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Real-Time Process Monitoring in Industry 4.0 Manufacturing Systems: Sensing, Smart, and Sustainable Technologies.

1. Introduction

In the framework of Industry 4.0, Internet of Things and cyber-physical system technologies bring forward cognitive automation to carry out intelligent manufacturing, thus catalyzing smart products and services. (Kunst et al., 2019) Industry 4.0 can qualitatively improve factory work, bringing about a more dynamic labor setting, superior self-governance and prospects for selfdevelopment. (Kaasinen et al., 2019) Industry 4.0 technologies can offer increased adjustability and may enhance the quality of the commodities due to superior monitoring of the production process. (Dachs et al., 2019)

2. Conceptual Framework and Literature Review

Organizations should refashion their operational structure to constitute a viable establishment for Industry 4.0. (Veile et al., 2019) The volume of energy demand has boosted because of the machine control of the manufacturing and industrial plants to conform them to Industry 4.0 requirements. (Shukla et al., 2020) Innovation can take place as a component of a high-tech networked sector and because of the tendency of firms to get up to date to a greater extent in Industry 4.0. (Buchi et al., 2020) Machine learning entails that a cutting-edge system or a smart algorithm is assimilating knowledge without being thoroughly programmed and inherently can bring to light models that facilitate prediction. (Hofmann et al., 2019) Industry 4.0 may diminish the required labor input and consequently reposition the balance between capital and labor inputs in preference to the former. (Dachs et al., 2019)

3. Methodology and Empirical Analysis

Building our argument by drawing on data collected from Accenture, BBC, BDO, Capgemini, McKinsey, and PwC, we performed analyses and made estimates regarding progress in the last year in implementing Industry 4.0 applications/strategies (%), digital intensity parameters (%), data monetization strategies (%), and current level of process integration (%). Data collected from 4,700 respondents are tested against the research model by using structural equation modeling.

4. Results and Discussion

Industry 4.0 technologies may beneficially impact the lifecycle management of commodities. (Rosa et al., 2019) Horizontal chains of command, adjustable structures and operations, and decentralized environments can constitute an agile company in conformity with Industry 4.0 standards. (Veile et al., 2019) A pivotal component of Industry 4.0 is human-centricity. (Kaasinen et al., 2019) Decreasing the servicing, infrastructure, and reorganization expenses, while furthering the adjustability are standard objectives for networking systems in Industry 4.0 (Zeng et al., 2019) (Tables 1-7)

5. Conclusions and Implications

In Industry 4.0, the technological developments are enabling competitive value creation in all advancements phases (Andrei et al, 2016; Balica, 2018; Neary et al., 2018; Popescu Ljungholm, 2018; Popescu et al., 2019), via cost-effective design, resource-efficient manufacturing operations, and repetitive and synergetic production systems. (Machado et al, 2019) Networking taking place in the physical realm may transform the processing performance in the virtual one, in a determining link that can be harnessed for the permanent enhancement of operations. (Delicato et al, 2019) Industry 4.0 attempts to assimilate production processes into a coherent digital unit (Androniceanu and Popescu, 2017; Lazaroiu, 2018; Nica et al, 2014; Popescu et al, 2018; Valaskova et al, 2018) by integrating adjustability, agility, reorganization, and soundness. (Barenji et al, 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 the Slovak Research and Development Agency under Grant no. APW-17-0546: Variant Comprehensive Model of Earnings Management in Conditions of The Slovak Republic as an Essential Instrument of Market Uncertainty Reduction.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare 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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Malcolm Gray-Hawkins

m.gray-hawkins@aa-er.org

The Internet-enabled Collective Intelligence Laboratory

at ISBDA, Glasgow, Scotland

(corresponding author)

Lucia Michalkova

lucia.michalkova@fpedas.uniza.sk Faculty of Operation and Economics of Transport and Communications, Department of Economics, University of Zilina, Zilina, Slovak Republic

Petr Suler

petr.suler@cez.cz

The School of Expertness and Valuation, The Institute of Technology and Business in Ceske Budejovice, Czech Republic

Natalia A. Zhuravleva

zhuravleva_na@mail.ru

Faculty of Economics and Management, Department of Transport Economics, Emperor Alexander I St. Petersburg State Transport University, St. Petersburg, Russia

Received 6 August 2019 * Received in revised form 10 December 2019

Accepted 12 December 2019 * Available online 15 December 2019

doi:10.22381/EMFM14420194
Table 1 Progress in the last year in implementing Industry 4.0
applications/strategies aimed at... (%)

                             Good/substantial            No or only
                             progress or implementation  limited
                             almost complete             progress

Suppliers... improving       47                          53
operational effectiveness
Suppliers... exploring       54                          46
new business models
Manufacturers... improving   48                          52
operational effectiveness
Manufacturers... exploring   38                          62
new business models

Sources: McKinsey; our survey among 4,700 individuals conducted June
2019.

Table 2 Digital intensity parameters (%)

Majority of processes digitized

                        Digital Masters with  Beginners with
                        more than 50%         more than 50%
                        processes digitized   processes digitized

Performance management  92                    36
Maintenance management  89                    33
Production              86                    32
Inventory management    83                    31
Quality management      80                    35

High leverage of technologies in operations

                               Digital Masters    Beginners highly
                               highly leveraging  leveraging the
                               the technology     technology

Big data                       89                 23
Mobility and                   86                 26
augmented reality
Industrial Internet of Things  78                 28
Advanced robotics              60                 24

Sources: Capgemini; our survey among 4,700 individuals conducted June
2019.

Table 3 How artificial intelligence could change the job market:
Estimated net job creation by industry sector (%, 2017-2037)

Health                               +19
Scientific and technical             +16
Communications                        +8
Hospitality                           +6
Education                             +5
Administrative and support services   -4
Other sectors                         -7
Wholesale and retail                  -8
Construction                          -8
Financial and insurance              -10
Public administration and defense    -14
Transportation and storage           -24
Manufacturing                        -30

Sources: PwC; BBC; our 2018 estimates.

Table 4 Top five prioritized Industry 4.0 applications (%)

Technology suppliers               Manufacturers
Smart energy consumption       23  Real-time supply                24
                                   chain optimization
Predictive maintenance         22  Digital quality management      23
Digital quality management     19  Predictive maintenance          20
Remote monitoring and control  18  Digital performance management  19
Real-time supply               18  Remote monitoring               14
chain optimization                 and control

Sources: McKinsey; our survey among 4,700 individuals conducted June
2019.

Table 5 How much of a priority is digital manufacturing on your
company's direction agenda? (%)

         Top priority  Average or low priority

Brazil   76            24
China    91             9
France   62            38
Germany  74            26
India    92             8
Japan    36            64
USA      69            31

Sources: McKinsey; our survey among 4,700 individuals conducted June
2019.

Table 6 Data monetization strategies (%)

Risk mitigation and fraud detection  74
Customer targeting                   69
Free value-add services              66
Product or service customization     68
Data syndication to customers        64

Sources: BDO; our survey among 4,700 individuals conducted June 2019.

Table 7 Current level of process integration (%)

End-to-end process integration with outside suppliers    4
Functional silos                                         6
Some process integration with outside suppliers         12
Some process integration                                34
End-to-end process integration within the organization  44

Sources: BDO; our survey among 4,700 individuals conducted June 2019.
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Author:Gray-Hawkins, Malcolm; Michalkova, Lucia; Suler, Petr; Zhuravleva, Natalia A.
Publication:Economics, Management, and Financial Markets
Date:Dec 1, 2019
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