Reducing Unplanned Downtime Losses in the Shaft Assembly Process with Overall Effectiveness Measurement
Article Sidebar
Main Article Content
Abstract
Reducing the losses resulting from unplanned equipment downtime is critical to uptime and increasing overall productivity as it can lower the loss cost. The objective of this research was to propose a solution to reduce the losses arising from unplanned downtime in the shaft assembly. This mixed-methods research design was performed. Quantitative data collection was carried out with a sample of 140 respondents who took a survey, while the qualitative data were collected using participatory observations, in-depth interviews, and a focus group of 27 key informants. The data were analyzed by value-added and nonvalue-added activities, value stream mapping, structural equation model of causal factors, along with Overall Equipment Effectiveness (OEE) measurement. The research found that shaft machining activities caused downtime losses where planning was the causal factor. Therefore, a method for reducing losses was proposed through the OEE measurement. The machine downtime was found to decrease from 110.58 to 52.24 minutes per day, resulting in an increase in the machinery OEE from 61.21 to 86.24 percent. Likewise, the amount of loss cost decreased from 5.26 to 1.70 baht per piece. It is recommended that the entrepreneurs place emphasis on minimizing the losses arising from unplanned downtime, which would further improve manufacturing process efficiency and its operations.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The articles published are the opinion of the author only. The author is responsible for any legal consequences. That may arise from that article.
References
V. Ravi, Theory of Machines. New Delhi: PHI Learning, 2011, p. 40.
W. Yongpisanphob. (2020, September). Business/ Industry Outlook 2020-16: Auto Parts Industry [Online]. Available: https://www.krungsri.com/ th/research/industry/industry-outlook/Hi-tech- Industries/Auto-Parts/IO/Industry-Outlook- Auto-Parts
Thansettakij. (2021, January). Insight into “Thai AutomotiveIndustry” 2021–2023 Opportunities for 4%. [Online]. Available: https://www.than settakij.com/content/motor/463939
Vutthipong Engineering and Service Part, Ltd., “Daily report,” Vutthipong Engineering and Service Part, Ltd., Khon Kaen, Thailand, 2020 (in Thai).
P. Suttikulsombat and P. Ruangchoengchum, “The loss reduction from downtime by improving overall equipment effectiveness and speed controlling in the offset printing process,” Journal of Accountancy and Management, Mahasarakham Business School, Mahasarakham University, vol. 12, no. 3, pp. 143–156, 2020 (in Thai).
S. Preangprom, S. Tumrongsuk, and W. Anuntajalerchork, “Increasing production efficiency by maintenance planning: A case study of a conveyor belt machine the receivingdistribution room,” The Journal of KMUTNB, vol. 30, no. 4, pp. 201–215, 2020.
P. Pornprasert, S. Karawek, K. Kittisuntaropas, and P. Boonrom, “An application of preventive maintenance principles to reduce the machine breakdown time in the handbag manufacturing: A case study in Thai Silk Industry Co., Ltd,” Thai Industrial Engineering Network Journal, vol. 3, no. 2, pp. 15–21, 2017 (in Thai).
F. K. De-La-Cruz-Arcela, J. S. Martinez-Castillo, E. Altamirano-Flores, and J. C. Alvarez-Merino, “Application of lean manufacturing tools to reduce downtime in a small metalworking facility,” presented at the IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Macao, China, Dec. 15–18, 2019.
R. Wolniak, “Downtime in the automotive industry production process-cause analysis,” Quality Innovation Prosperity, vol. 23, no. 2, pp. 101–118, 2019.
Y. Yin, Y. Wang, T. C. E. Cheng, and J. Li, “Parallelmachine scheduling of deteriorating jobs with potential machine disruptions,” Omega, vol. 69, pp. 17–28, 2017.
Y. Yin, T. C. E. Cheng, and D. J. Wang, “Rescheduling on identical parallel machines with machine disruptions to minimize total completion time,” European Journal of Operational Research, vol. 252, no. 3, pp. 737–749, 2016.
