Abstract:
Industry 4.0 has enabled technological integration of cyber physical systems and internet based communication in manufacturing value creation processes. As of now, many people use it as a collective term for advanced technologies, i.e. advanced robotics, artificial intelligence, machine learning, big data analytics, cloud computing, smart sensors, internet of things, augmented reality, etc. This substantially improves flexibility, quality, productivity, cost, and customer satisfaction by transforming existing centralized manufacturing systems towards digital and decentralized one. Despite having potential benefits of industry 4.0, the organizations are facing typical obstacles and challenges in adopting new technologies and successful implementation in their business models. This paper aims to identify potential barriers which may hinder the implementation of industry 4.0 in manufacturing organizations. The identified barriers, through comprehensive literature review and on the basis of opinions collected from industry experts, are: poor value-chain integration, cyber-security challenges, uncertainty about economic benefits, lack of adequate skills in workforce, high investment requirements, lack of infrastructure, jobs disruptions, challenges in data management and data quality, lack of secure standards and norms, and resistance to change. Interpretive Structural Modeling (ISM) is used to establish relationships among these barriers to develop a hierarchical model and MICMAC analysis for further classification of identified barriers for better understanding. An analysis of driving and dependence of the barriers may help in clear understanding of these for successful implementation of Industry 4.0 practices in the organizations.