planning 3 3 6 5 2 2.3 Risk hedging 3 2 3.1 Agile operations (short product development time) 1 1 3.2 Manufacturing flexibility 3 3 10 2 3.3. Enhance supply chain visibility 4 3 3.4 Logistics flexibility 3 4 9 6 3.5 Use of control information system 11 2 4 3.6 Velocity (pipeline time reduction) 1 12 3 4.1 Resilience-oriented talent management 4.2 Creating resilience awareness among employees 5 7 10 2 4.3 Continuity management 1 8 8 5 3 4.4 Factor risk considerations into decision making 6 2 1 6 4.5 Cross-training of employees 3 5.1 Blockchain technology, RFID 2 1 5.2 Digital supply chain twin 5.3 3D printing 5.4 Data analytics, machine learning, artificial intelligence 3 2 6.1 Circular economy capabilities 5 9 6.2 Sharing economy principles 1 1 6.3 Green supplier integration 1 3

Table 4: SCRes measures during varying disruption conditions Source: Own construction

5.2.1 Supply chain (re-) engineering

Overall, it can be stated that supply chain (re-) engineering includes valuable measures (table 4), but the short-term value as an immediate response to the COVID-19 disruption was limited. The main reason was that the automotive industry thinks in terms of projects, in terms of model life cycles, which means that, by and large, no suppliers are changed within the life cycle (I 3). Consequently, upstream supply chain tiers are bound to their contracts, and all changes within the supplier base must be qualified and approved by the OEMs.

Therefore, quick changes in the supplier selection (1.1), supply chain design (1.2) or diversification and dual sourcing (1.5) are challenging to use as reactive measures. However, the empirical data indicated that the awareness about the vulnerabilities of the supply chain increased (e.g. I 2). In line with van Hoek (2020), it can be assumed that a more resilience-oriented supplier selection and supply chain design and higher diversification will be a long-term impact of the COVID-19 disruption.

Besides, especially the aspect of extending safety stocks (1.3) was recognised as a practical measure as a part of the preparation, first response, and preparation for recovery. As mentioned in chapter 2.2.2, the automotive supply chains follow in general lean supply chain practices in non-crisis circumstances. However, during the COVID-19 disruption, this approach was slightly adjusted. Several companies throughout the supply chain tiers moved away from a strict lean approach to deal with the volatile demands and supplies (I 1, I 6, I 9, I 14). This is in line with the literature, where lean practices are described as vulnerable to disruptions, and safety stocks are assessed as a valuable approach to deal with uncertainties (Ruiz-Benítez et al., 2018). Also, as a long-term impact of the disruption, permanent higher stock levels are considered on a strategic level (I 20) to deal with the increased frequency and severeness of disruptions (Blackhurst et al., 2005; Craighead et al., 2007; Hohenstein et al., 2015).

Furthermore, the usage of forecasts or simulations (1.4) concentrated on the automotive companies that simulated shut down and especially recovery scenarios during the preparation for recovery (I 2, I 14, I 17). In line with Fu & Chien (2019), the creation of forecasts was assessed as valuable to making strategic decisions (I 11). However, the full

impact of the COVID-19 disruption exceeded the possibilities to anticipate or prepare for disruptions based on forecasts (I 15).

Supply chain mapping (1.6) was used during the first response and the preparation for recovery. Due to the globalisation of the automotive supply chains (chapter 2.2) and the described ripple effect (chapter 5.1), it provided additional valuable by creating an overview of the feasible production and delivery flow (I 7, I 17). Herein, our findings align with Zhu et al. (2020), who displayed the mapping approach as a measure to anticipate and prepare for disruptions.

The usage of disaster recovery plans (1.7) was somewhat limited in the empirical data. It mainly related to recovery and emergency plans for the disruption of just-in-time deliveries on the OEM level. This preparedness was useful during the first and second COVID-19 pandemic wave in Europe when governments released border closures affecting cross-border just-in-time deliveries.

