Maximizing Business Process Efficiency in Industry 4.0: A Techno-Functional Exploration of Process Mining Tools

Hari Lal Bhaskar (), Mohammad Osama () and Reeta ()
Additional contact information
Hari Lal Bhaskar: Starex University
Mohammad Osama: Madan Mohan Malviya University of Technology
Reeta: Shri Ramswaroop Memorial University

SN Operations Research Forum, 2025, vol. 6, issue 1, 1-34

Abstract: Abstract This paper examines business process optimization through process mining in the context of Industry 4.0 using a qualitative research approach. It underscores how process mining facilitates microeconomic principles and efficiency and discusses a techno-functional approach to realizing BPM value, which can propel organizational change management. By analyzing secondary data, the study identifies key process mining tools relevant to Industry 4.0 and BPM, establishing selection criteria and exploring real-world applications. A hypothetical case study demonstrates the role of process mining in enhancing predictive maintenance and asset management by analyzing established processes. Additionally, the paper provides a tactical roadmap and a comparative framework for selecting tools aimed at optimizing or re-engineering business processes applicable across various business functions. It highlights how digitally enabled organizations can leverage data-driven insights to revamp legacy systems and achieve operational excellence.

Keywords: Techno-functional; Process mining; Business process management; Industry 4.0; Business process improvement (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s43069-025-00428-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:snopef:v:6:y:2025:i:1:d:10.1007_s43069-025-00428-x

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43069

DOI: 10.1007/s43069-025-00428-x

Access Statistics for this article

SN Operations Research Forum is currently edited by Marco Lübbecke

More articles in SN Operations Research Forum from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().