IoT and big data technologies to avoid disruptions: creating a resilient model for global supply chain networks
Wadke, PP and Rezaei Vandchali, H, IoT and big data technologies to avoid disruptions: creating a resilient model for global supply chain networks, Proceedings of the 10th Asian Logistics Round Table Conference (ALRT), 19-20 November 2020, Launceston, Tasmania, pp. 1-24. (2020) [Refereed Conference Paper]
Historically, Global Supply Chain Networks (GSCNs) have failed to effectively respond to the disruptions induced by natural disasters, wars, terrorist attacks and public health crises with the most recent example of the global pandemic of COVID-19. Global Supply Chain Networks (GSCNs) have not only failed to design fail-proof systems for operating without any disruptions, but they have also been unable to adapt to the drastically changing market environment. Sudden variations in the demand for goods and services in combination with the lack of adaptability pose expensive risks to a firm. Moreover, vulnerable supply chains do not recover effectively from disruptions. The inability of firms to adopt IT-enabled services such as those offered by the big data ecosystems could been regarded as the major cause of such failures. The aim of this research paper is to formulate a conceptual framework of resilient GSCNs by linking the elements constituting towards GSCN disruption failures and the latest services offered by IoT (Internet of Things) and the big data technologies. The adopted methodology follows a literature review to investigate the major GSCN disruptions in recent decades and analyze the major causes for their vulnerability. It also examines the contribution of IoT and big data technologies in building resilient GSCNs. Based on the findings, major aspects of GSCN disruptions are addressed and appropriate solutions derived from IoT and big data technologies are provided. Furthermore, the paper provides additional suggestions for GSCNs to design future-proof systems to operate in uncertain environments.