5.2.2 Supply chain collaboration

Within the category of supply chain collaboration, valuable measures to enhance the SCRes and overcome the challenges caused by the COVID-19 disruption were found (table 4). The empirical data showed that information sharing (2.1) already played an essential role in the automotive industry before the COVID-19 disruption. To shorten the information pipeline time along the automotive supply chains, call-offs and demands were shared via EDI. In a non-crisis environment, this EDI data transfer was recognised as a valuable measure for planning manufacturing and supply operations. However, the information about the production stoppage was shared relatively late within the stages of preparation and first response, which contributed to the identified bullwhip effect (chapter 5.1.2).

Regarding the preparation for recovery, several examples showed that the call-offs within the EDI systems were not updated accurately, which limited the value of those systems to cope with the situation. Further, during the recovery phase, the figures within the EDI systems changed rather quickly and were recognised as volatile. Therefore, some suppliers criticised the accuracy of the shared information as low due to the fast recovery of the industry and OEMs (I 6, I 18). Above that, the interviews revealed a change on the

strategic level to extend information sharing due to the COVID-19 disruption. I 16 primarily described that the level of figures revealed by their customers increased massively, and they currently even receive as much long-term data as never before. Brandon‐Jones et al. (2014) highlight that the increase of information sharing and providing more reliable data could increase the supply chain visibility capability and thereby the SCRes and robustness. Our data shows that this learning already took place within the automotive industry as a result of the COVID-19 pandemic.

Collaborative planning measures (2.2), such as daily delivery meetings or discussion rounds with the most critical suppliers, were especially valuable during the preparation for the recovery and recovery stage (I 17). The empirical data showed that the frequency of collaborative crisis meetings decreased over the disruption timeline while meetings on other follow-up problems, such as the semi-conductor shortage, increased. The participating automotive companies acknowledged the straightness of the collaborative meetings without hidden agendas, which applied to the gravity of the disruption situation. Furthermore, collaboration was evaluated as essential since it is only possible to produce a vehicle together (I 1). Our results underline the theoretical assumption that increased collaboration throughout the supply chain helps companies react quickly to changing supply and demand conditions (Polyviou et al., 2020).

Compared to information sharing and collaborative planning, the usage of risk hedging (2.3) to deal with the disruption was somewhat limited. The OEM still possesses the dominant role within the supply chain (chapter 2.2.1), and there were no significant changes observable in that. However, a higher tolerance level towards deficiencies, especially during the recovery stage, was evaluated as mandatory in such crisis circumstances (I 2, I 12).

5.2.3 Agility

Within the agility category, we identified measures that sustain agility during the preparation stage and measures that enable agility during the recovery (table 4). In line with Abeysekara et al., 2019, we characterise agility as one of the main group measures that enabled the restart and quick recovery after the COVID-19 disruption in the second half of 2020.

During the preparation, the enhancement of supply chain visibility (3.3) and the usage of control information systems (3.5) were assessed as applicable to detect critical parts and adjust the production early enough. The monitoring of the supplier’s stocks from an OEM perspective (I 1) or the usage of backlog lists on supplier level (I 11) were already used before the pandemic, and they are perceived as common practice in the automotive industry (I 16). However, the full impact of the COVID-19 disruption exceeded the influence of the two SCRes measures at some point.

Whereas the manufacturing flexibility (3.2), logistics flexibility (3.4), and velocity (3.6) were especially valuable during the recovery stage since they enabled an agile and flexible ramp-up of production. To specify, the manufacturing flexibility (3.2) mainly relied on capabilities that were developed in the past apart from the disruption. For instance, the flexibility enabled companies to exploit the restrictions concerning the ratios of models on the same assembly line to mitigate the production decline due to shortages (I 1). However, the findings also revealed innovative approaches such as relocating upstream component manufacturing by an OEM into the own factory to avoid additional transportation risks (I 2). Therefore, in line with Rajesh (2020a), we acknowledge the importance of agility measures to mitigate the disruption impacts.

Further, logistics flexibility (3.4) and velocity (3.6) can be evaluated as emergency tools that warrant the continuance of the supply flow under massive efforts. The importance, especially of special flights to compensate for the long lead times and lack of components such as semi-conductors (chapter 5.1.1), was captured by I 16:

“Fortunately, we did not cause a complete band standstill. […] It has always been a hair’s breadth away from that, and it is also really the case because we have just flown.“

5.2.4 Supply chain risk management culture

SCRM culture can be classified as a valuable group of measures to mitigate the impact of the COVID-19 disruption (table 4). Even though the aspect of culture is difficult to objectify, the empirical data revealed that fundamental SCRM capabilities that relate to coordination and awareness enhance the SCRes. Further, the usage of the related SCRes

measures was spread across the disruption stages (table 4) and thereby contributed to the quick recovery of the automotive supply chains after the COVID-19 disruption.

