Artificial Intelligence-based Decision-Making Algorithms, Industrial Big Data, and Smart Connected Sensors in Cloud-based Cyber-Physical Manufacturing Systems.

• Author: Rommer, Derek

1. Introduction

   With the aim of handling constant variations and disruptions, numerous enterprises apply cutting-edge cyber-physical system technologies to maintain advanced manufacturing traceability and manageability throughout their factories. (Wang et al., 2020) Assimilation of cloud computing and the Internet of Things may have an impact on companies in altering their business systems for first-rate performance. (Narwane et al., 2020) Cloud operations and industrial artificial intelligence enable cutting-edge adjustable production systems. (Fragapane et al., 2020)

2. Conceptual Framework and Literature Review

   Industry 4.0 is developed on the components of Industrial Internet of Things, instantaneous data gathering and predictive analytics (Ainsworth-Rowen, 2019; Englund, 2019; Lazaroiu et al., 2019; Nica, 2015; Vilchez, 2019) by harnessing big data analytics, machine learning, and cloud manufacturing. (Tiwari and Khan, 2020) Cloud manufacturing constitutes a pattern of smart production systems with data opening, resource distribution, and wide-ranging services (Zhao et al., 2020), offering access when needed to a supply of manufacturing resources and assignments designed for employing them (Andrei et al., 2016; Graessley et al., 2019; Majerova et al., 2020; Popescu et al., 2017), although geographically scattered, in a service-oriented fashion. Such services are operated through the Industrial Internet of Things and its main IT framework and configuration instances (Cruciani, 2018; Keane, 2019; Mircica, 2019; Popescu et al., 2018; Zhuravleva et al., 2019), in addition to data exchange procedures and criteria. (Mourad et al., 2020)

3. Methodology and Empirical Analysis

   Using and replicating data from CompTIA, Deloitte, and McAfee, I performed analyses and made estimates regarding cloud-based applications in use (%), new/improved skills needed for cloud solutions (%), sources for procurement/management of cloud-based applications (%), and changes to IT function during cloud adoption (%). Data were analyzed using structural equation modeling.

4. Results and Discussion

   Crowdsourcing services and cloud-based planning and production platforms constitute an advancing network service pattern. (Chen et al., 2020) To progressively integrate smart manufacturing entities into logistics management Internet of Things applications in an omnipresent setting, task scheduling should be supplied for resource sharing in a consolidated fashion. (Hasan and Al-Rizzo, 2020) Smart sensors can reinforce soundly the operators by activating a pre-processing task so as to offer collected data to the human decision-makers. (Cimini et al., 2020) Massive demand and fierce competition have stimulated nearly all sectors to surpass large-scale manufacturing. (Aheleroff et al., 2020) Developments in information processing algorithms, in conjunction with decreased computing expenses, enable enterprises to inspect and enhance their systems by harnessing automated means, by having access to huge volumes of accurate...