Smart Connected Sensors, Cyber-Physical Networks, and Big Data Analytics Systems in Internet of Things-based Real-Time Production Logistics.

• Date: 01 June 2020
• Author: Moore, Kelly

1. Introduction

   Internet of Things is crucial to automated production settings where cutting-edge machines network to carry out the performance of smart manufacturing. (Taboada and Shee, 2020) Groundbreaking industry 4.0-related technologies are decisive in opening up the sharing economy, as harnessed to product advancement. (Chiappetta Jabbour et al., 2020) With the materialization of big data analytics, massive volumes of relevant information can be brought about throughout the whole product life cycle. (Feng et al., 2020)

2. Conceptual Framework and Literature Review

   Industry 4.0 reinforces data analytics applications to accomplish first-rate quality of decision-making furthering (Bargh, 2019; Krizanova et al., 2019; Mihaila, 2019; Popescu, 2014; Zhang, 2019) while handling serious disruptions. (Ivanov and Dolgui, 2020) Industry 4.0 is advancing swiftly, providing manufacturing enterprises with innovative prospects for digital transformations to supply commodities and services to large-scale markets at competitive prices. (Stentoft et al., 2020) Adoption of Industry 4.0 applications is instrumental in cutting down expenses, enhance output, coherence and adjustability, and improve product customization. (Ghadge et al., 2020) Attempting to satisfy the demands of transparency, consistency, and decentralization of smart manufacturing, product lifecycle management attempts to harmoniously supervise all products and information (Dusmanescu et al., 2016; Krylov, 2019; Nica et al., 2014; Popescu et al., 2017a, b) brought about for carrying out business competitiveness. (Liu et al., 2020) By harnessing big data technologies, the connectedness and interoperability between physical and virtual resources (Kliestik et al., 2020a, b; Lazaroiu et al., 2020a, b; Pilkington, 2018; Wingard, 2019) are feasible in cutting-edge production systems. (Zhang et al., 2020)

3. Methodology and Empirical Analysis

   Building my argument by drawing on data collected from Capgemini, CGI, CompTIA, and PwC, I performed analyses and made estimates regarding how organizations can accelerate and secure their smart factory transformation. The data for this research were gathered via an online survey questionnaire and were analyzed through structural equation modeling on a sample of 4,600 respondents.

4. Results and Discussion

   Industry 4.0-enabling technologies enable massive performance enhancements within supply chain operations by facilitating process assimilation, digitization, and automation, while generating advanced analytical capabilities. (Fatorachian and Kazemi, 2020) Industrial big data production indicates how breakthrough technologies assist enterprises in repositioning processes from internal company functions into external markets in line with strategic options as regards revenue potential. (Clark and Sudharsan, 2020) The extent to which companies can determine value from big data analytics and derive valuable insights is a distinct determinant in advancing monitoring that fully harnesses production while protecting resources. (Morariu et al., 2020) (Tables 1-7)

5. Conclusions and Implications

   Data represents the mainstay of real-world Internet of Things applications. (de Matos et al., 2020) Internet of Things sensing gathers massive volumes of data in the physical realm from production systems...