The increasing consideration of risks (4.4) can be related to the beginning and the end of the disruption stages. The automotive industry has already implemented backlog lists or GPS tracking for just-in-time deliveries before the COVID-19 disruption to prepare for disruptions. Furthermore, risk assessment was already indicated as a step within the supplier selection (chapter 5.2.1) to reduce the supply risk and preventative mitigate the disruption impact. Regarding the long-term impact, the empirical data indicated that SCRM is perceived as an emerging topic, and I 4 assumes that risk management might get bigger and bigger, more than cost reduction.

Further, the SCRes measures of resilience awareness (4.2) and continuity management (4.3) can be identified as necessary during the stages of the first response, the preparation for recovery and the actual recovery. Within resilience awareness (4.2), the involvement of the board level was especially valuable to shorten the decision making and ensure functioning supply chains. Due to the uncertainty, the short reaction time and flexibility in decision making were seen as essential.

Regarding continuity management (4.3), some companies deployed task forces as an emergency tool to deal with the situation while other enterprises just used their regular processes. Regarding the value of task forces, the picture within the empirical data is divided. On the one hand, automotive companies point out that they regard task forces as valuable and still rely on their implemented task forces to deal with the new challenges On the other hand, several companies did not establish task forces and relied on their regular processes to manage the challenges of the COVID-19 disruption. In line with Rajesh (2019a), both sides acknowledged the importance of predefined risk management procedures to enable the continuance of operations during disruptions.

5.2.5 Technological innovations

The empirical data showed that technological innovations were scarcely used to mitigate the impacts of the supply chain disruption. Consequently, it can be concluded that new technological innovations have a low value as SCRes measures during a disruption because the companies were primarily relying on their existing systems and described

them as sufficient to cope with the situation. Especially the more prominent automotive companies such as the OEMs, reported about tools that increase the transparency along the supply chain by digitalising the information about supply, demand, and stock levels along the supply chain (I 1, I 2). For instance, tracking tools for JIT deliveries were already in place and valuable for the automotive supply chains during non-crisis modes. Also, the usage of ERP systems and EDI were unrelated to the COVID-19 pandemic. Further, one of the more significant suppliers relied on business intelligence and statistical models as technological solutions to improve transparency and provide complementary information. Additionally, Microsoft Teams was implemented in most automotive companies to sustain the communication channels despite the contact restriction.

Regarding technological innovations like RFID tags and blockchain technologies (5.1), the empirical data did not show the implementation of these tools as a reaction to the COVID-19 disruption, even though authors like van Hoek (2020) emphasise the advantages of them. Likewise, SCRes measures related to data analytics (5.4) were already implemented before the crisis and supported automotive companies during the preparation. Hence, no innovation during the disruption was reported in this field. However, the respondents acknowledged the upcoming relevance of digitalisation and technological innovation as a long-term impact.

5.2.6 Sustainability

For sustainability, it can be concluded that the measures played a minor role to increase SCRes during the COVID-19 disruption. Although, the empirical data indicated that the awareness of the importance of sustainability increased due to this crisis. Concerning the literature, the increasing awareness and future consideration of sustainability will be essential because automotive supply chains face increasing pressure from their stakeholders to increase their environmental standards and thereby be compliant with the Paris climate agreement (Hervani et al., 2005; Srivastava, 2007; Gardner et al., 2019). Therefore, it can be concluded that further actions to increase the sustainability of supply chain operations will be a long-term impact of the COVID-19 disruption, as indicated by I 21:

“So, we have talked about sustainability in the automotive industry for a long time, but now it is happening. Now, the action is there, and that

started a little bit with Diesel gate, but mainly the pandemic was a starting point, I would say.”

