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A systematic literature review of the capabilities and performance metrics of supply chain resilience

Yu Han , Woon Kian Chong & Dong Li

To cite this article: Yu Han , Woon Kian Chong & Dong Li (2020) A systematic literature review of the capabilities and performance metrics of supply chain resilience, International Journal of Production Research, 58:15, 4541-4566, DOI: 10.1080/00207543.2020.1785034

To link to this article: https://doi.org/10.1080/00207543.2020.1785034

Published online: 02 Jul 2020.

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2020, VOL. 58, NO. 15, 4541–4566 https://doi.org/10.1080/00207543.2020.1785034

A systematic literature review of the capabilities and performance metrics of supply chain resilience

Yu Hana,c, Woon Kian Chongb and Dong Lic

aInternational Business School Suzhou (IBSS) at Xi’an Jiaotong-Liverpool University, Suzhou, People’s Republic of China; bSP Jain School of Global Management, Singapore, Singapore; cUniversity of Liverpool Management School, University of Liverpool, Liverpool, UK

ABSTRACT Research on supply chain resilience (SCRE) capabilities and its performance measurement has been growing in recent years. However, the investigation of these concepts has primarily been conducted independently despite the interdependence of these concepts. A systematic literature review of 153 papers was conducted based on the principles of rigour, transparency and replicability required by the methodology. For the first time, we structurally reviewed the 11 SCRE performance metrics cat- egories and its capabilities in SCRE Capabilities-Performance Metrics Framework (SCPM) developed based on the three resilience dimensions (readiness, response and recovery). The framework enables researchers to seek fundamental knowledge and to pursue further research regarding SCRE assess- ment. This study also provides practical value offering a guidance for decision-makers considering the trade-off among different capabilities and performance metrics.

ARTICLE HISTORY Received 9 January 2019 Accepted 11 June 2020

KEYWORDS Supply chain; resilience; capabilities; performance metrics; systematic literature review

1. Introduction

Supply chain resilience (SCRE) has attracted strong inter- est from researchers and practitioners because of the multiplicity of disruptive events and potential impacts on business competitiveness and continuity (Christopher and Peck 2004; Jüttner and Maklan 2011; Sheffi and Rice 2005). For example, routing operation interruptions, such as extreme weather disasters, information system failures and industrial disputes, can affect supply chain robustness and stability (Elliott, Swartz, and Herbane 2010). Supply chain managers are forced to adopt more resilient approaches to insulate the supply chain from disturbance (Christopher and Holweg 2011; Christopher and Lee 2004). Among the current SCRE definitions in the literature, a core concept is that SCRE is multidi- mensional and related to the system’s ability to eventually return to stabilisation (Day 2014; Hohenstein et al. 2015; Kamalahmadi and Parast 2016; Ponomarov and Hol- comb 2009). Building SCRE requires one to continuously adopt and develop capabilities (Pettit, Croxton, and Fik- sel 2013; Ponomarov and Holcomb 2009). Further devel- opment of SCRE requires information on its efficiency and a comparison with previous performances by eval- uating it via established performance metrics (Sillanpää 2015; Van Hoek 1998).

CONTACT Woon Kian Chong [email protected] SP Jain School of Global Management, 10 Hyderabad Road, Singapore 119579, Singapore

In terms of capabilities, it is recognised that it should be classified and integrated to make significant effects on formatting SCRE (Ponomarov and Holcomb 2009). Important studies in the SCRE field researched a vari- ety of capabilities (Christopher and Peck 2004; Jüttner and Maklan 2011; Pettit, Fiksel, and Croxton 2010; Sheffi and Rice 2005). Some applied literature reviews such as Ali, Mahfouz, and Arisha (2017) reviewed and classi- fied SCRE capabilities based on proactive, concurrent and reactive strategies. Some other extant studies focused on specific capabilities. For example, Fiksel et al. (2015) studied visibility, Ivanov, Dolgui et al. (2018) empha- sised redundancy and Ivanov, Sokolov, and Dolgui (2014) examined the importance of agility.

Regarding performance metrics, it is significant for organisations to conduct SCRE evaluation to facilitate the understanding of risk exposure in supply chains and to evaluate resilience and risk mitigation strategies (Soni, Jain, and Kumar 2014). Researchers have inves- tigated the measurement of SCRE by evaluating, for example, density (Smith et al. 2016), stock level (Cabral, Grilo, and Cruz-Machado 2012), service level, lead time and costs (Cabral, Grilo, and Cruz-Machado 2012). However, studies on SCRE performance metrics remain scarce (Chowdhury and Quaddus 2016; Kamalahmadi

© 2020 Informa UK Limited, trading as Taylor & Francis Group

4542 Y. HAN ET AL.

and Parast 2016; Spiegler, Naim, and Wikner 2012), as only a few articles have discussed SCRE measurement. Without understanding the level of resilience of a sys- tem, it would be difficult to assess the response and reaction of the supply chain during disruptions. Accord- ing to Ponomarov and Holcomb (2009), the potential of SCRE measurements is stated as a valuable research stream that can offer essential knowledge of SCRE and its outcomes.

Neely, Gregory, and Platts (1995) defined performance measurement as the process of quantifying the efficiency and effectiveness of action. It reflects the most essential parts of a process and shows the aspects needing fur- ther improvement. Well-established performance met- rics are essential to measure SCRE effectiveness. Perfor- mance metrics are important managerial mechanisms and support strategy implementation, communication, information and the control of processes (Kaplan and Norton 2000; Wouters and Sportel 2005). A measure- ment system states what is relevant and to be reviewed and not; it provides signals for where management has to intervene. Based on these grounds, studying change and evolution in performance metrics is highly impor- tant. Significant positive relationships exist among sup- ply chain management capabilities, and business per- formance has been expounded in many extant studies (Chowdhury and Quaddus 2016; Liao and Kuo 2014; Ponomarov and Holcomb 2009). Capabilities are essen- tial in the establishment of SCRE and therefore improve the performance of organisations when facing disrup- tive events (Pettit, Croxton, and Fiksel 2013); at the same time, appropriate performance metrics are neces- sary for evaluating SCRE performance to achieve fur- ther improvement (Sillanpää 2015). A systematic litera- ture review by Hohenstein et al. (2015) analysed eight studies on SCRE measurement and proposed a way to measure SCRE through readiness, responsiveness and recovery. Ponomarov and Holcomb (2009) developed a framework of measuring logistical capabilities based on pre- and post-disruption aspects. Chowdhury and Quaddus (2016) extended the measurement to readi- ness, response and recovery capabilities specifically. It could be seen that SCRE performance could be mea- sured through specific capabilities. We therefore pro- pose the existence of connections between the two top- ics and that they can be classified in a single frame- work. However, such connection between SCRE capabil- ities and performance metrics is ambiguous and requires sufficient understanding to explore the capabilities that deserve extra attention from the perspective of per- formance metrics, which indicates where the manage- ment should focus and intervene (Hald and Mouritsen 2018).

Extant literature reviews have mainly focused on three perspectives. First is the analysis of SCRE definition and identification of capabilities (e.g. Ali, Mahfouz, and Arisha 2017; Hohenstein et al. 2015; Kamalahmadi and Parast 2016; Kochan and Nowicki 2018). The second is the review on the evolution of SCRE research and identi- fication of future directions (e.g. Ali and Gölgeci 2019; Pettit, Croxton, and Fiksel 2019). The other perspec- tive is the review of research methods, such as quan- titative modelling methods applied in analysing SCRE (e.g. Hosseini, Ivanov, and Dolgui 2019; Pires Ribeiro and Barbosa-Povoa 2018). The purpose of this paper is to provide a systematic review and develop a frame- work to explore the aspects that contribute more to SCRE performance measurement through systematically studying the extant articles of SCRE capabilities and performance metrics and the link between them. This will build fundamental knowledge for SCRE measure- ment by evaluating specific capabilities that have not been sufficiently researched in the existing literature reviews.

The remainder of the paper is structured as fol- lows. Section 2 provides an explanation of the research methodology, research questions and the evaluation and selection criteria for the articles. The results of the sys- tematic review are presented in Section 3. This study concludes with a discussion of key findings, implications, limitations and recommendations for future research in Section 4.

2. Methodology

A systematic literature review (SLR) aims to acquire all evidence to address a specific research question for a given topic and involves a reproducible and thorough search of the literature and critical evaluation of eligible studies (Briner and Denyer 2012). An SLR is useful in synthesising the results and evidence from existing stud- ies to create new knowledge (Light and Pillemer 1984; Tranfield, Denyer, and Smart 2003), and it always offers an objective assessment of the whole literature, minimis- ing bias and errors through its strong focus on objec- tive observation and the repeatability of results (Denyer and Tranfield 2009; Tranfield, Denyer, and Smart 2003). Hence, an SLR is applied in this study. This research adopts Denyer and Tranfield’s (2009) five-step guidelines. This method has also been applied by other literature review studies focusing on SCRE, such as Ali, Mahfouz, and Arisha (2017) and Hohenstein et al. (2015) (see Figure 1). Furthermore, the organisation of the literature review and analysis follows the important features of SLR reported in Thomé, Scavarda, and Scavarda (2016) and Torraco (2005).

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 4543

Figure 1. FivestepsofanSLR(adaptedfromDenyerandTranfield 2009).

2.1. Step 1: question formulation

The first step of an SLR is to define the scope to develop a clear focus for the study (Booth, Papaioannou, and Sutton 2012; Light and Pillemer 1984). As explained, this study intends to enhance the understanding of SCRE perfor- mance evaluation and therefore identify the most impor- tant capabilities in the evaluation process through a sys- tematic summary of the literature on SCRE performance metrics, as well as capabilities that especially ensure the consistency of time range and databases. Therefore, this study proposes and attempts to address the following questions (from 2003 to 2019):

Q1: What are the capabilities in building SCRE that are normally discussed?

Q2: What are the performance metrics of SCRE?

Q3: How can SCRE be measured through capabilities and evaluation dimensions?