A. Tashakkor, R. B. Johnson, and C. Teddlie, Foundations of Mixed Methods Research, 2nd ed. New York: Sage, 2020, p. 101.
R. Kanjanapanyakom, Industrial Work Study. Bangkok: Top Publishing, 2019 (in Thai).
R. B. Kline, Principles and Practice of Structural Equation Modeling, 4th ed. New York: The Guilford Press, 2015, p. 16.
U. Sekaran and U. Bougie, Research Methods for Business: A skill-building approach, 7th ed. Handdington: John Wiley and Sons, 2016, pp. 113.
R. B. Khanna, Production and Operations Management. Delhi: PHI Learning Private, 2015, p. 122.
S. Bell, Run Grow Transform: Integrating Business and Lean IT. New York: CRC Press, 2017, pp. 24–25.
P. L. King and J. S. King, Value Stream Mapping for the Process Industries: Creating a Roadmap for Lean Industries. Florida: CRC Press, 2017, pp. 96–98.
N. Blunch, Introduction to Structural Equation Modeling Using IBM SPSS Statistics and Amos. London: Sage, 2012, p. 294.
M. R. R. Adam, A Practical Guide of the Integrated SEM (Structural Equation Modeling) with Lisrel & Amos for Marketing & Social Sciences. Yogyakarta: Deepublish Publisher, 2018, p. 102.
K. Singh, M. Junnarkar, and J. Kaur, Measures of Positive Psychology: Development and Validation. New Delhi: Springer, 2016 pp. 28–29.
D. H. Stamatis, The OEE Primer: Understanding Overall Equipment Effectiveness, Reliability, and Maintainability. New York: CPR Press, 2017, pp. 39–41.
L. Hassani and G. Hashemzadeh, “The impact of overall equipment effectiveness on production losses in Moghan cable & Wire manufacturing,” International Journal for Quality Research, vol. 9, no. 4, pp. 565–576, 2015.
S. C. Nwanya, J. I. Udofia, O.O. Ajayi, and T. Peng, “Optimization of machine downtime in the plastic manufacturing,” Cogent Engineering, vol. 4, no. 1, pp. 1–12, 2017.
P. Boonrod and P. Klomjit, “Efficiency improvement for powder coating process in brake lining factory,” Journal of Engineering, RMUTT, vol. 18, no. 1, pp. 1–12, 2020 (in Thai).
C. Maksuk, S. Thepchit, and S. Srikijsuwan, “Business development model for metal part manufacturing for the aerospace industry,” Journal of KMUTNB, vol. 30, no. 1, pp. 170–181, 2020 (in Thai).
T. Rattanakool, “Total productive maintenance for improving overall equipment effectiveness in steel manufacturing,” Industrial Technology Lampang Rajabhat University Journal, vol. 10, no. 1, pp. 50-62, 2017 (in Thai).
Z. Ales, J. Pavlu, V. Eagat, F. Mosna, and V. Jurca, “Methodology of overall equipment effectiveness calculation in the context of industry 4.0 environment,” Maintenance and Reliability, vol. 21, no. 3, pp. 411–418, 2019.
A. Fauzi and G. Doloksaribu, “Efforts in improving overall equipment effectiveness (OEE) of weaving machine in tire cord division at tire manufacturing company in Indonesia,” International Journal of Science and Research, vol. 5, no. 7, 2016, Art. no. 7303735.
A. Hadgaonkar, S. Sapkal, and A. Arora, “Downtime reduction of end of line pump testing machine,” Proceedings of International Conference in Mechanical and Energy Technology, 2020, pp. 145–156.
T. Prasertrungruang and B. H. Hadikusumo, “Study of factors influencing the efficient management and downtime consequences of highway construction equipment in thailand,” Journal of Construction Engineering and Management, vol. 135, no. 1, pp. 2–11, 2009.