Cognitive Decision-Making Algorithms, Real-Time Sensor Networks, and Internet of Things Smart Devices in Cyber-Physical Manufacturing Systems.

• Author: Smith, Alison

1. Introduction

   Industry 4.0 is based on horizontal networking throughout the supply chain, vertical networking throughout operational sectors, and sequential engineering from product advancement to recycling. (Rahman et al., 2020) Once the Industry 4.0 architecture has been configured, the difficult task of a prevalent production system is to attain the subsequent phase in the smart manufacturing, which carries out the adoption of cyber-physical production systems, connected to Internet of Things networks in certain operational sectors. (Lins and Oliveira, 2020)

2. Conceptual Framework and Literature Review

   The devices and systems in Industrial Internet of Things produce huge volumes of data streams (Andrei et al, 2016; Hecht et al, 2019; Krizanova et al, 2019; Lazaroiu et al, 2019; Mihaila, 2017; Nica et al, 2019) leading to the demand of cutting-edge computing systems for big data handling and analytics. (Khan et al, 2020) Internet of Things technology enables the networking of diverse smart objects online. (Kim, 2017) Improving manufacturing planning and control operations may result in a large-scale enhancement of production systems (Andrei et al, 2016; Jinyuan, 2019; Lazaroiu et al, 2017; Lazaroiu et al, 2020; Popescu et al, 2017), assisted by the Industry 4.0, considerable accessibility of data, high-computing potential, and significant storage capacity. (Usuga Cadavid et al, 2020) As smart manufacturing perseveres in refashioning and upgrading products and processes (Dusmanescu et al., 2016; Kovacova et al., 2019; Lazaroiu, 2018; Lyakina et al., 2019; Popescu et al, 2018), straightening out knowledge data is increasingly expedient. (Feng et al, 2020)

3. Methodology and Empirical Analysis

   The data used for this study was obtained and replicated from previous research conducted by Bain, Capgemini, Deloitte, Eclipse Foundation, Globant, MHI, and SME. I performed analyses and made estimates regarding the relationship between cognitive decision-making algorithms, real-time sensor networks, and Internet of Things smart devices. Data collected from 4,600 respondents are tested against the research model by using structural equation modeling.

4. Results and Discussion

   Industry 4.0 establishes a groundbreaking pattern of digital, self-governing, and dispersed supervision for manufacturing systems. (Sahal et al, 2020) Industry 4.0 production systems are comparable to the interconnected structures in which machines provide services and share data with products instantaneously. (Oztemel and Gursev, 2020) The Industrial Internet of Things facilitates the contiguous objects to network in heterogeneous working settings by harnessing their integrated underlying data communication technologies. (Umer et al, 2019) Harnessing blockchain, a consolidated tamperproof system can be deployed as an audit mechanism for Industrial Internet of Things hardware products. (Wang et al., 2020) The digital twin technology can be adopted in the product assembly domain. (Sun et al, 2020) (Tables 1-9)

5. Conclusions and Implications

   Groundbreaking manufacturing settings require more fluid and swift transitions taking into account inconstant business situations. (Lee, 2020) Digital production technologies constitute a vitally important range of support mechanisms in the incessant endeavors designed for the decrease in the advancement product time and cost while aiming the diversification in manufactured item...