Responding to the ripple effect from systemic disruptions: Empirical evidence from the semiconductor shortage during COVID-19

Kravchenko, Kateryna; Gruchmann, Tim; Ivanova, Marina; Ivanov, Dmitry Article Responding to the ripple effect from systemic disruptions: Empirical evidence from the semiconductor shortage during COVID-19 Modern Supply Chain Research and Applications Provided in Cooperation with: Emerald Publishing Limited Suggested Citation: Kravchenko, Kateryna; Gruchmann, Tim; Ivanova, Marina; Ivanov, Dmitry (2024) : Responding to the ripple effect from systemic disruptions: Empirical evidence from the semiconductor shortage during COVID-19, Modern Supply Chain Research and Applications, ISSN 2631-3871, Emerald, Bingley, Vol. 6, Iss. 4, pp. 354-375, https://doi.org/10.1108/MSCRA-03-2024-0011 This Version is available at: https://hdl.handle.net/10419/314931 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Responding to the ripple effect from systemic disruptions: empirical evidence from the semiconductor shortage during COVID-19 Kateryna Kravchenko Berlin School of Economics and Law, Berlin, Germany Tim Gruchmann Fachhochschule Westkuste, Heide, Germany Marina Ivanova Institute of Management and Factory Systems, Chemnitz University of Technology, Chemnitz, Germany, and Dmitry Ivanov Berlin School of Economics and Law, Berlin, Germany Abstract Purpose –The ripple effect (i.e. disruption propagation in networks) belongs to one of the central pillars in supply chain resilience and viability research, constituting a type of systemic disruption. A considerable body of knowledge has been developed for the last two decades to examine the ripple effect triggered by instantaneous disruptions, e.g. earthquakes or factory fires. In contrast, far less research has been devoted to study the ripple effect under long-term disruptions, such as in the wake of the COVID-19 pandemic. Design/methodology/approach –This study qualitatively analyses secondary data on the ripple effects incurred in automotive and electronics supply chains. Through the analysis of five distinct case studies illustrating operational practices used by companies to cope with the ripple effect, we uncover a disruption propagation mechanism through the supply chains during the semiconductor shortage in 2020–2022. Findings –Applying a theory elaboration approach, we sequence the triggers for the ripple effects induced by the semiconductor shortage. Second, the measures to mitigate the ripple effect employed by automotive and electronics companies are delineated with a cost-effectiveness analysis. Finally, the results are summarised and generalised into a causal loop diagram providing a more complete conceptualisation of long-term disruption propagation. Originality/value –The results add to the academic discourse on appropriate mitigation strategies. They can help build scenarios for simulation and analytical models to inform decision-making as well as incorporate systemic risks from ripple effects into a normal operations mode. In addition, the findings provide practical recommendations for implementing shortand long-term measures during long-term disruptions. Keywords Supply chain resilience, Ripple effect, Systems thinking, Systemic risk, Semiconductor shortage, Case-study Paper type Case study MSCRA 6,4 354 © Kateryna Kravchenko, Tim Gruchmann, Marina Ivanova and Dmitry Ivanov. Published in Modern Supply Chain Research and Applications. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2631-3871.htm Received 25 March 2024 Revised 25 April 2024 19 July 2024 Accepted 22 July 2024 Modern Supply Chain Research and Applications Vol. 6 No. 4, 2024 pp. 354-375 Emerald Publishing Limited 2631-3871 DOI 10.1108/MSCRA-03-2024-0011 1. Introduction The ripple effect (i.e. disruption propagation in networks) has been a visible topic in supply chain resilience and viability (Ivanov et al., 2014;Chowdhury et al., 2020;Li et al., 2021;Sawik, 2022), constituting a critical systemic risk (Ghadge et al., 2013;Garvey et al., 2015;Llaguno et al., 2022;Alikhani et al., 2023). While a considerable body of knowledge has been developed for the ripple effect triggered by instantaneous disruptions, e.g. earthquakes or factory fires, little is known about the ripple effect under long-term disruptions (Dolgui and Ivanov, 2021; Ivanov and Dolgui, 2021;Sindhwani et al., 2023). This novel context of long-term disruptions has appeared in the wake of the COVID19 pandemic and received increasing research attention (Ivanov, 2020;Singh et al., 2021;Brussetet al., 2022;Delasay et al., 2022). Since 2020, companies worldwide have experienced significant shortages in the supply of semiconductors. Many countries worldwide imposed lockdowns of different extents to prevent the rapid spread of the coronavirus (Paul and Chowdhury, 2021;Queiroz et al., 2022). Lockdowns and high levels of sickness led to employee shortages, leading to production disruptions (Rozhkov et al., 2022;Li et al., 2023). Further, bottlenecks at ports and shipping delays contributed to the shortage. Semiconductor producers had to carry additional shipping costs since the containers were stuck at ports for longer. Moreover, container shipping costs skyrocketed (Ramani et al., 2022). Several other disruptions apart from the pandemic intensified the impacts of the following ripple effects. “A cold wave in Texas in early 2021 impacted production at the Samsung, Infineon Tech, and NXP semiconductor plants. In addition, a fire at the Renesas Electronics Corp facility in Japan added to the production disruptions related to the production of automotive chips”(Ramani et al., 2022). Besides, the legally protected know-how involved in semiconductor production contributed to the propagation of the shortage. Facilities mainly belong to US companies, while the US government prohibited the export of manufacturing equipment to several Chinese companies. “In addition, the US government imposed sanctions on Huawei Technologies and coordinated with TSMC [Taiwan Semiconductor Manufacturing Company] to prevent the sale of semiconductor chips to Huawei and ZTE. In anticipation of being put on a US trade blacklist, the firm began stockpiling chips in 2019, contributing to tight capacity at Huawei’s leading foundry supplier TSMC”(Ramani et al., 2022). As a result, some clients started buying more and hoarding the components to ensure their availability, leading to supply chain uncertainty (Bloomberg, 2022). The semiconductor shortage accordingly represented a unique challenge for companies dealing with long-term and overlapping ripple effects resulting from supply chain complexity, vulnerability, and volatility. This study aims to complement and strengthen the existing research on the ripple effect in global supply chains, asking the following research question: RQ1. How can companies mitigate the ripple effect in their supply chains resulting from a long-term, systemic disruption? To answer the proposed research question, we qualitatively analysed secondary data on the ripple effects incurred by automotive and electronics supply chains. A multiple case study approach was used to study the complex