Maintenance Management Analysis to Improve the Effectiveness of Single Needle Sewing Machines at PT Alendros Global Production
DOI:
https://doi.org/10.35870/ijmsit.v6i2.8280Keywords:
Maintenance Management, Total Productive Maintenance, Overall Equipment Effectiveness, Sewing Machine, Six Big Losses, Garment IndustryAbstract
The operational efficiency of production machinery is essential for improving productivity and competitiveness in garment manufacturing. PT Alendros Global Production faces operational challenges in Single Needle sewing machines, including high downtime, declining production targets, and increasing defect rates. This study evaluates maintenance management, measures machine efficiency using Overall Equipment Effectiveness (OEE)—an indicator combining equipment availability, operating performance, and product quality—identifies major production losses using the Six Big Losses framework, and develops improvement strategies based on Total Productive Maintenance (TPM) principles. A qualitative case study supported by quantitative operational data was conducted using observations, structured interviews, company documents, and operational records from July to December 2025. The findings indicate that maintenance activities remain predominantly reactive, characterized by delayed preventive maintenance, fragmented records, and ineffective spare parts inventory control. These weaknesses resulted in average downtime of 22.80% and an OEE score of 57.63%, below the 85% World Class benchmark. The Six Big Losses analysis identified sudden mechanical breakdowns and spare parts delays as the main sources of equipment losses. Recommended improvements include strengthening preventive maintenance, implementing autonomous maintenance training for operators, establishing safety stock levels, digitizing maintenance records, and improving production–maintenance communication. These measures provide a practical framework for reducing downtime, stabilizing machine performance, minimizing defects, and improving production efficiency.
Downloads
References
Amengual, M., Distelhorst, G., & Tobin, D. (2020). Global purchasing as labor regulation: The missing middle. ILR Review, 73(4), 817–840. https://doi.org/10.1177/0019793919894240
Annamalah, S., Aravindan, K. L., & Ahmed, S. (2025). Resilience in the face of uncertainty: Navigating supply chain challenges through proactive risk surveillance and mitigation strategies among SMEs in ASEAN countries. F1000Research, 13, 1037. https://doi.org/10.12688/f1000research.153654.2
Arafat, Y., Das, A., Assiri, M. A., & Mia, R. (2026). Applications of lean manufacturing in the textile garments industry. The International Journal of Advanced Manufacturing Technology, 143(9), 4735–4774. https://doi.org/10.1007/s00170-026-17713-6
BHADURY, B. (2012). Principles and Practice of Total Productive Maintenance. Allied Publishers.
Cannas, V. G., Ciano, M. P., Saltalamacchia, M., & Secchi, R. (2024). Artificial intelligence in supply chain and operations management: a multiple case study research. International Journal of Production Research, 62(9), 3333–3360. https://doi.org/10.1007/s00170-026-17713-6
Carneiro, F., Lizarelli, F. L., Barraza, M. S., Antony, J., Sony, M., & Ma, J. (2026). Exploring the impact of kaizen practices on ESG performance in manufacturing: the role of digital technologies and organizational culture. Production Planning & Control. https://doi.org/10.1080/09537287.2026.2616009
Cheah, C. K., Prakash, J., & Ong, K. S. (2020). An integrated OEE framework for structured productivity improvement in a semiconductor manufacturing facility. International Journal of Productivity and Performance Management, 69(5), 1081–1105. https://doi.org/10.1108/IJPPM-04-2019-0176
