Even when supply chain resilience is a relatively new concept, there are already some established tools and frameworks in place that aim to increase supply chain resilience within an organization. Petit et al. (2019) composed a resilience framework through qualitative methods; this model of Supply Chain Resilience Assessment and Management, or SCRAM -model aims to identify linkages between organizational vulnerabilities and capabilities give specific recommendations to increase organizational resilience (See figure 8).
Figure 6 The SCRAM resilience improvement process (Petit et al. 2019, 59)
An organization's resilient ability is affected significantly by its customer- and supplier base;
thus, any framework aimed at increasing organizational resilience needs to be extended to the organization's supply partners. Extending assessment to suppliers enables the partners to assess and lets the buyer-organization have more unbiased validation towards self-assessment. The SCRAM process involves assessing an organization's resilience through a range of resilience perspectives (see chapter 4.1). (Petit et al. 2013; Petit et al. 2019) The SCRAM process or any other respective supply chain resilience process or strategy deals with multiple supply chain actors.
The resilience outcome of a supply chain event (e.g., a disruption) is the combination of interactions between numerous different supply chain actors (e.g., buyers and suppliers) (Wieland & Durach 2018). Besides identifying the relationships between vulnerabilities and capabilities, the SCRAM model follows the resilience gaps. These gaps are defined as the imbalance when comparing the current state of capability vs. vulnerability to the theoretically optimal state of resilience. These gaps can direct the organization's actions in maintaining and creating further resilience in the supply network. Improvement strategies are based on the current imbalance and aim to close the evident gap between the optimal resilience scenario and the current situation. The proposed model for resilience by Petit et al. (2019) is a relatively easily understandable and appliable model for quantifiable resilience improvements. As of the time of writing, one of the few established methods is currently in existence. The biggest hurdle in SCRAM is matching vulnerabilities with the right capability factors and sub-factors;
this means that defining the optimal resilience between values can stay somewhat subjective.
The linkage of elements relies on three aspects, theoretical, survey correlation, and focus group linkages (Petit et al., 2013). Quantifying an organization's supply chain resilience capacity has also been done by analyzing organizational capacity from the absorptive, adaptive, and restorative capacity perspectives (see figure 9).
These capacities aim to address disruption as three lines of defense. More specifically, supply resilience capacity refers to the organization's features that could increase its supply chain's ability to absorb risks, adapt to the current situation, and restore itself after a disruption event.
Absorptive capacity is the capability to absorb and rebound adverse events without significantly increased effort through, e.g., multi-sourcing or risk-mitigation inventory; thus, dispersion of supply is typical in this capacity. Absorptive capacities are proactive capacities, as the mitigation efforts are put in place before the actual disruption. Absorptive capacities are synonymous with creating robustness within supply chains through, e.g., redundancy and joint-communication and planning. In practice, absorptive capacity acts as the first line of defense to increase resilience. (Hosseini et al. 2019; Hohenstein et al. 2015) Adaptive capacity means that a company can overcome supply chain disruptions by implementing nonstandard tools and organizational practices without needing recovering activities.
Adaptivity acts as the second line of defense against disruptions in situations where the organization's absorptive capacity did not effectively contain the disruption. Adaptivity from an operational perspective can be, e.g., transportation rerouting. Adaptivity is a reactive quality with elements that should only come to play when the organization's proactive measures fail the prevent interference from a disruption. (Hosseini et al. 2019; Hohenstein et al. 2015) Finally, the third and final line of resilience against disruptions is the restorative capacity of an organization. In practice, restorative measures often require further investments in resilience through, e.g., increased production capacity or equipment. (Hosseini et al. 2019)
Figure 7 Three lines of resilience against disruptions (Hosseini et al. 2019, 291)
Comparatively speaking, the different methods for quantifying resilience vary in application and process maturity. The SCRAM process by Hohenstein et al. (2010; 2013; 2019) has been a decade-long endeavor with relatively established and thought-out procedures. Hosseini et al.
(2019) propose a similar framework, but as a process, it is more open-ended and, in practice, a collection of different tools for resilience factors and disruption situations. For this thesis and research, neither the SCRAM model nor the resilience capacity framework will not be fully utilized towards concrete resilience improvement measures towards the participating organizations, as the thesis acts as a more general look on supply chain resilience in industries rather than to create a specific resilience improvement plan for an organization, as the required data and resources are not there and relevant for the end goal. However, the before mentioned methods for supply chain resilience proved vital in creating themes for the semi-structured interviews.
4 RESEARCH METHODOLOGY & PROCESS
The research process began with the identification of the research topic. The key criteria when choosing a topic is its researchability (Eriksson & Kovalainen 2008, 3). The topic's choice was centered around the Covid-19 pandemic and the associated supply chain risk from a resilience perspective. Multiple companies were approached to participate in the semi-structured interview for the empirical portion of this thesis. Out of 32 contacted companies, five agreed to participate in the research process through the semi-structured interview, with six people attending interviews lasting around 7 hours in total. The business areas were varied, but the focus was generally on retail (see table 5 for details). The research design, data collection, and analysis methods are described in more detail in the following chapters.
Table 4 Number of interviews and roles from and within the case companies
Case underlying research questions(s). The research design includes and details the objectives derived from the research question, specifies the data collection sources, how the data will be collected and analyzed, and considers the research has had, e.g., time constraints, data limitations, etc. After establishing the study's design, an approach and techniques for data collection and analysis should be decided. (Saunders, Lewis & Thornhill 2015, 163-164).
Qualitative research design and methods are an effective way of gathering detailed and rich knowledge of a specific phenomenon.