structures of the ripple effect during COVID19, drawing on multiple sources of information (Eisenhardt and Graebner, 2007). The study focused on the empirical analysis of five published case studies and triangulated data from additional qualitative sources, analysing operational practices used by companies with a cost-effectiveness analysis (CEA) (Tuominen et al., 2015). Applying theory elaboration as proposed by Fisher and Aguinis (2017) in the second step, we sequence the triggers of the ripple effect and uncover the disruption propagation mechanism during the semiconductor shortage in 2020–2022. In the last step, the measures to mitigate the ripple effect employed by the companies are delineated through a systems thinking approach, resulting in a causal loop Modern Supply Chain Research and Applications 355 diagram (CLD) (Sterman, 2001). In this context, the CLD helps to gain sense of the behaviour of a nonlinear system based on specific feedback structures (Sedlacko et al., 2014). Our results show that most of the triggers for the shortage were similar among the manufacturing industries. For instance, a decreased demand for vehicles at the beginning of the COVID-19 pandemic forced car manufacturers to limit chip procurement. In turn, increased demand for consumer electronics led to increased orders of chips from the industry. Semiconductor manufacturers hence devoted their production capacities to the electronics sector. The research demonstrates that both industries experienced common effects: production capacity reduction, factory shutdowns, longer lead times, reduced outputs, employee layoffs, product mix changes, increased costs, product unavailability, and delivery delays (MacCarthy and Ivanov, 2022). Among the identified measures, all case companies dealt with the ripple effects by including stockpiling, production capacity restriction, product mix adjustments, and production of their own chips. Specific mitigation strategies were appliedonlybythecarmakers,whichincluded partial production, sales strategy modernisation, and chip usage reduction. Ourstudy contributesto the domainof resilience and viability research (Ivanov, 2020;Singh et al., 2021;Brusset et al., 2022;Delasay et al., 2022). We add to the academic discourse by explaining how specific strategies mitigate the ripple effect and synthesise the empirical findings into a CLD. The CLD particularly can be used in future research for building more nuanced scenarios in simulation and analytical models on the ripple effect and systemic risks under long-term disruptions. Our study provides managerial insights for implementing shortandlong-term measures during long-term disruptions. The remainder of this paper is organised as follows: Section 2 analyses literature related to the ripple effect and the semiconductor shortage during COVID19. Section 3 presents the research methodology. Section 4 presents the case study results. Cross-case analysis, theory elaboration and building of the CLD follow in Section 5.WeconcludeinSection 6 by discussing the main findings of our research. 2. Research background The ripple effect is one of the most prominent research avenues in supply chain resilience. Defined by Ivanov et al. (2014) as “the impact of a disruption on supply chain performance and disruptionbased scope of changes in the supply chain structures and parameters”and later by Dolgui et al. (2020) as “a downstream propagation of the downscaling in demand fulfilment in the supply chain as a result of a severe disruption,”research on the ripple effect has been grown considerably as documented in literature reviews by Dolgui et al. (2020),Hosseini et al. (2019),Ivanov and Dolgui (2021),andLlaguno et al. (2022). Research published before the COVID19 pandemic has focused chiefly on the propagation of a single disruption through some downstream echelons (Li and Zobel, 2020;Li et al., 2021;Hosseini and Ivanov, 2022). Valuable methods for mitigating the ripple effect through backup sourcing, capacity flexibility, and inventory optimisation have been developed (Ivanov, 2022a,b;Park et al., 2022;Aldrighetti et al., 2023). While many component shortage mitigation strategies exist in the literature, most consider short-term solutions (Ivanov, 2017;Pavlov et al., 2019;Lei et al., 2021). It is implied that the shortage is temporary and can be recovered by some adjustments to the company’s sourcing strategy, inventory, or ordering policy after a disruption (Ivanov et al., 2019). Component shortages before the semiconductor crisis were mainly caused by distinct disruptions, such as an accident at a factory or a machine breakdown at one of the suppliers. Such single disruptions can indeed cause a ripple effect across the whole supply chain, but their impact can be mitigated in the short-term (Hosseini et al., 2019;Dolgui et al., 2020). However, the semiconductor shortage resulted from the long-term COVID19 pandemic. This worldwide pandemic is a unique and systemic disruption for the following reasons (Ivanov, 2020;Paul and Chowdhury, 2021;Ghadge et al., 2022;Pavlov et al., 2022;H€ agele et al., 2023): MSCRA 6,4 356 (1) Long-lasting disruption with hardly predictable scaling and dynamics (2) Simultaneous disruption in supply, demand, and logistics infrastructure (3) Simultaneous disruption and epidemic spread (4) Recovery in the presence of a disruption The semiconductor shortage during the pandemic follows the above disruption specifics (Ramani et al., 2022). As the COVID19 pandemic started and propagated worldwide, the automotive supply chain experienced many shocks. As a result of a decline in demand and limited production capacities, the automotive industry procured fewer semiconductors. At the same time, the demand for consumer electronics increased significantly. People started working remotely and spending more time at home in general. Therefore, gadgets like computer screens, laptops, headsets, and entertainment electronics like gaming consoles were highly desired. This forced semiconductor producers to allocate their already limited capacities to this sector. As the demand for vehicles started recovering towards the end of 2020, car manufacturers increased their production volumes. Thus, they ordered more semiconductor chips, leading to increased demand that propagated upstream. However, supplies could not meet the higher demand because of limited capacities. Semiconductors were unavailable in the amount required, which disrupted supply for the automotive industry. During long-term crises, accordingly, disruptions are no longer only occasional incidents but transformed into long-term everyday challenges organizations face, which form a new business-as-usual-mode, blurring the lines between traditional operation’s mode separation (Ivanov, 2024). Hence, more research must be devoted to revealing the traits of corporate decisions, which tackling multiple dimensions (see Figure 1). The semiconductor shortage represents unique challenges for manufacturing companies by causing a ripple effect and disruption cascading along the entire value chain when recovery measures should be taken in the presence of a disruption. Recent research poses that this novel context extends a traditional understanding of resilience toward supply chain viability as an ability to survive in the presence of long-term crises and disruptions compounding economic and societal aspects (Ivanov and Dolgui, 2020;Ivanov, 2022c,2023;Ivanov and Keskin, 2023;Ivanov et al., 2023). Related mitigations strategies emphasise taking adaptive measures to ensure the continuity and survivability of the supply chain to face the newly emerging continuous base of disruptions, which is becoming an essential part of the normal operations mode (Ivanov, 2024). 