EQUIPMENT BREAKDOWN VISUALIZATION: A CASE STUDY IN AUTOMOTIVE MANUFACTURING PLANT
DOI:
https://doi.org/10.55197/qjssh.v7i2.1101Keywords:
automotive manufacturing, equipment, breakdown, maintenance, dashboardAbstract
In the body shop production of automotive manufacturing, automated production systems play a critical role, since equipment reliability plays a major role in production flow and performance. Many manufacturing plants have breakdown data available, however, overall maintenance decision-making is still reactive, due to manual data recording and poor analytical capability. This study investigate the current system of equipment breakdown data management used in an automotive body shop and suggesting a dashboard-based visualisation approach to enable maintenance action plan. A qualitative case study was used using interviews, observation and document analysis as data collection method. Results show that maintenance activities was carried out manually using spreadsheet-based logs of data which limited the analysis of historical trend, critical equipment identification, and recurring failure patterns. As a suggestion, a breakdown dashboard was created to consolidate breakdown data and visualize the main maintenance statistics such as total equipment downtime, breakdown frequency, machine-based comparison, time-based trend, breakdown classification by type of problem, and total breakdown cases. The results indicate that the dashboard visualisation enhances the visualization of breakdown and provides the basis for more structured decision-making during maintenance activity. This research also suggests the need for training and continued use of dashboard as a part of maintenance work, for successful long-term maintenance.
References
[1] Ahern, M., O’sullivan, D.T., Bruton, K. (2022): Development of a framework to aid the transition from reactive to proactive maintenance approaches to enable energy reduction. – Applied Sciences 12(13): 21p.
[2] Al-Duais, F.S., Mohamed, A.B., Jawa, T.M., Sayed-Ahmed, N. (2022): Optimal periods of conducting preventive maintenance to reduce expected downtime and its impact on improving reliability. – Computational Intelligence and Neuroscience 11p.
[3] Aripin, N.M., Mezhuyev, V., Nawanir, G., Yusuf, M.F., Haron, N.R.H.M. (2023): Unveiling Key Drivers of Industry 4.0 Adaptation in CKD Automotive Manufacturing Companies: Evidence From Asia and South America. – IEEE Access 11: 136049-136062.
[4] Aripin, N.M., Nawanir, G., Hussain, S., Muhamad Tamyez, P.F., Fauzi, M.A., Alimin, N.S.N. (2024): The Path to Sustainable Lean Implementation: A Case Study in Automotive Industry. In Operations Research Forum, Cham: Springer International Publishing 6(1): 24p.
[5] Bhattacharjee, J., Roy, S. (2025): Welding Automation and Robotics. – Advanced Welding Technologies 24p.
[6] Caliskan, A., Ozturkoglu, O., Ozturkoglu, Y. (2022): Ranking of responsible automotive manufacturers according to sustainability reports using PROMETHEE and VIKOR methods. – Advanced Sustainable Systems 6(6): 28p.
[7] Çetin, M., Demirci, O.K. (2024): Wastes in Automotive Maintenance Businesses, Its Effects on Employees and Precautions. – International Journal of Automotive Science And Technology 8(4): 431-438.
[8] Chuenmee, N., Phothi, N., Chamniprasart, K., Khaengkarn, S., Srisertpol, J. (2025): Machine learning for predicting resistance spot weld quality in automotive manufacturing. – Results in Engineering 25: 17p.
[9] Costa, F., Alemsan, N., Bilancia, A., Tortorella, G.L., Staudacher, A.P. (2024): Integrating industry 4.0 and lean manufacturing for a sustainable green transition: A comprehensive model. – Journal of Cleaner Production 465: 13p.
[10] Creswell, J.W., Klassen, A.C., Plano Clark, V.L., Smith, K.C. (2011): Best practices for mixed methods research in the health sciences. – National Institutes of Health 5p.
[11] Ding, B., Ferras Hernandez, X., Agell Jane, N. (2023): Combining lean and agile manufacturing competitive advantages through Industry 4.0 technologies: an integrative approach. – Production Planning & Control 34(5): 442-458.
[12] Dubey, D., Agarwal, S., Yadav, A.K., Singh, M.K., Singh, S.K. (2024): Sustainable manufacturing systems: Optimizing resource efficiency and minimizing environmental impact. – Indian Journal of Science and Research 4(2): 93-97.
[13] Erbiyik, H. (2022): Definition of maintenance and maintenance types with due care on preventive maintenance. In Maintenance Management-Current Challenges, New Developments, and Future Directions. – IntechOpen 27p.
[14] Hinrichs, M., Prifti, L., Schneegass, S. (2024): Data-driven decision-making in maintenance management and coordination throughout the asset life cycle: an empirical study. – Journal of Quality in Maintenance Engineering 30(1): 202-220.
[15] Kang, Z., Catal, C., Tekinerdogan, B. (2022): Product failure detection for production lines using a data-driven model. – Expert Systems with Applications 202: 13p.
[16] 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.
[17] Khan, M.A.A., Hasan, M.M. (2023): Smart Hybrid Manufacturing: A Combination Of Additive, Subtractive, And Lean Techniques For Agile Production Systems. – Journal of Sustainable Development and Policy 2(04): 174-217.