2.2. Step 2: locating studies

The second step of SLR is to locate, select, assess and list the core contributions related to the review ques- tions (Ali, Mahfouz, and Arisha 2017; Denyer and Tran- field 2009). To minimise bias and cover a wide range of sources and information, this study searched key online academic databases including Emerald, Science Direct, ABI/Inform, Taylor and Francis and Wiley Online. These databases were selected based on their availability in aca- demic institutions and having been considered in other similar studies.

Consistent with other systematic reviews in man- agement, especially SCRE (Colicchia and Strozzi 2012; Hohenstein et al. 2015), several keywords were defined as search criteria. To obtain broader coverage from the literature, we also adopted approaches used by Chen, Chiang, and Storey (2012) and other well- structured literature review approaches (e.g. Gupta et al.

2018; Sheng, Amankwah-amoah, and Wang 2017; Short 2009). The keywords consisted of the phrase ‘supply chain’ combined with at least one of the following: ‘resilience’, ‘resiliency’, ‘resilient’, ‘measurement’, ‘perfor- mance’, ‘assess’, ‘indices’ and ‘capabilities’; an example is the phrase ‘supply chain’ with ‘resilience’ in the abstract and the keywords and ‘performance’ in a full-text search. This literature study considers peer-reviewed academic articles published in 2003–2019. SCRE capabilities and performance metrics both have their critical years within this period. In 2003, the first crucial study on the capa- bilities of SCRE was published (Rice and Caniato 2003) – a turning point for SCM research. In addition, the first study that quantitatively researched SCRE performance metrics was published in 2007 (Datta, Allen, and Christo- pher 2007; Hohenstein et al. 2015). Given that the first paper was published in 2003, this review collected studies since then.

The search and locating of studies were started in December 2017, and were repeated in November 2018, October 2019 and March 2020. The review process was conducted interactively with frequent communications among the research teams which resulted a high level of agreement. The importance of extending the search beyond the keywords was considered for inclusiveness by including backward and forward searches (Thomé, Scavarda, and Scavarda 2016). Literatures from the arti- cles resulted from keyword search are reviewed for the backward search. Forward search was conducted through reviewing additional sources resulted from cited refer- ences of selected studies. No further studies were located during the process.

2.3. Step 3: study selection and evaluation

Explicit selection criteria (see Table 1) were applied for the inclusion and exclusion of relevant studies to main- tain the transparency of the process (see Figure 2). Titles and abstracts of 722 papers were read in the first screen- ing. All documents that did not meet the selection criteria or were duplicates were excluded; 302 articles remained for the next process of selection.

The third screening involved reading the introduc- tions and conclusions of the remaining articles, excluding 98 documents because of their irrelevance to the review questions. However, 6 articles were added as a result of cross-referencing citations, leaving 210 articles for the final screening. The final screening involved reading the articles in their entirety. This stage excluded research papers that did not provide related information in terms of the purpose of this literature review. In total, 153 arti- cles were selected for analysis, 36 of which discuss SCRE performance metrics, either with capabilities or simply

4544 Y. HAN ET AL.

Table 1. Inclusion criteria.

Inclusion criteria Rationale

Papers that discuss performance metrics of resilience

This study aims to review papers that include a discussion on SCRE performance metrics

Papers that discuss the capabilities of resilience

This study aims to review papers that include a discussion on SCRE capabilities

Published in English language The dominant language in the field of supply chain management

Different article types (e.g. empirical, conceptual and literature review)

To evaluate and synthesise the various research approaches

the performance metrics themselves, while the other 117 merely concerned capabilities.

3. Analysis and findings

This section discusses the analysis and findings of the review. First, a descriptive analysis is presented on the development and current situation of SCRE literature. The publication year, journal and methodologies of the 153 articles from 2003 to 2019 are discussed in detail. Second, SCRE capabilities and performance met- rics are identified and reviewed. The integrated analysis thoroughly examined the connection among reviewed

capabilities and performance metrics by discussing their underlying definitions and relevant practices.

3.1. Descriptive analysis

Figure 3 presents trends in the number of articles pub- lished in 2003–2019. In general, the number of articles related to SCRE increased dramatically during those 17 years. As noted, only a few attempted to analytically measure SCRE; most only briefly discussed SCRE per- formance metrics. The first attempt to analytically assess SCRE was that of Datta, Allen, and Christopher (2007), which evaluated the impact of different strategies when considering the dynamics of demand, production and distribution functions. They considered customer service level, average inventory level and production change over time to assess operational resilience.

Table 2 summarises the number of papers published in different academic journals and the methodologies applied by these articles. The 153 selected articles were published in 50 interdisciplinary academic journals, but nearly 50% were published in the 8 leading journals in the area of supply chain management (marked with * in Table 2). Among the leading academic journals, the Inter- national Journal of Production Research accounted for the highest share of articles published. Further, the diverse

Reading of introduction and

conclusion Cross-referencing

210 articles

Cross-referencing Reading of the articles in their

entirety

153 articles 36 articles on performance

metrics

Studies identified from electronic

databases

722 articles

Exclusion of duplication Exclusion after reading titles

and abstract

302 articles

Figure 2. Review process for study selection (adopted from Moher et al. 2010).

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 4545

1 1 2 2 1 1

6 5 3

9 9 11

15 17

20 23

26

0

5

10

15

20

25

30

20 03

20 04

20 05

20 06

20 07

20 08

20 09

20 10

20 11

20 12

20 13

20 14

20 15

20 16

20 17

20 18

20 19

Figure 3. Number of papers on SCRE, 2003–2019.

research themes of the journals (e.g. production, business logistics and transportation) are evidence of the multidis- ciplinary nature of the research topic and the increasing attention from various research communities.

Various research methodologies have been applied in the literature to address the research topic. Refer- ring to Table 2, four types of research methodologies are commonly used, including conceptual and empiri- cal research, case study and literature review. About 50% of the papers conducted conceptual research to study SCRE capabilities and performance metrics. Each paper was classified under its primary research methodolo- gies although a few papers applied a mixed research method. For example, Manning and Soon (2016) applied a mixed method including a literature review and con- ceptual research. However, this paper is classified here as a literature review paper because this was the primary research method adopted by the study.

Figure 4 provides a clearer view of the trends and development in this academic area, with the key times when study of SCRE capabilities and performance met- rics were developing rapidly highlighted. In 2003, the first work on SCRE capabilities was published (Rice and Caniato 2003), discussing crucial SCRE elements including security, redundancy, flexibility and knowledge management. Later, Datta, Allen, and Christopher (2007) published the first quantitative study on performance

metrics. The year 2009 saw a significant increase in not only the number of SCRE capability-related studies but also the types of capabilities discussed. Ponomarov and Holcomb (2009) developed the first definition highlight- ing preparation (readiness dimension) for unexpected events. Scholars started paying attention to performance metrics from 2012. Another important year was 2016, with two key papers published: Chowdhury and Quad- dus (2016), which applied a straightforward approach to measuring SCRE by directly evaluating the performance of capabilities, and Hohenstein et al. (2015), which is, to the best of our knowledge, the first literature review study on performance metrics (though numbers were limited).

3.2. SCRE definition

The definition of SCRE is examined in a considerable number of published studies. However, there is no com- monly accepted definition although many definitions from different studies are similar. What is commonly agreed is that SCRE is a multidisciplinary concept. To name a few important studies that contributed to defin- ing SCRE, Rice and Caniato (2003) described SCRE as an organisational ability to react to unexpected events and restore normal operations, Christopher and Peck (2004) proposed that SCRE is an ability to return to one’s original state after disruptions. A more comprehensive definition that can reflect the integrated multiple disci- plines is from Ponomarov and Holcomb (2009, 131), who stated that SCRE is an adaptive ability of the supply chain to prepare for, respond to and recover from unexpected events by maintaining the continuity of a desired level of operations and control over structure and function. Lit- erature review studies, such as Ali, Mahfouz, and Arisha (2017) and Hohenstein et al. (2015), worked on review- ing the definitions proposed by current literature to find an appropriate SCRE definition. Most of the definitions noted that SCRE is developed to prepare for, respond

Table 2. Number of papers published in academic journals and methodology applied.

Methodology

Academic journal No. of papers Frequency (% rounded) Conceptual Empirical Case study

Literature review

International Journal of Production Research* 30 19.6% 17 8 5 Supply Chain Management: An International Journal* 10 6.5% 2 1 5 2 International Journal of Physical Distribution and Logistics Management* 6 4.0% 2 1 3 International Journal of Production Economics* 11 7.2% 5 3 2 1 MIT Sloan Management Review* 5 3.3% 3 1 1 International Journal of Logistics: Research and Application* 3 2.0% 3 Journal of Business Logistics* 4 2.6% 1 3 Journal of Operation Management* 7 4.6% 2 5 Transportation Research Part E: Logistics and Transportation Review 4 2.6% 1 2 1 The International Journal of Logistics Management 6 4.0% 2 2 2 Journal of Supply Chain Management 1 0.7% 1 Others 66 43.1% 35 17 8 6 Total 153 100% 71 45 18 19

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Figure 4. Timeline of the development of SCRE capabilities and performance metrics.

to and recover from supply chain disruptions. Thus this paper adopts the dimensions of readiness, response and recovery to address the research questions.

3.2.1. SCRE dimensions for capabilities and performance evaluation Ponomarov and Holcomb (2009) clearly indicated the readiness, response and recovery dimensions as directly related to SCRE with regard to disruptions. Readiness is important for the supply chain to prepare for events to reduce its susceptibility to disruptions (Christopher and Peck 2004; Jüttner and Maklan 2011). Supply chain readiness implies the capabilities to recognise, antici- pate and prevent risks and disruptions before damage occurs (Chowdhury and Quaddus 2016). Pettit, Fiksel, and Croxton (2010) also mentioned that a supply chain should forecast, identify and assess risks, monitor devia- tions and mitigate disruptions by sensing early signals of risks. Readiness for unexpected events first appeared in the work of Datta, Allen, and Christopher (2007). Mean- while, response implies the ability to respond quickly to critical situations – an important variable that deter- mines a company’s resilience (Chowdhury and Quaddus 2016; Sheffi and Rice 2005). Response was mentioned by Rice and Caniato (2003); since then, it has been con- sidered a fundamental and reactive part of SCRE and is thus frequently discussed and stressed in SCRE defini- tions (Hohenstein et al. 2015). In the competitive busi- ness environment, companies that can respond quickly have the opportunity to gain market share and solidify or enhance their position in the industry (Sheffi and Rice 2005). Similar to response, recovery is also mentioned by Rice and Caniato (2003) and has since been a fundamen- tal and reactive part of SCRE (Hohenstein et al. 2015). Recovery refers to the aftershock of an event to restore and return to normal operations. In the literature, recov- ery is mostly related to recovery time (Christopher and Peck 2004; Ponomarov and Holcomb 2009; Sheffi and Rice 2005); the ability to recover implies a speediness in the supply chain to return to its original state (Christo- pher and Peck 2004; Losada, Scaparra, and O’Hanley 2012).

