# The Big Potential of Big Data in Manufacturing:
## Evidence from Emerging Economies

Marko Pavlović, Ugljesa Marjanovic, Slavko Rakić, Nemanja Tasić, Bojan Lalić

► To cite this version:

Marko Pavlović, Ugljesa Marjanovic, Slavko Rakić, Nemanja Tasić, Bojan Lalić. The Big Potential of Big Data in Manufacturing: Evidence from Emerging Economies. IFIP International Conference on Advances in Production Management Systems (APMS), Aug 2020, Novi Sad, Serbia. pp.100-107, 10.1007/978-3-030-57997-5_12. hal-03635690

HAL Id: hal-03635690

https://inria.hal.science/hal-03635690

Submitted on 8 Apr 2022

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# The big potential of Big Data in manufacturing: evidence from emerging economies

Marko Pavlović, Uglješa Marjanović, Slavko Rakić, Nemanja Tasić and Bojan Lalić

¹University of Novi Sad, Faculty of Technical Sciences, Novi Sad, Serbia
m.pavlovic@uns.ac.rs

²University of Novi Sad, Faculty of Technical Sciences, Novi Sad, Serbia
umarjano@uns.ac.rs

³University of Novi Sad, Faculty of Technical Sciences, Novi Sad, Serbia
slavkorakic@uns.ac.rs

⁴University of Novi Sad, Faculty of Technical Sciences, Novi Sad, Serbia
nemanja.tasic@uns.ac.rs

⁵University of Novi Sad, Faculty of Technical Sciences, Novi Sad, Serbia
blalic@uns.ac.rs

**Abstract.** In the last years, the manufacturing sector of developed economies is going through extensive changes to adopt Industry 4.0 principles. Prior studies investigated key enabling technologies for Industry 4.0 and their applications focusing on developed economies. However, there is a lack of studies covering emerging economies (e.g., Serbia). This research provides an overview of the use of technologies for automatic storing of operational data and the exchange of operational data between different entities from the manufacturing sector. For this purpose, the Serbian dataset of 240 companies from the European Manufacturing Survey gathered in 2018 is used. The empirical results indicate that 43% of manufacturing companies are utilizing the systems that automatically record operational data, 88.3% of manufacturing companies are creating an immense amount of data through ERP systems, and 78.6% of companies are using a digital exchange with suppliers or customers. The results reveal the big potential for the Big Data in the manufacturing sector in emerging economies.

**Keywords:** Industry 4.0, Big Data, Manufacturing.

## 1 Introduction

The transformation of manufacturing systems characterizes Forth-Industrial Revolu-
tion or Industry 4.0 through deeper digitalization [1]. The concept of Industry 4.0 was
first declared as a German strategic initiative in 2013 [1]. Industry 4.0 is enabled
through the use of technologies like Cyber-Physical Systems (CPS), Internet of
Things (IoT), Cloud Computing, Big Data, and Data Science [2]. Prior studies investi-
gated key enabling technologies for Industry 4.0 and their applications focusing on
developed economies. However, there is a lack of studies covering emerging econo-
mies (e.g., Serbia). In this paper, the analysis will be conducted on how many compa-
nies from the manufacturing sector of the Republic of Serbia automatically record
data from their production and examine the potential for the use of Big Data in the
manufacturing sector of the Republic of Serbia. Moreover, we investigate which sys-
tems are used to record data automatically and in which manufacturing sectors. Our
analysis used the Serbian dataset from the European Manufacturing Survey (EMS)
conducted in 2018.

The remainder of the paper is structured as follows. A literature review is presented in Section 2. Section 3 describes the research questions and method that has been used in this paper, Section 4 presents the research results and discussion, and Section 5 presents the conclusion of this paper with identified limitations of the study and suggestions for further research.

