Industry 4.0 which has no longer remained future of manufacturing world. It has started to root itself in manufacturing industry at varying level. One of the key aspects of its adoption is how it get integrated with tradition competencies. One such competency is Industrial Engineering which is here to stay as foundation of manufacturing. Smart factories can improve efficiency, profitability, compliance, and customer satisfaction by implementing components of Industry 4.0. During the Pandemic 67% of the Manufacturers have accelerated towards digital Projects and it is very much expected that digital manufacturing market to cross $767 Billion by 2025. Industrial engineers will undoubtedly play a key role in Industry 4.0 environments, such as designing, implementing, and maintaining the enabling technologies of a fully automated smart factory.

Today there is less to refer on how Industry 4.0 can be integrated with Industrial Engineering. Some of the researchers have done great work to rethink on Industrial Engineering curriculum. IE Community has a huge scope for research and knowledge sharing for integration of actual applications of Industry 4.0 components in Industrial Engineering competency. All of Industry 4.0's connection - sensors, IoT, AI, and so on – is aimed at one thing: improving production processes. Automation allows factories to work more quickly, while data analytics enables leadership to make data-driven decisions to boost productivity. Industry 4.0 enables greater flexibility across the industrial operation, resulting in higher asset utilization and, as a result, the possibility for increased profitability.

This paper will discuss about such specific applications of different industry 4.0 components like Big Data, IIoT, Cloud Computing, Cybersecurity, Robotics, AR, Additive Manufacturing, Real Time Simulation etc. These technologies can contribute greatly to reach the full potential of manufacturing 4.0 movement. We will review the IE areas of application with Industry 4.0 components with their approach, advantages, benefits, and challenges. Some of the examples that we will discuss in this paper are Data Analytics of Standard time data and time study data, Analytic of standard time data for ergonomics improvement opportunities, IIoT for motion economy analysis, Machining Learning for predicating the time standards, Additive for developing efficient methods, collaborative robots to combine people and robots in manufacturing operations to increase efficiency and revenue, autonomous mobile robots (AMR) for efficient material handling.