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Economics, Management, and Financial Markets 15(2), 2020 pp. 23–29, ISSN 1842-3191, eISSN 1938-212X

Internet of Things Sensing Networks, Deep Learning- enabled Smart Process Planning, and Big Data-driven

Innovation in Cyber-Physical System-based Manufacturing

Melissa Connolly-Barker [email protected]

The Center for Digital Labor Markets at CLI, Sydney, Australia

(corresponding author) Elena Gregova

[email protected] Department of Economics,

Faculty of Operation and Economics of Transport and Communications,

University of Zilina, Zilina, Slovak Republic Victor V. Dengov

[email protected] Faculty of Economics,

Department of Economics and Economic Policy, Saint Petersburg State University,

Saint Petersburg, Russia Ivana Podhorska

[email protected] Department of Economics,

Faculty of Operation and Economics of Transport and Communications,

University of Zilina, Zilina, Slovak Republic

ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore cyber-physical system-based manufacturing. Using and replicating data from Capgemini, CompTIA, EY, Microsoft, PAC, and PwC, we performed analyses and made estimates regarding the relationship between Internet of Things sensing networks, deep learning-enabled smart process planning, and big data-driven inno- vation. Data were analyzed using structural equation modeling. JEL Codes: E24; J21; J54; J64

Keywords: Internet of Things; big data; cyber-physical system-based manufacturing

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How to cite: Connolly-Barker, M., Gregova, E., Dengov, V. V., and Podhorska, I. (2020). “Internet of Things Sensing Networks, Deep Learning-enabled Smart Process Planning, and Big Data-driven Innovation in Cyber-Physical System-based Manufacturing,” Economics, Management, and Financial Markets 15(2): 23–29. doi:10.22381/EMFM15220203

Received 6 March 2020 • Received in revised form 15 June 2020 Accepted 17 June 2020 • Available online 23 June 2020

1. Introduction As a factory eventually is highly automated, an essential component of an operational life-cycle is physical asset management. (Lee et al., 2020) Deep learning methods can identify iterative multiple assembly processes (Dușmă- nescu et al., 2016) and assess their operating times. (Chen et al., 2020)

2. Conceptual Framework and Literature Review With the harnessing of Internet of Things and big data technology, the pro- curement of demand (Drennan-Stevenson, 2019; Kliestik et al., 2020; Lăză- roiu et al., 2020; Nica, 2015; Popescu et al., 2019; Tooby, 2019) turns out to be more coherent, convenient, and swift. (Feng et al., 2020) Because of operational barriers in a realistic production environment (Andrei et al., 2016a, b; Plumpton, 2019; Reicher, 2019), it may be challenging to collect massive quantities of data (Andrei et al., 2020; Gutschow, 2019; Lăzăroiu et al., 2017; Popescu et al., 2017a, b; Rueda Garrido, 2019) for deep learning network training and enhancement. (Xiong et al., 2020)

3. Methodology and Empirical Analysis Using and replicating data from Capgemini, CompTIA, EY, Microsoft, PAC, and PwC, we performed analyses and made estimates regarding the relation- ship between Internet of Things sensing networks, deep learning-enabled smart process planning, and big data-driven innovation. Data were analyzed using structural equation modeling.

4. Results and Discussion Industrial Internet of Things in the development of enterprises approaches the next level so as to detect and monitor the operations of their wireless devices (Rathee et al., 2020), networking smart physical objects to each other and enabling elaborate equipment units to have incorporated sensors and particular modules (Krizanova et al., 2019; Majerova et al., 2020) that configure the link to the controlling center. (Ferrer, 2020) (Tables 1–9)

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Table 1 Corporate approach to technology (%) Aiming for cutting edge of technology 27 Focusing on maintenance with selective new elements 21 Balancing current operations with emerging adoption 52 Sources: CompTIA; our survey among 3,600 individuals conducted February 2020. Table 2 Possible results of technology backlash. Select all that apply. (%) Increases likelihood of government regulation 52 Greater scrutiny from customers 47 Downstream effects for small tech firms 44 Potential to inhibit innovation 39 May deter young people from working in tech 30 Negative impact to employee morale/productivity 26 Sources: CompTIA; our survey among 3,600 individuals conducted February 2020. Table 3 Share of organizations with advanced data capabilities. Select all that apply. (%) We can store, retrieve and analyze the data at all levels of the value chain 49 We have the required methods and tools to scan and create digital mock-ups of our existing assets

