Big Data-driven Decision-Making Processes, Industry 4.0 Wireless Networks, and Digitized Mass Production in Cyber-Physical System-based Smart Factories.

• Author: Hyers, Douglas

1. Introduction

   The advancement of information technologies has facilitated the gathering and inspection of massive volumes of data. (Tao et al., 2020) Big data analytics capability assists companies in gaining insight that is instrumental in fortifying their dynamic performance, positively shaping marketing and technological maintenance. (Mikalef et al., 2020) Cyber-physical production systems are elaborate, networked, smart, and innovative sustainable manufacturing aggregates. (Vogel-Heuser et al., 2020) Cyber-physical systems network instantaneously both internally and throughout organizational services within the value chain by use of industrial big data analytics. (Antonucci and Costa, 2020)

2. Conceptual Framework and Literature Review

   By use of data mining, models constituting knowledge inherently stored in huge volumes of information can be digitally derived for enabling human decision-making. (Tao et al, 2020) A company's big data analytics capability has indirect consequences on marketing and technological performance and competitive operations (Adami, 2019; Du?manescu et al., 2016; Kliestik et al., 2020a, b, c; Lazaroiu et al, 2019; Majerova et al, 2020) that are moderated by an improved impact on dynamic maintenance. (Mikalef et al, 2020) Connected production equipment typifies cyber-physical systems and generates significant upsides. (Mahan and Menold, 2020) Industry 4.0 improves manufacturing performance and the function of the operator, which is steadily demanded to coordinate and control cyber-physical production systems. (Papetti et al, 2020) Industry 4.0 is instrumental in configuring the shared, interactive, and automated design together with production workflow (Chuanyue, 2019; Kliestik et al, 2018; Kovacova et al, 2019; Lazaroiu et al, 2020; Peters et al, 2020) across Internet of Things-based real-time manufacturing logistics. (Li et al, 2020)

3. Methodology and Empirical Analysis

   Building my argument by drawing on data collected from Accenture, Capgemini, Catapult, Deloitte, Forrester, Globant, MHI, PwC, and ZDNet, I performed analyses and made estimates regarding the link between big data-driven decision-making processes, Industry 4.0 wireless networks, and digitized mass production. Data collected from 4,600 respondents are tested against the research model by using structural equation modeling.

4. Results and Discussion

   Network analysis is typically harnessed to inspect graph-structured data for setting up knowledge systems. (Tao et al, 2020) Automation and assistance technologies have become fashionable in smart production and logistics, leading to an effective digital transformation. (Neumann et al., 2020) Industry 4.0 wireless networks are thoroughly reshaping manufacturing, boosting adjustability, mass personalization, quality, and output. (Silvestri et al., 2020) Big data-driven factory is a subsequent state of an entirely networked production system, chiefly operating robotically by articulating, distributing, receiving, and handling required input to conduct tasks for generating manufactured goods. (Osterrieder et al, 2020) Coherent deploying of cyberphysical systems configures cost-effective and low-carbon production across manufacturing networks. (Garcia et al, 2019) (Tables 1-12)

   By furthering a big data analytics capability, companies fortify their ability to identify developing prospects and threats, grasp options available for selection before competitors, and reconfigure coherently the organizational resource base. (Mikalef et al., 2020) In the first instance planned for digital process automation, big data-driven technologies shape markets and business patterns and considerably refashion supply chain management. (Panetto et al., 2019) Manufacturing companies harness deep learning-assisted smart process planning throughout the Industry 4.0 route, analyzing consequences on business approaches and the levels of innovation and technology implementation, while reorganizing their operational structures and supporting their managerial performance across supply chain management and end user relationships through cognitive automation. (Pessot et al., 2020)

   Physical and digital realms are progressively connected, articulating cyberphysical systems and Internet of Things-based real-time production logistics by use of deep learning-assisted smart process planning. (Panetto et al., 2019) Industry 4.0 operational enhancements are attained by (i) optimizing asset use and decreasing machine discontinuance with the assistance of remote supervision systems and predictive maintenance, (ii) boosting labor output through manual automation, (iii) decreasing inventory levels and enhancing the quality of services and manufactured goods by deploying inspection of big data generated instantaneously by smart sensors. (Corallo et al., 2020) Embracing Industry 4.0 is the key approach for industrial operations, as companies have to manufacture first-rate goods to satisfy the escalating end-user demands viably. (Devi et al., 2020)

5. Conclusions and Implications

   The impact of big data analytics capability is identifiable by harnessing improved operational performance leading to competitive functional gains. (Mikalef et al., 2020) Smart factories necessitate...