Artificial Intelligence-based Decision-Making Algorithms, Automated Production Systems, and Big Data-driven Innovation in Sustainable Industry 4.0.

• Author: Duft, Gerald

1. Introduction

   The merging of production, networking, and operations necessitates progressively transdisciplinary expertise for setting up an economically effective and competitive smart factory. (Peruzzini et al., 2020) Smart manufacturing is consistently validated and extensively implemented as a result of the emergent characteristics of sustainability, adjustability, and teamwork. (Zhang et al., 2020)

2. Conceptual Framework and Literature Review

   Industry 4.0 configures a smart factory typified by the thoroughgoing networking of manufacturing components and operations (Andrei et al, 2016a, b; Englund, 2019; Kliestik et al, 2020a, b; Lazaroiu and Adams, 2020; Peters et al, 2020; Popescu et al, 2018), encompassing instantaneous supervision through cyber-physical systems, accelerated harnessing of advanced robotics, intelligent and flexible production systems, leading to increased output by use of resource efficiency. (Peruzzini et al, 2020) While data-driven sustainable smart manufacturing develops, companies deploy cutting-edge monitoring tools facilitated by real-time control systems (Clark, 2020; Ionescu, 2020; Kovacova et al, 2019; Lazaroiu, 2018; Popescu et al, 2017a, b) that enable the advancement of precise planning patterns that reinforce product decision-making information systems. (Sgarbossa et al, 2020)

3. Methodology and Empirical Analysis

   Using and replicating data from AlphaWise, CompTIA, Deloitte, Globant, McKinsey, Morgan Stanley, PAC, PwC, SME, and we.CONECT, we performed analyses and made estimates regarding the link between artificial intelligence-based decision-making algorithms, automated production systems, and big data-driven innovation. The results of a study based on data collected from 5,300 respondents provide support for our research model. Using the structural equation modeling, we gathered and analyzed data through a self-administrated questionnaire.

4. Results and Discussion

   Smart factories should bolster sustainable industrial value creation, big data-driven innovation, digitized mass manufacturing, and Internet of Things-based real-time production logistics. (Peruzzini et al, 2020) Industry 4.0 requires connectivity and teamwork guidelines by collecting feedback from consumers and supplying swift added value to deep learning-assisted smart process planning. (Oztemel and Gursev, 2020) (Tables 1-13)

   Articulating an Industry 4.0 setting is typified by the urgency to configure a vertical networking of product decision-making information systems (Du?manescu et al., 2016; Kliestik et al., 2018; Kral et al, 2019; Lazaroiu et al., 2020a, b; Popescu, 2018), and the connection of smart logistics, manufacturing, marketing, and services, across a robust needs-oriented and customized deep learning-assisted process planning. (Peruzzini et al, 2020) Big data in cyber-physical systems covers huge heterogeneous input flows collected from autonomous sources and assessed in shared information storage systems. (Gifty et al, 2019) Smart maintenance of facilities and equipment shapes the coherence of automated manufacturing systems reinforced by Industry 4.0 wireless networks. (Senechal and Trentesaux, 2019)

   Robust horizontal integration through groundbreaking value-creation networks encompasses connection between business partners and end users, in addition to cutting-edge business and collaboration patterns. (Peruzzini et al., 2020) A thorough assimilation of Industry 4.0 technologies in Internet of Things-based real-time production logistics is instrumental for data-driven sustainable smart manufacturing. (Sgarbossa et al., 2020) Collected customer requirement data are inspected and optimized with input from each phase of the product life cycle. (Feng et al., 2020) Manufacturing companies may not handle the unprocessed data gathered by breakthrough technological infrastructures as a consequence of their intricacies and massive volumes. (Park et al., 2020) Technological and scientific developments articulate the rise of advanced prospects for cyber-physical manufacturing systems by use of artificial intelligence-based decision-making algorithms. (Da Silva et al., 2020)

5. Conclusions and Implications

   Industry 4.0 is established on Internet of Things-based real-time production logistics by use of cutting-edge technologies that improve sustainable manufacturing: isolated, advanced machines and/or cells are connected to carry out a thoroughly integrated and automated production stream, resulting in increased performances and refashioned production relationships. (Peruzzini et al., 2020) Service discovery and distribution in Industry 4.0 supply on-demand manufacturing capabilities to satisfy personalized production requirements. (Zhang et al., 2020) Companies should configure their business model in circular economy around sustainable development that diminishes use of natural resources and perpetuates the determining features of the natural world. (Centobelli et al., 2020) As limitations, this article...