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We have established a data governance framework governing the data flow, access control, and data retention

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We have accurate digital mock-ups of our plants 42 We have complete view of the data flows across all processes and all IT-OT systems

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Sources: Capgemini; our survey among 3,600 individuals conducted February 2020. Table 4 Which of the following smart factory initiatives has your organization evaluated, planned or already deployed? Select all that apply. (%) Distributed ledger technologies like blockchain and similar 62 Deep learning technologies using AI 57 Quantum computing 26 Sources: PAC; our survey among 3,600 individuals conducted February 2020. Table 5 Primary goals of personalization. Select all that apply. (%) Build greater customer loyalty 87 Target new customers with more effective digital campaigns 84 Increase customer satisfaction 83 Increase order values 80 Reduce customer churn with more effective experiences 76 Increase conversation rates. 73 Reduce marketer effort in personalization 70 Increase relevancy of the brand experience/brand relevancy 67 Sources: Microsoft; our survey among 3,600 individuals conducted February 2020.

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Table 6 What companies need to do in a smart products/factories world. Select all that apply. (%) Increase worker responsiveness to data insights 88 Train and hire people who can generate value from data and analytics 86 Facilitate sensors are adopted at scale 84 Identify new sensor data-based services 81 Target fully-harmonized sensor environment and corporate data lake 76 Foster a data- and analytics-centered mindset and culture 80 Remain at leading edge of technologies that facilitate sensor-led business transformation, including AI, the cloud, robotics and 5G

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Promote use of soft sensors 84 Target greater customization of products and services 87 Continuously improve products and services based on demand and feedback

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Reduce equipment downtime through predictive maintenance 84 Improve quality control 86 Reduce waste 82 Improve understanding of cost structures 79 Sources: EY; our survey among 3,600 individuals conducted February 2020. Table 7 Are manufacturers adequately prepared to deal with cybersecurity concerns? Select all that apply. (%) Data from each device on the network is analyzed only after the device is authenticated

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We have created a Security Operations Center to prevent cyberattacks 44 We conduct security audits of our networks periodically 39 We ensure that all the communications between the devices (sensors) and the servers are always encrypted

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We have developed a separate security strategy for smart factory implementation

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Our smart factory devices are “air-gapped”, i.e. they do not have a direct connection to public internet

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We have quantified the exposure to cyber risk for major components of our smart factory initiatives

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Sources: Capgemini; our survey among 3,600 individuals conducted February 2020. Table 8 What are the biggest challenges with regard to the utilization of data analytics? Select all that apply. (%) Lack of skills and competencies in your company's workforce 47 Lack of analytical methods or algorithms to be applied 39 Poor existing data quality 38 Uncertainty regarding data property or data security 32 Lack of top management support or willingness to invest 30 Sources: PwC; our survey among 3,600 individuals conducted February 2020.

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Table 9 How are data analytics capabilities organized in your company? (%) Dedicated department for data analysis serving many functions across the company

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Data analytics is embedded within specific functions 36 No significant data analysis capabilities 7 Selective, ad hoc data analysis capabilities of single employees 30 Data analysis services are outsourced and performed by external service providers

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Sources: PwC; our survey among 3,600 individuals conducted February 2020.

5. Conclusions and Implications Big data analytics is relevant in various industrial sectors because of its capacity to transform massive volumes of information into knowledge for judicious business and operational decisions. (Tao et al., 2020) Cloud manufacturing enables the harnessing of machine learning and big data technologies instantaneously, decreasing the expenses of implementing and deployment. (Morariu et al., 2020) The applications of digital twin models deal with smart manufacturing systems. (Sun et al., 2020) Survey method 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. Sampling errors and test of statistical significance take into account the effect of weighting. Stratified sampling methods were used and weights were trimmed not to exceed 3. Average margins of error, at the 95% confidence level, are +/-2%. For tabulation purposes, percentage points are rounded to the nearest whole number. The precision of the online polls was measured using a Bayesian credibility interval. An Internet-based survey software program was utilized for the delivery and collection of responses.

Data and materials availability All research mentioned has been published and data is available from respective outlets.

Funding This paper was supported by Grant GE-1937571 from the Cognitive Automation Research Unit, New Haven, CT.

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 com- mercial or financial relationships that could be construed as a potential conflict of interest.

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