Cyber-Physical Smart Manufacturing Systems: Sustainable Industrial Networks, Cognitive Automation, and Big Data-driven Innovation.

• Author: Coatney, Karen

1. Introduction

   Industry 4.0 will transform how supply networks are fashioned and run. (Hofmann et al., 2019) Embedded in the technological prospects of networked remote monitoring and augmented or virtual reality tools, personnel are not supposed to supervise machines continuously, facilitating thus adjustable working time patterns. A predetermined harnessing of data and technological advancements are decisive in shaping and precisely harmonize subsequent workplaces to human resources' distinct necessities. (Veile et al., 2019)

2. Conceptual Framework and Literature Review

   With the development of Industry 4.0, organizations have been directing their endeavors to attain first-rate performance (Chijioke et al., 2018; Du?manescu et al., 2016; Havu, 2017; Krizanova et al., 2019; Nica, 2018; Popescu et al., 2018) by upgrading degrees of automation and interconnectivity. (Tortorella et al., 2019) Automation, networking, digitalization, the adoption of renewable energies, and the coherent employment of resources are paramount in Industry 4.0, that leverages cutting-edge machinery, devices, and growing data and communication technologies that use the Internet of Things capabilities. (Miranda et al., 2019) Various sensor networks are instrumental in connecting machines, components, commodities, and workforce (De Gregorio Hurtado, 2017; Hardingham et al., 2018; Kovacova and Kliestik, 2017; Nica, 2015; Popescu, 2014) so as to set up groundbreaking applications to reinforce intelligent and self-governing decision making. (Raza et al., 2019) The smart factory is an inevitable state of an entirely networked production system, chiefly functioning without personnel by catalyzing, transmitting, collecting, and processing required data to supervise all specified tasks for manufacturing goods. (Osterrieder et al., 2019)

3. Methodology and Empirical Analysis

   The data used for this study was obtained and replicated from previous research conducted by BCG, Capgemini, DAA, IoT Analytics GmbH, Management Events, and PwC. I performed analyses and made estimates regarding the biggest challenges for organizations to progress toward Industry 4.0 (%), top challenges in formulating smart factory strategy (%), and proportion of categories based on digital maturity across industries (%). Data collected from 4,600 respondents are tested against the research model by using structural equation modeling.

4. Results and Discussion

   As the implementation of Industry 4.0 technologies in production processes represents a competence-creating undertaking, the function of interaction and monitoring is a relevant provision for manufacturing operation innovation. (Dachs et al., 2019) Sound and prompt data distribution is decisive for accomplishing the agile production objectives of Industry 4.0. (Zeng et al., 2019) To adjust to an Industry 4.0 setting, extensive groundbreaking applications have been integrated within logistics 4.0. (Hasan et al., 2020) Interoperability enables manufacturing...