Elahi, M., Afolaranmi, S. O., Mohammed, W. M., & Martinez Lastra, J. L. (2022). Energy-based prognostics for gradual loss of conveyor belt tension in discrete manufacturing systems. Energies, 15(13), 4705. https://doi.org/10.3390/en15134705
Eusufzai, Z. (2025). Impact of Advanced Lubrication Management Systems on Equipment Longevity And Operational Efficiency In Smart Manufacturing Environments. ASRC Procedia: Global Perspectives in Science and Scholarship, 1(01), 618–653. https://doi.org/10.63125/r0n6bc88
Ewin, G., & Oye, E. (2025). Transitioning from corrective to preventive maintenance strategies: Enhancing equipment reliability and efficiency. https://doi.org/10.63125/r0n6bc88
Fani, V., Bucci, I., Bandinelli, R., & Rossi, M. (2025). Advancing human-centric zero defect manufacturing: a theoretical framework and practical insights. International Journal of Production Research, 1–18. https://doi.org/10.1080/00207543.2025.2579770
Fasuludeen Kunju, F. K., Naveed, N., Anwar, M. N., & Ul Haq, M. I. (2022). Production and maintenance in industries: impact of industry 4.0. Industrial Robot: The International Journal of Robotics Research and Application, 49(3), 461–475. https://doi.org/10.1108/IR-09-2021-0211
Fernandes, J., Reis, J., Melão, N., Teixeira, L., & Amorim, M. (2021). The role of Industry 4.0 and BPMN in the arise of condition-based and predictive maintenance: A case study in the automotive industry. Applied Sciences, 11(8), 3438. https://doi.org/10.3390/app11083438
Gomaa, A. H. (2025). Asset management excellence: a roadmap for integrating lean six sigma and ISO 55001 to achieve operational excellence. Intelligent and Sustainable Manufacturing, 2(2), 10020. https://doi.org/10.70322/ism.2025.10020
Gomez, A., Peceros, Y., & Pozo, J. C. (2025). Reducing garment manufacturing errors in a textile company using TPM and SCQM tools. In 2025 Congreso Internacional de Innovación y Tendencias en Ingeniería (CONIITI) (Vol. 1, pp. 1–6). IEEE. https://doi.org/10.1108/IJPPM-01-2025-0044
González-Viralta, D., Veas-González, I., Egaña-Bruna, F., Vidal-Silva, C., Delgado-Bello, C., & Pezoa-Fuentes, C. (2023). Positive effects of green practices on the consumers’ satisfaction, loyalty, word-of-mouth, and willingness to pay. Heliyon, 9(10). https://doi.org/10.1016/j.heliyon.2023.e20353
Holgado, M., Macchi, M., & Evans, S. (2020). Exploring the impacts and contributions of maintenance function for sustainable manufacturing. International Journal of Production Research, 58(23), 7292–7310. https://doi.org/10.1016/j.heliyon.2023.e20353
Juraev, S. (2022). Overall equipment effectiveness (OEE) analysis in CHEP. Politecnico di Torino. https://doi.org/10.1016/j.heliyon.2023.e20353
Karim, M. R. (2025). Optimizing Maintenance Strategies in Smart Manufacturing: A Systematic Review Of Lean Practices, Total Productive Maintenance (TPM), And Digital Reliability. Review of Applied Science and Technology, 4(02), 176–206. https://doi.org/10.63125/np7nnf78
Koç, B., & Eryürük, S. H. (2025). Achieving Sustainable Overall Equipment Effectiveness (OEE) in Apparel Industry With Lean and Digital Integration. Engineering Reports, 7(7), e70162. https://doi.org/10.1002/eng2.70162
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, 142(11), 110805. https://doi.org/10.1115/1.4047856
Limon, G. Q. (2023). Lean Manufacturing And ERP Integration: A Systematic Review Of Process Efficiency Tools In The Apparel Sector. Available at SSRN 5268414. https://dx.doi.org/10.2139/ssrn.5268414
Márquez, A. C. (2022). Digital maintenance management. Springer.
Muhaba, A. M. (2026). A systematic review of reliability, availability, maintainability, and dependability (RAMD) modeling for maintenance optimization in production systems: Trends, gaps, and future directions. Advances in Mechanical Engineering, 18(4), 16878132261438568. https://doi.org/10.1177/16878132261438568
Nakajima, S. (1988). Introduction to TPM: total productive maintenance. (Translation). Productivity Press, Inc., 1988, 129.