3. Research design This research applies a multiple-case study approach suitable for (middle-range) theory development and refinement (Voss, 2010). Based on the empirical evidence, the study Risks from ripple effects Volatility and process Global structure Quantity and demand Availability and supply Source(s): Ivanov et al. (2014) Figure 1. Risks from ripple effects Modern Supply Chain Research and Applications 357 elaborates on mitigation strategies to extend the understanding of how companies can cope with the ripple effect in their supply chains resulting from a long-term, systemic disruption. Figure 2 provides an overview of the research design. The unit of analysis is the mitigation practice already realised at the companies. The cases were selected based on the theoretical sampling method proposed by Eisenhardt (1989), involving 4 to 10 cases from multiple industries. Furthermore, quality procedures regarding external validity, construct validity, and reliability were in place to ensure methodological rigour (Yin, 2009)(Table 1). 3.1 Case selection and data collection Following the scope of the study, cases were chosen from the population of existing companies affected by the ripple effects of COVID19. The cases were chosen from the automotive and consumer electronics industries, as the pandemic significantly affected those industries. Secondly, the selected companies had to represent different regions of the world to consider if the location impacted any noticeable decisions undertaken. Furthermore, the supply chains of the companies had to be global. Thirdly, the cases with different strategies applied were chosen to get a comprehensive overview of possible approaches. Finally, since the research is based on secondary data, choosing case companies with sufficient publicly available information was essential. The study’s focus on the automotive and consumer electronics industries limits applicability of the findings, acknowledging that extant literature already tackled other industries such as the apparel and textile industry (Polyviou et al., 2023). Table 2 gives an overview of the observed companies and initiatives and the analysed data sources. This research applies secondary data collection, which serves as a reliable source for case study research and theory development (Eisenhardt and Graebner, 2007). Several operations and supply chain management studies have already conducted case study research on secondary data sources as they particularly provide up-to-date data (e.g. Meier et al., 2023). • Case selection • Data collection Phase 1 • Qualitative content analysis (coding) •Triangulationacross sources Phase 2 • Mapping of causal connections •Mentalmodel •CausalLoop Diagram (CLD) Phase 3 • Theory elaboration •Strategy development • Cost-effectiveness analysis Phase 4 Source(s): Figure created by authors Criteria Realisation Internal validity Data analysis was performed by two researchers External validity Triangulation, comparisons across multiple sources Construct validity Collecting data from multiple sources Inter-rater reliability Exposing relevant parallels across multiple sources Source(s): Yin (2009) Figure 2. Research design Table 1. Quality procedures MSCRA 6,4 358 To achieve a high reputation and trustworthiness of the data, we draw on multiple authoritative third-party sources, also to avoid researcher bias (Calantone and Vickery, 2010). The sources included public reports and websites, as well as professional newspapers and magazines such as Reuters, Forbes, and other journals. The triangulation of multiple data sources helped to achieve construct validity. For instance, information on the market share, production volumes, sales and revenue values were retrieved from Statista and compared with the main sources to conclude overall performance. The third-party data further reduced over-reliance on internal data, increasing reliability. The collected data of each case were saved in separate documents to prepare for the subsequent coding and analysis. 3.2 Data analysis and theory elaboration To analyse the qualitative data, a qualitative content-analysis approach was conducted in a structured, abductive manner (Schreier, 2012). A deductive category system derived from the literature was used first to code the empirical data (see Figure 1). Final codes were built inductively when mentioned frequently in the documents based on the researcher’s interpretation of the specific construct (see Figure 3). This allowed for flexible coding and clustering of the results. The codes on costs and revenues were particularly valuable to subsequently conduct the CEA (Bryan et al., 2007). Following Fisher and Aguinis (2017),a theory-elaboration technique of structuring sequence relations was further used to refine the emerging constructs regarding industry contexts and their relationships with each other. In this approach, theory elaboration can be described as a process of conceptualising and executing empirical research using pre-existing conceptual models as a basis to Cases Scope Sources Tesla Tesla, Inc., is an automotive company founded in 2003. It is focused on designing, developing, manufacturing, and selling electric vehicles with self-driving capability, stationary, as well as solar energy generation and storage systems Media interviews/press releases, firm website pages, literature Hyundai Hyundai Motors is a multinational automotive manufacturer from South Korea founded in 1967. As an automotive manufacturer operating in all segments, Hyundai has mainly grown in the SUV, electric vehicle, and luxury segments in recent years Media interviews/press releases, firm website pages, literature Ford Ford Motor Company is an American multinational automotive company founded in 1903 by Henry Ford. It owns the Ford and Lincoln car brands. Ford, as well, is operating in all car segments Media interviews/press releases, firm website pages, literature Sony Sony Group Corporation is a Japanese multinational corporation founded in 1946. It is one of the world’s largest consumer and professional electronics manufacturers. Its product portfolio includes various electronic products such as audio/video equipment, digital cameras, home appliances, video games, and gaming consoles Media interviews/press releases, firm website pages, literature Apple Apple Inc. is an American multinational tech company founded in 1976. Apple’s product portfolio includes smartphones, tablets, PCs, laptops, and smartwatches, as well as related software, accessories, services, and applications. Its supply chains are considered a benchmark among manufacturing companies Media interviews/press releases, firm website pages, literature Source(s): Table created by authors Table 2. Case characteristics Modern Supply Chain Research and Applications 359 develop new theoretical insights by structuring theoretical constructs and relations to explain empirical observations (cf. Fisher and Aguinis, 2017). Accordingly, the observed ripple effects were sequenced to establish cause-effect relationships. As a result, the propagation of the ripple effect through the supply chains could be demonstrated. Finally, ripple effect mitigation strategies could be deduced as practical guidelines for manufacturing companies. 