Assessment of Industrial Maintenance Management Strategies on Productivity.
Keywords:
Assessment, Industrial, Maintenance, Management, Productivity.Abstract
The effectiveness of industrial operations is closely linked to the success of maintenance management techniques, which are crucial for preventing operational disruptions, minimizing equipment downtime, and preserving ideal production levels. This research examines the effects of various maintenance management strategies, including preventive, corrective, and predictive maintenance, on the productivity of industrial systems. By examining both theoretical models and practical applications, the study assesses each strategy's impact on equipment performance, cost-effectiveness, and overall production output. A quantitative approach was applied by carefully analyzing secondary data from the three-year 2019 Plant Engineering Maintenance and 2020 State of Industrial Maintenance surveys (2018-2020). Descriptive statistics, ranking analysis, comparative analysis, and correlation approaches were deployed to investigate the effectiveness of predictive, preventive, and reliability-centered maintenance processes together with as their relationship to significant productivity measures. The findings of the ANOVA and post-hoc analysis showed that, although maintenance strategies were not different between sectors, they were significantly different throughout the course of the three years that were studied. Chi-square analysis supports the significant switch in maintenance tactics between 2018 and 2020. The research's ranking analysis identifies the top maintenance techniques, difficulties, and technology as being preventive maintenance, financial restrictions, and computerized maintenance management systems, respectively. Technology's contribution to maintenance is becoming more widely acknowledged. As per the polls, the deployement of IoT and CMMS has increased from 60% in 2019 to 65% in 2020. Additionally, predictive analytics increased, suggesting a tendency toward using digital technologies to maximize productivity and efficiency. The study's findings emphasize the importance of a comprehensive strategy to management of maintenance, whereby well-considered maintenance expenditures lead to enhanced equipment reliability, longer machinery lifespans, and better overall productivity. This research provides industry practitioners with useful insights on how to develop and implement maintenance programs that support productivity targets.
References
Achouch, M., Dimitrova, M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H., & Adda, M. (2022). On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges. In Applied Sciences (Switzerland) (Vol. 12, Issue 16). https://doi.org/10.3390/app12168081
Buhr, L. & Schicktanz, S., (2022). Individual benefits and collective challenges: Experts’ views on data-driven approaches in medical research and healthcare in the German context. Big Data & Society, 9(1), p. 15.
Hamasha, M. M., Bani-Irshid, A. H., Al Mashaqbeh, S., Shwaheen, G., Al Qadri, L., Shbool, M., Muathen, D., Ababneh, M., Harfoush, S., Albedoor, Q., & Al-Bashir, A. (2023). Strategical selection of maintenance type under different conditions. Scientific Reports,
13(1). https://doi.org/10.1038/S41598-023-42751-5
Handoyo, S., Suharman, H., Ghani, E. K., & Soedarsono, S. (2023). A business strategy, operational efficiency, ownership structure, and manufacturing performance: The moderating role of market uncertainty and competition intensity and its implication on open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 9(2), 100039. https://doi.org/10.1016/J.JOITMC.2023.100039
Kumaresan, V., Saravanasankar, S., & Di Bona, G. (2024). Identification of optimal maintenance parameters for best maintenance and service management system in the
SMEs. Journal of Quality in Maintenance Engineering, 30(1). https://doi.org/10.1108/JQME-10-2022-0070
Lee, J., Ni, J., Singh, J., Jiang, B., Azamfar, M., & Feng, J. (2020). Intelligent Maintenance
Systems and Predictive Manufacturing. Journal of Manufacturing Science and Engineering, Transactions of the ASME, 142(11). https://doi.org/10.1115/1.4047856
Lee, S. M., Lee, D., & Kim, Y. S. (2019). The quality management ecosystem for predictive maintenance in the Industry 4.0 era. International Journal of Quality Innovation, 5(1). https://doi.org/10.1186/s40887-019-0029-5
Manenzhe, M. T., Telukdarie, A., & Munsamy, M. (2023). Maintenance work management process model: incorporating system dynamics and 4IR technologies. Journal of Quality in Maintenance Engineering, 29(5). https://doi.org/10.1108/JQME-10-2022-0063
Nakajima, S. (2019). Introduction to Total Productive Maintenance, Productivity Inc, Cambridge. Idling and Minor Stoppages (%) Reduced Speed Lose.
Pramesh K., Bon-Gang H., Carlos H. C., Sriya M. and Daniel P. (2019) Assessing the Implementation of Best Productivity Practices in Maintenance Activities, Shutdowns, and Turnarounds of Petrochemical Plants, Sustainability, 11, Pp 1-27, 1239; doi:10.3390/su11051239
Yang, Y. et al., 2022. Current Status and Applications for Hydraulic Pump Fault Diagnosis: A Review. Sensors, 22(24), p. 9714.