Cognitive Automation, Big Data-driven Manufacturing, and Sustainable Industrial Value Creation in Internet of Things-based Real-Time Production Logistics.

• Author: Keane, Eliot

1. Introduction

   Sensors are cutting-edge advancements in big data technologies that are pivotal in optimized process monitoring, adding value across industrial automation. (Eifert et al., 2020) The digital data stream from the plant to management by the cyber-physical system furthers smart production, optimizing efficiency in a mass manufacturing setting. (Prathima et al, 2020)

2. Conceptual Framework and Literature Review

   The capacity of the process sector and its providers to sustainably manufacture first-rate goods at competitive prices (Chuanyue, 2019; Kovacova et al., 2019a, b; Lazaroiu and Adams, 2020; Peters et al., 2020; Watson et al., 2020) and swiftly adjust to ever-increasing end user demands (Andrei et al, 2016; Kliestik et al, 2018; Majerova et al, 2020; Popescu et al, 2018) is instrumental to Industry 4.0 wireless networks. (Eifert et al, 2020) Manufacturing is the multi-staged operation of configuring a product originating from raw materials: smart manufacturing constitutes the subcategory that harnesses computer monitoring and advanced flexibility tools. (Oztemel and Gursev, 2020) The digital twin represents a means of enhancing the operation of physical entities by deploying computational approaches (Kliestik et al., 2020a, b, c; Lazaroiu et al., 2020a, b; Popescu et al., 2017) facilitated through virtual counterparts. (Jones et al, 2020)

3. Methodology and Empirical Analysis

   Building our argument by drawing on data collected from BCG, CompTIA, Deloitte, IW Custom Research, Kronos, McKinsey, PAC, PwC, and Software AG, we performed analyses and made estimates regarding the link between cognitive automation, big data-driven manufacturing, and sustainable industrial value creation. Structural equation modeling was used to analyze the collected data.

4. Results and Discussion

   Historically and perpetually archived information is inspected by cutting-edge data analysis software, assisting in the adoption of cyber-physical manufacturing systems as components of the process industry. (Eifert et al, 2020) Industry 4.0 wireless networks attempt to attain superior levels of operational coherence, output, and automation, as manual assembly systems are typified by significant adjustability and inferior productivity, in contrast to thoroughly cognitive automated ones. (Bortolini et al, 2020) (Tables 1-10)

   Industry 4.0 wireless networks comprise heterogeneous digital technologies carrying out the business operations of production companies. (Zheng et al., 2020) Cyber-physical systems connect the physical and digital twins, enabling instantaneous performance of hybrid sensors for groundbreaking feedback process monitoring and coherent experimental design: digital twins facilitate supervision of elaborate operations factually for process optimization. (Eifert et al., 2020) For the purpose of satisfying the high-frequency refashioning of the manufacturing tasks constituted by tailor-made demands, the software and hardware resources have to perform and act jointly unsupervised, adjustably, conveniently, and swiftly. (Zhang et al., 2020a)

   Smart sensors systematically streamline their performance, maintenance, and adjustment, monitored by digital twin intelligence and enabled by plug-and-play assimilation in a cyber-physical system by consistency and a module type re-design of operational networking and connectivity. (Eifert et al., 2020) To facilitate the configuration of a digital twin, physical entities connect with the digital realm by harnessing sensors and networks able to collect and reorganize real values in data flows. (Mazzei et al., 2020) Incessant process enhancements preserve and perform an industrial process effectively. (Bhadani et al., 2020) The adoption of robotic automation and adjustable manufacturing systems increases long-term output. (Camina et al., 2020)

5. Conclusions and Implications

   Cutting-edge technological advancements articulated in Industry 4.0 have brought about a disruptive impact on the manufacturing and service systems, and also on value chains. (Asif, 2020) The networking of industrial applications is a pivotal demand in smart manufacturing. (Zhang et al., 2020b) Fog computing is a groundbreaking infrastructure providing adjustable resources at the border of the network, handling and loading data and application resources to final operators. (Wang et al., 2020) Decision-making developed on inaccurate data from smart sensors may result in detrimental, unreliable, and big-budget operations. (Mouapi et al., 2020) Automation and data collection in smart manufacturing necessitates the use of heterogeneous wireless sensor devices in cyber-physical systems. (Zorbas et al., 2020) As limitations, this article focuses only on cognitive automation, big data-driven manufacturing, and sustainable industrial value creation in Internet of Things-based real-time production logistics. Further research should consider business process optimization, cognitive decision-making algorithms, and artificial intelligence data-driven Internet of Things systems in sustainable smart manufacturing.

   Survey method

   The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and...