3.3 Systems thinking and causal loop diagram Systems thinking and system dynamics (SD) modelling deals with the nonlinear behaviour of complex systems over time (Morecroft, 1992), aiming to understand how feedback structures determine a system’s behaviour (Coyle, 1996). Following Davis et al.(2007),SDis also increasingly used as a methodology for theory development. Particularly for longitudinal and nonlinear processes, they can help to build a more comprehensive and precise theory from so-called simple theory (Davis et al., 2007). CLDs are the most important qualitative modelling method in systems thinking (Coyle, 1996;Sterman, 2001). They comprise a set of nodes and edges, connected by arrows denoting the causal influences among them. To better understand the propagation of the ripple effect, a systems thinking approach was applied to determine causal connections and establish cause-effect relationships between the variables, followed by an attempt to lead back these effects directly to the causes. In our analysis, we sequenced the impacts of the ripple effect (i.e. demand variations, labour shortages, lockdowns, facility shutdowns, and operating with limited capacities), to construct the cause-effect relationships. The feedback structure was incorporated by closing cycles between the single actor’s actions (i.e. automakers could increase their production levels and ordered more semiconductors, at the same time suppliers could not satisfy the increased demand since orders from other industries overtook their capacities). Such structured mapping incrementally added and connected the observed variables to the CLD. Figure 3. Coding scheme from the qualitative content analysis MSCRA 6,4 360 4. Within-case analysis 4.1 Tesla When the COVID-19 pandemic started, the semiconductor shortage caused rollout delays of Tesla’s long-awaited electric pickup and semi-trailer trucks (Ashcroft, 2022). The start of production of both models was planned for 2021 but was postponed to 2022 and 2023, respectively. Tesla had to temporarily close one of its plants in California at the beginning of 2021 because of component shortages. However, in the second half of 2020, Tesla’s orders reached the highest level in the company’s history, increasing by 45%. This number was under the 500.000-unit sales goal for the year, even though the COVID-19 pandemic was at its peak (Cohen, 2021). As of 2021, Tesla reported that deliveries in 2021 increased by 87% compared to 2020 (Ashcroft, 2022). When looking at the rest of the car manufacturers, such results were surprising, considering that Tesla cars usually require more chips than others. Several factors enabled Tesla’s resilience during the disruption. Besides traditional strategies, such as building safety stock, Tesla found creative ways to approach the problem. Firstly, they usually “produce iterations of vehicle models that often stretch back over generations”(Ashcroft, 2022). Tesla is a more flexible company that designs and builds vehicles from scratch. The expertise of internal software engineers helped to maintain operations and production plans. According to the company’s CFO Zachary Kirkhorn: “our expertise in the chip industry and consistent messaging to suppliers has helped us manage supply chain challenges”(Ashcroft, 2022). He also claimed that Tesla did not reduce its production forecasts with suppliers. Instead, they were adding capacity in the fastest way possible. CEO Elon Musk admitted that Tesla managed to alter the software rapidly to use different types of chips for the vehicles. “We were able to substitute alternative chips and then write the firmware in a matter of weeks. It is not just a matter of swapping out a chip; you also have to rewrite the software”(Hawkins, 2021). In some cases, after rewriting the software, one chip could perform dual functions. As a result, the number of semiconductors needed for vehicles was decreased due to the company’s strategic use, leading to production maximisation (Zimmerman, 2022). According to Elon Musk, the shortage “has served as a forcing function for us to reduce the number of chips in the car”(Zimmerman, 2022). Tesla’s semiconductor supplier base comprises 43 vendors, which provide around 1,600 unique silicon chips. Discovering alternative ways of applying them enabled cost reductions, production maximisation, and decreased failure points. Secondly, the company’s management realised a need to decrease dependence on Asian semiconductor vendors even before the pandemic. Therefore, it was decided to put effort into producing its chips in-house. Additionally, Tesla decided to use a new material technology–silicon carbide (SiC) instead of commonly used pure silicon. “The unique properties of silicon-carbide make it much more energy efficient and durable relative to traditional silicon wafers. Due to their improved thermal conductivity, SiCs reduce energy loss by as much as 50%”(Cohen, 2021). By producing its own semiconductor materials during the pandemic, Tesla has made its supply chain more resilient and avoided “a shortterm crisis”(Cohen, 2021). 4.2 Hyundai Despite the semiconductor shortage impacting automotive supply chains worldwide, Hyundai maintained constant production levels. For instance, Hyundai Motor India was ahead of its primary competitors in the country, Maruti Suzuki and Mahindra & Mahindra. They both were forced to cut down production because of chip shortages. On the contrary, Hyundai handled the crisis by altering the product mix and allocating available components to produce high-demand models. “The semiconductor supply issue is common for all OEMs, and everyone is under the same challenging conditions. But the results are totally different Modern Supply Chain Research and Applications 361 during long-term disruptions and identify which measures were implemented by each company to mitigate the ripple effect. Data from multiple sources, such as company websites, websites of supply chain consulting agencies, and articles from business magazines, were merged to get a complete overview of each company’s actions in 2020–2022. The findings particularly contribute to the growing academic discourse on appropriate mitigation strategies. While Polyviou et al. (2023) found supplier concentration and carrier diversification as potential measures to mitigate the ripple impact of supply disruptions during COVID19 in the apparel and textile industry, the present results particularly vote for investment in engineering change activities, sales modernisation, as well as investments into the flexibilization of production facilities. 