Cyber-Physical Production Networks, Artificial Intelligence-based Decision-Making Algorithms, and Big Data-driven Innovation in Industry 4.0-based Manufacturing Systems.

• Author: Davidson, Robert

1. Introduction

   Technological, administrative, and environmental components may facilitate the adoption of digital manufacturing breakthroughs and improve firm performance. (Gillani et al., 2020) With the harnessing of heterogeneous smart devices, physical entities are advanced to smart ones that can be detected and shared in every part of the production process. (Guo et al., 2020) Particular assignments and decisions are automatable by use of smart systems and autonomous operations. (Ralston and Blackhurst, 2020)

2. Conceptual Framework and Literature Review

   Industry 4.0 technologies strategically shape applications (Ainsworth-Rowen, 2019; Dusmanescu et al., 2016; Lazaroiu et al, 2017; Nica et al., 2014; Tuyls and Pera, 2019) in production and service sectors (Jabbar and Dani, 2020) essentially configuring the quality of business operations and outcomes. (Zavadska and Zavadsky, 2020) A continuously growing volume of data has to be handled instantaneously (Andrei et al, 2016; Kral et al, 2019; Laza roiu et al, 2020; Popescu et al, 2018) and feed simulation patterns that can swiftly analyze various options available for selection and supply decision support and enhancement (Culkin, 2019; Lazaroiu, 2017; Michalkova et al, 2019; Sion, 2019) of the system performance. (Mourtzis, 2020) Data-driven product design constitutes a coherent and fashionable design approach, which can supply significant support for designers in making operationally smart decisions. (Feng et al, 2020)

3. Methodology and Empirical Analysis

   Using and replicating data from Accenture, Deloitte, MHI, PwC, and SME, I performed analyses and made estimates regarding the relationship between cyber-physical production networks, artificial intelligence-based decisionmaking algorithms, and big data-driven innovation. The results of a study based on data collected from 3,800 respondents provide support for my research model. Using the structural equation modeling, I gathered and analyzed data through a self-administrated questionnaire.

4. Results and Discussion

   Productivity and performance in the industrial domain can be enhanced by assimilating cutting-edge data technologies that are refashioning the industrial manufacturing patterns. (Segura et al, 2020) Numerous production resources are designed and advanced with virtual/digital) ones that will be related to the physical ones in every part of their lifecycle. (Zhang et al, 2020) Supervising a machine may supply huge volumes of data that have to be handled to extract valuable information. (Lee et al, 2020) Notwithstanding the advancement of robotics, certain sectors have been robot-reluctant to a great degree as their operations entail large or particular components and nonserialized products. (Perez et al, 2020) Human action recognition is instrumental in the carrying out of human-robot collaboration, by supplying the grounds for subsequent operational forecast and robot planning. (Xiong et al, 2020) Digital twins constitute real objects combined with their data and operations in the digital realm. (Piros et al, 2020) (Table 1-6)

5. Conclusions and Implications

   Numerous manufacturing companies forecast a relevant effect of Industry 4.0 on their supply chains, performances, and business patterns. (Wagire et al., 2020) Smart manufacturing attempts to fully harness concept generation, production, and outcome dealings from conventional methods to digitized and self-governing systems. (Oztemel and...