Sustainable Internet-of-Things-based Manufacturing Systems: Industry 4.0 Wireless Networks, Advanced Digitalization, and Big Data-driven Smart Production.
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)
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.
This paper was supported by Grant GE-1707348 from the Artificially Intelligent Algorithmic Systems Research Unit, Westminster, CO.
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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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
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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|Publication:||Economics, Management, and Financial Markets|
|Date:||Dec 1, 2019|
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