6.1 Theoretical implications Literature on the ripple effect mitigation is relatively nascent, having its roots in the seminal work by Ivanov et al. (2014). While there are already studies providing specific insights into the ripple effect of COVID19 in single industries, i.e. in the medical industry in Turkey (Yilmaz et al., 2023), the present study complements existing research by studying the semiconductor shortage in the automotive and electronics industry. In this vein, it can be indeed concluded that the semiconductor shortage is not a regular disruption but a systematic one. For the specific context of the automotive industry, the study draws parallels to other specific types of short-term ripple effects, such as the horizontal bullwhip effect (cf. Gruchmann and Neukirchen, 2019), showing that demand variations are not just present between first-tier suppliers within one industry but also between two or more industries. Thus, the research adds to the academic discourse by describing the mechanism of systemic disruptions through a CLD and explaining how certain strategies mitigate the related ripple effects for intertwined supply networks (Ivanov, 2024). Future research may particularly use the proposed CLD and observed mitigation strategies for building more nuanced scenarios in simulations and analytical models on the ripple effect studying systemic risks under long-term disruptions. While we observed differences between the automotive and customer electronics case companies (e.g. a prioritised access to component suppliers for the electronics cases), the simulations may provide further insights into proactively managing ripple effects. While the observed mitigation practices incorporate the (traditional) resilience practices, such as supply chain Measures Cost implications Effectiveness Cases Production cut downs Cutting variable costs, but decreased sales Short-term Tesla, Ford, Sony, Apple Prioritising production with higher margins Increased revenues Short-term Hyundai Partial production Cutting variable costs, but decreased sales Short-term Ford Engineering changes Investment in re-engineering activities Midto longterm Tesla Supplier negotiations Increased component costs Short-term Sony, Apple Stock pilling Increased stockingand shipping costs Short-term Tesla, Ford, Sony Sales modernisation Increased revenues Mid-term Ford Investment in addition component supply Remarkable investments into new production facilities Long-term Tesla, Hyundai, Sony, Apple Source(s): Table created by authors Table 4. Cost-effectiveness analysis MSCRA 6,4 368 flexibility that arises from product substitution, flexible contracting, supplier switching, portfolio diversification, and dynamic pricing practices (Balakrishnan and Ramanathan, 2021), some particularly go beyond reactive resilience towards supply chain viability. First, long-term disruptions lead to the long-term adaptation of supply chain structures (supply chain strategy level, i.e. closing factories) that may last after the end of the disruption. Second, the required adaptions affected not just operations but also marketing (i.e. product mix, sales strategy) or sustainability strategies (He and Harris, 2020). Third, engineering change management practices are coming to the fore in the context of long-term disruptions (Gollmann et al., 2023). 6.2 Managerial implications Implications for the semiconductor industry: Our analysis showed that companies in both industries applied stockpiling, which implies accumulating more extensive semiconductor stocks to ensure the future availability of components. Some companies tried to place orders well in advance (e.g. Sony), while others established closer communication with critical suppliers and negotiated prioritisation for their orders (e.g. Apple). For instance, Apple decided to procure a share of semiconductors from a future plant in Arizona. However, poor supply chain visibility was mentioned as one of the contributors to the adverse effects companies experience due to semiconductor shortage. Accordingly, one of the objectives is to increase supply chain visibility and improve forecasting and procurement decisions (Ivanov et al., 2021). In this vein, companies may make use of digital technologies. Adopting digital twin technologies (DTT), for instance, enhances resilience by providing real-time visibility, facilitating quick decision-making, and enabling immediate actions or responses to disruptions in the supply chain. Moreover, visibility can be achieved from enhanced monitoring using DTT (Burgos and Ivanov, 2021). Implications for the semiconductor industry: Specifically in the automotive industry, partial assembly is a widely implemented strategy during COVID19. Semiconductor scarcity has made building parks of unfinished vehicles wait for the availability of components (e.g. FORD). Such an approach is likely not applicable to consumer electronics since electronic devices are highly integrated with chips. Their installation is embedded into the production process, which does not allow adding them later for most products. Some companies, such as Ford, decided to modernise sales strategy by sacrificing the number of cars available at dealerships and using a build-to-order production approach. This gives a better overview of orders and facilitates inventory management. Such a strategy can be applied in the automotive industry. To reduce the number of chips required for a vehicle, Tesla developed a unique solution to rewrite the software of several semiconductor types (Ashcroft, 2022). Such an approach, however, requires high technological engineer expertise, which is only available for some companies. Notably, automotive firms allocated available components to the most demanded and profitable models. The same trend could be observed in consumer electronics, where firms halted production of specific models temporarily or even permanently. Implications for digital transformation: One major element decreasing ripple effect during long-term disruptions is the application of digital technologies (Balakrishnan and Ramanathan, 2021;Ivanov and Dolgui, 2021). Digital technologies in supply chain management can be considered disruptive technologies that influence modern SC management (Ivanov et al., 2019). They significantly support viability as increasing complexity and structural variety in supply networks require data availability and capable data processing technology (Balakrishnan and Ramanathan, 2021). To mitigate long-term ripple effects, blockchain technology is promising (Gruchmann et al., 2023), acknowledging that blockchain initiatives are often on a pilot stage (Gong et al., 2022). In this vein, blockchain technology particularly enhances collaboration practices by supporting the sharing of Modern Supply Chain Research and Applications 369 information between two or more parties in a transparent way and recording the data among the single supply chain members (Balakrishnan and Ramanathan, 2021;Gong et al., 2022; Gruchmann et al., 2023). 