Concerning the empirical data (chapter 4.2.6), one measure that can be highlighted is supplier relocalisation as a part of the circular economy capabilities (6.1). The COVID-19 disruption demonstrated that global sourcing is constantly exposed to risks and disruptions. Regarding sustainability within supply chains, long transportation routes have a larger carbon emission footprint, and the empirical data showed the increasing awareness that higher emissions will likely be connected to higher costs. This is in line with several authors who emphasise that the rising demand for sustainability requires carbon emission reductions in international supply chains (Kagawa et al., 2015; Nabernegg et al., 2019).

Nevertheless, the case companies rarely used relocalisation to respond to the COVID-19 disruption. Instead, the empirical data indicates that supplier relocalisation is a strategic consideration. On the one hand, automotive companies already rely to a high degree on localised production networks which can be related to the stage of preparation. On the other hand, the increasing awareness that relocalisation increases SCRes will influence the future consideration of local sourcing and nearshoring as a long-term impact of the COVID-19 pandemic.

Above that, milk-runs and returnable packaging were mentioned within the empirical data. Those measures were already used before the crisis but were also valuable during the COVID-19 pandemic. Regarding SCRes, these concepts might be positively impacted since enhanced planning efforts also increase collaboration throughout the supply chain. Furthermore, the planning efforts might increase the supply chain visibility since the focal company needs to determine the milk runs’ routes and how the packaging can be returned and reused.

6. Conclusion ______In the following section, we conclude and answer our three research questions. ______RQ1: How can the trajectory of the COVID-19 disruption be described and visualised regarding the supply chain performance in the automotive industry? The analysis and visualisation of the supply chain performance trajectory revealed a fast recovery of the production volume from the COVID-19 disruption, especially on the OEM level. Nevertheless, we further differentiated the automotive supply chain performance and characterised the causes of the supply chain turbulences that the companies experienced. The causes are the high market demand, the reduced freight capacities, the raw material shortages, further local virus outbreaks, and the additional blockage of the Suez Canal. Additionally, we specified the turbulences by displaying the existing demand-supply gap on upstream parts of the supply chains that involve component shortages and volatile lead times, indicating the bullwhip effect. In conclusion, the automotive industry has a high level of SCRes, even though it experienced supply chain turbulences in the aftermath of the disruption.

RQ2: How have supply chain resilience measures been used in the automotive industry in the varying disruption conditions of the COVID-19 disruption? During the preparation stage for the COVID-19 disruption, the automotive industry used its risk management and control information systems to monitor the situation. As a first response, they increased their safety stocks, intensified the information sharing along the supply chain, implemented task forces, and created resilience awareness in the company. The recovery was prepared by exploiting the high inventories and quick decision-making due to board-level involvement and coordination with the help of task forces. Above that, possible recovery scenarios were simulated, and the collaborative planning was enhanced along the supply chain even though the accuracy of the shared information was affected by the volatility. Agility measures mainly enabled the fast recovery. On the one hand, SCRes was created by flexible manufacturing. On the other hand, the high production volume was achieved by using air freight that relates to logistics flexibility and velocity measures. Furthermore, the increased usage of sustainability measures and the importance of risk factor consideration in decision making will be a long-term impact of the COVID- 19 disruption.

RQ3: How valuable were the supply chain resilience measures that had been used in the automotive industry in the varying disruption conditions of the COVID-19 disruption? The value of the different SCRes measures varies in the automotive industry. Supply chain (re-) engineering included measures such as supplier selection, supply chain design and diversification that had limited value during the COVID-19 disruption due to the lack of flexibility caused by tight contracts, certification processes, and a focus on product life cycles. In contrast, higher safety stocks and simulations were valuable SCRes measures of supply chain (re-) engineering. Regarding supply chain collaboration, the value of information sharing and collaborative planning can be emphasised. Within agility, the use of control information systems, manufacturing and logistics flexibility, velocity were essential to compensate for the extended lead times and lack of material and thereby sustain the production. They include innovative solutions such as the temporary relocation of upstream component manufacturing into the own factory to avoid additional transport risks or the switch to small shipping companies and private ports. Also, SCRM culture measures such as resilience awareness, continuity management, and risk consideration were perceived as valuable to overview the disruption situation. Furthermore, the value of technological innovation and sustainability were low due to the fast-moving and volatile environment of the COVID-19 disruption. However, the awareness increased that technological innovation and sustainability measures like relocalisation enhance SCRes.

