Industry 4.0 enabler technology	Description	Reference	Discussed SCRES antecedents							Referred SCRES phases
			Visibility	Velocity	Collaboration	SC understanding	SC design	Sourcing	SCRM culture	Readiness	Response	Recovery	Growth
Cloud computing	Comparison of cloud-based vs. non-cloud automotive SME’s organizational resilience in an uncertain business environment	Arsovski et al. (2017)	x							x	x
	Analysis of the interplay of CC and logistics service providers based on innovation diffusion theory within an SC risk assessment framework	Subramanian and Abdulrahman (2017)			x					x
Internet of Things	Assessment of IoT’s effect on SCRM processes, pathways and outcomes based on a multiple case study methodology	Birkel and Hartmann (2020)	x	x				x	x	x	x
	Discussion of IoT’s impact on internal and external risks under consideration of specific technologies, e.g., 3G, RFID, GPS	Gao et al. (2020)	x	x						x	x
Big data analytics	Analysis of the barriers successful BDA implementation faces in humanitarian supply chains	Bag et al. (2020)	x	x						x	x
	Presentation of a framework to apply Petri Net and Agent Based Model techniques to global SC disruptions	Blos et al. (2018)				x				x	x
	Introduction of an analytical framework (Twitter Analytics) for analyzing tweets with the ambition to generate insights on SCDs and reporting them to SC partners	Chae (2015)	x	x	x						x
	Application of variance-based structural equation modelling to analyze the relationship between data analytics and SCRES	Dubey et al. (2021)	x	x						x	x	x
	Presentation of a data-driven, disruption sensitive demand forecast framework	Fu and Chien (2019)	x							x
	Introduction of an SCM multi-agent-based system with dedicated big data and risk management agents to improve SC agility	Giannakis and Louis (2016)	x	x							x
	Review of literature on quantitative methods, e.g., mathematical models and optimization, for SCRES analysis	Hosseini et al. (2019)	x	x			x	x		x	x	x
	Development of an SC simulation model for single and dual sourcing under consideration of capacity disruption and big data	Ivanov (2017)					x	x		x
	Discussion of digital SC twins simulating real-world SCs based on the combination of data analytics and model-based decision-support	Ivanov & Dolgui (2020a)	x			x				x	x	x
	Assessment of supplier response diversity in case of SC disruptions and the impact on SCRES	Kahiluoto et al. (2020)						x		x	x
	Development of a multi-criteria decision support approach for supplier selection problems under consideration of SC risk	Kellner et al. (2019)						x		x
	Analysis unveiling the positive influence of BDA planning, BDA coordination and BDA control on SCRES	Mandal (2019)	x	x	x					x	x
	Presentation of a SLR on evidence-based, BDA-related papers in SCM	Meriton et al. (2020)	x	x	x	x				x	x
	Review of literature on supplier selection including data analytics and mathematical programming approaches to assess individual risk characteristics	Ocampo et al. (2018)						x		x
	Application of BDA to identify antecedents and propose and test frameworks in the context of SCRES	Papadopoulos et al. (2017)			x					x	x
	Discussion of BDA application in SC inventory management and supplier assessments to mitigate risks in the SC	Sanders (2016)					x	x		x
	Creation of a rule-based resilience support system for collaborative decision-making on the optimal state for initiating production and logistics recovery activities in the network	Singh et al. (2019)	x	x								x
	Analysis on the role of BDA in building SCRES with a special focus on the relationship with organizations’ IT and managerial capabilities	Singh and Singh (2019)	x								x
	Presentation of a hybrid simulation model using big data and statistical distribution allowing risk scenarios to be analyzed	Vieira et al. (2019a)				x				x	x
	Presentation of a decision-support-system empowered by a big data warehouse and a simulation model, allowing the analysis of risk scenarios	Vieira et al. (2019b)	x	x						x	x
	Identification of key SCRES antecedents through BDA for improved SC design, resource allocation and risk mitigation	Wu et al. (2017)					x			x
Artificial intelligence	Presentation of a data-driven ML approach facilitating resilient supplier selection	Cavalcante et al. (2019)		x				x		x	x
	Development of a machine-based learning algorithm to convert data from multiple news feeds into risk impact and probability indicators resulting in a visualization of country-level supply base risks	Handfield et al. (2020)		x		x		x		x
	Review of literature on the application of Bayesian networks for supply chain risk, resilience and ripple effect analysis including further development options using ML techniques	Hosseini and Ivanov (2020)				x				x	x
	Recommendation and examples for AI and Natural Language Processing use in supplier monitoring and SC mapping	Linton and Vakil (2020)				x		x		x
	Description of a Natural Language Processing and Deep Learning solution to automatically extract buyer–supplier relationships from newsfeeds and generate supply maps	Wichmann et al. (2020)				x				x	x
Blockchain	Contribution on how BC can be applied to facilitate the implementation of mean–variance risk analysis for global supply chain operations	Choi et al. (2019)	x		x						x