6.3 Limitations The usage of only secondary sources is a limitation of the study. Only publicly available information could be used to identify measures that companies took to deal with the semiconductor shortage. Companies may not communicate sensitive information about their operations. Accordingly, future research may collect primary data through interviews and surveys to blend with the present findings. Additionally, the study is focused to the automotive and consumer electronics industries representing a boundary condition for transferability of the results. Experience in other manufacturing industries, such as the LED lightning or power turbines/solar industries, might lead to developing additional ripple effect mitigation strategies applicable more generally. Finally, only the practices of manufacturing companies were considered in the study. The semiconductor shortage is critical, and other stakeholders may contribute to its solution. For instance, governments of different countries realise the importance of chip availability and invest in new production facilities. Their actions must also be considered in the decision-making process by the supply chain managers. Future research may tackle these limitations, for instance, by investigating the use of supply chain digitalisation for advanced mitigation strategies. Future research can focus on determining how digital transformation can support companies and their supply chains in case of systemic disruption. References Aldrighetti, R., Battini, D. and Ivanov, D. (2023), “Efficient resilience portfolio design in the supply chain with consideration of preparedness and recovery investments”,Omega, Vol. 117, 103841, doi: 10.1016/j.omega.2023.102841. Alikhani, R., Ranjbar, R., Jamali, A., Torabi, S.A. and Zobel, C.W. (2023), “Towards increasing synergistic effects of resilience strategies in supply chain network design”,Omega, Vol. 116, 102819, doi: 10.1016/j.omega.2022.102819. Balakrishnan, A.S. and Ramanathan, U. (2021), “The role of digital technologies in supply chain resilience for emerging markets’automotive sector”,Supply Chain Management: An International Journal, Vol. 26 No. 6, pp. 654-671. Brusset, X., Davari, M., Kinra, A. and La Torre, D. (2022), “Modelling ripple effect propagation and global supply chain workforce productivity impacts in pandemic disruptions”,International Journal of Production Research, Vol. 61 No. 8, pp. 2493-2512, doi: 10.1080/00207543.2022. 2126021. Bryan, S., Williams, I. and McIver, S. (2007), “Seeing the NICE side of cost-effectiveness analysis: a qualitative investigation of the use of CEA in NICE technology appraisals”,Health Economics, Vol. 16 No. 2, pp. 179-193, doi: 10.1002/hec.1133. Burgos, D. and Ivanov, D. (2021), “Food retail supply chain resilience and the COVID-19 pandemic: a digital twin-based impact analysis and improvement directions”,Transportation Research Part E: Logistics and Transportation Review, Vol. 152, 102412, doi: 10.1016/j.tre.2021.102412. Calantone, R.J. and Vickery, S.K. (2010), “Introduction to the special topic forum: using archival and secondary data sources in supply chain management research”,Journal of Supply Chain Management, Vol. 46 No. 4, pp. 3-11, doi: 10.1111/j.1745-493x.2010.03202.x. Chowdhury, M.T., Sarkar, A., Saha, P.K. and Anik, R.H. (2020), “Enhancing supply resilience in the COVID-19 pandemic: a case study on beauty and personal care retailers”,Modern Supply Chain Research and Applications, Vol. 2 No. 3, pp. 143-159, doi: 10.1108/mscra-07-2020-0018. MSCRA 6,4 370 Coyle, R.G. (1996), System Dynamics Modelling: A Practical Approach, CRC Press, London. Davis, J.P., Eisenhardt, K.M. and Bingham, C.B. (2007), “Developing theory through simulation methods”,Academy of Management Review, Vol. 32 No. 2, pp. 480-499, doi: 10.5465/amr.2007. 24351453. Delasay, M., Jain, A. and Kumar, S. (2022), “Impacts of the COVID-19 pandemic on grocery retail operations: an analytical model”,Production and Operations Management, Vol. 31 No. 5, pp. 2237-2255, doi: 10.1111/poms.13717. Dolgui, A. and Ivanov, D. (2021), “Ripple effect and supply chain disruption management: new trends and research directions”,International Journal of Production Research, Vol. 59 No. 1, pp. 102-109, doi: 10.1080/00207543.2021.1840148. Dolgui, A., Ivanov, D. and Rozhkov, M. (2020), “Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain”,International Journal of Production Research, Vol. 58 No. 5, pp. 1285-1301, doi: 10.1080/00207543.2019. 1627438. Eisenhardt, K.M. (1989), “Building theories from case study research”,Academy of Management Review, Vol. 14 No. 4, pp. 532-550, doi: 10.5465/amr.1989.4308385. Eisenhardt, K.M. and Graebner, M.E. (2007), “Theory building from cases: opportunities and challenges”,Academy of Management Journal, Vol. 50 No. 1, pp. 25-32, doi: 10.5465/amj.2007. 24160888. Fisher, G. and Aguinis, H. (2017), “Using theory elaboration to make theoretical advancements”, Organizational Research Methods, Vol. 20 No. 3, pp. 438-464, doi: 10.1177/1094428116689707. Garvey, M.D., Carnovale, S. and Yeniyurt, S. (2015), “An analytical framework for supply network risk propagation: a Bayesian network approach”,European Journal of Operational Research, Vol. 243 No. 2, pp. 618-627, doi: 10.1016/j.ejor.2014.10.034. Ghadge, A., Dani, S., Chester, M. and Kalawsky, R. (2013), “A systems thinking approach for modelling supply chain risk propagation”,Supply Chain Management: An International Journal, Vol. 18 No. 5, pp. 523-538, doi: 10.1108/scm-11-2012-0366. Ghadge, A., Er, M., Ivanov, D. and Chaudhuri, A. (2022), “Visualisation of ripple effect in supply chains under long-term, simultaneous disruptions: a System Dynamics approach”,International Journal of Production Research, Vol. 60 No. 20, pp. 6173-6186, doi: 10.1080/00207543.2021. 1987547. Gollmann, T., Gangl, R. and Gruchmann, T. (2023), “Engineering change management–an empirical study on IT, processual, and organizational requirements”,Logistics Management Conference, Cham, Springer Nature Switzerland, pp. 99-112. Gong, Y., Xie, S., Arunachalam, D., Duan, J. and Luo, J. (2022), “Blockchain-based recycling and its impact on recycling performance: a network theory perspective”,Business Strategy and the Environment, Vol. 31 No. 8, pp. 3717-3741, doi: 10.1002/bse.3028. Gruchmann, T. and Neukirchen, T. (2019), “Horizontal bullwhip effect-empirical insights into the system dynamics of automotive supply networks”,IFAC-PapersOnLine, Vol. 52 No. 13, pp. 1266-1271, doi: 10.1016/j.ifacol.2019.11.372. Gruchmann, T., Elgazzar, S. and Ali, A.H. (2023), “Blockchain technology in pharmaceutical supply chains: a transaction cost perspective”,Modern Supply Chain Research and Applications, Vol. 5 No. 2, pp. 115-133, doi: 10.1108/mscra-10-2022-0023. H€ agele, S., Grosse, E. and Ivanov, D. (2023), “Supply chain resilience: a tertiary study”,International Journal of Integrated Supply Management, Vol. 16 No. 1, pp. 52-81, doi: 10.1504/ijism.2023. 