Activity-Level Waste-Time Diagnosis in Corrective Maintenance Using WAM-T: Evidence from 40 Repair Events in Manufacturing Lines
DOI:
https://doi.org/10.55826/jtmit.v5i3.1897Keywords:
Corrective maintenance, WAM-T, waste-time, VA-NNVA-NVA, maintenance efficiencyAbstract
Corrective maintenance plays an important role in maintaining machine availability and the stability of the production process. However, maintenance performance measurement still relies heavily on macro indicators such as Overall Equipment Effectiveness (OEE), Mean Time Between Failures (MTBF), and Mean Time To Repair (MTTR). These indicators are useful for evaluating system performance, but they are not sufficient to identify which activities cause time waste during the repair process. This study aims to diagnose waste time in corrective maintenance activities using Waste Analysis Mapping–Time (WAM-T). The research data came from 40 repair events on manufacturing production lines collected through field observations, Computerized Maintenance Management System (CMMS) data, and technician interviews. Each repair activity is mapped, timed, and classified into value-added (VA), necessary non-value-added (NNVA), and non-value-added (NVA). The results showed that, out of a total of 7,055 repair minutes, VA activity accounted for 2,340 minutes (33.17%), NNVA for 4,054 minutes (57.46%), and NVA for 661 minutes (9.37%). The largest types of waste come from the Skill/Knowledge Gap (260 minutes) and the Inventory (197 minutes). These findings show that WAM-T provides more detailed visibility into the repair-time structure and the points at which waste time occurs. Thus, WAM-T can be used as a practical diagnostic tool to help maintenance management determine improvement priorities based on actual data in the field.
References
[1] A. Tayal, N. S. Kalsi, M. K. Gupta, D. Y. Pimenov, M. Sarikaya, and C. I. Pruncu, “Effectiveness improvement in manufacturing industry: Trilogy study and open innovation dynamics,” J. Open Innov. Technol. Mark. Complex., vol. 7, no. 1, pp. 1–20, 2021, doi: 10.3390/joitmc7010007.
[2] J. Friederich and S. Lazarova-Molnar, “Reliability assessment of manufacturing systems: A comprehensive overview, challenges and opportunities,” J. Manuf. Syst., vol. 72, pp. 38–58, 2024, doi: 10.1016/j.jmsy.2023.11.001.
[3] C. Lundgren, J. Bokrantz, and A. Skoogh, “Performance indicators for measuring the effects of Smart Maintenance,” Int. J. Product. Perform. Manag., vol. 70, no. 6, pp. 1286–1306, 2020, doi: 10.1108/IJPPM-03-2019-0132.
[4] F. Cunha, J. Dinis-Carvalho, and A. C. Alves, “Performance measurement systems in continuous improvement environments: Barriers to effectiveness and ways to overcome them,” Sustainability, vol. 15, no. 1, p. 867, 2023, doi: 10.3390/su15010867.
[5] M. S. Tanuwijaya, Ahmad, and M. A. Saryatmo, “Penerapan Lean Maintenance pada aktivitas maintenance mesin untuk minimasi waste,” J. Mitra Tek. Ind., vol. 1, no. 1, pp. 1–11, 2022, doi: 10.24912/jmti.v1i1.18277.
[6] W. Azhari, M. A. Adi, A. C. Dewi, and H. Mahfud, “Hoist tulangan machine maintenance design using lean maintenance method: Case study of PT. XYZ,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1125, no. 1, p. 012111, 2021, doi: 10.1088/1757-899X/1125/1/012111.
[7] M. F. Hassan, S. H. Barakat, and A. A. Sobh, “Effect of applying lean maintenance in oil and gas fields,” IOP Conf. Ser. Mater. Sci. Eng., vol. 973, p. 012045, 2020, doi: 10.1088/1757-899X/973/1/012045.
[8] M. T. H. Irfan, M. Rafiquzzaman, and Y. A. Manik, “Productivity Improvement through Lean Tools in Cement Industry: A Case Study,” Heliyon, vol. 11, no. 4, p. e42057, 2025, doi: 10.1016/j.heliyon.2025.e42057.
[9] L. F. Romero, A. Arce, and F. Ballestin, “Applying value stream mapping in manufacturing: A systematic literature review,” IFAC-Pap., vol. 50, no. 1, pp. 1075–1086, 2017, doi: 10.1016/j.ifacol.2017.08.385.
[10] B. P. Sullivan, P. Ghafoorpoor Yazdi, A. Suresh, and S. Thiede, “Digital value stream mapping: Application of UWB real time location systems in manufacturing,” J. Manuf. Syst., vol. 63, pp. 98–112, 2022, doi: 10.1016/j.jmsy.2022.03.004.