3.3. SCRE capabilities

During the review process of analysing SCRE capability, we found an inconsistency in the terminologies. Some authors used the term ‘capabilities’ (Jüttner and Mak- lan 2011; Pettit, Croxton, and Fiksel 2013; Pettit, Fiksel, and Croxton 2010) while others referred to ‘elements’ (Christopher and Peck 2004), ‘antecedents’ (Ponomarov and Holcomb 2009) or ‘competencies’ (Wieland and Wal- lenburg 2013). This study uses ‘capabilities’, which is also suggested by Jüttner and Maklan (2011), and in line with that formative resilience, elements should be captured at a capability level.

To consolidate various SCRE capabilities into the dimensions of readiness, response and recovery, based on rigorous previous studies grounded in theory (Pono- marov and Holcomb 2009), 11 capabilities were consid- ered essential in constituting SCRE; they matched the three dimensions and attracted the largest number of SCM studies in past decades. We rigorously reviewed and identified four resilience capabilities (situation aware- ness, visibility, security and redundancy) in the readi- ness dimension, four (agility, flexibility, collaboration and leadership) in the response dimension and three (knowl- edge management, contingency planning and market position) in the recovery dimension (see Appendix).

The readiness dimension contains four capabilities: situation awareness, visibility, security and redundancy. Redundancy entails maintaining excess capacity, safety stock, multiple suppliers and backup sites (Dabhilkar, Bengtsson, and Lakemond 2016; Hasani and Khosrojerdi 2016; Ivanov 2018; Ivanov and Dolgui 2019; Manning and Soon 2016). It is expected to improve the ability to respond to disruption via the strategic use of excess resources (Sheffi and Rice 2005; Wieland and Wallen- burg 2013). Visibility involves the use of information technology to enable transparency of information and awareness of the current supply chain situation (Fik- sel et al. 2015; Jüttner and Maklan 2011; Melnyk et al. 2010; Pettit, Fiksel, and Croxton 2010). Security is help- ful in areas such as personnel security, physical security and cyber-security. Situation awareness is the ability to sense and forecast a possible disruption; such capability requires knowledge of supply chain vulnerabilities and

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 4547

the sharing of information (Ali, Mahfouz, and Arisha 2017; Chowdhury and Quaddus 2017; Eltantawy 2016).

In terms of response, four capabilities were identi- fied and reviewed. Flexibility was widely discussed as the ability to adapt and adjust to a disruption rapidly rather than merely withstand the damage of the disrup- tion (Dolgui, Ivanov, and Sokolov 2018; Ishfaq 2012; Jüt- tner and Maklan 2011; Ponis and Koronis 2012; Wieland and Wallenburg 2013). Collaboration is what integrates the supply chain network, allowing the holistic deci- sion to build a resilient supply chain (Scholten, Sharkey, and Fynes 2014; Sheffi 2001). According to Christopher and Peck (2004), collaboration concerns the exchange of information and the application of shared knowl- edge to decrease uncertainty and increase visibility and customer service (Scholten, Sharkey, and Fynes 2014). Agility is the ability to rapidly respond to unpredictable changes in demand or supply in the marketplace since customer requirements are continuously changing (Car- valho, Duarte, and Machado 2011; Christopher and Peck 2004). Quick reactions through agility will help the sup- ply chain to reduce the damage of disruption (Cabral, Grilo, and Cruz-Machado 2012). Leadership refers to the execution of management in companies, which requires support from top management, engagement of employ- ees and high-quality decision-making (Manning and Soon 2016; Seville, Opstal, and Vargo 2015).

As for the recovery dimension, three capabilities were identified and reviewed – knowledge management, con- tingency planning and market position. Knowledge man- agement is the ability to learn from feedback from a disruption to develop better plans and solutions for future ones (Ponomarov and Holcomb 2009). Contingency planning enhances the ability to recover by assessing processes such as supply chain reconfiguration, scenario analysis and resource reconfiguration (Birkie, Trucco, and Campos 2017; Boone et al. 2013; Pavlov et al. 2018; Ponomarov and Holcomb 2009; Zsidisin and Wagner 2010). Market position is related to financial perspec- tives, including financial strength, market share and loss absorption (Day 2014; Fiksel et al. 2015; Wu et al. 2013); for example, a strong market position will ensure a high market share that allows for more investment in SCRE (Sheffi and Rice 2005).

3.4. Identification and categorisation of SCRE performance metrics

SCRE performance metrics focus on the evaluation of the impact of resilience. We identified 36 papers studying the performance metrics of supply resilience, applying differ- ent research methods and perspectives (see Appendix 2 for a detailed summary of SCRE performance metrics).

Table 3. Categorisation of performance metrics of SCRE.

Category Studies (e.g.)

Performance of main- taining customer satisfaction

Cabral, Grilo, and Cruz-Machado (2012); Rajesh (2016); Chen, Xi et al. (2017); Schmitt et al. (2017); Ivanov, Dolgui, and Sokolov (2018); Kinra et al. (2019)

Efficiency of completing supply chain processes

Day (2014); Rajesh (2016); Azevedo, Carvalho, and Cruz-Machado (2016); Schmitt et al. (2017)

Efficiency of recovering to normality

Todo, Nakajima, and Matous (2015); Chowdhury and Quaddus (2016); Zeng and Yen (2017); Chen, Xi et al. (2017); Hosseini and Ivanov (2019); Chang and Lin (2019); Tan, Cai, and Zhang (2019)

Performance of production and inventory

Azevedo, Carvalho, and Cruz-Machado (2016); Wicher et al. (2016); Lücker and Seifert (2017); Ivanov (2018); Tan, Zhang, and Cai (2019)

Performance of relationship management

Chowdhury and Quaddus (2016); Rajesh (2016); Wicher et al. (2016); Li et al. (2017)

Financial performance Ambulkar, Blackhurst, and Grawe (2015); Dixit, Seshadrinath, and Tiwari (2016); Rajesh (2016); Loh and Thai (2016); Wicher et al. (2016); Ivanov, Dolgui, and Sokolov (2018)

Performance of overseeing the supply chain situation

Azevedo, Carvalho, and Cruz-Machado (2016); Ivanov, Pavlov, and Sokolov (2016)

Performance of discerning possible disruptions

Cabral, Grilo, and Cruz-Machado (2012); Rajesh (2016); Li et al. (2017); Chen, Xi et al. (2017); Hosseini and Ivanov (2019)

Damage of disruptions Munoz and Dunbar (2015); Ambulkar, Blackhurst, and Grawe (2015); Ivanov (2018); Kinra et al. (2019)

Efficiency of responding the disruptions

Chowdhury and Quaddus (2016); Rajesh (2016); Vonderembse et al. (2006); Li et al. (2017); Chang and Lin (2019)

Reconstruction of the supply chain

Ambulkar, Blackhurst, and Grawe (2015); Loh and Thai (2016); Lam and Bai (2016); Pavlov et al. (2018)

Most of the studies had their own measurement mod- els and identified specific performance metrics for SCRE. However, from the results of the literature review, no common agreement on a measurement model has been achieved; the assessment of SCRE performance was stud- ied structurally using SCRE dimensions or phases in most published SCRE measurement studies.

To improve understanding and conceptual clarity, we first integrated and consolidated various performance metrics and primarily categorised them into 11 categories according to the underlying definitions of the perfor- mance metrics in Table 3 (see Appendix 3 with listed performance metrics from the 36 studies of SCRE mea- surement). The performance metrics adopted by each article are listed in Table 3, and the details can be found in Appendix 2. We found that the assessment of SCRE is rigorously studied by scholars from variety of perspectives. For example, in 2016, Ivanov, Pavlov and

4548 Y. HAN ET AL.

Sokolov attempted quantifying reliability from the man- agerial perspective, which enables supply chain managers to assess and compare the reliability of different sup- ply chain settings. Later, Ivanov (2018) and Kinra et al. (2019) contribute in measuring ripple effect. The former study is a simulation-based research that identifies the sustainability factors that mitigate or enhance the rip- ple effect. The latter study develops a model based on possible maximum loss in assessing the ripple effect of a supplier disruption. Hosseini and Ivanov (2019) also looks at the ripple effect and examine resilience by using a Bayesian network and a real-life case study. The research quantifies the resilience through a multi-stage assessment of suppliers’ proneness to disruptive events and the sup- ply chain exposure to ripple effect. Pavlov et al. (2018) assess the total structural resilience for a given reconfigu- ration path. Using a hybrid fuzzy-probabilistic approach, the authors suggest a method of comparing resilience of different supply chain design, considering both the disruption propagation and recovery strategies. Ivanov, Dolgui, and Sokolov (2018) analyse the control policy performance by measuring service level and profit under different scenarios. If both performance indicators are above the minimum bounds for all possible disruptions, the supply chain can be considered resilient within the analysed perturbation range.

Based on analysing the categories of these studies on performance measurement, this paper attempts to link SCRE capabilities and performance metrics to consoli- date them into the SCRE evaluation framework as illus- trated in Section 3.5. Subsequently, we have carefully reviewed and analysed the selected scientific articles and explained the categories specifically for SCRE evaluation as follows:

(1) Performance of maintaining customer satisfaction refers to the measurement with regard to the performance of managing customer satisfaction particularly during disruption periods (Cabral, Grilo, and Cruz-Machado 2012; Datta, Allen, and Christopher 2007; Loh and Thai 2016; Rajesh 2016). Under disruption risks, many companies try to develop an efficient manner to maximise customer service level and seek better metrics to measure their performance in order to enhance services in a customer-driven supply chain envi- ronment (Rajesh 2016; Sawik 2016). For instance, Sawik (2014) developed an integrated measure- ment model to equitably optimise expected cost and expected customer service level to improve the selection of supply portfolio and scheduling of customer orders in a global supply chain under disruption risks.