## 2 Literature review

One of the turning points of human history is the emergence of the First-Industrial Revolution, which dramatically changed human production. The use of water and steam powers introduced mechanized industry systems in the manufacturing sector. Second-Industrial Revolution introduced the use of electrical energy in manufacturing that paved the path to mass production. Third-Industrial Revolution saw further automation in manufacturing through increased use and development of informational technologies. For the implementation of Industry 4.0 in their operation, companies can use various novel technologies that include CPS, automated robotics, Big Data analytics, and cloud computing, etc. [3]. Developed countries formed government strategies, which included Industry 4.0 as guiding principles. Germany created “High Tech Strategy 2020” and “Industry 4.0” [1], the United States “Advanced Manufacturing Partnership” [4], China “Made in China 2025” [5], and the United Kingdom “The Future of Manufacturing: A New Era of Opportunity and Challenge for the U.K.” [6]. Prior studies investigated key enabling technologies for Industry 4.0 and their applications focusing on developed economies [7]. However, there is a lack of studies covering emerging economies (e.g., Serbia). In this paper, we will focus on the connection between CPS, IoT, Big Data, and Data Science regarding Industry 4.0, specifically the manufacturing sector. To respond to constant pressure from the competitors and to improve total production performance, companies in the manufacturing industry are integrating new solutions [8]. CPS presents one of the essential pillars of Industry 4.0 implementation [9]. CPS is defined as processing technologies with the high interconnection between physical assets and computational tools [10]. CPS provides integration of objects from the virtual world with artifacts from the physical world through embedded computing technologies [11]. Regarding manufacturing, every component of CPS will be capable to sense environment, itself and to collect and transmit data and consequently take actions that will be constructed through input from the acquired information [12]. Likewise, one of the critical pillars of industry 4.0 is IoT. IoT will completely change current ways of manufacturing and set-up a path to advanced manufacturing [13], and manufacture will progress from automated to intelligent manufacturing [14]. IoT is defined as a network of interconnected devices,
systems, and services through Internet infrastructure. IoT enables mutual communica-
tion between connected devices, systems, services and objects and provides deeper
integration and joint relations among the virtual and physical world [15]. Implementa-
tion of IoT in production will inevitably be followed by the creation of massive
amounts of data from manufacturing processes and the stacking of data from various
devices will create Big Data [16]. Prediction from Cisco and Ericsson is that by 2020
there will be 50 billion devices connected to the internet and Machine Research pre-
dicted that the total number of M2M connections would grow from 5 billion in 2014
to 27 billion in 2024 [17]. There can be found a lot of various predictions about the
number of connected devices in the future. Still, all of the projections are united in the
conclusion that the number will grow substantially. Big Data is defined as dynamic
information that is generated in complex systems that is characterized by three V's,
by volume represented in enormous amount of the acquired data, by velocity repre-
sented in pace of data processing and by variety represented in data coming from
highly diverse sources [18]. Permanent acquiring, storing and exchanging of data
from different devices from miscellaneous sources and systems will form Big Data.
The manufacturing sector generates and stores a larger amount of data than any other
sector [19]. Only one particular device, from production, could generate thousands of
records within a second, which would amount to several trillion records in a year.
Beside large amount of data that is generated from the companies internally, also
horizontal integration through value networks that includes processes and data flows
between customers, suppliers and various external partners, will produce enormous
quantities of data [1] [20] [21]. Trough processing and analysis of data provided by
the implementation and use of IoT in the manufacturing sector, valuable information
can be extracted that could provide useful knowledge to companies to face the
business challenges and to recognize opportunities and form competitive advantages
[22]. Gaining insight from data is achieved through the use of artificial intelligence
technologies, like Data Science, Data Mining, and Machine Learning [23]. Using Data
Mining or Machine Learning algorithms to analyze and study collected data can sig-
nificantly improve the efficiency of companies' operations in a very efficient manner
[24]. In the manufacturing sector Data Science can facilitate better production rates,
supply chain innovations, reduction in malfunction rates, improved decision making,
development of new business models, etc. [25] [26].

3 Research questions and method

Based on the literature review, the following research questions were proposed to
increase the available knowledge about the potential for the use of Big Data in the
manufacturing sector in developing countries:

* RQ1: What is the utilization of the systems that automatically record opera-
tional data in the manufacturing sectors of an emerging economy?

* RQ2: Which systems for automatic recording of operational data are used and which are the most common?
* **RQ3:** Is it and to what extent Electronic Data Interchange between manufacturers, suppliers, and customers is represented?

