Big data and analytics software applications in manufacturing implementations: Industry 4.0

Chuks Medoh, Charles Mbohwa

Abstract


Big Data and Analytics is recognized as one of the pillars of Industry 4.0. It enables real-time data exchange, automation, and intelligent decision-making across factories, supply chains, and entire value networks. Therefore, Data analysis tools and applications are vital in the manufacturing sector implementations. These tools give manufacturing sectors a competitive edge through data-driven decision-making. A constraint could be choosing an appropriate data analysis tool tailored to an industrial use. To assist manufacturing sectors with decision making, this paper presents a methodology for reviewing, comparing, and evaluating data analytics tools. With a discussion of significant case examples, the paper, additionally covers the basic concepts of data analytics, its operating principles, and emerging trends for sustainability. When selecting a data analysis tool, the analytic hierarchy method offers a framework for evaluating illustrative application examples. These provide a novel perspective that improves upon the shortcomings of previous insights on data analysis tool selection.

 

Keywords: Analytic hierarchy process, Data analysis tools, Decision-making, Industry 4.0, Manufacturing sectors.

 

Received 20 March 2026

Accepted 03 June 2026

Published 10 September 2026


Keywords


Analytic hierarchy process, Data analysis tools, Decision-making, Industry 4.0, Manufacturing sectors

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References


J. B. Awotunde, K. Muduli, and B. Brahma, Computational Intelligence for Analysis of Trends in Industry 4.0 And 5.0. Boca Raton: Auerbach Publications, 2025. doi: 10.1201/9781003533023.

W. Babel, Industry 4.0, China 2025, IoT. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. doi: 10.1007/978-3-658-37852-3.

D. G. S. Pivoto, L. F. F. de Almeida, R. da Rosa Righi, J. J. P. C. Rodrigues, A. B. Lugli, and A. M. Alberti, “Cyber-Physical Systems Architectures for Industrial Internet of Things Applications in Industry 4.0: A Literature Review,” Journal of Manufacturing Systems, vol. 58, pp. 176–192, Jan. 2021, doi: 10.1016/j.jmsy.2020.11.017.

S.-L. Taguimdje, S. Wamba, J. R. Kamdjoug, and C. E. Wanko, “Influence of Artificial Intelligence (AI) on Firm Performance: The Business Value of AI-based Transformation Projects,” Business Process Management Journal, vol. 26, no. 7, pp. 1893–1924, May 2020, doi: 10.1108/BPMJ-10-2019-0411.

P. Mikalef, J. Krogstie, I. O. Pappas, and P. Pavlou, “Exploring the Relationship Between Big Data Analytics Capability and Competitive Performance: The Mediating Roles of Dynamic and Operational Capabilities,” Information & Management, vol. 57, no. 2, p. 103169, May 2020, doi: https://doi.org/10.1016/j.im.2019.05.004.

D. Pamucar, V. Simic, Ö. F. Görçün, and H. Küçükönder, “Selection of the Best Big Data Platform Using COBRAC-ARTASI Methodology With Adaptive Standardized Intervals,” Expert Systems with Applications, vol. 239, p. 122312, Apr. 2024, doi: 10.1016/j.eswa.2023.122312.

A. Sarkar and S. S. Goswami, “A Systematic Review of MCDM Techniques for Decision-Making in Smart Manufacturing Systems Under Industry 4.0,” Management Science Advances, vol. 3, no. 1, pp. 290–306, Apr. 2026, doi: 10.31181/msa31202649.

W. Nyikana and T. Iyamu, “A Guide for Selecting Big Data Analytics Tools in an Organisation,” Proceedings of the Annual Hawaii International Conference on System Sciences, 2022, doi: 10.24251/hicss.2022.664.

S. Fosso Wamba and D. Mishra, “Big Data Integration With Business Processes: A Literature Review,” Business Process Management Journal, vol. 23, no. 3, pp. 477–492, Jun. 2017, doi: 10.1108/bpmj-02-2017-0047.

Y. Lu, “The Current Status and Developing Trends of Industry 4.0: A Review,” Information Systems Frontiers, vol. 27, no. 1, Nov. 2021, doi: 10.1007/s10796-021-10221-w.

V. P. Gupta, “Smart Sensors and Industrial IoT (IIoT): A Driver of the Growth of Industry 4.0,” Internet of Things, pp. 37–49, 2021, doi: 10.1007/978-3-030-52624-5_3.