(2) Efficiency in completing a certain supply chain pro- cess concerns the time and efficiency between the initiation and execution of a process during the time of disruptions (Azevedo, Carvalho, and Cruz- Machado 2016; Pettit, Croxton, and Fiksel 2013; Rajesh 2016). For example, lead time is used to measure the time needed to deliver the product to market to complete customers’ requirement. Com- panies usually strive to reduce lead time; if the production lead time increases, the total lead time and cost will increase (Cabral, Grilo, and Cruz- Machado 2012). Previous literature (e.g. Chopra and Sodhi 2014; Rumyantsev and Netessine 2007) indicates that lead time has a positive relation- ship with companies’ preparation to absorb the impact of a disruption. The method aims to elim- inate potential duplicate and improve work flows within production and responsiveness of suppliers to end-users during any disruption events. There- fore, companies should develop effective metrics for lead time in determining companies’ resilience performance when businesses are exposed to a greater risk of disruptions (Carvalho et al. 2012).

(3) Efficiency of recovery to normality refers to the speed of the supply chain to fully recover to its normal operation after a disruption (Pant et al. 2014; Raj et al. 2015; Todo, Nakajima, and Matous 2015). It is different from the efficiency in respond- ing to disruptions that focus on the speed of taking responses and actions at the beginning of an event. For example, Pant et al. (2014) proposed ‘the time to full system service’ resilience to measure the time from when recovery activities commence to the time when the system is completely restored.

(4) Performance of production and inventory capacity concerns the measure of stock level and capacity during disruptions. Inventory levels increase mate- rial availability, allowing for a quicker response to unexpected demand (Cabral, Grilo, and Cruz- Machado 2012). Normally, if the inventory level of critical materials is low, the supply chain is more vulnerable to unexpected events that affect the supply of these materials (Carvalho, Duarte, and Machado 2011).

(5) Performance of relationship management refers to performance metrics such as the extent of connec- tion and interaction (Smith et al. 2016) and the quality of relationships in the supply chain network under disruptive conditions (Lam and Bai 2016). For instance, building flexible relationships with suppliers is one of the effective ways to respond to the uncertainty of productions such as supply and demand volatility (Ivanov and Dolgui 2019;

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Ivanov, Das, and Choi 2018). According to Smith et al. (2016), there are many dimensions to mea- sure the flexibility of suppliers’ relationships (such as connectivity or adaptability), and this measure- ment is essential to effectively respond to supply chain networks under disruptions.

(6) Financial performance mainly includes the eval- uation of cost, profits, financial benefits, fines and penalties that occurred during the disruption (Cabral, Grilo, and Cruz-Machado 2012; Loh and Thai 2016). In other words, it measures whether the supply chain has the ability to maximise the profits and minimise the costs during the time of disruptions.

(7) Performance of overseeing the supply chain sit- uation concerns the assessment of the quality in monitoring the supply chain situation to enable a longer preparation time for the supply chain before the disturbances take place (Chen, Xi et al. 2017); Li et al. 2017; Rajesh 2016). This indicator measures specifically the performance of the supply chain in the overall monitoring and control of the condi- tions from the beginning to the end of disruption.

(8) Performance of discerning possible disruptions measures the supply chain’s ability to sense and interpret events through assessing, for example, the quality of forecast (Rajesh 2016). Unlike the perfor- mance of overseeing the supply chain that focus on the situation during the disruption happening, this indicator targets specifically on whether the sup- ply chain has the ability or has sufficient ability to forecast a disruptive even before it happens.

(9) Damage from disruptions relates to the assessment of the severity of the event, which focus only on the calculation and measurement of the loss caused by the disruption. This is also an appropriate indicator to assess the final results of a company’s resilience building. For example, Ambulkar, Blackhurst, and Grawe (2015) adopted a disruption impact measure to capture how supply chain disruptions reported by respondents affected their firm’s overall effi- ciency of operations, delivery reliability to cus- tomers and procurement costs.

(10) Efficiency in responding to disruptions evaluates specifically on how quick the supply chain (e.g. time, speed) can recognise a disruptive event and start taking actions when a disruption appears. It is different from the Efficiency of recovery to nor- mality, which emphasises on the post-disruption recovery. According to the literature, for example, it can relate to the assessment of the ability to provide quick, appropriate resources to meet dynamically shifting needs (Chen, Xi et al. 2017); Li et al. 2017;

Rajesh 2016) that allows for sufficient adaptabil- ity to external influences and unforeseen problems, which can improve overall relief effort performance (Day 2014; Pettit and Beresford 2005).

(11) Reconstruction of the supply chain involves the redesign and restructure of the system and the reconfiguration and realignment of resources after the impact of disruptions (Ambulkar, Blackhurst, and Grawe 2015; Ivanov, Dolgui, and Sokolov 2018; Loh and Thai 2016). Ambulkar, Blackhurst, and Grawe (2015) applied a seven-point Likert scale to evaluate resource reconfiguration, renewal and restructure in response to the dynamic environ- ment and to react to the changing business envi- ronment (Ambulkar, Blackhurst, and Grawe 2015; Munoz and Dunbar 2015).

3.5. SCRE evaluation framework

The literature review conducted by Hohenstein et al. (2015) proposed that the assessment of readiness, response and recovery should be based on robustness measures (e.g. inventory holding, multiple sourcing), reaction time to disturbance and time to recover to normal performance, respectively. The authors further revealed that the overall SCRE performance could be assessed through customer service, market share and financial performance. However, through our review of SCRE capabilities and performance metrics from 2003 to 2019, it is clear that extant studies on SCRE assess- ment focused on more aspects, and it could be noticed that SCRE capabilities and different categories of per- formance metrics share similar underlying concepts; in other words, they are conceptually connected. This section intends to illustrate such connection and hence present capabilities and performance metrics in a sin- gle framework. Furthermore, to identify the most crucial SCRE aspects in the studies’ perspective, this review allo- cates capabilities and performance metrics correspond- ingly using dimensions including readiness, response and recovery. Building certain SCRE capabilities is expected to enable better SCRE performance, and the correspond- ing performance metrics can measure SCRE perfor- mance resulting from the establishment of capabilities.

According to the explanation of different capabili- ties and performance metrics (refer to Appendix 1–3 and Sections 3.3–3.4.), for example, situation awareness is the ability to forecast a possible disruption (Birkie, Trucco, and Campos 2017; Eltantawy 2016; Rajesh and Ravi 2015) while the ability to discern possible disrup- tions involves the assessment of accuracy and quality in forecasting the disruptive events (Chen, Xi et al. 2017); Li et al. 2017; Rajesh 2016). Similarly, redundancy is the

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Figure 5. SCRE Capability-Performance Metrics Framework (SCPM).

resilience capability of having excess inventory, multiple suppliers, backup sites and capacity (Dabhilkar, Bengts- son, and Lakemond 2016; Hasani and Khosrojerdi 2016; Manning and Soon 2016). Contingency plans appear as a key SCRE capability, including the practices of resource reconfiguration and restoration plans, while the category of performance metrics of the reconstruction of the sup- ply chain also seeks to measure supply chain reconfigu- ration and redesign. The efficiency of completing certain supply chain process evaluates, for example, lead time, which refers to the amount of time needed to deliver the product to market. Both scholars and industries strongly suggest the importance of reducing lead time (Christo- pher and Peck 2004; Vonderembse et al. 2006).

Built upon the three SCRE dimensions (readiness, response and recovery), capabilities associated with SCRE and the categories of performance metrics identi- fied from previous studies, this paper presents the SCRE Capability-Performance Metrics Framework (SCPM) which comprehensively demonstrates the overall SCRE structure (as in Figure 5). Through the literature review and linking the capabilities and performance metrics, we were able to identify the eight capabilities that are frequently measured using corresponding performance metrics. Building on the literature, this framework pro- vides a comprehensive and holistic view of the develop- ment of SCRE research and a guideline associated with capabilities development and performance metrics. Two important implications of the SCPM are therefore:

• It indicates that the SCRE performance can be mea- sured from a capability perspective and the SCRE

performance measurement framework reveals how such measurement is achieved.

• -It bridges the two academic areas, SCRE capabili- ties and SCRE performance metrics, that are used to be independently researched in most of the literature. SCPM suggests that the two areas should be consid- ered and discussed as a whole for successful SCRE development.

After the review and analysis, three capabilities are found to be left out by the SCPM, namely security, leader- ship and knowledge management. This is mainly because the corresponding performance metrics for evaluating the three capabilities are not located during the review process. However, it is not suggesting to overlook the importance of these capabilities as the advancement of technologies, management skills, globalisation, etc. could imply a starting point for researchers to conduct further research into these capabilities and establish relationships with SCRE performance metrics. We therefore suggest the future studies to pay specific attention to the measure- ment of security, leadership and knowledge management.

According to the review, research on SCRE capabilities exhibits a mature and unified framework. The develop- ment of performance metrics is still far from that of capabilities. We argue in this study that capabilities enable SCRE development, and performance metrics are needed to evaluate SCRE and future improvement. Therefore, we suggest that future studies conduct research on per- formance metrics directly based on SCRE capabilities to offer a more straightforward approach to demon- strate how SCRE performance is measured and guides

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companies to focus on crucial capabilities to improve per- formance. To provide a simple example, if an organisation adopted capabilities including flexibility, collaboration, redundancy and visibility to enable the building of SCRE, it could apply available performance metrics that directly target these four capabilities. This would provide a clear view of the requirements to improve these capabilities and enable better SCRE performance.

