Industry 4.0 has witnessed several changes in the way the industries are operating with technology applications and computational intelligence. Industrial automations and decision makings are augmented with artificial intelligence (AI) and robotic process automation (RPA) is reaping several benefits in the form of improvement of operational, organisational and business processes. The objective of current work is to explore the scenario of RPA and AI in varied fields of industry and management and improve the organisational, business and operational functionality with the support of machine learning (ML), deep learning (DL) and network linguistic programs (NLP with the use of algorithms. Deliberations have been made in the paper stating the uses, differentiation, trends, and applications of RPA and AI, in management and industry. The chapter supports the advancement of the body of knowledge detailing the theoretical implications of computational intelligence.
TopIntroduction
The Industrial Revolution, a term historically associated with the profound socio-economic changes of the 18th and 19th centuries, has taken on a new dimension in contemporary discussions. In the present day, the focus is on the advent of Industry 4.0, a paradigm shift in manufacturing and industrial processes that leverages advanced technologies to create smart industries and factories (IBM, 2020).
One of the key pillars of Industry 4.0 is the seamless exchange of information among various components in industrial technologies (B2BNN Newsdesk, 2021). This interconnectedness is facilitated by the Internet of Things (IoT), which enables devices and machines to communicate and share data in real-time. The integration of IoT allows for the creation of intelligent systems that can make informed decisions, leading to enhanced productivity and efficiency (NICE, 2023).
Robotic process automation plays a pivotal role in Industry 4.0 by automating routine tasks and processes (Biscotti, Tornbohm, Villa, Bhullar, & Mehta, 2021). This not only reduces human intervention in mundane activities but also contributes to precision and consistency in manufacturing processes. As a result, industries can achieve higher levels of output and operational excellence (Assistedge, 2023).
Cognitive computing, another cornerstone of Industry 4.0, involves the use of artificial intelligence (AI) and machine learning to simulate human thought processes (SoftwareReviews, 2023). This enables machines to learn from data, adapt to changing circumstances, and make decisions without explicit programming. Cognitive computing enhances the adaptability and responsiveness of industrial systems (Violino, 2016).
Cloud computing provides the necessary infrastructure for the storage, processing, and analysis of vast amounts of data generated by smart industries (Khan, et al., 2023). This cloud-based approach offers scalability, flexibility, and accessibility, empowering organizations to efficiently manage and utilize their data resources (Aceto, Persico, & Pescapé, 2020).
IBM, a prominent player in the tech industry, emphasizes that Industry 4.0 represents the digital transformation of the field, ushering in real-time decision-making capabilities (IBM Cloud Education, 2020). The adoption of Industry 4.0, however, is not uniform across industries. Different sectors face varying degrees of challenges related to absorptive capacity – the ability to assimilate and apply new technologies effectively (IBM, 2020).
For some industries, the journey toward Industry 4.0 may be prolonged due to obstacles such as outdated infrastructure, workforce skill gaps, and concerns regarding data security and privacy (IBM, 2020). Overcoming these challenges requires strategic planning, investments in technology and training, and a collaborative effort from industry stakeholders.
Industry Revolutions 4.0 features are considered by growing automation and the engagement of smart factories well-versed in information to produce commodities more competently and efficiently (Gartner G., 2023). In the manufacturing and service industry, one of the major advantages of RPA is exploring news of work with the automated business process using RPA tools and thereby obtaining real-time data from smart devices (Schroer, 2023). Reducing human involvement and possible errors in the operational process, RPA replaces the traditional repetitive tasks through automated programmes (IBM Cloud Education, 2020) leading sharp reduction in the incurring cost. It is reported that there is a 30% to 50% reduction in operational costs of transactional undertakings with joint services with the application of RPA technologies (Bhatt, 2021). RPA has extended flexible operational processes that support the manufacturers to have mass customization and eventually attain efficiency.