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Sustainable Internet-of-Things-based Manufacturing Systems: Industry 4.0 Wireless Networks, Advanced Digitalization, and Big Data-driven Smart Production.

1. Introduction

Cutting-edge Industry 4.0 technologies enable companies to diminish the volume of resources misdirected and the emissions, bringing forth a prevailing low-carbon upside in addition to a decrease of the marginal production expense. (Liu and De Giovanni, 2019) The transformative capacity that functions within Industry 4.0, digitalization, and digital twins is instrumental in enhancing operational performance and cutting down process safety accidents. (Lee et al., 2019) Industry 4.0 can offer cost benefits of mass manufacturing with the adjustability of a small-batch producer. (Dachs et al., 2019)

2. Conceptual Framework and Literature Review

The socially impacted undertakings in Industry 4.0 entail collection investment and dimension of the customer market that regulates the product returns, shaping the competitive reverse logistics system. (Dev et al., 2020) Integrated high tech and networks supervise via sensors and coordinate via actuators the physical operations (Andrei et al., 2016; Mengoli et al., 2017; Nica, 2018a, b; Sandal and Krupka, 2018), commonly with input loops where physical operations and data processing shape each other. (Delicato et al., 2019) The production tools can make decisions in real time and clarify with the end user the alterations that can be implemented, in conformity with the assigned work streaming through the manufacturing system. (Rossit et al., 2019) Assimilating industrial automation systems leads to significant and groundbreaking characteristics via networking with team members (Ionescu, 2018; Nica, 2015; Popescu et al., 2017a, b; Valaskova et al., 2018), and assists in generating links between the cyber and physical realms. (Buchi et al., 2020) Repetitive and physically challenging tasks are handled by assistance systems, resulting in growing demands in respect of human resources' mental processes and performance. (Veile et al., 2019) Becoming competent at a distinct level is not instrumental as a mediator in the influence of Industry 4.0-based technologies on operational effectiveness. (Tortorella et al., 2020)

3. Methodology and Empirical Analysis

Using and replicating data from Capgemini, DAA, IoT Analytics GmbH, The Manufacturer, McKinsey, Oracle, PwC, US BLS, and WEF, I performed analyses and made estimates regarding smart factory transformation approach taken by players in different categories (%) and drivers of technological change and time to impact on employee skills (%). Data were analyzed using structural equation modeling.

4. Results and Discussion

With the swift advancement of Industry 4.0, cutting-edge technologies (e.g., big data, Internet of Things, and cloud computing) are progressively being applied, while established industrial production technologies will steadily develop or be replaced. (Lu et al., 2019) Industry 4.0 facilitates the monitoring of manufacturing operations by supplying instantaneous integration of flows and by furthering the fashioning of distinctive and custom-tailored commodities. (Moeuf et al., 2019) A digital production company is networked and interacts, assesses and harness data to more thoroughly handle smart operations back into the physical realm. (Hofmann et al., 2019) (Tables 1-7)

5. Conclusions and Implications

Wireless technologies driven by the Internet of Things will remodel the industry as presently designed. (Garrido-Hidalgo et al., 2019) Human resource determinants are pivotal causal agents and constraints of Industry 4.0. (Horvath and Szabo, 2019) Organizations can harness Industry 4.0 technologies to catalyze economic, sustainable, and social value by fashioning the logistics role as a competitive mechanism, a social value producer, and a driving force for performance. (Tang and Veelenturf, 2019) The advancement of leandigitized manufacturing system constitutes a feasible business approach for corporate longevity in the Industry 4.0 environment. (Ghobakhloo and Fathi, 2019)

Note

The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States. The precision of the online polls was measured using a Bayesian credibility interval.

Funding

This paper was supported by Grant GE-1707348 from the Artificially Intelligent Algorithmic Systems Research Unit, Westminster, CO.