4. Concluding remarks

4.1. Implications

The distinctiveness of this literature review focuses on the connection between SCRE capabilities and performance metrics and establishment of the SCPM framework to offer a better conceptual framework for these important research topics in supply chain management. Practition- ers can benefit from the findings and develop a better understanding of what capabilities they need to develop and further improve using performance metrics. Young researchers can have a comprehensive understanding and knowledge about this research area, which can concep- tually help them build their knowledge base. For senior researchers, this literature review suggests that future studies on SCRE performance metrics can conduct the study based on the conceptual connectedness between SCRE performance metrics and capabilities.

4.1.1. Academic implications Building on previous research, this study contributes to the analysis of supply chain capabilities and perfor- mance metrics guided by an SLR covering studies under- taken over a period of 17 years (2003–2019). First, this study identified SCRE capabilities from 2003 to 2019 which provides researchers the updated knowledge of relevant studies. Second, this paper addresses the gap of lacking studies on SCRE measurement by offering the first structured review on extant studies; this pro- vides future researchers a clearer structure and picture of current research in this area. Most of all, this paper is the first attempt that structurally reviews performance metrics for SCRE measurement and contributes as the first review study in providing structured knowledge involving SCRE capabilities and performance metrics to future research. The development of the SCPM frame- work reveals the important capabilities reflected from the measurement perspective. The framework points to a research direction and provides an efficient approach of SCRE performance evaluation. Assessing readiness, situ- ation awareness, visibility and redundancy measures fos- ters the SCRE to quickly prepare for the shock. Responses can be measured by evaluating agility-, collaboration-

and flexibility-related indicators to analyse the perfor- mance level of reacting to disruptions. The assessment on contingency planning and market position can indicate the supply chain’s performance in maintaining opera- tion status. Meanwhile, overall SCRE performance can be reflected through the damage caused by the events.

4.1.2. Practical implications This review of capabilities and performance metrics can help managers towards a better understanding of the requirements of building a resilient supply chain. This literature review indicates that if companies intend to develop and improve certain SCRE capabilities such as flexibility (the category of efficiency of responding to disruptions), performance metrics related to flexibility should be applied. Eleven SCRE capabilities were iden- tified, which encompass a wide range of supply chain dimensions to manage disruptions (readiness, response and recovery). Therefore, this can offer companies a framework for cultivating and building their capabili- ties based on real situations. Managers now have a clear picture of the evaluation of their SCRE capabilities and potential for further improvement. For example, man- agers could directly refer to performance metrics such as level of capacity and inventory to evaluate the capability of redundancy.

Further, SCRE is a relatively new terminology to some developing countries. For example, companies in China have only begun to realise the value and importance of improving supply chains in recent years. The Belt and Road project, which is currently under the spotlight, is an example of applying SCRE strategies to build sustain- able business models (Sheu and Kundu 2017). Therefore, this literature review could serve as a good set of instruc- tions for understanding the establishment, evaluation and improvement of SCRE. A similar case can be found in India, as Indian firms and their partners within the country are seeking global competitiveness, and a better understanding of resilience building and risk mitigation strategies is crucial (Rogers et al. 2016).

Moreover, this study emphasises and reminds the pub- lic about the critical role of resilience in supply chain disruption management in the current special period of COVID-19 and future epidemic outbreaks. Epidemic outbreaks start with small scale, but scale up fast and disperse over many geographic regions with great uncer- tainty which makes it difficult to fully understand the impacts of epidemic outbreaks on supply chains and take appropriate measures to response (Ivanov 2020). This makes it even more important and urgent for not only businesses but also authorities and government sectors to invest in establishment of core capabilities of resilient supply chains to enhance the performance in such a crisis.

4552 Y. HAN ET AL.

Thus this systematic review would serve as a strong fun- damental and comprehensive knowledge for the develop- ment of a more robust supply chain resilience framework in responding to emergencies such as the COVID-19.

4.2. Recommendations for future research

Through this study, important paths for future research can be highlighted with following research agendas.

From a general perspective of SCRE research, the review has found sufficient conceptual research observ- ing and analysing existing SCRE related concepts and definitions. Therefore, more rigorous and exploratory empirical studies are needed to justify SCRE capabili- ties and performance metrics with practical evidence. Case studies have been proven particularly useful in exploring the right direction to understand the rela- tionships between SCRE capabilities and performance metrics (Stuart et al. 2002). We suggest empirical stud- ies such as case study in combination with quanti- tative methods to validate both theoretical concepts and practical models would be effective methodological approaches. We encourage researchers to further inves- tigate all the elements provided in the SCPM framework to discover specific measures for SCRE in various indus- tries. For instance, researchers could conduct empirical research focusing on different countries and industries that requires significant exploration from competitive advantage and sustainable perspectives. Given that the COVID-19 has not only immensely affected all areas of economy and society but also put the SCRE to the test, we suggest that the future research focuses more on extending the constructs of SCRE capabilities and per- formance measurements to tackle unknown disruptions and systemic threats. For example, Ivanov and Dolgui (2020) introduces the term ‘Intertwined Supply Network’ (ISN) that encapsulates entireties of interconnected sup- ply chains which secure the provision of society and markets with goods and services, and elaborates on the integrity of ISNs and viability to ensure the survivabil- ity. Future studies may continuously work on measur- ing the impact of epidemic outbreaks (e.g. COVID-19), and supply chain capabilities that need to be developed and improved to achieve a quicker recovery from the epidemic outbreaks to enhance the SCRE performance.

We also suggest researchers to follow up the SCRE research for deeper understanding on how the SCRE capabilities and performance metrics can be effectively generated and developed. In our study, the SCPM frame- work developed in this study enhances the under- standing of relationships among the SCRE dimen- sions, SCRE capabilities and their associated enabling business practice factors, and the performance metrics

(as presented in Figure 5 and Appendix 1). For instance, the development of collaboration relies on information sharing, collaborative forecasting and communication as seen in Appendix 1. We believe that further investiga- tion about these relationships with strengthened forms, e.g. differentiated priorities or ranked proximity, etc. would add significant value to building key capabilities and evaluate critical measures for improving SCRE in businesses. An effective option for such research would be, a step further from the breadth-oriented study pre- sented in this paper, developing depth-oriented studies on the literature, e.g. identifying the strength of the rele- vant relationships through content analysis. As a widely adopted method for qualitative research, content anal- ysis may be used to ‘inference about matters of impor- tance’ (Stemler 2000) of the enabling business practice elements to relevant SCRE capabilities. Through identi- fying the concurring frequency of coded key capability terms and business practice factors in selected litera- ture with proper tools, the targeted relationships with various level of interest would be revealed reflecting their importance of the practice factors to building rele- vant capabilities and improving SCRE performance. Such a study would help to develop theoretical or practical frameworks for SCRE management with more tangible sense.

Furthermore, the study on SCRE measurement and related capabilities can be extended by taking the imple- mentation and operationalisation factors into account. From the perspective of SCRE measurement, first, this study only identified a limited number of articles on performance metrics (Chowdhury and Quaddus 2016; Kamalahmadi and Parast 2016) further studies can be conducted among variety of research types, theoret- ically and empirically. As a future research agenda, researchers could examine SCRE capabilities for assess- ing the resilience status and most importantly for the design of a sustainable SCRE framework with reason- able and practical procedures. Second, security, leader- ship and knowledge management are not included in the SCPM framework because corresponding performance metrics for these capabilities were not identified in this study. However, according to Chowdhury and Quaddus (2017) and Manning and Soon (2016), these three impor- tant capabilities have values associated with the ability to synthesise research and can provide a fundamental understanding of SCRE phenomenon and create further research advancement. For example, evaluating the per- formance of security related to information and data would be promising in SCRE research in the big data era (Richey et al. 2016). Therefore, we suggest future research to pay extra attention to the measurement of the three capabilities.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 4553

From the perspective of SCRE capabilities, it is observed during the review process that apart from the 11 capabilities selected, there are many other capabil- ities whose definitions are similar to or same as the identified capabilities in this paper, but using different terminologies. It is widely recognised that opinions and discussions among many concepts within SCRE are still divided (Christopher and Peck 2004; Jüttner and Mak- lan 2011; Pettit, Croxton, and Fiksel 2013; Ponomarov and Holcomb 2009). Therefore, though studies through literature review help with summarisation and compre- hensive understanding of the topics, further conceptual or empirical studies are urgently needed to clarify the variety of SCRE capabilities. In practice, building capa- bilities for performance could be expensive. For instance, redundancy, a SCRE capability that is characterised by holding excess stock, facilities, multiple suppliers, etc., is widely discussed as an efficient way of achieving SCRE (e.g. Rajesh and Ravi 2015; Zsidisin and Wagner 2010). However, Tukamuhabwa et al. (2015) note that building redundancy could be an expensive method to achieve SCRE. Zsidisin and Wagner (2010) also found that the benefits of holding redundant resources might be overestimated by firms, as this may not reduce fre- quency of disruptive events. Therefore, it is important for future research to pay extra attention to the invest- ment into SCRE capability building, so that plausible bal- ance between the benefits and costs of developing certain SCRE capabilities can be explored.

4.3. Limitations

This study has certain limitations. First, it only covers literature over the past 17 years drawn from key elec- tronic academic databases. Second, the articles selected for review and analysis are limited to peer-reviewed aca- demic journal articles that provide higher quality. Other types of texts, such as conference papers and book chap- ters, are ignored; these sources might offer a deeper understanding of this topic.

Acknowledgments

The research has been partly sponsored by EC H2020-MSCA- RISE-2017 Project 777742.

Disclosure statement

No potential conflict of interest was reported by the author(s). Statement of the length: This is a literature review paper that contains a lot of information and knowledge from published papers. The reference list is also much longer than that of a normal paper.

Funding

This work was supported by National Natural Science Foun- dation of China [grant number 71402143]; EC H2020-MSCA- RISE-2017 Project [grant number AMD-777742-57].

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4560 Y. HAN ET AL.

Appendices

Appendix 1. SCRE capabilities from selected articles.

