Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience

Abstract

The supply chain resilience and data analytics capability has generated increased interest in academia and among practitioners. However, existing studies often treat these two streams of literature independently. Our study model reconciles two different streams of literature: data analytics capability as a means to improve information-processing capacity and supply chain resilience as a means to reduce a ripple effect in supply chain or quickly recover after disruptions in the supply chain. We have grounded our theoretical model in the organizational information processing theory (OIPT). The four research hypotheses are further tested using responses from 213 Indian manufacturing organizations collected via a survey-based pre-tested instrument. We further test our model using variance based structural equation modelling, popularly known as PLS-SEM. All of hypotheses were supported. The findings of our study offer a unique contribution to information systems (IS) and operations management (OM) literature. The findings further provide numerous directions to the supply chain managers. Finally, we note our study limitations and provide further research directions.

Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case

“…Making innovations and data work for the SC resilience in crisis times is a promising future research avenue with a particular focus on data analytics, artificial intelligence, and machine learning. The understanding and progressing the research of how all these technologies can be used to operate the SCs in a resilient way in cases of epidemic outbreaks is an important future research area (Choi et al, 2017(Choi et al, , 2019Choi and Lambert, 2017b, Dubey et al, 2019a, Ganasegeran and Abdulrahman, 2020, Queiroz and Wamba, 2019, Yoon et al, 2019. In particular, digital SC twins ) -i.e., the computerized SC models that represent the network state for any given moment in real time -can be used to support the decision-making during the epidemic outbreaks.…”

Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak

“…The studies (Basole and Bellamy 2014;Dolgui 2014a, 2014b;Kim, Chen, and Linderman 2015;Brintrup, Wang, and Tiwari 2015;Sawik 2017;Macdonald et al 2018;Yoon et al 2018;Scheibe and Blackhurst 2018;Pavlov et al 2018;Ojha et al 2018;Giannoccaro, Nair, and Choi 2017;Ivanov 2018Ivanov , 2019Dolgui, Ivanov, and Sokolov 2018;Li et al 2019;Pavlov et al 2019b) recognised the structural SC properties as crucial determinant to maintain stability and robustness and to achieve resilience. Another important observation in literature is a linkage of SC complexity and resilience (Blackhurst et al 2005;Nair and Vidal 2011;Bode and Wagner 2015;Dubey et al 2019a;Tan, Cai, and Zhang 2020). emphasise that complex networks become more vulnerable to severe disruptions which change the SC structures and are involved with SC structural dynamics.…”

Internet of Things and Big Data as enablers for business digitalization strategies

“…IoT and Big Data are united by their emphasis on information that is rich in volume, velocity and variety, and which requires innovative forms of processing ( Lycett, 2013 ). Velocity indicates the speediness of data generation ( Dubey et al, 2019 ) due to real-time and continuous connections. Volume indicates the size of the data flows (generally huge).…”