The role of big data and predictive analytics in developing a resilient supply chain network in the South African mining industry against extreme weather events

Most parts of the world have suffered the negative impacts from extreme weather events, in whatever form they may take. To mitigate such impacts, attention in the operations management literature has focused on how firms build resilience in their supply chains, in order to quickly respond to such events and also to return, as soon as possible, to a business-as-usual state. Prior studies have examined the process of building a supply chain (SC) in different countries, industries and in response to various disruptions, such as the COVID-19 pandemic, whilst, at the same time, calling for further research in different contexts. We respond to these calls by exploring SC resilience ability in the South African mining industry under extreme weather events. We situated our study in the dynamic capability view (DCV) view of the firm. We examined the direct effect of big data and predictive analytics (BDPA) capabilities on SC visibility and the final effects on community and resource resilience. We adopted a sequential mixed methods research design, collecting data from interviews with 10 industry practitioners and from 219 respondents to an online survey. We built and tested our theoretical model using partial least squares structured equation modelling (PLS-SEM). Notable theoretical contributions of our study are that big data enables a more efficient supply chain monitoring system, which, in turn, improves SC visibility. BDPA capability improves a company's ability to make the best use of its available resources. It improves the South African mining industry's dynamic capability, allowing them to adjust their strategies in response to diverse adverse weather conditions. Furthermore, BDPA capability's ability to improve SC visibility is enhanced when there is strong alignment between BDPA strategy and initiatives. Finally, having a high level of SC visibility helps develop community and resource resilience, which are necessary to ensure that firms in the industry fulfil their responsibilities in relation to social sustainability. • Emerging technologies such as big data and predictive analytics (BDPA) can reduce the negative impacts of extreme weather events. • BDPA is most effective when there is a strong alignment between BDPA strategy and BDPA initiatives. • Supply chain (SC) visibility improves the resilience of the supply chain network. • Community resilience and resource resilience are crucial in developing SC resilience under extreme weather conditions. • Managing operations and supply chains in a socially responsible way are vital when facing extreme weather events.