Resilience dimensions Capability

Number of articles Related business practices Authors

Readiness Situation awareness

14 Sensing events, forecasts, continuity planning, warning strategies

Christopher and Peck (2004); Stecke and Kumar (2009); Ponomarov and Holcomb (2009); Pettit, Fiksel, and Croxton (2010); Sawik (2013); Rajesh and Ravi (2015); Birkie, Trucco, and Campos (2017); Eltantawy (2016); Ali, Mahfouz, and Arisha (2017); Chowdhury and Quaddus (2017); Machado, Paiva, and da Silva (2018); Stone and Rahimifard (2018); Lima et al. (2018); Yu et al. (2019)

Visibility 26 Tracking and monitoring, information technology capabilities, information exchange, transportation visibility, information transparency, perceiving potential opportunities

Christopher and Peck (2004); Stecke and Kumar (2009); Pettit, Fiksel, and Croxton (2010); Jüttner and Maklan (2011); Ponis and Koronis (2012); Aigbogun, Zulkipli, and Radzuan (2014); Brandon-Jones et al. (2014); Fiksel et al. (2015); Rajesh and Ravi (2015); Thekdi and Santos (2016); Dabhilkar, Bengtsson, and Lakemond (2016); Ivanov, Mason, and Hartl (2016); Tukamuhabwa, Stevenson, and Busby (2017); Ali, Mahfouz, and Arisha (2017); Parkouhi and Ghadikolaei (2017); Aigbogun, Zulkipli, and Radzuan (2017); Gunessee, Subramanian, and Ning (2018); Machado, Paiva, and da Silva (2018); Namdar et al. (2018); Gunasekaran, Subramanian, and Rahman (2015); Stone and Rahimifard (2018); Lima et al. (2018); Singh, Soni, and Badhotiya (2019); Dubey, Gunasekaran, Childe, Papadopoulos et al. (2019); López and Ishizaka (2019); Kumar and Anbanandam (2019)

Security 17 Access restriction, cyber-security, personnel security, layered defence, security partnership, public–private partnership, physical security

Rice and Caniato (2003); Sarathy (2006); Stecke and Kumar (2009); Williams, Ponder, and Autry (2009); Pettit, Fiksel, and Croxton (2010); Voss and Williams (2013); Ivanov and Sokolov (2013); Fiksel et al. (2015); Rajesh and Ravi (2015); Thekdi and Santos (2016); Ali, Mahfouz, and Arisha (2017); Chowdhury and Quaddus (2017); Kochan and Nowicki (2018); Stone and Rahimifard (2018); Lima et al. (2018); Singh, Soni, and Badhotiya (2019); López and Ishizaka (2019)

Redundancy 53 Safety stock, multiple suppliers, multiple sourcing, multiple production locations, backup sites, capacity, transportation capacity

Rice and Caniato (2003); Sheffi and Rice (2005); Tang (2006); Stecke and Kumar (2009); Klibi, Martel, and Guitouni (2010); Pettit, Fiksel, and Croxton (2010); Zsidisin and Wagner (2010); Thun, Drüke, and Hoenig (2011); Ponis and Koronis (2012); Mandal (2012); Klibi and Martel (2012); Schmitt and Singh (2012); Boone et al. (2013); Wu et al. (2013); Wieland and Wallenburg (2013); Ivanov and Sokolov (2013); Ivanov, Sokolov, and Dolgui (2014); Pereira, Christopher, and Silva (2014); Sáenz and Revilla (2014); Urciuoli et al. (2014); Aigbogun, Zulkipli, and Radzuan (2014); Fiksel et al. (2015); Matsuo (2015); Tukamuhabwa et al. (2015); Manning and Soon (2016); Hasani and Khosrojerdi (2016); Dabhilkar, Bengtsson, and Lakemond (2016); Tukamuhabwa, Stevenson, and Busby (2017); Ali, Mahfouz, and Arisha (2017); Parkouhi and Ghadikolaei (2017); Chowdhury and Quaddus (2017); Ivanov (2017); Ivanov et al. (2017); Aigbogun, Zulkipli, and Radzuan (2017); Beheshtian et al. (2017); Schmitt et al. (2017); Rajesh (2017); Sharma and George (2018); Adobor and McMullen (2018); Machado, Paiva, and da Silva (2018); Kochan and Nowicki (2018); Ivanov and Dolgui (2019); Ivanov (2018); Dolgui, Ivanov, and Sokolov (2018); Ivanov, Dolgui, and Sokolov (2018); Namdar et al. (2018); Stone and Rahimifard (2018); Lima et al. (2018); Tan, Cai, and Zhang (2019); Dolgui, Ivanov, and Rozhkov (2020); Hosseini, Morshedlou et al. (2019); Thomas and Mahanty (2019)

Response Agility 25 Velocity, channel to detect change, execution of supply chain activities, fast reaction to perceived change

Pereira (2009); Ponomarov and Holcomb (2009); Ismail, Poolton, and Sharifi (2011); Jüttner and Maklan (2011); Khan, Christopher, and Creazza (2012); Mandal (2012); Ponis and Koronis (2012); Wieland and Wallenburg (2013); Kristianto et al. (2014); Durach, Wieland, and Machuca (2015); Rajesh and Ravi (2015); Tukamuhabwa et al. (2015); Thekdi and Santos (2016); Ali, Mahfouz, and Arisha (2017); Parkouhi and Ghadikolaei (2017); Machado, Paiva, and da Silva (2018); Kochan and Nowicki (2018); Stone and Rahimifard (2018); Lima et al. (2018); Abeysekara, Wang, and Kuruppuarachchi (2019); Singh, Soni, and Badhotiya (2019); Vishnu, Sridharan, and Kuma (2019); Kumar and Anbanandam (2019); Gligor et al. (2019); Sridharan, Gunasekaran, and Ram Kumar (2019)

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Collaboration 48 Information sharing, collaborative forecasting, communication, risk sharing, joint knowledge creation, joint relationship effort, employee engagement, connecting people in dynamic ways, trust, business relationships, joint decision-making

Christopher and Peck (2004); Pereira (2009); Pettit, Fiksel, and Croxton (2010), Jüttner and Maklan (2011); Mandal (2012); Ponis and Koronis (2012); Voss and Williams (2013); Leat and Revoredo-giha (2013); Urciuoli et al. (2014); Aigbogun, Zulkipli, and Radzuan (2014); Fiksel et al. (2015); Rajesh and Ravi (2015); Tukamuhabwa et al. (2015); Seville, Opstal, Vargo (2015); Scholten and Schilder (2015); Dabhilkar, Bengtsson, and Lakemond (2016); Tukamuhabwa, Stevenson, and Busby (2017); Ali, Mahfouz, and Arisha (2017); Papadopoulos et al. (2017); Chowdhury and Quaddus (2017); Brusset and Teller (2017); Zeng and Yen (2017); Aigbogun, Zulkipli, and Radzuan (2017); Chen, Zhao et al. (2017); Rajesh (2017); Botes, Niemann, and Kotze (2017); Liu and Lee (2018); Adobor and McMullen (2018); Turner, Aitken, and Bozarth (2018); Durach and Machuca (2018); Friday et al. (2018); Machado, Paiva, and da Silva (2018); Kochan and Nowicki (2018); Namdar et al. (2018); Gunasekaran, Subramanian, and Rahman (2015); Stone and Rahimifard (2018); Lima et al. (2018); Ivanov, Mason, and Hartl (2016); Abeysekara, Wang, and Kuruppuarachchi (2019); Singh, Soni, and Badhotiya (2019); Kim and Bui (2019); Dubey, Gunasekaran, Childe, Papadopoulos et al. (2019); Lawson et al. (2019); Hendry et al. (2019); Li et al. (2017); López and Ishizaka (2019); Kumar and Anbanandam (2019); Li et al. (2019)

Flexibility 56 Auditing supplier process, monitoring, flexibility in sourcing, flexibility in order fulfilment, flexible products

Rice and Caniato (2003); Sheffi and Rice (2005); Tang (2006); Tang and Tomlin (2008); Pereira (2009); Stecke and Kumar (2009); Yang and Yang (2010); Pettit, Fiksel, and Croxton (2010); Zsidisin and Wagner (2010); Jüttner and Maklan (2011); Thun, Drüke, and Hoenig (2011); Ponis and Koronis (2012); Khan, Christopher, and Creazza (2012); Ishfaq (2012); Wieland and Wallenburg (2013); Ivanov and Sokolov (2013); Aigbogun, Zulkipli, and Radzuan (2014); Ivanov, Sokolov, and Dolgui (2014); Urciuoli et al. (2014); Chopra and Sodhi (2014); Mari et al. (2015); Rajesh and Ravi (2015); Gunasekaran, Subramanian, and Rahman (2015); Tukamuhabwa et al. (2015); Sokolov et al. (2016); Ivanov, Sokolov et al. (2016); Tukamuhabwa, Stevenson, and Busby (2017); Ali, Mahfouz, and Arisha (2017); Parkouhi and Ghadikolaei (2017); Chowdhury and Quaddus (2017); Brusset and Teller (2017); Aigbogun, Zulkipli, and Radzuan (2017); Beheshtian et al. (2017); Rajesh (2017); Adobor and McMullen (2018); Turner, Aitken, and Bozarth (2018); Gunessee, Subramanian, and Ning (2018); Machado, Paiva, and da Silva (2018); Kochan and Nowicki (2018); Ivanov and Dolgui (2019); Ivanov, Das, and Choi (2018); Dolgui, Ivanov, and Sokolov (2018); Kumar et al. (2018); Stone and Rahimifard (2018); Lima et al. (2018); Ivanov, Mason, and Hartl (2016); Singh, Soni, and Badhotiya (2019); Dubey, Gunasekaran, Childe, Wamba et al. (2019); Li et al. (2019); Vishnu, Sridharan, and Kuma (2019); López and Ishizaka (2019); Thomas and Mahanty (2019); Bag, Gupta, and Foropon (2019); Kumar and Anbanandam (2019); Chunsheng et al. (2019); Sridharan, Gunasekaran, and Ram Kumar (2019)

Leadership 5 Top management support, sound decision-making, execution of decisions, staff engagement

Seville, Opstal, and Vargo (2015); Manning and Soon (2016); Adobor and McMullen (2018); Stone and Rahimifard (2018); Lima et al. (2018)