7. Discussion ______This last chapter of our thesis displays the managerial, ethical, and theoretical implications of our study. It further discusses the limitations of our investigation, complemented by suggestions for future research. ______7.1 Managerial and societal implications

The results of our research promote several implications for managers on the different supplier chain tiers of the automotive supply chains. At first, our findings give insights into a severe disruption regarding the trajectory of the supply chain performance on several levels of a supply chain. Herein, our research implies that managers within the automotive industry must be aware of the bullwhip effect as a cause of a rapid and steep increase of demands after a disruption that caused production stoppages for a considerable period. Our findings imply that increased collaboration, information sharing, and long- term forecasts are vital measures to reduce such bullwhip effects.

In terms of SCRes measures, we suggest managers within the automotive sector which measures were perceived as valuable by the industry and how these measures can be used during varying disruption conditions. Thereby, we want to encourage the industry to show creativity and find innovative solutions to further disruptions. The empirical data demonstrated examples just as relocating upstream component manufacturing into the own manufacturing facility to avoid additional border crossings or the switch to small shipping companies and private ports to avoid port congestion in big harbours. Above that, technological innovations should be further considered to increase SCRes. Tools such as Microsoft Teams were widely implemented during the COVID-19 disruption. However, the automotive industry has extended possibilities for implementing technologies such as RFID tags or artificial intelligence. Those capabilities seemed to be not widely used yet even though they contain the potential for increased SCRes.

Further implications are that the COVID-19 disruption evidenced that the philosophy of pure lean management and the focus on cost factors within decision-making has its drawbacks. Suggestions for managerial implications are first to move away from a pure low-inventory philosophy as a certain redundancy level proved to be advantageous in crisis settings. Above that, risk considerations should be included when establishing a

supply base strategy. Backup suppliers were rarely implemented yet even though they would have been beneficial to deal with shortages. Linked to that, the offshoring strategy showed its vulnerabilities during a global crisis like a pandemic. Since the chance for a future global disruptive event is given, nearshoring should be considered in managerial decisions. As a result, transportation costs and supply chain risks would decrease while the level of sustainability within the supply chain would increase.

Concerning the societal implications, the automotive industry is currently in a stage of significant changes. One of the major upcoming changes is the swap towards electrical mobility, which the automotive industry identified as a key technology to reduce carbon emissions and thereby fulfil its societal responsibilities (Günther et al., 2015). However, the reduction of carbon emissions is not only related to the mode of driving. This transformation goes along with new challenges regarding the decarbonisation of the supply chain, reverse logistics, and supplier changes (Günther et al., 2015; Borgstedt et al., 2017). Therefore, a focus on local suppliers and reusable packaging would also enhance the efforts towards sustainability. As shown by our research, the level of SCRes would also increase as a positive side-effect of these sustainability and societal measures.

7.2 Theoretical implications

Our research contributes to the theory as it gives empirical insights into the development of the supply chain performance trajectory during the current COVID-19 pandemic within the automotive industry. Further, it discloses linkages between the SCRes research and its practical implications. Hence, our contribution to the SCRes literature is twofold.

First, we showed that the theoretical concept developed by Sheffi and Rice (2005) is applicable to describe the trajectory of the supply chain performance within the automotive industry during the COVID-19 pandemic. However, slight changes from this model were described. A considerable stage of preparation did not take place within the observed automotive supply chains. The disruptive event of the COVID-19 outbreak led to several uncertainties and decisions made within a short period, often due to governmental decisions and regulations. Furthermore, the trajectory as developed by Sheffi and Rice (2005) follows a U-shape for the recovery stage. However, during the COVID-19 pandemic, the trajectory followed a V-shape scenario instead since the supply chains recovered surprisingly quickly and steadily due to constant high demands for cars.

Concerning the long-term impact, our research showed that the trajectory follows instead an up and down caused by the bullwhip effect than a specific performance gap as shown in Sheffi and Rice's framework (2005).

