Striking a balance between supply chain resilience and supply chain vulnerability in the cross-border e-commerce supply chain

Figures & data

Figure 1. The flow of the proposed methodology.

Table 1. List of interviewees, their job titles and organisations.

Number	Company	Title	Years of working	Interview method	Time
1	Osell	Co-Founder and Vice President	14	Face to face	July 24, 2020
2	Osell	Overseas Executive President	11	Face to face	July 24, 2020
3	Osell	Project Manager	10	Face to face	July 24, 2020
4	Zongteng	Deputy General Manager	16	Face to face	August 3 2020
5	Bizark	General Manager of South China	13	Face to face	August 12, 2020
6	Linca	Director of Overseas Business Unit	15	Face to face	August 21, 2020

Figure 2. Supply chain resilience drivers.

Table 2. SCR drivers and corresponding supporting literature.

Drivers	Supporting literature
Supply chain agility	Prater, Biehl, and Smith (2001); Christopher and Peck (2004); Ponomarov and Holcomb (2009); Naughton, Golgeci, and Arslan (2020)
Supply chain structure	Jüttner, Peck, and Christopher (2003); Jüttner (2005); Tang (2006); Ivanov (2018); Dixit, Verma, and Tiwari (2020); Trkman and McCormack (2009)
Supply chain visibility	Chopra and Sodhi (2004); Christopher and Lee (2004); Wei and Wang (2010); Rogerson and Parry (2020)
Information sharing	Lee, Padmanabhan, and Whang (1997); Christopher and Peck (2004)
Risk and revenue sharing	Narayanan and Raman (2004); Soni, Jain, and Kumar (2014)
Geographic distribution	Manuj and Mentzer (2008); Pettit, Fiksel, and Croxton (2010); Chopra and Sodhi (2004); Christopher and Peck (2004); Habermann, Blackhurst, and Metcalf (2015)
Collaboration with supply chain partners	Christopher and Peck (2004); Simatupang and Sridharan (2008); Jüttner and Maklan (2011); Dubey et al. (2020)

Table 3. Survey result about SCR drivers from interview^a.

Drivers	Interviewee 1	Interviewee 2	Interviewee 3	Interviewee 4	Interviewee5	Interviewee6
Supply chain agility	√	√	√	√	√	√
Supply chain structure	√	√	√		√	√
Supply chain visibility	√	√		√	√	√
Information sharing	√	√	√	√	√
Risk and revenue sharing	√		√	√	√	√
Geographic distribution	√	√	√		√	√
Collaboration with supply chain partners	√		√		√	√
Corporate strength		X
Trust among supply Chain partners				X

^a Note: √ indicates that the driver is identified by the interviewee, and X indicates that the driver is added by the interviewee.

Figure 3. Supply chain vulnerability drivers.

Table 4. SCV drivers and corresponding supporting literature.

Drivers	Supporting literature
Quality risk	Samvedi, Jain, and Chan (2013); Tse and Tan (2011); Tse and Tan (2012)
Delivery risk	Milgate (2001); Gaudenzi and Borghesi (2006); Vilko and Hallikas (2012)
Suppliers’ dependency	Wagner and Neshat (2010); Hallikas et al. (2005); Wagner, Bode, and Koziol (2009)
Worker strikes	Samvedi, Jain, and Chan (2013); Chopra and Sodhi (2004)
Machine failure	Samvedi, Jain, and Chan (2013); Ivanov (2010)
Risks in global logistics network	Creazza, Dallari, and Melacini (2010); Vilko and Hallikas (2012)
Sudden demand fluctuation	Lee, Padmanabhan, and Whang (1997); Samvedi, Jain, and Chan (2013); Ivanov (2010)
Forecasting error	Zhao, Xie, and Leung (2002); Samvedi, Jain, and Chan (2013); Christopher and Peck (2004)
Customers’ dependency	Awaysheh and Klassen (2010); Wagner and Neshat (2010); Wagner and Bode (2006)
Natural and man-made disasters	Vilko and Hallikas (2012); Samvedi, Jain, and Chan (2013)
Political instability	Samvedi, Jain, and Chan (2013); Wilson (2007)
Exchange rate fluctuation	Liu and Nagurney (2011); Meixell and Gargeya (2005); Pereira, Scarpin, and Neto (2020)

Table 5. Survey result about SCV drivers from interview^a.

Drivers	Interviewee 1	Interviewee 2	Interviewee 3	Interviewee 4	Interviewee 5	Interviewee 6
Quality risk	√	√	√	√	√	√
Delivery risk	√		√	√	√	√
Suppliers’ dependency	√	√		√		√
Worker strikes		√	√	√	√
Machine failure	√		√	√		√
Risks in global logistics network	√	√		√	√	√
Sudden demand fluctuation	√		√		√	√
Forecasting error	√	√	√	√		√
Customers’ dependency		√		√
Natural and man-made disasters	√		√	√	√	√
Political instability				√	√
Exchange rate fluctuation	√	√	√	√	√	√
Tariff policy	X	X	X	X	X	X

^a Note: √ indicates that the driver is identified by the interviewee, and X indicates that the driver is added by the interviewee.

Figure 4. Illustration of the balance state of resilience and vulnerability.

Table 6. Scenarios and strategies.

Table 7. Pairwise comparison of SCR drivers in linguistic variables.