Recovery Knowledge management

12 Learning, innovation, education and training

Rice and Caniato (2003); Ponomarov and Holcomb (2009); Dowty and Wallace (2010); Pereira, Christopher, and Silva (2014); Rajesh and Ravi (2015); Manning and Soon (2016); Birkie, Trucco, and Campos (2017); Eltantawy (2016); Tukamuhabwa, Stevenson, and Busby (2017); Cheng and Lu (2017); Ali, Mahfouz, and Arisha (2017); Stone and Rahimifard (2018)

Contingency planning

11 Supply contingency plans, supply chain reconfiguration, scenario analysis

Blackhurst et al. (2005); Ponomarov and Holcomb (2009); Khan, Christopher, and Creazza (2012); Boone et al. (2013); Zsidisin and Wagner (2010); Birkie, Trucco, and Campos (2017); Adobor and McMullen (2018); Stone and Rahimifard (2018); Vlajic, Vorst, and Djurdjevic (2019); Abeysekara, Wang, and Kuruppuarachchi (2019); Tan, Cai, and Zhang (2019)

Market position 11 Financial strength, market share, cost efficiency, loss absorption

Sheffi and Rice (2005); Pettit, Fiksel, and Croxton (2010); Boone et al. (2013); Wu et al. (2013); Day (2014); Fiksel et al. (2015); Adobor and McMullen (2018); Kochan and Nowicki (2018); Stone and Rahimifard (2018); López and Ishizaka (2019); Kumar and Anbanandam (2019)

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Appendix 2. SCRE performance metrics from selected articles

Measures Explanation Authors

Customer service level Total quantity sold to the end customer over the total quantity ordered, averaged across all products, all markets and the entire time horizon

Datta, Allen, and Christopher (2007)

Production change over time and aver- age production run length

Planning of production, how long it should be produced for

Average inventory at each distribution centre

Total on-hand stock level in the whole network across all distribution cen- tres and products averaged over the time horizon

Total average inventory across all distri- bution centres

Average number of days the system takes to attend to a large drop in inventory

Operational performance Inventory level, quality, customer satisfaction, time

Just in time Cabral, Grilo, and Cruz-Machado (2012)

Supplier relationships Cycle/setup time reduction

Economic performance

Cost, environmental cost, cash-to-cash cycle Speed in improving responsiveness to changing market needs

Producing in large or small batches Environmental performance

Business waste Ability to change delivery times of supplier’s order

Developing visibility for a clear view of upstream inventories and supply conditions

Lead time reduction Demand-based management Reduction in the variety of materials employed in manufacturing the products

Working with product designers and suppliers to reduce environmental impacts

Lead time ratio Ratio between actual and promised lead time Carvalho et al. (2012) Total cost Evaluate all costs associated with each supply chain entity on a time period

Actual inventory or cover time in the MTS (market-to-stock)

MTS system products are produced based on a demand forecast; the cus- tomer is more interested in the amount of inventory still available

Spiegler, Naim, and Wikner (2012)

Delivery lead time or the order book in the MTO (market-to-order) system

MTO products are manufactured only after an order is confirmed. Hence, MTO supply chains are concerned with delivering the orders in a minimum reasonable time

Availability Tested with seven global manufacturing and service companies. Needs a longitudinal study

Pettit, Fiksel, and Croxton (2010)

Inventories Delivery lead time Order accuracy Customer complaints

Agility, collaboration, information shar- ing, sustainability, risk and revenue sharing, trust, visibility, risk manage- ment culture, adaptive capability and structure

Using graph theory to explain the interdependence of enablers and select the values of the enablers

Soni, Jain, and Kumar (2014)

Transient recovery Recovery Records the time required for performance to return to 95% from the time of impact where performance first falls below 95%

Munoz and Dunbar (2015)

Impact Severity of the impact, the difference between the initial per- formance level and the performance at full onset

Profile length Thelengthoftherecoverycurve,measuredfromthetimeofthe full onset of the disruption until the performance level returns to 95%

Performance loss The area above the performance curve from the time of the ini- tialperformancedropuntilthetimeatwhichthesystemreturns to 95% performance,

Contingency plan Achieving redundancy, maintaining response capacity Lam and Bai (2016) Forecast accuracy Increase visibility and responsiveness Strategic alliance Establishment of collaborative programs Supply chain relationship

Development and maintenance of good supply chain relationships

IT system (real-time tracking) Synchronising the flow of goods with the flow of information Monitoring and maintenance

Control and monitoring activities to ensure the performance of employee and suppliers

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 4563

Supply chain disruption scale Alertness to disruptions, analysis of disruption Ambulkar, Blackhurst, and Grawe (2015)

Risk management infrastructure

The presence of a person/department in risk management, use of informa- tion system, use of KPIs and metric in risk monitoring

Resource reconfiguration scale

Ability to realign, reconfigure, restructure, renew the resource

Disruption impact How disruption impacts the overall efficiency of operation, delivery reliabil- ity, procurement cost

Speed Speed of critical activities Day (2014) Efficiency Reasonable costs, balance between efficiency and effectiveness Responsiveness Ability to provide appropriate resource quickly

Time to total system restoration Total time spent from the point when recovery activities commence to the time when all recovery activities are finalised

Pant et al. (2014)

Time to full system service resilience Total time spent from the point when recovery activities are started to the exact time when system service is completely restored

Time to a *100% resilience Total time spent from the point when recovery activities commence to the exact time when the system service is restored to au(t0)

Recovery time Based on the CoxPH model, the variables represent various sources of dis- ruptions, the input variable represents an event (failure event), and the output variable is the time

Raj et al. (2015)

Network density Ratio of the total existing arcs to possible arcs in the network Kim, Chen, and Linderman (2015)

Average degree Average number of possible arcs in the network Average, maximum, minimum walk length

The average, maximum and minimum length of the identified multiple walks

Connectivity Minimum number of nodes/arcs that must be removed to disconnect the network

Betweenness centrality How often the nodes in a network lie on the shortest path between all combinations of pairs of nodes

Recovery Number of days before resulting production (number of suppliers in/outside of the affected area, number of workers and sales per work)

Todo, Nakajima, and Matous (2015)

Scale The extent to which production and distribution is locally/nationally/globally located

Smith et al. (2016)

Density The number of social, economic and environmental actors, skills, functions Responsiveness Speed of the reaction (flexibility, efficiency, adaptability and learning) Cohesion Extent of connection and interaction

Percentage of unfulfilled demand Analyses resilience of a supply chain network while addressing trade-off between the two performance measures

Dixit, Seshadrinath, and Tiwari (2016)

Total transportation cost post-disaster Disaster preparation Readiness training, readiness resource, early-warning signal, contingency

planning Chowdhury and Quaddus (2016)

Flexibility Production flexibility, customisation, multi-skilled workforce, contract flex- ibility, sourcing flexibility, distribution flexibility

Redundancy Reserve capacity, stock, backup utility Visibility Information sharing, track of information on operation, business intelli-

gence, flow of real-time information Collaboration Collaborative demand forecasting, collaborative decision, investing in sup-

plier’s plant Response Quick response, effective response, response team Recovery Quick recovery, loss absorption, reduction of impact, recovery cost Flexibility indicators Stock-out rate, inventory accurate rate, number of small disruptions man-

aged through flexibility, percentage increase in sales from design flexibility Rajesh (2016)

Responsiveness indicators On-time delivery ratio, contract issue time, contract approval time, put- away time ratio

Quality indicators Quality of forecasts, testing quality, shipping accuracy, security measures Productivity indicators Order compliance, fill rate, storage space utilisation, units moved per

person-hour Accessibility indicators Dealer accessibility, retailer accessibility, customer accessibility, network

intensity Resilient behaviour Sourcing strategies to switch suppliers, flexible supply base, strategic stock,

lead time reduction, total supply chain visibility, flexible transportation, visibility of downstream inventories and demand conditions

Azevedo, Carvalho, and Cruz-Machado (2016)

Financial perspective Presence of financial difficulties, financial growth, financial benefits, fines and penalties received, financial ratios and profits

Loh and Thai (2016)

Customer perspective Market share, customer retention, customer complaints, attraction of new customers, customer satisfaction

Process perspective Ability to redesign and resume internal operations, improvement in opera- tional efficiencies, experience in disruptions

Learning perspective Skills and knowledge of employees, engagement in technology and acquirement of capabilities, intensity and frequency of training and learning opportunities, improvement in disruption management process, improvement in employee turnover rates

4564 Y. HAN ET AL.

Number of cooperating partners Number of enterprises weighted by the size of material flow Wicher et al. (2016) Investment in cooperation develop- ment

Million per year

Width of portfolio Number of groups in the NACE classification Alternative options to ensure produc- tion

Percentage of own capacities

Number of enterprises sharing basic information

Number of enterprises weighted by the size of material flow

Number of enterprises using an inte- grated ERP system

Number of enterprises weighted by the size of material flow

Reserve capacity Percentage of own capacities Creditworthiness index Kralicek’s Quick Test scale

Supply chain design reliability Use the genome concept and its dual analogue to quantify the supply chain structure reliability; allows determining the upper bound and the approximate lower bound of the supply chain reliability

Ivanov, Pavlov, and Sokolov (2016)

Recovery rate Probability of the supply chain returning from chaos to normality Zeng and Yen (2017) Financial performance

Supply chain preparedness Capability of a supply chain to endure the influence of potential changes (contingency plan and interest alignment)

Li et al. (2017)

Supply chain alertness Capability of a supply chain to detect changes Supply chain agility Capabilityofasupplychaintorespondtoactualchangesinatimelymanner

by adapting supply chain processes (reconfiguration, reduce lead time and reduce non-value action)

Reliability Ability to satisfy immediate demand before any risk mitigating actions, preventative or post-disruption, are taken

Chen, Xi et al. (2017)

PEDC (pre-disruption mitigation capa- bility)

Ability to prognose and prevent a disruption before its occurrence

PODC (post-disruption mitigation capability)

Ability to recover from a disruption after it occurs (time allowance, safety inventory and quantity required by buyers)

Operational resilience ρ = M M + S S is defined as the stock-out surface (quantity times time), surface M is thearea that has been successfully mitigated

