Cognitive Decision-Making Algorithms for Sustainable Manufacturing Processes in Industry 4.0: Networked, Smart, and Responsive Devices.

AuthorHollowell, Jane Catherine
  1. Introduction

    Companies are burdened with unpredictable customer demands and affected by worldwide competition resulting in essential alterations of current industry. (Winkelhaus and Grosse, 2019) Industry 4.0 represents the cutting-edge direction of automation and data transfer in production technologies, connecting digital and physical systems. (Qian et al., 2019) Industry 4.0 systems can supervise unassisted the manufacturing management processes. (Rossit et al., 2019) Smart, sensitive, self-managing, and self-designing ubiquitous systems can be fashioned to enhance the quality of operations. (Delicato et al., 2019)

  2. Conceptual Framework and Literature Review

    Industry 4.0 represents both a technological concern and a relevant social achievement (Andrei et al., 2016; Krizanova et al., 2019; Lazaroiu, 2018; Nica et al., 2014; Szkutnik and Szkutnik, 2018), offers prospects for performance assessment, and leads to the reorganization of management functions. (Horvath and Szabo, 2019) Industry 4.0 transition necessitates the operational assimilation of numerous IT-based groundbreaking technologies and the complete digitization of value chains. (Ghobakhloo and Fathi, 2019) By harnessing advanced technologies, more and more firms are designing cyber-physical systems that can transform the competition setting. (Tang and Veelenturf, 2019) Data analytics constitutes a high-potential way to develop information into by-products, improve decision making, make data-driven accomplishments, diminish risk, and bring to light pivotal insights. (Gurdur et al., 2019)

  3. Methodology and Empirical Analysis

    Using and replicating data from Capgemini, Deloitte, McKinsey, Optus Business, and PwC, we performed analyses and made estimates regarding Industry 4.0 levers mapped to the main value drivers (%), current status of smart factory initiatives (%), and transformation management intensity parameters (%). Data were analyzed using structural equation modeling.

  4. Results and Discussion

    Industry 4.0 is a system of innovative digital and physical technologies that provide cutting-edge values and services to individuals and companies (Pacchini et al., 2019), bringing about disruptive options and weaknesses that have to be regulated adequately to positively shape both business and society. (Buchi et al., 2020) In Industry 4.0, decision making is for the most part dispersed, and system components make self-governing, carefully designed operations. (Hofmann et al., 2019) (Tables 1-6)

  5. Conclusions and Implications

    Industry 4.0 technologies enable and activate data collecting and interaction. (Tortorella et al., 2020) Industry 4.0-related transformations lead to the alteration of work composition, labor conditions, and task design, which consequently impact personnel planning. (Veile et al., 2019) The accomplishment of smart products and...

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