Management of V.U.C.A. (Volatility, Uncertainty, Complexity and Ambiguity) Using Machine Learning Techniques in Industry 4.0 Paradigm

Abstract

With the fast advancement in technology and induction of information technology and internet into various aspects of organizations, there has been both large scale of both quantitative as well as qualitative changes in industries. These revolutionary changes being done to attain digital transformation for organizational improvements, has been termed as Fourth Industrial Revolution also known as Industry 4.0. However, transforming the traditional processes and machineries for digitization involves lots of risk factors such as volatility, ambiguity, complexity and uncertainty. The amalgamations of all risk factors can be abbreviated as V.U.C.A. where V stands for volatility, U stands for uncertainty, C stands for complexity and A stands for ambiguity. It’s just like the cancerous cells that are present in each organization, if ignored at an early stage then it can lead to the deterioration of the organization. Hence for identifying and defining the contingencies, V.U.C.A. can play a highly pioneer role and thereby avoiding catastrophic results and cascaded issues in an organization. One effective way to manage V.U.C.A. is integrating machine learning (ML) techniques into Industry 4.0 applications. ML acts as a guide to identifying, getting prepared for, and responding to events in each category. In this paper, therefore, we have reviewed the V.U.C.A. terminologies and its importance associated with Industry 4.0 practices; the various effects of V.U.C.A. which are presented in a systematic overview about Industry 4.0 along with its peripherals, challenges, and finally identifying some future research directions. It also includes the different ML techniques with their applications towards each factor.