Lücker and Seifert (2017)

Warehouse performance measurers

Accuracy Inventory accuracy, accuracy in order picking, accuracy in order shipping, % product transferred without transaction errors, % order/lines received with correct shipping documents

Laosirihongthong et al. (2018)

Resource utilisation Space utilisation, equipment utilisation, labour productivity and utilisation

Financial outcome Shipping cost, inventory holding cost, product damage rate, insurance cost, shortage cost

Responsiveness and flexibility

Responsiveness to urgent deliveries, transportation speed, customer query time, order size flexibility, delivery flexibility, service system flexibility

Supply management resilience perfor- mance

suppliers’ flexibility, supply location flexibility and suppliers’ reliability

Das (2018)

Production management resilience performance

levels of production capacity flexibility, plant reliability and quality assur- ance performance of the product

Distribution management resilience performance

level of distribution flexibility, the provision of safely located distribution capacities and extra inventory for ensuring product distribution to a market

Performance impact of disruption propagation in the supply chain

Withconsiderationofsustainabilityfactorsinordertodesignaresilientsup- ply chain structure in regard to ripple effect mitigation and sustainability increase

Ivanov (2018)

Supply chain design resilience Describes the total structural resilience for a given reconfiguration path by changes in the structure failure values during the reconfiguration on a certain path

Pavlov et al. (2018)

J1 as service level and J2 as profit Comparison of J1 and J2 in a disruption scenario with ‘ideal’ minimum val- uesoftheseindicatorsinadisruption-freescenariowithdifferentdisruption scenarios

Ivanov, Dolgui, and Sokolov (2018)

Suppliers’ proneness to disruptions and the supply chain exposure to the ripple effect

Examine and test the developed notion of resilience as a function of sup- plier vulnerability and recoverability using a Bayesian network

Hosseini and Ivanov (2019)

Ripple effect of a supplier disruption A risk exposure model that quantifies the ripple effect based on possi- ble maximum loss. Comprehensively combining features such as financial, customer and operational performance impacts

Kinra et al. (2019)

Crisis readiness, response effectiveness, recovery speed, and impact propaga- tion rate

Measured under different supply chains characterised by various lead time durations

Chang and Lin (2019)

Time-to-Recover Measures the time taken to recover its operations after a disruption Tan, Cai, and Zhang (2019) Total cost Economic consequences of disruption can be influenced by the mitigation

and contingency strategies taken by the fir Redundancy Structural redundancy is measured using graph theory} Tan, Zhang, and Cai (2019)

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Appendix 3. Categorisation of performance metrics.

Category Number of articles Performance metrics from the 36 papers

Performance of fulfilling customer requirements

11 Customer service level (Datta, Allen, and Christopher 2007) Customer satisfaction (Cabral, Grilo, and Cruz-Machado 2012) Customer complaints (Pettit, Croxton, and Fiksel 2013) Demand-based management (Cabral, Grilo, and Cruz-Machado 2012) Percentage of unfulfilled demand (Dixit, Seshadrinath, and Tiwari 2016) Customer accessibility (Rajesh 2016) Customer perspective (e.g. customer retention, customer complaints) (Loh and Thai 2016) Post-disruption mitigation capabilities (PODC) (quantity required by buyers) (Chen, Xi et al. 2017)) Service level and system expediting (Schmitt et al. 2017) Service level (Ivanov, Dolgui, and Sokolov 2018) Customer (service level) (Kinra et al. 2019)

Efficiency of completing supply chain processes

8 Lead time ratio (Carvalho et al. 2012) Delivery lead time or the order book in the market-to-order (MTO) system (Spiegler, Naim, and Wikner 2012) Delivery lead time (Pettit, Croxton, and Fiksel 2013) Cycle/setup time reduction, lead time reduction (Cabral, Grilo, and Cruz-Machado 2012) Speed (of critical activities) (Day 2014) On-time delivery ratio, contract issue time, contract approval time, put-away time ratio, shipping accuracy (Rajesh 2016) Lead time reduction (Azevedo, Carvalho, and Cruz-Machado 2016) System expediting (Schmitt et al. 2017)

Efficiency of recovery to normality

10 Profile length, performance loss, recovery (Munoz and Dunbar 2015) Time to total system restoration, time to full system service resilience, time to *100% resilience (Pant et al. 2014) Recovery time (Raj et al. 2015) Recovery (days passed before resulting production) (Todo, Nakajima, and Matous 2015) Recovery (quick recovery) (Chowdhury and Quaddus 2016) Recovery rate (Zeng and Yen 2017) Post-disruption mitigation capability (PODC) (Chen, Xi et al. 2017)) Recoverability (Hosseini and Ivanov 2019) Recovery speed (Chang and Lin 2019) Time-to-recovery (Tan, Cai, and Zhang 2019)

Performance of production and inventory

15 Production change over time, average production run length (Datta, Allen, and Christopher 2007) Average inventory at each distribution centre, total average inventory across all distribution centres (Datta, Allen, and Christopher 2007) Inventory level, producing in large or small batches, reduction in the variety of materials employed in manufacturing products (Cabral, Grilo, and Cruz-Machado 2012) Actual inventory or cover time in market-to-stock (MTS) (Spiegler, Naim, and Wikner 2012) Availability, inventories (Pettit, Croxton, and Fiksel 2013) Scale (Smith et al. 2016) Stock-out rate, inventory accurate rate, order compliance, fill rate, storage space utilisation, units moved per person-hour (Rajesh 2016) Strategic stock (Azevedo, Carvalho, and Cruz-Machado 2016) Reserve capacity (Wicher et al. 2016) Operational resilience (Lücker and Seifert 2017) PODC (safety inventory) (Chen, Xi et al. 2017) System inventory (Schmitt et al. 2017) Extra inventory for ensuring product distribution to a market (Das, 2018) Facility fortification, inventory replacement, sourcing (Ivanov 2018) Structural redundancy (Tan, Zhang, and Cai 2019)

Performance of relationship management

10 Collaboration, information sharing, trust, risk and revenue sharing (Soni, Jain, and Kumar 2014) Supplier relationships, ability to change delivery times of supplier’s order, working with product designers and suppliers to reduce environmental impacts (Cabral, Grilo, and Cruz-Machado 2012) Strategic alliance, supply chain relationship (Lam and Bai 2016) Risk management infrastructure (Ambulkar, Blackhurst, and Grawe 2015) Network density, average degree, average, maximum, minimum walk length, connectivity, betweenness centrality (Kim, Chen, and Linderman 2015) Density, cohesion (Smith et al. 2016) Collaboration (Chowdhury and Quaddus 2016) Dealer accessibility, retailer accessibility, customer accessibility, network intensity (Rajesh 2016) Number of cooperating partners, width of portfolio, number of enterprises sharing basic information, number of enterprises using an integrated ERP system, investment in cooperation development (Wicher et al. 2016) Supply chain agility (Li et al. 2017)

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Financial performance 10 Total cost (Carvalho et al. 2012) Efficiency (reasonable costs, balance between efficiency and effectiveness) (Day 2014) The total transportation cost post-disaster (Dixit, Seshadrinath, and Tiwari 2016) Percentage increase in sales from design flexibility (Rajesh 2016) Financial perspective (e.g. financial growth, financial benefits) (Loh and Thai 2016) Creditworthiness index (Wicher et al. 2016) Financial outcome (Laosirihongthong et al. 2018) Profit (Ivanov, Dolgui, and Sokolov 2018) Financial (costs, revenue) (Kinra et al. 2019) Total cost (Tan, Cai, and Zhang 2019)

Performance of overseeing the supply chain situation

7 Order accuracy (Pettit, Croxton, and Fiksel 2013), visibility, developing visibility to a clear view of upstream inventories and supply conditions (Cabral, Grilo, and Cruz-Machado 2012)

Monitoring and maintenance (Lam and Bai 2016) Reliability Ivanov, Dolgui, and Sokolov (2018) Disaster preparation (early warning signal), visibility (Chowdhury and Quaddus 2016) Visibility of downstream inventories and demand conditions, total supply chain visibility (Azevedo, Carvalho, and Cruz-Machado 2016) Accuracy (Laosirihongthong et al. 2018)

Performance of discerning possible disruptions

5 Quality of forecast (Cabral, Grilo, and Cruz-Machado 2012) Quality of forecasts (Rajesh 2016) Supply chain alertness (Li et al. 2017) Pre-disruption mitigation capability (PEDC) (Chen, Xi et al. 2017)) Disruption probability (Hosseini and Ivanov 2019)

Damage of disruptions 4 Impact (Munoz and Dunbar 2015) Supply chain disruption scale, disruption impact (Ambulkar, Blackhurst, and Grawe 2015) Ripple effect (Ivanov 2018; Kinra et al. 2019)

Efficiency of responding the disruptions

11 Agility, adaptive capability (Soni, Jain, and Kumar 2014) Responsiveness (ability to provide appropriate resource quickly) (Day 2014) Responsiveness (Smith et al. 2016) Disaster preparation (readiness training, readiness resource, contingency planning), flexibility (production flexibility, sourcing flexibility, distribution flexibility), response (quick response, effective response, response team) (Chowdhury and Quaddus 2016) Number of small disruptions managed through flexibility, security measures (Rajesh 2016) Alternative options to ensure production (Wicher et al. 2016) Supply chain preparedness (contingency plan), supply chain agility (Li et al. 2017) Reliability (Chen, Xi et al. 2017)) Responsiveness and flexibility (Laosirihongthong et al. 2018), suppliers’ flexibility, supply location flexibility, suppliers’ reliability, production capacity flexibility, plant reliability, distribution flexibility (Das, 2018) Response effectiveness (Chang and Lin 2019)

Reconstruction of the supply chain

4 Resource reconfiguration scale (Ambulkar, Blackhurst, and Grawe 2015) Process perspective (e.g. ability to redesign and resume internal operations, improvement in operational efficiencies), learning perspective (e.g. skills and knowledge of employees, engagement in technology and acquirement of capabilities) (Loh and Thai 2016) Contingency plan (Lam and Bai 2016) Total structural resilience for a given reconfiguration path (Pavlov et al. 2018)