Networked, Smart, and Responsive Devices in Industry 4.0 Manufacturing Systems.

• Author: Kliestik, Tomas

1. Introduction

   How to harness the resource data of the smart manufacturing system to handle product life-cycle operations is pivotal in improving the value chain of the production sector. (Feng et al, 2020) Established software firms offer solutions that assimilate Industry 4.0 technologies in integrated platforms (Andrei et al, 2016; Cuicui, 2019; Lazaroiu, 2017; Mihaila et al., 2016; Pickard et al, 2019; Popescu Ljungholm, 2019; Wingard, 2019) while leveraging machine learning techniques. (Mourtzis, 2020)

2. Conceptual Framework and Literature Review

   Smart technologies as an essential element of Industry 4.0 integrate series and large-scale manufacturing (Andrei et al, 2020; Dusmanescu et al, 2016; Lazaroiu et al, 2019; Nica et al, 2014; Popescu, 2014; Svabova et al, 2020) of perpetual operations and material product. (Zavadska and Zavadsky, 2020) Companies deploy smart systems to thoroughly shape firm performance (Berke, 2019; Lazaroiu et al, 2017; Lazaroiu et al., 2020; Popescu et al, 2018; Szewieczek, 2019) and to maximize Industry 4.0 processes for a costeffective supply chain advantage. (Ralston and Blackhurst, 2020) Personnel and robots can share the working space synchronously without physical dissociation responsibly, bringing about mutually beneficial teamwork and cutting down times, risks, and expenses. (Perez et al, 2020)

3. Methodology and Empirical Analysis

   Building our argument by drawing on data collected from BCG, Capgemini, Deloitte, McKinsey, PwC, SME, and Software AG, we performed analyses and made estimates regarding networked, smart, and responsive devices. The structural equation modeling technique was used to test the research model.

4. Results and Discussion

   The main function of groundbreaking data and communication technology infrastructures in smart manufacturing is to gather heterogeneous digital data from production resources. (Park et al., 2020) With the concerted advancement and adoption of data and communication technologies in smart manufacturing, huge volumes of complex incongruous data are being produced, collected, and stored. (Kozjek et al, 2020) Cutting-edge manufacturing technologies chiefly designate automation by use of robotics and networked assembly lines. (Kumar et al, 2020) Traceability of individual components is key in automated manufacturing. (Wigger et al, 2020) (Tables 1-9)

5. Conclusions and Implications

   The swift advancement of data communication technology has configured the materialization of the networked realm typified by the ubiquitous integration of smart technologies. (Papagiannidis and Marikyan, 2020) Mass customization demands can be exemplarily carried out by harnessing cutting-edge Industry 4.0 technologies. (Aheleroff 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...