The impact of industry 4.0 on supply chain capability and supply chain resilience: A resource-based view

Influences of artificial intelligence and blockchain technology on financial resilience of supply chains

Data science and big data analytics: a systematic review of methodologies used in the supply chain and logistics research

Supply chain (SC) resilience is imperative to cope with disruptions using some preparedness and recovery capabilities such as network redundancy (e.g., backup suppliers) and process flexibility (e.g., capacity agility). These capabilities frame an SC resilience portfolio. Both designing a resilient portfolio and recovering in case of a real disruption require investments. This paper presents a new mathematical model for designing an efficient resilience portfolio in a multi-echelon SC. Through computational and comparative analyses using a real-life case-study, we demonstrate that our model allows increasing resilience at minimal costs by determining an optimal combination of preparedness and recovery investments. Interestingly, the optimal solutions (i.e., efficient resilient SC designs) increase SC efficiency even in business-as-usual scenarios. This result contributes to the literature on transforming resilience from an expensive spend to a value-creation asset. We illustrate our approach using a real-life industrial example that allows for the identification of important relations between disruption duration/magnitude and efficiency of preparedness and recovery strategies. Based on computational, comparative, and case-study analyses, we deduce and generalize managerial implications at the network, supplier, and manufacturer levels. We take an extra step by extrapolating our major findings and generalized managerial implications toward the COVID-19 pandemic setting. The outcome of our research can be instructive for SC managers when deciding on investments in resilient redundancy allocation as a part of preparedness strategy and efficient recovery deployment. 

Efficient resilience portfolio design in the supply chain with consideration of preparedness and recovery investments

Recent years have witnessed increased pressure across the global healthcare system during the COVID-19 pandemic. The COVID-19 pandemic shattered existing healthcare operations and taught us the importance of a resilient and sustainable healthcare system. Digitisation, specifically adoption of Artificial Intelligence (AI) has positively contributed to developing a resilient healthcare system in recent past. To understand how AI contributes to building a resilient and sustainable healthcare system, this study based on systematic literature review of 89 articles extracted from Scopus and Web of Science databases is conducted. The study is organised around several key themes such as applications, benefits, and challenges of using AI technology in healthcare sector. It is observed that AI has wide applications in radiology, surgery, medical, research, and development of healthcare sector. Based on the analysis, a research framework is proposed using an extended Antecedents, Practices, and Outcomes (APO) framework. This framework comprises AI applications’ antecedents, practices, and outcomes for building a resilient and sustainable healthcare system. Consequently, three propositions are drawn in this study. Furthermore, our study has adopted the theory, context and methodology (TCM) framework to provide future research directions, which can be used as a reference point for future studies. 

Application of artificial intelligence for resilient and sustainable healthcare system: systematic literature review and future research directions

The COVID-19 pandemic’s impacts have been devastating for the global economy, and particularly for manufacturing companies. Many firms were unprepared for a crisis of this magnitude and adapted their operations in an ad-hoc manner. Some of these adaptations were facilitated by digital technologies (DTs). This article examines extant literature using a systematic review methodology in order to extend our understanding of what DTs have been used in manufacturing companies during the pandemic, how DTs have helped manufacturing companies cope with the pandemic, and how these innovations can be used in the post-pandemic world. The major outcome of our study is a novel classification of DT applications in the pandemic setting in the manufacturing industry. In addition, we propose an agenda for future research. 

The Impacts of digital technologies on coping with the COVID-19 pandemic in the manufacturing industry: a systematic literature review

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The Impacts of digital technologies on coping with the COVID-19 pandemic in the manufacturing industry: a systematic literature review