Our analysis used the Serbian dataset from the European Manufacturing Survey (EMS) conducted in 2018. EMS investigates technological and non-technological innovation in the European manufacturing sector [27]. The survey is carried out on a triennial basis and targets a random sample of manufacturing companies with more than 20 employees (NACE Rev 2 codes from 10 to 33). The dataset includes 240 companies. Concerning descriptive statistics, the sampled companies report, on average, a company size of 124 employees (SD = 207). In total, 110 companies are small firms (fewer than 50 employees), 103 companies are medium-sized (between 50 and 249 employees), and 27 companies are large enterprises (more than 250 employees).

# 4 Research results and discussion

This section presents the results of the research and discusses the results. The presentation of results follows the order of the research questions introduced in the previous section.

## 4.1 Automatically recording operating data

To provide the answer on RQ1 (i.e., What is the utilization of the systems that automatically record operational data in the manufacturing sectors of an emerging economy), we used the data gathered through the following question:

*Do you use machines or systems in your production that automatically store operating data?*

We found that a large number of manufacturing companies (103 or 43%) already in their operations use systems that automatically record operational data.

Table 1 depicts an overview of companies that automatically record operating data by manufacturing industries. Most of the companies are manufacturers of fabricated metal products, except machinery and equipment, manufacturers of rubber and plastic products and manufacturers of food products.

<table><caption>**Table 1.** companies automatically recoding data by industries</caption><thead><tr><td>NACE</td><td>Manufacturing industry</td><td>Nb. of companies</td><td>% of companies</td></tr></thead><tbody><tr><td>25</td><td>fabricated metal products, except machinery and equipment</td><td>21</td><td>20.1</td></tr><tr><td>22</td><td>rubber and plastic products</td><td>15</td><td>14.6</td></tr><tr><td>10</td><td>food products</td><td>15</td><td>14.6</td></tr><tr><td>28</td><td>machinery and equipment</td><td>7</td><td>6.8</td></tr></tbody></table>
Since the largest number of the all companies that were included in the survey are NACE 10, 22, 25 and 28, it is logical that most companies which already automatically record data are coming from specified manufacturing sectors. No significant deviations have been established by manufacturing sectors.

## 4.2 Technologies used for automatic storage of operational data

To provide the answer on RQ2 (i.e., Which systems for automatic recording of operational data are used and which are the most common), we used the data gathered through the following questions:

*Which of the following technologies are currently used in your factory?*

* Software for production planning and scheduling (e.g., ERP system)

* Near real-time production control system (e.g., Systems of centralized operating and machine data acquisition, MES – Manufacturing Execution System)

* Systems for automation and management of internal logistics (e.g., Warehouse management systems, RFID)

* Product-Lifecycle-Management Systems (PLM) or Product/Process Data Management

* Which of the specified technologies do you plan to use by 2021?

The results in Table 2 show that most of the companies use ERP and MES systems. Also, we can see that a certain number of companies plan to implement and use specified systems in their production by the year 2021.

<table><caption>**Table 2.** technologies used/planned to be used for automatic data storage</caption><thead><tr><td></td><td>Companies using 2018 [%]</td><td>Planned use by 2021</td></tr></thead><tbody><tr><td>ERP</td><td>91 [88.3]</td><td>24</td></tr><tr><td>MES</td><td>66 [64.0]</td><td>32</td></tr><tr><td>WMS</td><td>38 [36.9]</td><td>33</td></tr><tr><td>PLM</td><td>29 [28.2]</td><td>23</td></tr></tbody></table>

Results are in line with the findings from studies covering developed countries, in which most common systems used are ERP and MES [28]. So, there is no deviation regarding systems used for automatic data storage. Thus, already in the manufacturing sector of the Republic of Serbia, we have a lot of operational data recorded and stored through various specified systems. Also, we can conclude that the number of companies that automatically store operational data will rise in the future.
## 4.3 Electronic Data Interchange

To provide the answer on RQ3 (i.e., Is it and to what extent Electronic Data Interchange between manufacturers, suppliers, and customers is represented), we used the data gathered through the following questions:

*Do you use digital exchange of product/process data with suppliers / customers (Electronic Data Interchange – EDI)?*

*Do you plan to use digital exchange of product/process data with suppliers / customers (Electronic Data Interchange – EDI) by the year 2021?*

The results in Table 3 show that 81 companies use EDI, and 21 companies plan to implement EDI by the year 2021.