A. R. Santhi and P. Muthuswamy, “Industry 5.0 Or Industry 4.0S? Introduction to Industry 4.0 And a Peek Into the Prospective Industry 5.0 Technologies,” International Journal on Interactive Design and Manufacturing (IJIDeM), vol. 17, no. 2, pp. 947–979, Feb. 2023, doi: 10.1007/s12008-023-01217-8.

M. Y. Santos et al., “A Big Data Analytics Architecture for Industry 4.0,” Advances in Intelligent Systems and Computing, pp. 175–184, 2017, doi: 10.1007/978-3-319-56538-5_19.

G. Sariyer, S. K. Mangla, Y. Kazancoglu, C. Ocal Tasar, and S. Luthra, “Data Analytics for Quality Management in Industry 4.0 From a MSME Perspective,” Annals of Operations Research, vol. 350, no. 2, Aug. 2021, doi: 10.1007/s10479-021-04215-9.

H. Castro et al., “Data Science for Industry 4.0 And Sustainability: A Survey and Analysis Based on Open Data,” Machines, vol. 11, no. 4, p. 452, Apr. 2023, doi: 10.3390/machines11040452.

S. Jaskó and T. Ruppert, “The Future of Manufacturing and Industry 4.0,” Applied Sciences, vol. 15, no. 9, pp. 4655–4655, Apr. 2025, doi: 10.3390/app15094655.

A. M. Ojeda, J. B. Valera, and O. Diaz, “Artificial Intelligence of Big Data for Analysis in Organizational Decision-Making,” Global Journal of Flexible Systems Management, vol. 26, no. 3, Jul. 2025, doi: 10.1007/s40171-025-00450-2.

P. Tambare, C. Meshram, C.-C. Lee, R. J. Ramteke, and A. L. Imoize, “Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review,” Sensors, vol. 22, no. 1, p. 224, Dec. 2021.

W. Zhao, H. Chen, and A. Bulis, “How Are Industry 4.0 Technologies Transforming a Sustainable Society Across Industries?,” Digital Transformation and Society, vol. 4, no. 3, May 2025, doi: 10.1108/dts-11-2024-0225.

Z. Zong and Y. Guan, “AI-Driven Intelligent Data Analytics and Predictive Analysis in Industry 4.0: Transforming Knowledge, Innovation, and Efficiency,” Journal of the knowledge economy, vol. 16, no. 1, pp. 1–40, 2024, doi: 10.1007/s13132-024-02001-z.

M. Forozandeh and A. Mohammadnezhadiyan, “Industry 4.0 Technologies Transforming Operations in the Oil and Gas Industry: An Overview,” The IUP Journal of Operations Management, vol. 24, no. 2, pp. 5–22, May 2025, doi: 10.71329/iupjom/2025.24.2.5-22.

S. Thangamani and R. Saranya, “Future Trends in Data Analytics,” Advances in systems analysis, software engineering, and high performance computing book series, pp. 289–306, Jul. 2024, doi: 10.4018/979-8-3693-3609-0.ch013.

R. Lovelace, J. Nowosad, and J. Muenchow, Geocomputation With R. Boca Raton: Chapman and Hall/CRC, 2025. doi: 10.1201/9781003280569.

R. S. Mangrulkar and P. V. Chavan, “Introduction to Analytics,” Predictive Analytics with SAS and R, pp. 1–30, 2025, doi: 10.1007/979-8-8688-0905-7_1.

D. Mortelmans, Doing Qualitative Data Analysis With NVivo. Cham: Springer Nature Switzerland, 2024. doi: 10.1007/978-3-031-66014-6.

J. Rogel-Salazar, Data Science and Analytics With Python. Boca Raton: Chapman and Hall/CRC, 2025. doi: 10.1201/9781003482062.

T. A. Runkler, Data Analytics. Wiesbaden: Springer Fachmedien Wiesbaden, 2024. doi: 10.1007/978-3-658-45951-2.

R. W. Saaty, “The Analytic Hierarchy Process—What It Is and How It Is Used,” Mathematical Modelling, vol. 9, no. 3–5, pp. 161–176, 1987, doi: 10.1016/0270-0255(87)90473-8.

C. Medoh and A. Telukdarie, “Business Process Modelling Tool Selection: A Review,” 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Dec. 2017, doi: 10.1109/ieem.2017.8289946.




DOI: https://dx.doi.org/10.21622/ACE.2026.06.2.2021

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Copyright (c) 2026 Chuks Medoh, Charles Mbohwa

Advances in Computing and Engineering

E-ISSN: 2735-5985

P-ISSN: 2735-5977

 

Published by:

Academy Publishing Center (APC)

Arab Academy for Science, Technology and Maritime Transport (AASTMT)

Alexandria, Egypt

ace@aast.edu