Mitigation Strategies for Challenges in Adoption of Data Science in Industry 4.0 - Balasubramanian, Somasundaram (Digital Repository of Theses, 2024)
Manufacturing Industries are currently undergoing a digital transformation called Industry 4.0 (I 4.0) to face the challenges of the Volatile, Uncertain, Complex and Ambiguous (VUCA) world. I 4.0 makes factories smart with cyber physical systems, Internet of Things (IOT) and high-performance computing technologies. Industrial Internet of Things (IIOT) provides a lot of data on the performance of cyber physical systems. These data tell stories both good and bad about the condition of the product it produces, the process that is used for production and performance of the process as well as performance of the product when used by customer like Original Equipment Effectiveness, Specific energy consumption etc. With the advancement and availability of information, communication, and computing technologies these data can be processed to improve the production capacity, efficiency, and reliability. Without the wisdom of data science, I 4.0 would not be able to decode and bring out value from these data to understand and adapt the challenges the manufacturing industries are facing in this VUCA world. Data driven approaches are not new for Manufacturing Industries, for example Lean Six Sigma (LSS) has been in practice for a long time. Though it is not new, adaptation of data science in the digital transformation journey of a manufacturing industry is facing many challenges like change management, human / social management etc. This research focuses on identifying the challenges the manufacturing industry faces while adopting data science in the digital transformation journey and suggests mitigation strategies for those challenges. The finding of this research would support faster adaptation of data science in industry which is taking a new avatar through I4.0.