[11] A. ur Rehman, M. B. Ramzan, M. Shafiq, A. Rasheed, M. S. Naeem, and M. M. Savino, “Productivity improvement through time study approach: a case study from an apparel manufacturing industry of Pakistan,” Procedia Manuf., vol. 39, pp. 1447–1454, 2019, doi: 10.1016/j.promfg.2020.01.306.
[12] W. Shou, J. Wang, P. Wu, and X. Wang, “Value adding and non-value adding activities in turnaround maintenance process: classification, validation, and benefits,” Prod. Plan. Control, vol. 31, no. 1, pp. 60–77, 2020, doi: 10.1080/09537287.2019.1629038.
[13] F. Rahman, S. Sugiono, A. A. Sonief, and O. Novareza, “Optimization maintenance performance level through collaboration of overall equipment effectiveness and machine reliability,” J. Appl. Eng. Sci., vol. 20, no. 3, pp. 917–936, 2022, doi: 10.5937/jaes0-35189.
[14] R. Nurcahyo, D. W. Maulida, D. A. Susanto, and E. Kristiningrum, “Assessment of maintenance performance using the maintenance scorecard method and prioritization of problem control strategies with the USG method,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 6, pp. 2267–2273, 2023, doi: 10.18517/ijaseit.13.6.18563.
[15] F. Ahmadi, E. Molewijk, J. Helmus, and S. RazaviAlavi, “Key result indicators (KRIs) and key performance indicators (KPIs) for maintenance management,” presented at the 2022: The Digital Reality of Tomorrow, University of New Brunswick, 2022, pp. 1–9. doi: 10.57922/tcrc.611.
[16] F. Z. Berrabah, C. Belkacemi, and L. Zemmouchi-Ghomari, “Essential and new maintenance KPIs explained,” Int. J. Educ. Manag. Eng., vol. 12, no. 6, pp. 11–20, 2022, doi: 10.5815/ijeme.2022.06.02.
[17] J. Ge, K. V. Sigsgaard, B. S. Andersen, N. H. Mortensen, J. K. Agergaard, and K. B. Hansen, “An adaptable end-to-end maintenance performance diagnostic framework,” Int. J. Qual. Reliab. Manag., vol. 41, no. 2, pp. 732–753, 2024, doi: 10.1108/IJQRM-02-2022-0071.
[18] M. Baur, P. Albertelli, and M. Monno, “A review of prognostics and health management of machine tools,” Int. J. Adv. Manuf. Technol., vol. 107, pp. 2843–2863, 2020, doi: 10.1007/s00170-020-05203-0.
[19] R. Ahmad, R. F. Mohd Amin, and S. A. Mustafa, “Value stream mapping with lean thinking model for effective non-value added identification, evaluation and solution processes,” Oper. Manag. Res., vol. 15, no. 3, pp. 1490–1509, 2022, doi: 10.1007/s12063-022-00265-9.
[20] A. Kumar, R. Kumar, and A. Gul, “Reduction of Lean Wastes by Using Value Stream Mapping: A Case Study of Textile Company in Pakistan,” Int. J. Supply Chain Manag., vol. 9, no. 2, pp. 37–61, 2024, doi: 10.47604/ijscm.2399.
[21] I. Jebbor, Z. Benmamoun, and H. Hachimi, “Optimizing manufacturing cycles to improve production: Application in the traditional shipyard industry,” Processes, vol. 11, no. 11, p. 3136, 2023, doi: 10.3390/pr11113136.
[22] P. Ghafoorpoor Yazdi, A. Azizi, and M. Hashemipour, “An empirical investigation of the relationship between overall equipment effectiveness (OEE) and manufacturing sustainability using a time study approach,” Sustainability, vol. 10, no. 9, p. 3031, 2018, doi: 10.3390/su10093031.
[23] N. T. Sheehan and N. J. Foss, “Using Porterian activity analysis to understand organizational capabilities,” J. Gen. Manag., vol. 42, no. 3, pp. 41–51, 2017, doi: 10.1177/0306307017690518.
[24] P. D. Karningsih, W. Puspitasari, and M. L. Singgih, “Cost-integrated lean maintenance to reduce maintenance cost,” J. Optimasi Sist. Ind., vol. 22, no. 1, pp. 69–80, 2023, doi: 10.25077/josi.v22.n1.p69-80.2023.
[25] H. M. Dara, A. Raut, M. Adamu, Y. E. Ibrahim, and P. V. Ingle, “Reducing non-value added (NVA) activities through lean tools for the precast industry,” Heliyon, vol. 10, no. 7, p. e29148, 2024, doi: 10.1016/j.heliyon.2024.e29148.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Rohmad Basuki, Imam Baihaqi
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