Second, our synthesis of SCRes measures and the reflection of their usage and value contribute to SCRes literature's advancement. The investigation of the synthesised SCRes measures provides additional information about the relevance in practice. Further, our research showed that the framework of Christopher and Peck (2004) still has a high relevance since most of the categorised measures were found in our empirical data. Moreover, technological innovations, described as a key category of SCRes measures (Rajesh, 2017), are still in a developing stage, just as the category of sustainability. However, our study indicates that those two categories will have an increased impact on SCRes and should therefore be considered in future theoretical concepts of SCRes.

7.3 Limitations

Our primary focus was the COVID-19 disruption in Europe that took place at the end of March 2020. We are confident that our study revealed the essential insights of the disruption. However, since our research started in January 2021 and ended in May 2021, a similar investigation at a later point might provide new perspectives. Lastly, since we were conducting interviews with experts from Sweden and Germany, the transferability of our research results is limited. Regarding the estimated recovery paths of the automotive markets in Asia or North America, a data sampling with a different regional focus might have led to different results (BCG, 2020; Statista, 2021).

Furthermore, we identified the COVID-19 disruption as a special case of disruption due to its immediate impact, fast recovery, and collateral damages (chapter 5.1). From a practical perspective, several long-term impacts of the COVID-19 disruption could be specified (chapter 5.2). Nevertheless, from a theoretical perspective, the transferability of the insights about the COVID-19 disruption towards other future disruptions might be limited and has to be further investigated.

Based on our qualitative research approach, the value of the SCRes measure was primarily expressed in words and relations. Even though we quantify the usage of SCRes measures, our study assesses the SCRes in a subjective, qualitative way based on the

interviews and documents provided by the cases. An in-depth quantitative review of corporate data and numbers on production volumes, lead times, and material availability would have enhanced the significance of our research results. However, the search for case companies that are willing to share their company data and the process of approval would have exceeded the scope of a master thesis considering our high number of cases.

7.4 Future research

Our master thesis creates opportunities for future research within the field of SCRes. At first, the study could be expanded timewise and industry-wise. Since the pandemic is not over yet and new challenges caused by new virus mutations occur, a similar investigation at a later point might provide new insights. Further, other manufacturing or service industries besides the automotive industry might also provide other perspectives and results. Also, the study could be expanded over the automotive supply chain. Our research focused on the OEM, first, and second supply chain tier. Above that, we included suppliers with third supply chain tier business and industry analysts for a macro- perspective. Therefore, the study could be expanded upstream to investigate more specifically on third supply chain tiers and raw material suppliers. The study could also be expanded downstream to include the importers, dealerships, and even end-customers.

Our research observes backlashes that occurred during the beginning of 2021, such as semi-conductor shortages or the Suez Canal blockade to some degree. However, a further avenue for research would be to highlight those backlashes as the central theme for investigations of disruptions and assess the trajectory and the resilience measures. Last, another approach for future research would be assessing the SCRes measures based on quantitative data like Belhadi et al. (2021). Herein, a scale could be used to create a questionnaire and request a valuation of the SCRes measures from companies.

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Appendix

Appendix 1: SCRes definition matrix Christopher Sheffi Ponomarov Brandon- Tukamu- and Peck and Rice & Holcomb Jones et al. habwa et (2004) (2005) (2009) (2014) al., 2015 Aslam et al. (2020) X x Brandon-Jones et al. (2014) X X Costa et al. (2018) X Dubey et al. (2019) X X X Dubey et al. (2021) X X Emenike et al. (2020) X Esmizadeh et al. (2020) X Gligor et al. (2019) X Golgeci et al. (2013) X Hosseini et al. (2016) X Huang et al. (2020) X Ji et al. (2020) X Kristianto et al. (2017) X Li et al. (2020) X López et al. (2020) X X Lotfi et al. (2018) X Lücker et al. (2017) X Mari et al. (2019) X Nandi et al. (2020) X Parast (2020) X Pavlov et al. (2018) X Polyviou et al. (2020) X X Rajesh (2018a) X Rajesh (2019a) X X Rajesh (2019b) X Rajesh (2020b) X X Ramezankhani et al. (2018) X Saglam et al. (2020) X Shekarian (2020) X X Singh et al. (2019) X Thomas et al. (2015) Um et al. (2020) X Verghese et al. (2019) X Total 12 8 16 3 3

The matrix illustrates the most cited definitions of SCRes based on the sampled 85 SCRes articles from our systematic literature review (chapter 3.3.2). The columns display that five definitions were cited more than two times. To simplify the matrix, the lines only exhibit the 33 articles from the sample that cite one of the definitions in the columns.