10050753. He, H. and Harris, L. (2020), “The impact of Covid-19 pandemic on corporate social responsibility and marketing philosophy”,Journal of Business Research, Vol. 116, pp. 176-182, doi: 10.1016/j. jbusres.2020.05.030. Modern Supply Chain Research and Applications 371 Hosseini, S. and Ivanov, D. (2022), “A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach”,Annals of Operations Research, Vol. 319 No. 1, pp. 581-607, doi: 10.1007/s10479-019-03350-8. Hosseini, S., Ivanov, D. and Dolgui, A. (2019), “Review of quantitative methods for supply chain resilience analysis”,Transportation Research Part E: Logistics and Transportation Review, Vol. 125, pp. 285-307, doi: 10.1016/j.tre.2019.03.001. Ivanov, D. (2017), “Simulation-based ripple effect modelling in the supply chain”,International Journal of Production Research, Vol. 55 No. 7, pp. 2083-2101, doi: 10.1080/00207543.2016.1275873. Ivanov, D. (2020), “Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case”, Transportation Research Part E: Logistics and Transportation Review, Vol. 136, 101922, doi: 10. 1016/j.tre.2020.101922. Ivanov, D. (2022a), “Lean resilience: AURA (active usage of resilience assets) framework for postCOVID-19 supply chain management”,International Journal of Logistics Management, Vol. 33 No. 4, pp. 1196-1217, doi: 10.1108/ijlm-11-2020-0448. Ivanov, D. (2022b), “Probability, adaptability and time: some research-practice paradoxes in supply chain resilience and viability modelling”,International Journal of Integrated Supply Management, Vol. 15 No. 4, pp. 454-465, doi: 10.1504/ijism.2022.125995. Ivanov, D. (2022c), “Viable Supply Chain Model: integrating agility, resilience and sustainability perspectives –lessons from and thinking beyond the COVID-19 pandemic”,Annals of Operations Research, Vol. 319 No. 1, pp. 1411-1431, doi: 10.1007/s10479-020-03640-6. Ivanov, D. (2023), “The Industry 5.0 framework: viability-based integration of the resilience, sustainability, and human-centricity perspectives”,International Journal of Production Research, Vol. 61 No. 5, pp. 1683-1695, doi: 10.1080/00207543.2022.2118892. Ivanov, D. (2024), “Two views of supply chain resilience”,International Journal of Production Research, Vol. 62 No. 11, pp. 4031-4045, doi: 10.1080/00207543.2023.2253328. Ivanov, D. and Dolgui, A. (2020), “Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak”,International Journal of Production Research, Vol. 58 No. 10, pp. 2904-2915, doi: 10. 1080/00207543.2020.1750727. Ivanov, D. and Dolgui, A. (2021), “OR-Methods for coping with the ripple effect in supply chains during COVID-19 pandemic: managerial insights and research implications”,International Journal of Production Economics, Vol. 232, 107921, doi: 10.1016/j.ijpe.2020.107921. Ivanov, D. and Keskin, B. (2023), “Post-pandemic adaptation and development of supply chain viability theory”,Omega, Vol. 116, 102806, doi: 10.1016/j.omega.2022.102806. Ivanov, D., Sokolov, B. and Dolgui, A. (2014), “The Ripple effect in supply chains: trade-off ‘efficiencyflexibility-resilience’in disruption management”,International Journal of Production Research, Vol. 52 No. 7, pp. 2154-2172, doi: 10.1080/00207543.2013.858836. Ivanov, D., Dolgui, A. and Sokolov, B. (2019), Handbook of Ripple Effects in the Supply Chain, Springer, New York. Ivanov, D., Blackhurst, J. and Das, A. (2021), “Supply chain resilience and its interplay with digital technologies: making innovations work in emergency situations”,International Journal of Physical Distribution and Logistics Management, Vol. 51 No. 2, pp. 97-103, doi: 10.1108/ijpdlm03-2021-409. Ivanov, D., Dolgui, A., Blackhurst, J. and Choi, T.-M. (2023), “Toward supply chain viability theory: from lessons learned through COVID-19 pandemic to viable ecosystems”,International Journal of Production Research, Vol. 61 No. 8, pp. 2402-2415, doi: 10.1080/00207543.2023.2177049. Lei, Z., Lim, M.K., Cui, L. and Wang, Y. (2021), “Modelling of risk transmission and control strategy in the transnational supply chain”,International Journal of Production Research, Vol. 59 No. 1, pp. 148-167, doi: 10.1080/00207543.2019.1698782. MSCRA 6,4 372 Li, Y. and Zobel, C.W. (2020), “Exploring supply chain network resilience in the presence of the ripple effect”,International Journal of Production Economics, Vol. 228, 107693, doi: 10.1016/j.ijpe.2020. 107693. Li, Y., Chen, K., Collignon, S. and Ivanov, D. (2021), “Ripple effect in the supply chain network: forward and backward disruption propagation, network health and firm vulnerability”,European Journal of Operational Research, Vol. 291 No. 3, pp. 1117-1131, doi: 10.1016/j.ejor.2020.09.053. Li, M., Sodhi, M., Tang, C. and Yu, J. (2023), “Preparedness with a system integrating inventory, capacity, and capability for future pandemics and other disasters”,Production and Operations Management, Vol. 32 No. 2, pp. 564-583, doi: 10.1111/poms.13887. Llaguno, A., Mula, J. and Campuzano-Bolarin, F. (2022), “State of the art, conceptual framework and simulation analysis of the ripple effect on supply chains”,International Journal of Production Research, Vol. 60 No. 6, pp. 2044-2066, doi: 10.1080/00207543.2021.1877842. MacCarthy, B. and Ivanov, D. (2022), “The Digital Supply Chain—emergence, concepts, definitions, and technologies”, in MacCarthy, B. and Ivanov, D. (Eds), The Digital Supply Chain, Elsevier, Amsterdam, pp. 3-14. Meier, O., Gruchmann, T. and Ivanov, D. (2023), “Circular supply chain management with blockchain technology: a dynamic capabilities view”,Transportation Research Part E: Logistics and Transportation Review, Vol. 176, 103177, doi: 10.1016/j.tre.2023.103177. Morecroft, J.D. (1992), “Executive knowledge, models and learning”,European Journal of Operational Research, Vol. 59 No. 1, pp. 9-27, doi: 10.1016/0377-2217(92)90004-s. Park, Y.W., Blackhurst, J., Paul, C. and Scheibe, K.P. (2022), “An analysis of the ripple effect for disruptions occurring in circular flows of a supply chain network”,International Journal of Production Research, Vol. 60 No. 15, pp. 4693-4711, doi: 10.1080/00207543.2021.1934745. Paul, S.K. and Chowdhury, P. (2021), “A production recovery plan in manufacturing supply chains for a highdemand item during COVID-19”,International Journal of Physical Distribution and Logistics Management, Vol. 51 No. 2, pp. 104-125, doi: 10.1108/ijpdlm-04-2020-0127. Pavlov, A., Ivanov, D., Pavlov, D. and Slinko, A. (2019), “Optimization of network redundancy and contingency planning in sustainable and resilient supply chain resource management under conditions of structural dynamics”,Annals of Operations Research. doi: 10.1007/s10479-01903182-6. Pavlov, A., Ivanov, D., Werner, F., Dolgui, A. and Sokolov, B. (2022), “Integrated detection of disruption scenarios, the ripple effect dispersal and recovery paths in supply chains”,Annals of Operations Research, Vol. 319 No. 1, pp. 609-631, doi: 10.1007/s10479-019-03454-1. Polyviou, M., Wiedmer, R., Chae, S., Rogers, Z.S. and Mena, C. (2023), “To concentrate or to diversify the supply base? Implications from the US apparel supply chain during the COVID-19 pandemic”,Journal of Business Logistics, Vol. 44 No. 3, pp. 502-527, doi: 10.1111/jbl.12335. Queiroz, M.M., Ivanov, D., Dolgui, A. and Fosso Wamba, S. (2022), “Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review”,Annals of Operations Research, Vol. 319 No. 1, pp. 1159-1196, doi: 10.1007/ s10479-020-03685-7. Ramani, V., Ghosh, D. and