Role of Business Analytics in Improving Operational Efficiency: A Study on Cme Laboratories Bharat Pvt. Ltd
Authors
Prasanth K, II MBA-BA
Department of Management Studies, School of Management Studies, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai (IN)
Dr.M.Kotteeswaran
Associate Professor & Research Supervisor, School of Management Studies, Vels Institute of Science, Technology and Advanced Studies (IN)
Article Information
DOI: 10.51583/IJLTEMAS.2026.150400116
Subject Category: Business Analytics
Volume/Issue: 15/4 | Page No: 1355-1366
Publication Timeline
Submitted: 2026-05-20
Published: 2026-05-20
Abstract
Businesses now, because of the amount of data available, are using business analytics more and more to work better, improve how things are done, and help them make big plans. This research looks at how business analytics helps CME Laboratories Bharat Pvt. Ltd. in Chennai, India (a condition monitoring lab that is officially ISO/IEC 17025 accredited) to be more efficient in their daily work.
The research is descriptive and a questionnaire with specific questions was used to gather information from 110 employees in different sections of the company. It considers five main areas: how well things work (operational efficiency), the issues with running things, how key performance indicators are watched, using business analytics, and how analytics generally affects the lab’s work. To be sure the answers were consistent, and to understand how different things relate to each other, the research used statistical methods, specifically Cronbach’s Alpha and Pearson correlation.
The outcome of the research is that businesses which do use analytics and see improvements in how well they’re running are very strongly and demonstrably connected. Effectively using analytics in daily work and consistently following KPI’s leads to being more efficient, getting things done faster, and making better choices. This supports the idea that analytics is becoming a really important part of a lab’s strategy.
Keywords
Business Analytics, Operational Efficiency, KPI Monitoring, Laboratory Operations, Condition Monitoring, SPSS
Downloads
References
1. Alqarni, M. A., Alharthi, R. J., Alsubhi, A. M., & Alotaibi, S. T. (2024). Industry 4.0 technologies and operational efficiency: A systematic review of analytics frameworks in industrial service organisations. International Journal of Production Research, 62(3), 101–118. [Google Scholar] [Crossref]
2. Benhanifia, K., Mesloub, A., Zerouali, B., & Sahraoui, T. (2025). Predictive maintenance analytics and operational efficiency: A systematic review across industrial sectors. Journal of Industrial Engineering and Management, 14(1), 45–67. [Google Scholar] [Crossref]
3. Brown, T. A., Williams, C. J., & Patel, D. K. (2025). KPI dashboards and data-driven monitoring for process optimisation in industrial service organisations. International Journal of Productivity and Performance Management, 74(2), 310–328. [Google Scholar] [Crossref]
4. Carvalho, T. P., Soares, F. A. A. M. N., Vita, R., Francisco, R., Basto, J. P., & Alcalá, S. G. S. (2022). A systematic literature review of machine learning algorithms for predictive maintenance in industrial environments. Computers and Industrial Engineering, 163(1), 107869. [Google Scholar] [Crossref]
5. Cheng, J., Chen, W., Tao, F., & Lin, C. L. (2022). Visual analytics and KPI dashboards in predictive maintenance and industrial decision-making environments. Industrial Management and Data Systems, 122(5), 1231–1249. [Google Scholar] [Crossref]
6. Garcia, R. M., Torres, J. A., López, F. R., & Sánchez, M. V. (2025). Condition monitoring systems and predictive maintenance strategies in industrial environments: A comprehensive review. Mechanical Systems and Signal Processing, 204(1), 110890. [Google Scholar] [Crossref]
7. George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference (4th ed.). Allyn & Bacon. [Google Scholar] [Crossref]
8. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning. [Google Scholar] [Crossref]
9. Jain, R., & Kumar, S. (2023). Business intelligence tools for process optimisation in industrial service-based organisations: A KPI-driven approach. International Journal of Operations and Production Management, 43(8), 1100–1125. [Google Scholar] [Crossref]
10. Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. [Google Scholar] [Crossref]
11. Kumar, S., Dwivedi, Y. K., & Hughes, L. (2023). Transforming condition monitoring data into actionable insights: Predictive analytics for industrial operational performance. Journal of Business Research, 156(1), 113540. [Google Scholar] [Crossref]
12. Mallioris, P., Aivazidou, E., & Bechtsis, D. (2024). Predictive maintenance in Industry 4.0 environments: Managerial strategies and operational efficiency outcomes. Computers and Industrial Engineering, 189(1), 109978. [Google Scholar] [Crossref]
13. Mohammed, A., & Ali, M. (2025). Business analytics in predictive maintenance and asset performance management: A KPI-based framework. Journal of Quality in Maintenance Engineering, 31(2), 78–95. [Google Scholar] [Crossref]
14. Murtaza, Q., Iqbal, M., & Ilyas, M. (2024). Condition monitoring from Industry 4.0 to Industry 5.0: A systematic review of analytics applications and operational impacts. Journal of Manufacturing Systems, 74(1), 205–225. [Google Scholar] [Crossref]
15. Nkosi, B., & Emuze, F. (2024). Predictive maintenance analytics and operational efficiency in modern industrial service organisations. Engineering, Construction and Architectural Management, 31(4), 650–668. [Google Scholar] [Crossref]
16. Nunes, A., & Santos, R. (2023). Business intelligence and analytics systems for operational excellence in industrial service organisations. International Journal of Information Management, 68(1), 102598. [Google Scholar] [Crossref]
17. Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill. [Google Scholar] [Crossref]
18. Orošnjak, M., Brkljač, N., Šević, D., Čavić, M., Oros, D., & Penčić, M. (2024). Machine learning and analytics in fluid condition monitoring: Applications in lubricant and coolant diagnostic testing. Tribology International, 192(1), 109267. [Google Scholar] [Crossref]
19. Patel, H., & Mehta, R. (2023). KPI-based analytics for improving operational efficiency in laboratory and industrial testing environments. Journal of Laboratory Management, 11(2), 34–52. [Google Scholar] [Crossref]
20. Saleh, M., Abdullah, R., Ismail, A., & Hassan, W. (2023). Predictive analytics for improving operational efficiency in business and industrial service organisations. Business Process Management Journal, 29(3), 700–722 [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Performance Analysis of Silicon PV Array Using Infrared Thermography and Detecting Temperature Non-Uniformities
- Dynamic Capabilities and Digital Transformation Performance: The Role of Organizational Agility in the Public Sector
- Comparative Effectiveness of Neural Mobilization Versus Positional Release Technique in Cervical Radiculopathy – An Experimental Study
- Soil Moisture Mapping of Solano Nueva Vizcaya: A Comparison and Review
- Silent Assistance for Emergencies (SAFE): A Mobile-Based Emergency Reporting Application for the Deaf and Mute Community