Peng, H., Wang, X., Wang, T., Liu, Y., & Wang, J. (2021). Extended failure mode and effect analysis approach based on hesitant fuzzy linguistic Z-numbers for risk prioritisation of nuclear power equipment failures. Journal of Intelligent & Fuzzy Systems, 40(6), 10489–10505. https://doi.org/10.3233/JIFS-201154
Pereira, M. de S., de Castro, B. S., Cordeiro, B. A., de Castro, B. S., Peixoto, M. G. M., da Silva, E. C. M., & Gonçalves, M. C. (2025). Factors of customer loyalty and retention in the digital environment. Journal of Theoretical and Applied Electronic Commerce Research, 20(2), 71. https://doi.org/10.3390/jtaer20020071
Romero Meza, L., & D’Urso, G. (2024). User’s dilemma: a qualitative study on the influence of Netflix recommender systems on choice overload. Psychological Studies, 69(3), 349–367. https://doi.org/10.1007/s12646-024-00807-0
Säfsten, K., & Elgh, F. (2020). The possibilities of improving maintenance through CMMS data analysis. In SPS2020: Proceedings of the Swedish Production Symposium, October 7-8, 2020 (Vol. 13, p. 249). SAGE Publications Limited. https://doi.org/10.1007/s12646-024-00807-0
Samal, R., Sahoo, S., & Badavath, N. (2023). Static stability analysis of bamboo grid-reinforced slopes. Engineering Proceedings, 56(1), 112. https://doi.org/10.3390/ASEC2023-15887
Sari, D. I., Saputra, R., & Handinata, D. (2024). SUPPLY CHAIN MANAGEMENT RESILIENCE: HOW IT WORKS TO THE SUSTAINABILITY OF CULINARY SMES THROUGH DECADE. Indonesian Journal of Business & Entrepreunership, 10(2). http://doi.org/10.17358/IJBE.10.2.367
Shaheen, B. W., & Németh, I. (2022). Integration of maintenance management system functions with industry 4.0 technologies and features—A review. Processes, 10(11), 2173.
Shamim, M. M. R. (2025). Maintenance optimization in smart manufacturing facilities: A systematic review of lean, TPM, and digitally-driven reliability models in industrial engineering. American Journal of Interdisciplinary Studies, 6(1), 144–173. https://doi.org/10.63125/xwvaq502
Sheel, C. C. (2025). Optimizing Overall Equipment Effectiveness (OEE) in the Textile Industry Bangladesh (Esquire Knit Composite Limited): A Mixed-Methods Approach to Manufacturing Efficiency. https://urn.fi/URN:NBN:fi-fe2025051948022
Syahid, M. N., Guritno, A., & Raharjo, S. T. (2026). Evaluation of Offshore Production Performance in Mature Fields: Case Study of Foxtrot Flow Station. Finance Innovations Digest, 4(1), 156–184. https://doi.org/10.54518/fid.4.1.2026.1411
Titu, A. M., Grecu, D., Pop, A. B., & Șugar, I. R. (2025). Service process modeling in practice: A case study in an automotive repair service provider. Applied Sciences, 15(8), 4171. https://doi.org/10.3390/app15084171
Velmurugan, R. S., Dhingra, T., & Velmurugan. (2021). Asset Maintenance Management in Industry. Springer.
Wascom, W. C. (2026). Reliability Modeling and Maintenance Optimization for Repairable Systems: A Stochastic Modeling and Learning-Based Approach.
West, J., Siddhpura, M., Evangelista, A., & Haddad, A. (2024). Improving equipment maintenance—switching from corrective to preventative maintenance strategies. Buildings, 14(11), 3581. https://doi.org/10.3390/buildings14113581
Xiao, D., Huang, Y., Qin, C., Liu, Z., Li, Y., & Liu, C. (2019). Transfer learning with convolutional neural networks for small sample size problem in machinery fault diagnosis. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 233(14), 5131–5143. https://doi.org/10.1177/0954406219840381
Yazdi, M. (2024). Maintenance strategies and optimization techniques. In Advances in computational mathematics for industrial system reliability and maintainability (pp. 43–58). Springer. https://doi.org/10.1007/978-3-031-53514-7_3
Zineb, M., Maryam, G., Safae, M., Aziz, S., & Mario, D. N. (2025). Total productive maintenance and Industry 4.0: a literature-based path toward a proposed standardized framework. Applied System Innovation, 8(4), 98. https://doi.org/10.3390/asi8040098
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Tita Puspita, Rini Mulyani Sari
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