SCR drivers	Supply chain agility	Supply chain structure	Supply chain visibility	Information sharing	Risk and revenue sharing	Geographic distribution	Collaboration
Supply chain agility	EQ	WK	ES	VS	WK	ES	ES
Supply chain structure		EQ	LWK^1	LES	LES	WK	WK
Supply chain visibility			EQ	WK	LES	VS	ES
Information sharing				EQ	LES	AB	VS
Risk and revenue sharing					EQ	AB	WK
Geographic distribution						EQ	LWK
Collaboration							EQ

¹ L=Less.

Table 8. Pairwise comparison of SCV drivers in linguistic variables.

SCV drivers	Supply side risk	Operation process risk	Demand side risk	Environment risk
Supply side risk	EQ	VS	ES	VS
Operation process risk		EQ	WK	WK
Demand side risk			EQ	ES
Environment risk				EQ
Supply side risk	Quality risk	Delivery risk	Suppliers’ dependency
Quality risk	EQ	ES	ES
Delivery risk		EQ	LWK^1
Suppliers’ dependency			EQ
Operation process risk	Worker strikes	Machine failures	Risks in global logistics network
Worker strikes	EQ	LVS	LVS
Machine failures		EQ	LWK
Risks in global logistics network			EQ
Demand side risk	Sudden demand fluctuation	Forecasting error
Sudden demand fluctuation	EQ	WK
Forecasting error		EQ
Environment risk	Natural and man-made disasters	Tariff policy	Exchange rate fluctuation
Natural and man-made disasters	EQ	LAB	LVS
Tariff policy		EQ	VS
Exchange rate fluctuation			EQ

¹ L=Less.

Table 9. Linguistic variables for evaluation.

Ranking level	A qualitative explanation for performance of drivers	A qualitative explanation for probability and impact of risk occurrence	Triangular fuzzy numbers
1	Extremely poor (EP)	Extremely low (EL)	(0.0,0.1,0.2)
2	Quite poor (QP)	Quite low (QL)	(0.1,0.2,0.3)
3	Poor (P)	Low (L)	(0.2,0.3,0.4)
4	Slightly poor (SP)	Slightly low (SL)	(0.3,0.4,0.5)
5	Moderate (M)	Moderate (M)	(0.4,0.5,0.6)
6	Slightly good (SG)	Slightly high (SH)	(0.5,0.6,0.7)
7	Good (G)	High (H)	(0.6,0.7,0.8)
8	Quite good (QG)	Quite high (QH)	(0.7,0.8,0.9)
9	Extremely good (EG)	Extremely high (EH)	(0.8,0.9,1.0)

Table 10. Result of SCR index.

SCR Drivers	Performance	Closeness coefficient	Weight	Score
Supply chain agility	SP(0.3,0.4,0.5)	0.3750	0.3521	0.1320
Supply chain structure	M(0.4,0.5,0.6)	0.5000	0.0753	0.0377
Supply chain visibility	G(0.6,0.7,0.8)	0.7500	0.1398	0.1049
Information sharing	M(0.4,0.5,0.6)	0.5000	0.1184	0.0592
Risk and revenue sharing	P(0.2,0.3,0.4)	0.2500	0.2473	0.0618
Geographic distribution	SG(0.5,0.6,0.7)	0.6250	0.0227	0.0142
Collaboration among supply chain parterners	G(0.6,0.7,0.8)	0.7500	0.0444	0.0333
SCR index				0.4431

Table 11. Result of SCV index.

SCV Drivers	Probability	Impact	Closeness coefficient	Weight	Score
Supply side risk				0.6403
Quality risk	SH(0.5,0.6,0.7)	EH(0.8,0.9,1.0)	0.6660	0.6753	0.2880
Delivery risk	M(0.4,0.5,0.6)	QH(0.7,0.8,0.9)	0.4927	0.1166	0.0368
Suppliers’ dependency	SL(0.3,0.4,0.5)	H(0.6,0.7,0.8)	0.3442	0.2081	0.0459
Operation process risk				0.1816
Worker strikes	L(0.2,0.3,0.4)	QH(0.7,0.8,0.9)	0.2965	0.0602	0.0032
Machine failures	M(0.4,0.5,0.6)	M(0.4,0.5,0.6)	0.3061	0.3371	0.0187
Risks in global logistics network	SH(0.5,0.6,0.7)	H(0.6,0.7,0.8)	0.5170	0.6027	0.0566
Demand side risk				0.1175
Sudden demand fluctuation	L(0.2,0.3,0.4)	QH(0.7,0.8,0.9)	0.2965	0.7044	0.0245
Forecasting error	SL(0.3,0.4,0.5)	H(0.6,0.7,0.8)	0.3442	0.2956	0.0120
Environment risk				0.0606
Natural and man-made disasters	EL(0.0,0.1,0.2)	EH(0.8,0.9,1.0)	0.1250	0.0512	0.0004
Tariff policy	H(0.6,0.7,0.8)	QH(0.7,0.8,0.9)	0.6904	0.7483	0.0313
Exchange rate fluctuation	H(0.6,0.7,0.8)	H(0.6,0.7,0.8)	0.6036	0.2005	0.0073
SCV index					0.5247

Figure B1. Triangular fuzzy number $\tilde{A}$.

Table B1. Linguistic variables for the relative importance weights.

Linguistic expressions	Equivalent fuzzy numbers	Triangular fuzzy number
Equally important (EQ)	1	(1, 1, 3)
Weakly important (WK)	3	(1, 3, 5)
Essentially important (ES)	5	(3, 5, 7)
Very strongly important (VS)	7	(5, 7, 9)
Absolutely important (AB)	9	(7, 9, 9)

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Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article.