	Examination of BC application areas in multiple party disaster relief SC operations	Dubey et al. (2020)	x		x						x
	Analysis of resilience strategies in BC-supported SCs through agent-based simulation	Lohmer et al. (2020)	x	x	x					x	x	x
	Description of asset and order fulfillment tracking possibilities to mitigate risks associated with intermediaries’ interventions and improve SCRES	Min (2019)	x		x					x	x
	Discussion of BC resources at 24 companies and their role in improving agility and digitalization capabilities	Nandi et al. (2020)	x	x	x					x	x	x
Various technologies	Review of AI and BDA literature in SCRM with a focus on identifying related approaches and application possibilities in the SCRM process	Baryannis et al. (2019)	x						x	x	x
	Analysis of SCRES measures in the automotive and airline industries during the COVID-19 pandemic	Belhadi et al. (2021)	x	x		x				x	x
	Elaboration of a big data driven SC analytics architecture supported by CC with the goal of mitigating business risks, among others	Biswas and Sen (2016)	x	x							x
	Discussion on the application of BDA and ML in predicting first tier SCDs using historical data	Brintrup et al. (2020)	x	x						x	x
	Development of an analytical-based resilience model for CPSs to facilitate resource allocation decisions between agility, design and sourcing measures in case of SCDs	Chen et al. (2020)	x	x			x			x	x
	Presentation of BDA and ML application areas in operations management including potential data sources, commonly used techniques and implications for SCRM	Choi et al. (2018)	x							x
	Discussion of a new SC typology (“the X-network”) with resilience and digitalization characteristics	Dolgui et al. (2020)	x	x						x	x	x
	Application of BDA and IoT approaches in SC planning under causal and temporal uncertainty	Dunke et al. (2018)				x				x
	Description of possibilities to use BDA in long linked supply chains for risk mitigation under consideration of IoT data provisions	Engelseth and Wang (2018)	x	x						x	x
	Introduction of Data Mining frameworks in an SCRM context including the use of simulation techniques and IoT data	Er Kara et al, (2020)	x	x		x				x	x
	Introduction of the I4.0 supported low-certainty-need SC concept ensuring efficient disruption resistance and recovery resource allocation	Ivanov and Dolgui (2019)	x	x							x	x
	Development of a conceptual framework for researching the relationships between SCD risks and digitalization, including AM, BC, BDA, CPSs and IoT	Ivanov et al. (2019)	x	x						x	x	x
	Development of a multi-stage hybrid model for supplier selection and order allocation considering disruption risks and several I4.0 technologies	Kaur and Prakash Singh (2021)	x	x				x		x	x
	Illustration of BC mechanisms to achieve main SC objectives (including risk reduction) under consideration of IoT	Kshetri (2018)	x	x						x	x
	Introduction of an enterprise capability evaluation model and sharing system using BC, IoT and AI to achieve risk reduction through real-time data collection and automated assessment mechanisms	Li et al. (2020)	x	x	x					x	x
	Presentation and application of predictive analytics tools for forecasting demand shifts in various industries based on actual COVID-19 infection cases	Nikolopoulos et al. (2020)	x	x							x
	Investigation on the relationship of real-time data processing, data analytics and managerial capabilities under consideration of supporting technologies, e.g., CC	Oliveira and Handfield (2019)	x								x
	Presentation of an architectural framework for a cyber-physical logistics system including technical functionalities for a digital SC twin simulation engine	Park, Son, & Noh, 2020	x								x
	Presentation of a mathematical production recovery model supported by BC and AM capacities to ensure the provision of essential and high-demand products following SCDs	Paul and Chowdhury (2020)	x	x	x		x					x
	Discussion on various digital supply chain capabilities and enabler technologies at the intersection of I4.0 and human resource management to improve SC performance	Queiroz et al. (2021)	x	x					x	x
	Presentation of a CC and IoT supported grey prediction model forecasting key indicators for SCRES performance allowing firms to proactively rearrange SCRM strategies and resources	Rajesh (2016)	x							x
	Analysis whether I4.0 is a driver of capability enhancement or capability loss including AI, BDA, CPSs and IoT	Ralston and Blackhurst (2020)	x	x	x		x	x	x	x
	Discussion of several I4.0 enabler technologies to support SCRES in a shipbuilding SC	Ramirez-Peña et al. (2020)	x	x		x			x	x	x
	Application of a multi-stage algorithm to assess and improve data quality in supplier selection for risk prevention	Shabani-Naeeni and Ghasemy Yaghin (2021)	x					x		x
	Discussion of SCRES advantages from AM and CPSs	Shih (2020)						x	x	x
	Review of literature with a focus on the achievement of main SC objectives in the I4.0 era under consideration of AM, BC and IoT	Zhang et al. (2020)	x	x						x	x
	Analysis of digital maturity’s effect on SCRES through a sample of SCM practitioners	Zouari et al. (2020)	x	x	x			x		x	x