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

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 services (Campbell et al., 2017; Lazaroiu et al., 2017; Nica, 2018; Popescu et al., 2017) is in the setting up of predictive, risk-safety, and groundbreaking manufacturing systems. (Neal et al., 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 is an output of the scientific project VEGA 1/0210/19 - Research of Innovative Attributes of Quantitative and Qualitative Fundaments of the Opportunistic Earnings Modelling.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare 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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Jane Catherine Hollowell

The Center for Data-driven Automated Decision-Making at CLI, Melbourne, Australia

(corresponding author)

Boris Kollar

Faculty of Operation and Economics of Transport and Communications, Department of Economics, University of Zilina, Zilina, Slovak Republic

Jaromir Vrbka

The Institute of Technology and Business in Ceske Budejovice, The School of Expertness and Valuation, Czech Republic

Erika Kovalova

Faculty of Operation and Economics of Transport and Communications, Department of Economics, University of Zilina, Zilina, Slovak Republic

Received 17 August 2019 * Received in revised form 11 December 2019

Accepted 12 December 2019 * Available online 15 December 2019

Table 1 What impact do you believe each of the following technologies
will have on innovation and/or disruption in your industry in the next
three years? (%)

Cyber/Information security          88
Big data, analytics, algorithms     84
Application programming interface   80
Artificial intelligence             79
Internet of Things                  74
Advanced 5G wireless networks       53
Robotic Process Automation          52
Blockchain and distributed ledgers  47
Advanced robotics                   44
Quantum computing                   37
Virtual/Augmented reality           34
Autonomous vehicles                 31
Nanotechnology                      28
Biotechnology                       27
3D printing                         24

Sources: Optus Business; our survey among 4,500 individuals conducted
July 2019.

Table 2 Transformation management intensity parameters (%)

Excellence in digital skills

                    Digital Masters highly     Beginners highly skilled
                    skilled in the technology  in the technology

Analytics experts   98                         41
Cyber security      92                         32
Automation experts  66                         20

Appropriate governance for transformation

                             Digital Masters  Beginners
                             strongly agree   strongly agree

Established a roadmap to     99               43
monitor progress
Coordinated the initiatives  98               47
at organizational level
Set up committees and        97               42
decision making processes
Formulated strategy at       95               50
the top management level
Appointed a leader           91               37

Sources: Capgemini; our survey among 4,500 individuals conducted July

Table 3 Industry 4.0 levers mapped to the main value drivers (%)

Service/Aftersales    Virtually guided self-service            82
                      Remote maintenance                       79
                      Predictive maintenance                   77
Resource/ process     Smart energy consumption                 87
                      Intelligent IoTs                         84
                      Real-time yield optimization             83
Asset utilization     Routing flexibility                      82
                      Machine flexibility                      79
                      Remote monitoring and control            78
                      Predictive maintenance                   76
                      Augmented reality for MRO1 Maintenance,  74
                      repair, and operations
Labor                 Human-robot collaboration                83
                      Remote monitoring and control            81
                      Digital performance management           79
                      Automation of knowledge work             77
Inventories           In situ 3D printing                      75
                      Real-time SC optimization                73
                      Batch size 1                             71
Quality               Digital quality management               84
                      Advanced process control (APC)           82
                      Statistical process control (SPC)        78
Supply/ demand match  Data-driven design to value              85
                      Data-driven demand prediction            83
Time to market        Rapid experimentation and simulation     82
                      Concurrent engineering                   80
                      Rapid experimentation and simulation     78

Sources: McKinsey; our survey among 4,500 individuals conducted July

Table 4 What benefits do you expect from your investments in digital
technologies cumulatively over the next five years?

30% or over  14
20-29%       27
10-19%       35
0-9%         24

Sources: PwC; our survey among 4,500 individuals conducted July 2019.

Table 5 Current status of smart factory initiatives (%)

                 Early Stage  Good or better than  Struggling
                              expected progress

Beginners        22           31                   47
Conservatives    19           39                   42
Digital Masters  18           69                   13

Sources: Capgemini; our survey among 4,500 individuals conducted July

Table 6 Which of the following issues will have the greatest impact on
your organization over the next 5 years? (%, Select up to three)

Changing regulatory environment                    47
Emergence of new businesses or delivery models     44
Evolving economic trade landscape                  42
Smart autonomous technologies                      39
More powerful and tech savvy customers             38
Blurred lines between physical and digital worlds  38
Blurred lines between industries                   37
Increasing threat of cyber risk                    36
Uncertain impact on workforce                      34
Potential geopolitical instability                 27

Sources: Optus Business; Deloitte; our survey among 4,500 individuals
conducted July 2019.
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Author:Hollowell, Jane Catherine; Kollar, Boris; Vrbka, Jaromir; Kovalova, Erika
Publication:Economics, Management, and Financial Markets
Date:Dec 1, 2019
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