<table><caption>**Table 3.** overview of companies using/planning to use EDI</caption><thead><tr><td>Technology</td><td>Companies using EDI 2018 [%]</td><td>Planned use EDI by 2021</td></tr></thead><tbody><tr><td>EDI</td><td>81 [78.6]</td><td>24</td></tr></tbody></table>

The substantial number of companies exchange their data with other companies, suppliers, and customers, thus creating additional quantities of different operational data across the manufacturing sector of the Republic of Serbia. These results indicate the existence of a reasonable basis for the use of Data Science, Data Mining, and Machine Learning to extract hidden knowledge from Big Data.

## 5 Conclusion

This paper focuses on determining the potential for the use of Big Data in the manufacturing sector of the Republic of Serbia. The empirical results indicate that 43% of manufacturing companies are utilizing the systems that automatically record operational data. Our results show that 88.3% of manufacturing companies are creating an immense amount of data through ERP systems. Moreover, 78.6% of companies are using a digital exchange of product/process data with suppliers/customers. These findings indicate actionable insights for managers of manufacturing firms to expand their understanding of how to use various systems to collect data and provide a proper application for the use of Big Data analysis. Furthermore, there are significant quantities of operational data that are recorded and stored, which represents an appropriate basis for the use of Big Data in the manufacturing sector in emerging economies. Through this research, we have shown that companies from emerging economies, to some extent, already use technologies for automatic recording of operational data. We have shown that a certain number of companies, from the manufacturing sectors, exchange operational data with suppliers and customers. Moreover we have determined that part of the companies also plan to implement technologies that automatically record operational data and to exchange data with other entities. Furthermore,
this research contributes to the existing literature by showing the potential for the
creation of Big Data and the possibilities for further use in emerging economies.
This research is limited to the dataset from the manufacturing sector of the single
country. There are other emerging economies and companies from their manufactur-
ing sector that could be included in future research. Also, future research could in-
clude the potential for the use of Big Data covered through different manufacturing
sectors, company sizes, regions, etc.

References

[1] H. Kagermann, W. Wahlster, and J. Helbig, "Securing the future of German manufacturing industry: Recommendations for implementing the strategic initiative INDUSTRIE 4.0," *Final Report of the Industrie 4.0 Working Group*, 2013.

[2] M. Hermann, T. Pentek, and B. Otto, "Design principles for industrie 4.0 scenarios," in *Proceedings of the Annual Hawaii International Conference on System Sciences*, 2016, doi: 10.1109/HICSS.2016.488.

[3] A. Fedorov, E. Goloschchapov, O. Ipatov, V. Potekhin, V. Shkodyrev, and S. Zobnin, "Aspects of smart manufacturing via agent-based approach," in *Procedia Engineering*, 2015, doi: 10.1016/j.proeng.2015.01.530.

[4] Steering Committee of the Advanced Manufacturing Partnership 2.0, "Accelerating U.S. Advanced Manufacturing," *Rep. to Pres. Accel. U.S. Adv. Manuf.*, 2014, doi: 10.1111/j.0033-0124.1964.033_g.x.

[5] The State Council, "'Made in China 2025' plan issued, "People's Repub. China, 2015, doi: 10.2332/allergolint.R-07-146.

[6] Department for Trade and Industry, "Future of manufacturing: a new era of opportunity and challenge for the UK - summary report - GOV.UK," [*Online*] Gov.uk, 2013.

[7] T. D. Oesterreich and F. Teuteberg, "Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry," *Computers in Industry*. 2016, doi: 10.1016/j.compind.2016.09.006.

[8] H. Zhao, L. McLoughlin, V. Adzhiev, A. Pasko, "Why do we not buy mass customized products?"- An investigation of consumer purchase intention of mass customized products, *International Journal of Industrial Engineering and Management*, 2019, doi: 10.24867/IJIEM-2019-2-238.

[9] Y. Lu, "Industry 4.0: A survey on technologies, applications and open research issues," *Journal of Industrial Information Integration*. 2017, doi: 10.1016/j.jii.2017.04.005.

[10] R. Baheti and H. Gill, *Cyber-Physical Systems: From Theory to Practice*. 2011.

[11] V. Gunes, S. Peter, T. Givargis, and F. Vahid, "A survey on concepts, applications, and challenges in cyber-physical systems," *KSII Trans. Internet Inf. Syst.*, 2014, doi: 10.3837/tiis.2014.12.001.