Author Contributions

The author confirms being the sole contributor of this work and approved it for publication.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Clive Lafferty

c.lafferty@aa-er.org

The Center for Cognitive Internet of Things

at AAER, Manchester, England

Received 19 August 2019 * Received in revised form 7 December 2019

Accepted 8 December 2019 * Available online 15 December 2019

doi:10.22381/EMFM14420192
Table 1 Drivers of technological change and time to impact on employee
skills (%)

Mobile Internet, cloud technology     82
Processing power, big data            76
New energy supplies and technologies  69
Internet of Things                    62
Sharing economy, crowdsourcing        54
Robotics, autonomous transport        48
Artificial intelligence               36
Advanced manufacturing, 3D printing   22
Advanced materials, biotechnology     19

Sources: WEF; my survey among 4,200 individuals conducted June 2019.

Table 2 How important are the following data analytics skills and how
well are they integrated in your company? (%)

                               Important/Very  All skills on board
                               important

Data science                   94              26
Project management and         91              69
implementation
Machine learning techniques    86              37
and algorithms
Industrial process know-how    77              42
Cloud/Data storage             76              41
Computer engineering/          74              38
programming
IoT/M2M infrastructure         72              21
Business intelligence          67              46
Enterprise system integration  66              29

Sources: DAA; IoT Analytics GmbH; my survey among 4,200 individuals
conducted June 2019.

Table 3 Which statement best describes your supply chain integration by
digital maturity level? (%)

                            Digital  Digital   Digital    Digital
                            Novice   Follower  Innovator  Champion

Isolated solutions and      34       36        25         5
optimization of individual
processes
Internal functions are      29       36        12         23
integrated\and close
collaboration
Digitally connected with     2       10        59         29
external partners,
integrated platforms
for collaboration
Near-real-time end-to-end    2        3        35         60
integration and planning
platforms across external
network

Sources: PwC; my survey among 4,200 individuals conducted June 2019.

Table 4 Which role do the following technologies play in your
industrial data analysis? (%)

Spreadsheets                     57
Advanced analytics platforms     52
Business intelligence tools      44
Predictive analytics tools       36
Simulation tools                 35
Statistical package              34
Artificial intelligence          29
Event/Streaming analytics tools  27
Cognitive analytics              22
Edge/Fog Analytics               17

Sources: DAA; IoT Analytics GmbH; my survey among 4,200 individuals
conducted June 2019.

Table 5 Smart factory transformation approach taken by players in
different categories (%)

                                    Digital Masters  Conservatives

Business case and                   88               67
roadmap definition by
consulting firms
Focused transformation such as      72               40
operating model transformation,
people transformation, and
infrastructure transformation etc.
Partnership with                    70               58
tech providers for
feasibility study
End-to-end technology               67               49
solutions (e.g. Industrial
IoT connecting all key
manufacturing process etc.)

                                    Beginners

Business case and                   46
roadmap definition by
consulting firms
Focused transformation such as      19
operating model transformation,
people transformation, and
infrastructure transformation etc.
Partnership with                    33
tech providers for
feasibility study
End-to-end technology               15
solutions (e.g. Industrial
IoT connecting all key
manufacturing process etc.)

Sources: Capgemini; my survey among 4,200 individuals conducted June
2019.

Table 6 What area(s) do you plan on investing in Industry 4.0? (%)

Finance                  6
R&D                     48
Logistics               37
Production              76
Maintenance             42
Sales                   28
IT                      46
Don't know               2
Other (please specify)   3

Sources: The Manufacturer; Oracle; my survey among 4,200 individuals
conducted June 2019.

Table 7 Net growth in work involving more application of expertise,
interaction, and management (total work hours by activity type):

                                   Displaced  Added  Net change
                                   hours      hours  in hours

Applying expertise                   569      2,293  1,724
Interacting with stakeholders        756      1,658    902
Managing and developing people       152        977    824
Unpredictable physical activities  1,054      1,198    144
Processing data                    2,678      1,411  1,267
Collecting data                    3,413      1,906  1,507
Predictable physical               3,097      1,521  1,576

Sources: ONET skill classification, US BLS; McKinsey Global Institute
analysis; my 2019 data.
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