[18] Kovarikova, Z., Duchon, F., Trebula, M., Nagy, F., Dekan, M., Labat, D., Babinec, A. (2023): Prototyping an intelligent robotic welding workplace by a cyber-physic tool. – The International Journal of Advanced Manufacturing Technology 125(9): 4855-4882.
[19] 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): 23p.
[20] Lemya, M. (2025): Assessment of the service sector's performance in Malaysia during the period 2020-2023. – Journal of Economics & Finance 11(1): 261-275.
[21] Liu, R., Zhang, Y., Zhou, B. (2024): Solving a Real-Life Stochastic Car Batching and Sequencing Problem With Dynamic Programming Approaches. – IEEE Transactions on Automation Science and Engineering 22: 5012-5028.
[22] Lu, Y.J., Lee, W.C., Wang, C.H. (2023): Using data mining technology to explore causes of inaccurate reliability data and suggestions for maintenance management. – Journal of Loss Prevention in the Process Industries 83: 13p.
[23] Madhav, S., Math, M.M., KVS, R.R., BW, S., AC, P.C. (2023): Exploring Effective Approaches To Minimize Downtime In Final Assembly Line Of Braking Systems. – Journal of Namibian Studies 35(S1): 3560-3592.
[24] Molęda, M., Małysiak-Mrozek, B., Ding, W., Sunderam, V., Mrozek, D. (2023): From corrective to predictive maintenance-A review of maintenance approaches for the power industry. – Sensors 23(13): 47p.
[25] Ojeda, J.C.O., De Moraes, J.G.B., Filho, C.V.D.S., Pereira, M.D.S., Pereira, J.V.D.Q., Dias, I.C.P., da Silva, E.C.M., Peixoto, M.G.M., Goncalves, M.C. (2025): Application of a Predictive Model to Reduce Unplanned Downtime in Automotive Industry Production Processes: A Sustainability Perspective. – Sustainability 17(9): 29p.
[26] Pohan, F., Saputra, I., Tua, R. (2023): Scheduling Preventive Maintenance to Determine Maintenance Actions on Screw Press Machine. – Jurnal Riset Ilmu Teknik 1(1): 1-14.
[27] Raoufi, K., Sutherland, J.W., Zhao, F., Clarens, A.F., Rickli, J.L., Fan, Z., Huang, H., Wang, Y., Lee, W.J., Mathur, N. (2024): Current state and emerging trends in advanced manufacturing: smart systems. – The International Journal of Advanced Manufacturing Technology 134(7): 3031-3050.
[28] Roberts, R., Cullinane, N. (2023): Skilled maintenance trades under lean manufacturing: Evidence from the car industry. – New Technology, Work and Employment 38(1): 103-124.
[29] Rosati, R., Romeo, L., Cecchini, G., Tonetto, F., Viti, P., Mancini, A., Frontoni, E. (2023): From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in Industry 4.0. – Journal of Intelligent Manufacturing 34(1): 107-121.
[30] Shahin, M., Chen, F.F., Hosseinzadeh, A., Zand, N. (2023): Using machine learning and deep learning algorithms for downtime minimization in manufacturing systems: An early failure detection diagnostic service. – The International Journal of Advanced Manufacturing Technology 128(9): 3857-3883.
[31] Sharma, J., Mittal, M.L., Soni, G. (2024): Condition-based maintenance using machine learning and role of interpretability: a review. – International Journal of System Assurance Engineering and Management 15(4): 1345-1360.
[32] Silva, G.M., Gomes, P.J. (2023): Lean production, green supply chain management and environmental performance: a configurational perspective based on the Portuguese context. – International Journal of Lean Six Sigma 16(2): 518-541.
[33] Stauder, M., Kühl, N. (2022): AI for in-line vehicle sequence controlling: development and evaluation of an adaptive machine learning artifact to predict sequence deviations in a mixed-model production line. – Flexible Services and Manufacturing Journal 34(3): 709-747.
[34] Tortorella, G.L., Fogliatto, F.S., Cauchick-Miguel, P.A., Kurnia, S., Jurburg, D. (2021): Integration of industry 4.0 technologies into total productive maintenance practices. – International Journal of Production Economics 240: 14p.
[35] Utku, D.H. (2023): The evaluation and improvement of the production processes of an automotive industry company via simulation and optimization. – Sustainability 15(3): 17p.
[36] West, J., Siddhpura, M., Evangelista, A., Haddad, A. (2024): Improving equipment maintenance-switching from corrective to preventative maintenance strategies. – Buildings 14(11): 16p.
[37] Yin, R. (1994): Case Study Research: Design and Methods. – Sage Publications 94p.
[38] Zehra, K., Mirjat, N.H., Shakih, S.A., Harijan, K., Kumar, L., El Haj Assad, M. (2024): Optimizing auto manufacturing: a holistic approach integrating overall equipment effectiveness for enhanced efficiency and sustainability. – Sustainability 16(7): 32p.
[39] Zhou, P., Han, M., Shen, Y. (2023): Impact of Intelligent Manufacturing on Total-Factor Energy Efficiency: Mechanism and Improvement Path. – Sustainability 15(5): 22p.
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Copyright (c) 2026 NUR AZLEEN YAHAYA, NUR SOFIA NABILA ALIMIN, NOR ROKIAH HANUM MD HARON, KAMARULZAMAN MAHMAD KHAIRAI, NORHANA MOHD ARIPIN, MOHD YUSRIZAL MOHD YUSOOF
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