Appendix 2: SCRes concept matrix Christopher and Sheffi and Pettit et al. Scholten and Peck (2004) Rice (2005) (2013) Schilder (2015)

Abeysekara et al. (2019) X Fan et al. (2020) X X Gligor et al. (2019) X X X Golgeci et al. (2013) X Ivanov (2020b) X Ivanov et al. (2020) X Polyviou et al. (2020) X X X Rajesh (2019a) X X X Rajesh (2020b) X X X Shekarian et al. (2020) X X X Verghese et al. (2019) X X X Total 10 6 3 6

The matrix illustrates the most cited concepts of SCRes based on the sampled 85 SCRes articles from our systematic literature review (chapter 3.3.2). The columns display the four concepts with more than two references in the sampled articles. Thus, the columns also contain the framework of Christopher and Peck (2004) and the disruption stages of Sheffi and Rice (2005), which both contribute to our theoretical study and theory-based research frameworks. The lines only exhibit the eleven articles from the literature review that refer to at least one concept to simplify the matrix.

We assume that the overall low number of cited concepts in the reviewed literature is based on two reasons. First, as stated in chapter 2.4.1, there are no accepted concepts in the SCRes literature that relate to the assessment of SCRes and the usage of SCRes measures (Rajesh, 2020b; van Hoek, 2020). Second, it confirms our identified research gap that the literature is scarce on concepts to qualitatively assess SCRes that encompass SCRes measures (Rajesh, 2019a).

Appendix 3: Interview request To whom it may concern,

We are Jan Schliebener and Thomas Nickel, two master’s students of Logistics and Supply Chain Management from Jönköping University, Sweden. Currently, we are working on our master thesis about supply chain resilience in the automotive industry during the COVID-19 pandemic. The motivation for our study is based on our previous working experience in the automotive industry. For our master thesis, we are conducting a qualitative study based on expert interviews, which is why we are reaching out to you.

We would like to schedule a phone interview of 30-45 minutes with you to get further insights into the attached questions. With the interview questions, we aim to answer our three main questions: 1. How can the trajectory of the COVID-19 disruption be described and visualised regarding the supply chain performance in the automotive industry (with regards to response, recovery, and potential backlashes)? 2. How have supply chain resilience measures been used in the automotive industry in the varying disruption conditions of the COVID-19 disruption? 3. How appropriate were the supply chain resilience measures that had been used in the automotive industry in the varying disruption conditions of the COVID-19 disruption?

Therefore, we would be thankful if you could suggest us a date to conduct the interview that fits your schedule.

However, if it is not possible for you to find time for an interview with us, we would also be thankful for a response to our attached questions in written form. In both ways, your response will be treated as confidential, and we will preserve anonymity. Also, the results of our study will be shared with you afterwards to ensure that you also benefit from our study.

We hope to hear from you soon, and we are looking forward to your date proposal for the interview.

Best regards Jan Schliebener & Thomas Nickel Jönköping University Gjuterigatan 5, 553 18 Jönköping

Jan Schliebener E-Mail: [email protected] Phone: +491632090215 Thomas Nickel E-Mail: [email protected] Phone: +4915165160788

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Appendix 4: Interview guide List of questions