Sodhi, M. (2022), “Understanding systemic disruption from the Covid-19induced semiconductor shortage for the auto industry”,Omega: International Journal of Management Sciences, Vol. 113, 102720, doi: 10.1016/j.omega.2022.102720. Rozhkov, M., Ivanov, D., Blackhurst, J. and Nair, A. (2022), “Adapting supply chain operations in anticipation of and during the COVID-19 pandemic”,Omega, Vol. 110, 102635, doi: 10.1016/j. omega.2022.102635. Sawik, T. (2022), “Stochastic optimization of supply chain resilience under ripple effect: a COVID-19 pandemic related study”,Omega, Vol. 109, 102596, doi: 10.1016/j.omega.2022.102596. Schreier, M. (2012), Qualitative Content Analysis in Practice, Sage, London. Modern Supply Chain Research and Applications 373 Sedlacko, M., Martinuzzi, A., Røpke, I., Videira, N. and Antunes, P. (2014), “Participatory systems mapping for sustainable consumption: discussion of a method promoting systemic insights”, Ecological Economics, Vol. 106, pp. 33-43, doi: 10.1016/j.ecolecon.2014.07.002. Sindhwani, R., Jayaram, J. and Saddikuti, V. (2023), “Ripple effect mitigation capabilities of a hub and spoke distribution network: an empirical analysis of pharmaceutical supply chains in India”, International Journal of Production Research, Vol. 61 No. 8, pp. 2795-2827, doi: 10.1080/ 00207543.2022.2098073. Singh, S., Kumar, R., Panchal, R. and Tiwari, M.K. (2021), “Impact of COVID-19 on logistics systems and disruptions in food supply chain”,International Journal of Production Research, Vol. 59 No. 7, pp. 1993-2008, doi: 10.1080/00207543.2020.1792000. Sterman, J.D. (2001), “System dynamics modeling: tools for learning in a complex world”,California Management Review, Vol. 43 No. 4, pp. 8-25, doi: 10.2307/41166098. Tuominen, P., Reda, F., Dawoud, W., Elboshy, B., Elshafei, G. and Negm, A. (2015), “Economic appraisal of energy efficiency in buildings using cost-effectiveness assessment”,Procedia Economics and Finance, Vol. 21, pp. 422-430, doi: 10.1016/s2212-5671(15)00195-1. Voss, C. (2010), “Case research in operations management”,Researching Operations Management, pp. 176-209. Yılmaz, € O.F., Yeni, F.B., Yılmaz, B.G. and € Ozçelik, G. (2023), “An optimization-based methodology equipped with lean tools to strengthen medical supply chain resilience during a pandemic: a case study from Turkey”,Transportation Research Part E: Logistics and Transportation Review, Vol. 173, 103089, doi: 10.1016/j.tre.2023.103089. Yin, R.K. (2009), Case Study Research: Design and Method, 4th ed., Sage, Thousand Oaks. Secondary data sources Ashcraft, B. (2022), “Sony cuts PlayStation 5 sales expectations due to global chip shortage”,Kotaku, available at: https://kotaku.com/ps5-playstation-5-sony-chip-shortage-semiconductor-supp1848465342 Ashcroft, S. (2022), “Analysis: how buoyant Tesla is defying global chip shortage”,Supply Chain Magazine, available at: https://supplychaindigital.com/sustainability/analysis-how-buoyanttesla-defying-global-chip-shortage Bloomberg (2022), “Wait times for chip deliveries grow again as shortages persist”, available at: https://www.bloomberg.com/news/articles/2022-03-11/wait-times-for-chip-deliveries-growagain-as-shortages-persist#xj4y7vzkg (accessed 12 March 2023). Carlier, M. (2022), Ford –Statistics and Facts, Statista, available at: https://www.statista.com/topics/ 1886/ford/ Cenizo, S. (2022), Hyundai Is Handling The Semiconductor Chip Shortage Better Than Most, CarBuzz, available at: https://carbuzz.com/news/hyundai-is-handling-the-semiconductor-chip-shortagebetter-than-most Cohen, A. (2021), “Tesla Flexes innovative Muscle by manufacturing own chips during supply Crunch”,Forbes, available at: https://www.forbes.com/sites/arielcohen/2021/09/22/tesla-flexesinnovative-muscle-by-manufacturing-own-chips-during-supply-crunch/?sh553407afa1618 Das, S. (2022), Chip Shortage May Not Fundamentally Alter Apple Supply Chain: Tim Cook, Mint, available at: https://www.livemint.com/companies/news/chip-shortage-may-not-fundamentallyalter-apple-supply-chain-tim-cook-11643362609255.html Erg€ oç€ un, G. (2021), “Japan to allocate about $3.5B for chip facility”, available at: https://www.aa.com. tr/en/economy/japan-to-allocate-about-35b-for-chip-facility-/2616037 Fingas, J. (2021), “Sony and TSMC attempt to address chip shortages with a factory in Japan”, Engagadget. MSCRA 6,4 374 Garland, M. (2021), “Ford shifts focus to built-to-order to mitigate semiconductor shortage’s effects”, Supply Chain Dive, available at: https://www.supplychaindive.com/news/ford-semiconductorshortage-chip-sales-bank/604270/ Gurman, M. (2022), Apple Prepares to Get Made-In-US Chips in Pivot from Asia, Bloomberg, available at: https://www.bloomberg.com/news/articles/2022-11-15/apple-prepares-to-get-made-in-uschips-in-pivot-from-asia-supply Hawkins, A.J. (2021), “Tesla rewrote its own software to survive the chip shortage”,The Verge, available at: https://www.theverge.com/2021/7/26/22595060/tesla-chip-shortage-softwarerewriting-ev-processor Horn, G. (2022), Hyundai Has Big Plans To Battle The Chip Shortage, CarBuzz, available at: https:// carbuzz.com/news/hyundai-has-big-plans-to-battle-the-chip-shortage Jin, H. (2021), “Hyundai Motor aims to develop chips, cut reliance on chipmakers”,Reuters, available at: https://www.reuters.com/technology/hyundai-motor-says-it-wants-develop-chips-cutreliance-chipmakers-2021-10-13/ Leonard, M. (2021), “Ford limits production at multiple factories through July due to semiconductor shortages”,Supply Chain Dive, available at: https://www.supplychaindive.com/news/fordsemiconductor-shortage-manufacturing-supplier-factory/602855/ Mangold, L. (2021), Semiconductor Shortage Impact on Consumer Electronics, Corintech, available at: https://www.corintech.com/news/posts/2021/september/the-chips-are-down-for-the-electronicsindustry/ Mayo, B. (2021), “TSMC to prioritize Apple and automaker silicon orders as global semiconductor shortage continues”, 9to5Mac, available at: https://9to5mac.com/2021/06/22/tsmc-to-prioritizeapple-and-automaker-silicon-orders-as-global-semiconductor-shortage-continues/ Schneider, J. (2021), “Sony: global chip shortage ‘more serious’than expected”, PetaPixel, available at: https://petapixel.com/2021/11/24/sony-global-chip-shortage-more-serious-than-expected/ Wayland, M. (2021), “Ford cutting shifts, partially building F-150 pickups and Edge SUVs due to chip shortage”,CNBC, available at: https://www.cnbc.com/2021/03/18/ford-cutting-shifts-partiallybuilding-f-150s-due-to-chip-shortage.html Zimmerman, S. (2022), “Tesla rewrites software to get around chip shortages hampering EV production”,Utility Dive, available at: https://www.utilitydive.com/news/tesla-chipsemiconductor-shortage/628150/ Further reading Aldrighetti, R., Battini, D., Ivanov, D. and Zennaro, I. (2021), “Costs of resilience and disruptions in supply chain network design models: a review and future research directions”,International Journal of Production Economics, Vol. 235, 108103, doi: 10.1016/j.ijpe.2021.108103. Corresponding author Tim Gruchmann can be contacted at: [email protected] For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected] Modern Supply Chain Research and Applications 375