Real-Time Process Monitoring in Industry 4.0 Manufacturing Systems: Sensing, Smart, and Sustainable Technologies.

• Author: Gray-Hawkins, Malcolm

1. Introduction

   In the framework of Industry 4.0, Internet of Things and cyber-physical system technologies bring forward cognitive automation to carry out intelligent manufacturing, thus catalyzing smart products and services. (Kunst et al., 2019) Industry 4.0 can qualitatively improve factory work, bringing about a more dynamic labor setting, superior self-governance and prospects for selfdevelopment. (Kaasinen et al., 2019) Industry 4.0 technologies can offer increased adjustability and may enhance the quality of the commodities due to superior monitoring of the production process. (Dachs et al., 2019)

2. Conceptual Framework and Literature Review

   Organizations should refashion their operational structure to constitute a viable establishment for Industry 4.0. (Veile et al., 2019) The volume of energy demand has boosted because of the machine control of the manufacturing and industrial plants to conform them to Industry 4.0 requirements. (Shukla et al., 2020) Innovation can take place as a component of a high-tech networked sector and because of the tendency of firms to get up to date to a greater extent in Industry 4.0. (Buchi et al., 2020) Machine learning entails that a cutting-edge system or a smart algorithm is assimilating knowledge without being thoroughly programmed and inherently can bring to light models that facilitate prediction. (Hofmann et al., 2019) Industry 4.0 may diminish the required labor input and consequently reposition the balance between capital and labor inputs in preference to the former. (Dachs et al., 2019)

3. Methodology and Empirical Analysis

   Building our argument by drawing on data collected from Accenture, BBC, BDO, Capgemini, McKinsey, and PwC, we performed analyses and made estimates regarding progress in the last year in implementing Industry 4.0 applications/strategies (%), digital intensity parameters (%), data monetization strategies (%), and current level of process integration (%). Data collected from 4,700 respondents are tested against the research model by using structural equation modeling.

4. Results and Discussion

   Industry 4.0 technologies may beneficially impact the lifecycle management of commodities. (Rosa et al., 2019) Horizontal chains of command, adjustable structures and operations, and decentralized environments can constitute an agile company in conformity with Industry 4.0 standards. (Veile et al., 2019) A pivotal component of Industry 4.0 is human-centricity. (Kaasinen et al., 2019) Decreasing the servicing, infrastructure, and reorganization expenses, while furthering the adjustability are standard objectives for networking systems in Industry 4.0 (Zeng et al., 2019) (Tables 1-7)

5. Conclusions and Implications

   In Industry 4.0, the technological developments are enabling competitive value creation in all advancements phases (Andrei et al, 2016; Balica, 2018; Neary et al...