1. Introduction and general background information 1.1. Research motivation and purpose 1.2. Confidentiality, anonymity research consent, permission for recording and GDPR 1.3. Interviewee’s role and responsibility within the company, years of experience, and supply chain tier 1.4. How did the interviewee's job contribute to the supply chain response strategy during the COVID-19 disruption? 2. Supply chain performance trajectory of the COVID-19 disruption 2.1. How would you describe the trajectory of the supply chain performance during the COVID-19 disruption until now? 3. Supply chain resilience measures as a response to the COVID-19 disruption 3.1. Supply chain (re-) engineering 3.1.1. Which role played supply chain engineering (e.g. SC understanding, supply base strategy, supply chain design principles) during the initial response? 3.1.2. Which role played supply chain engineering during the recovery? 3.2. Supply chain collaboration 3.2.1. Which role played supply chain collaboration (e.g. collaborative planning, SC intelligence) during the initial response? 3.2.2. Which role played supply chain collaboration during the recovery? 3.3. Supply chain agility 3.3.1. Which role played agility (e.g. visibility, velocity & acceleration) during the initial response? 3.3.2. Which role played agility during the recovery? 3.4. Supply chain risk management culture 3.4.1. Which role played supply chain risk management culture (e.g. SC continuity teams, board-level responsibility, factor risk consideration in decision-making) during the initial response? 3.4.2. Which role played supply chain risk management culture during the recovery? 3.5. Supply chain technological innovation 3.5.1. Which role played SC technological innovation during the initial response? 3.5.2. Which role played SC technological innovation during the recovery? 3.6. Supply chain sustainability 3.6.1. Which role played SC sustainability during the initial response? 3.6.2. Which role played SC sustainability during the recovery?

Appendix 5: GDPR Thesis Study Consent Form

GDPR Thesis Study Consent Form Required by European Union General Data Protection Regulation 2016/679

GDPR Consent for “Assessing supply chain resilience within the automotive industry in the event of a Pandemic”

Please tick the appropriate boxes Yes No

Taking part in the study I consent to JIBS processing my personal data in accordance   with current data protection legislation and the data delivered.

I consent voluntarily to be a participant in this study and   understand that I can refuse to answer questions and I can withdraw from the study at any time, without having to give a reason.

My signature below indicates that I choose to take part in the thesis study and consent to JIBS treating my personal data in accordance with current data protection legislation and the data delivered.

______Name of participant [IN CAPITALS] Signature Date

Thesis contact details for further information: Jan Schliebener & Thomas Nickel Phone: +491632090215 | +4915165160788 E-mail: [email protected] | [email protected] Jönköping University Gjuterigatan 5, 553 18 Jönköping

Participant Information Sheet

You are being invited to take part in a thesis study. Before you decide whether or not to take part, it is important for you to understand why the research is being done and what it will involve. Please take time to read the following information carefully.

What is the purpose of the study collecting personal data? Based on phone interviews of 30-45 minutes, we aim to answer the three main questions of our master thesis: 1. How can the trajectory of the COVID-19 disruption be described and visualised regarding the supply chain performance in the automotive industry (with regards to response, recovery, and potential backlashes)? 2. How have supply chain resilience measures been used in the automotive industry in the varying disruption conditions of the COVID-19 disruption? 3. How appropriate were the supply chain resilience measures that had been used in the automotive industry in the varying disruption conditions of the COVID-19 disruption?

It is entirely up to you to decide whether or not to take part. If you decide to do so, you will be given this information sheet to keep and will be asked to give your consent.’ All the information that we collect about you during the course of the research will be kept strictly confidential. You will not be able to be identified in any ensuing reports or publications.

Under GDPR you have the following rights over your personal data:  The right to be informed. You must be informed if your personal data is being used.  The right of access. You can ask for a copy of your data by making a ‘subject access request’.  The right to rectification. You can ask for your data held to be corrected.  The right to erasure. You can ask for your data to be deleted.  The right to restrict processing. You can limit the way an organisation uses your personal data if you are concerned about the accuracy of the data or how it is being used.  The right to data portability. You have the right to get your personal data from an organisation in a way that is accessible and machine-readable. You also have the right to ask an organisation to transfer your data to another organisation.  The right to object. You have the right to object to the use of your personal data in some circumstances. You have an absolute right to object to an organisation using your data for direct marketing.  How your data is processed using automated decision making and profiling. You have the right not to be subject to a decision that is based solely on automated processing if the decision affects your legal rights or other equally important matters; to understand the reasons behind decisions made about you by automated processing and the possible consequences of the decisions, and to object to profiling in certain situations, including for direct marketing purposes.

You should also know that you may contact the data protection officer if you are unhappy about the way your data or your participation in this study are being treated at [email protected]

Thank you for reading this information sheet and for considering whether to take part in this research study.’

Contact details for further information:

Imoh Antai (thesis supervisor) [email protected] Jan Schliebener [email protected] Thomas Nickel [email protected]

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