[12] M. Dassisti et al., "Industry 4.0 paradigm: The viewpoint of the small and medium enterprises," *Int. Conf. Inf. Soc. Technol. ICIST 2017*, 2017.
[13] A. J. C. Trappey, C. V. Trappey, U. Hareesh Govindarajan, A. C. Chuang, and J. J. Sun, "A review of essential standards and patent landscapes for the Internet of Things: A key enabler for Industry 4.0," *Advanced Engineering Informatics*. 2017, doi: 10.1016/j.aei.2016.11.007.

[14] M. Weber, C. G. Chatzopoulos, "Digital customer experience: the risk of ignoring the non-digital experience," *International Journal of Industrial Engineering and Management*. 2019, doi: 10.24867/IJIEM-2019-3-240.

[15] Lalic B., Marjanovic U., Rakic S., Pavlovic M., Todorovic T., Medic N, "Big Data Analysis as a Digital Service: Evidence Form Manufacturing Firms," in *5th International Conference on the Industry 4.0 Model for Advanced Manufacturing*, 2020, pp. 263–269.

[16] R. Y. Zhong, X. Xu, E. Klotz, and S. T. Newman, "Intelligent Manufacturing in the Context of Industry 4.0: A Review," *Engineering*, 2017, doi: 10.1016/J.ENG.2017.05.015.

[17] C. Maple, "Security and privacy in the internet of things," *J. Cyber Policy*, 2017, doi: 10.1080/23738871.2017.1366536.

[18] D. Laney, "3D Data Management: Controlling Data Volume, Velocity, and Variety.," *Appl. Deliv. Strateg.*, 2001, doi: 10.1016/j.infsof.2008.09.005.

[19] Y. Chen, H. Chen, A. Gorkhali, Y. Lu, Y. Ma, and L. Li, "Big data analytics and big data science: a survey," *J. Manag. Anal.*, 2016, doi: 10.1080/23270012.2016.1141332.

[20] V. S. R. Guido, G. Mirabelli, E. Palermo, "A framework for food traceability: case study – Italian extra-virgin olive oil supply chain," *Int. J. Ind. Eng. Manag.*, vol. 11, no. 1, pp. 50–60, 2020.

[21] N. Medić, Z. Anišić, B. Lalić, U. Marjanović, and M. Brezocnik, "Hybrid fuzzy multi-attribute decision making model for evaluation of advanced digital technologies in manufacturing: Industry 4.0 perspective," *Adv. Prod. Eng. Manag.*, 2019, doi: 10.14743/apem2019.4.343.

[22] D. Chong and H. Shi, "Big data analytics: a literature review," *J. Manag. Anal.*, 2015, doi: 10.1080/23270012.2015.1082449.

[23] J. Siryani, B. Tanju, and T. J. Eveleigh, "A Machine Learning Decision-Support System Improves the Internet of Things' Smart Meter Operations," *IEEE Internet Things J.*, 2017, doi: 10.1109/JIOT.2017.2722358.

[24] X. V. Wang, L. Wang, A. Mohammed, and M. Givehchi, "Ubiquitous manufacturing system based on Cloud: A robotics application," *Robot. Comput. Integr. Manuf.*, 2017, doi: 10.1016/j.rcim.2016.01.007.

[25] J. Manyika, M. Chui Brown, B. B. J., R. Dobbs, C. Roxburgh, and A. Hung Byers, "Big data: The next frontier for innovation, competition and productivity," *McKinsey Glob. Inst.*, 2011.

[26] L. Da Xu and L. Duan, "Big data for cyber physical systems in industry 4.0: a survey," *Enterp. Inf. Syst.*, 2019, doi: 10.1080/17517575.2018.1442934.

[27] B. Lalic, S. Rakic, and U. Marjanovic, "Use of industry 4.0 and organisational innovation concepts in the Serbian textile and apparel industry," *Fibres Text. East. Eur.*, 2019, doi: 10.5604/01.3001.0013.0737.

[28] T. Niebel, F. Rasel, and S. Viete, "BIG data–BIG gains? Understanding the link between big data analytics and innovation," *Econ. Innov. New Technol.*, 2019, doi: 10.1080/10438599.2018.1493075.