Application of Big Data Analytics in Supply Chain Management to Mitigate Pandemic Risk
K. Jnaneswar
CET School of Management, Thiruvananthapuram, India.
Zahwa Shirin *
CET School of Management, Thiruvananthapuram, India.
*Author to whom correspondence should be addressed.
Abstract
The majority of industry executives and policymakers are looking for effective strategies and policies to revamp manufacturing patterns and meet market demand. Most transportation links and distribution systems between manufacturers, production facilities, and consumers have been disrupted by the COVID-19 pandemic. The complexities of production and operations management in pandemic circumstances and policy solutions for enhancing the system's stability and sustainability are adequately proposed in this paper. The research employs both main and secondary data sets. Secondary data was gathered through a comprehensive online search that turned up important articles and publications; data was then collected and analysed to better understand the current supply chain situation. It is found that pandemic has caused a major destruction in the supply chain management like transportation barriers, decreased demands, lack of labour, import export barriers, improper inventory management etc. The organizations that used big data analytics to make proper decisions, optimisation purposes has overcome the situation to an extent.
Keywords: Leaf extracts, Big data analytics, effect, Effect of Parthenium, supply chain management, Stomatal features, pandemic risk, correlation, regression, anova