Sustainable Value Creation Networks, Digitized Mass Production, and Networked Information-driven Technologies in Industry 4.0-based Manufacturing Systems.

• Date: 01 June 2020
• Author: Haskins, Peter

1. Introduction

   A smart factory necessitates manufacturing units operating as data nodes. (Lass and Gronau, 2020) Technologies activating Industry 4.0 assist manufactoring enterprises both in reducing their expenses and in improving their quality, in addition to delivery and flexibility performance, thus constituting robust grounds for competitive advantage. (Szasz et al., 2020)

2. Conceptual Framework and Literature Review

   Industry 4.0 entails the industrial automation and assimilation of cutting-edge manufacturing technologies so as to enhance labor conditions and boost productivity and quality. (Di Nardo et al., 2020) Industry 4.0 endeavors to incorporate groundbreaking advancements developed on digital technologies (Andrei et al., 2016; Ionescu, 2018; Kliestik et al., 2020; Lazaroiu, 2018; Majerova et al., 2020; Nica, 2015) together with the networking operations across them (Andrei et al., 2020; Ionescu, 2019; Krizanova et al., 2019; Lazaroiu et al., 2019; Meila, 2018; Nica et al., 2018; Popescu et al., 2017a, b), enabling companies to convey instantaneous data as regards behavior and performance. (Lezoche et al., 2020) Organizations can harness business intelligence and big data to enhance their operational performance (Durica et al., 2019; Jouet, 2018; Lazaroiu et al., 2017; Lazaroiu et al., 2020; Mihaila, 2017; Vochozka et al., 2018) and generate industrial value creation. (Bordeleau et al., 2020)

3. Methodology and Empirical Analysis

   Building our argument by drawing on data collected from Capgemini, CompTIA, IW Custom Research, Kronos, The Manufacturer, and PwC, we performed analyses and made estimates regarding production lines that are currently managed by digital platforms/technologies. Data collected from 4,200 respondents are tested against the research model by using structural equation modeling.

4. Results and Discussion

   Enterprises should implement breakthrough technologies in their business operations and handle the growing smart data streams in their value chain for adequate administration of advanced digital supply chains. (Ghadge et al., 2020) As a consequence of their self-activating data collecting capability, instantaneous sensing techniques not necessitating non-automatic material sampling are appropriate for harnessing in monitoring systems for process streamlining and as networked sensors in smart manufacturing. (Lewis Bowler et al., 2020) In Industry 4.0, human operators confront intensifying job requirements (e.g. elevated cognitive intricacy). (Van Acker et al., 2020) (Tables 1-7)

5. Conclusions and Implications

   Industry 4.0 leads to configuring cutting-edge, networked, and decentralized production systems, articulating a smooth interconnection between decisionmakers, smart machines, and products throughout the manufacturing operations driven by the digital ecosystem. (Shekhar et al., 2020) Groundbreaking supply chain platforms regulate the consequences of digital technologies, related to environmental vitality, on both economic and low-carbon performance. (Li 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...