Performance Maintenance Analysis Using QFD Method: A Case Study in Fabrication Company in Indonesia

Authors

  • Wardah Rizlan Master of Industrial Engineering Program, Mercu Buana University
  • Humiras Hardi Purba Mercu Buana University
  • Sudiyono Sudiyono Master of Industrial Engineering Program, Mercu Buana University

DOI:

https://doi.org/10.21512/comtech.v9i1.4456

Keywords:

Performance maintenance, Quality Function Deployment (QFD), Voice of the Customer (VOC), total productive maintenance

Abstract

This research aimed to determine the needs and wants of production and management team to improve the machine and performance maintenance. Voice of the Customer (VOC) method was used to capture the needs and wants of production and management team. Meanwhile, the method of Quality Function Deployment (QFD) was used to translate the needs and wants of production and management to the technical requirement that should be done to improve machine and performance maintenance. Moreover, Total Productive Maintenance (TPM) was a part of technical requirement. From the result, it is known that long breakdown machine, huge inventory of spare part, and high cost of maintenance become the first of priority of maintenance team to achieve customer satisfaction. To develop the performance maintenance, the company should standardize the procedure, identify the critical spare part, reduce the time to purchase critical spare part, and increase lifetime of spare part.

Dimensions

Plum Analytics

Author Biographies

Wardah Rizlan, Master of Industrial Engineering Program, Mercu Buana University

Engineering Department

Humiras Hardi Purba, Mercu Buana University

Master of Industrial Engineering Program

Sudiyono Sudiyono, Master of Industrial Engineering Program, Mercu Buana University

Engineering Department

References

Akbaş, H., & Bilgen, B. (2017). An integrated fuzzy QFD and TOPSIS methodology for choosing the ideal gas fuel at WWTPs. Energy, 125, 484-497. https://doi.org/10.1016/j.energy.2017.02.153

Charaf, K., & Ding, H. (2015). Is Overall Equipment Effectiveness (OEE) universally applicable? The case of Saint-Gobain. International Journal of Economics and Finance, 7(2), 241-252. https://doi.org/10.5539/ijef.v7n2p241

Chauhan, N. D., & Pancholi, N. H. (2013). Guidelines to understanding to estimate MTBF. International Journal of Scientific Research & Development, 1(3), 493-495.

Chowdhury, M. M. H., & Quaddus, M. A. (2016). A multi-phased QFD based optimization approach to sustainable service design. International Journal of Production Economics, 171, 165-178. https://doi.org/10.1016/j.ijpe.2015.09.023

Eldermann, M., Siirde, A., & Gusca, J. (2017). QFD framework for selection of industry development scenarios. Energy Procedia, 128, 230-233. https://doi.org/10.1016/j.egypro.2017.09.060

Franciosi, C., Lambiase, A., & Miranda, S. (2017). Sustainable maintenance: A periodic preventive maintenance model with sustainable spare parts management. IFAC-PapersOnLine, 50(1), 13692-13697. https://doi.org/10.1016/j.ifacol.2017.08.2536

Garg, A., & Deshmukh, S. G. (2006). Maintenance management: Literature review and directions. Journal of Quality in Maintenance

Engineering, 12(3), 205-238. https://doi.org/10.1108/13552510610685075

Goetsch, D. L., & Davis, S. B. (2014). Quality management for organizational excellence. Upper Saddle River, NJ: Pearson.

Ionica, A. C., & Leba, M. (2015). QFD integrated in new product development - Biometric identification system case study. Procedia Economics and Finance, 23, 986-991. https://doi.org/10.1016/S2212-5671(15)00454-2

Jaiswal, E. S. (2012). A case study on Quality Function Deployment (QFD). IOSR Journal of Mechanical and Civil Engineering, 3(6), 27-35. https://doi.org/10.9790/1684-0362735

Jin, J., Ji, P., & Liu, Y. (2015). Translating online customer opinions into engineering characteristics in QFD: A probabilistic language analysis approach. Engineering Applications of Artificial Intelligence, 41, 115-127. https://doi.org/10.1016/j.engappai.2015.02.006

Kamath, N. H., & Rodrigues, L. L. R. (2016). Simultaneous consideration of TQM and TPM influence on production performance: A case study on multicolor offset machine using SD Model. Perspectives in Science, 8, 16-18. https://doi.org/10.1016/j.pisc.2016.01.005

Ko, W. C. (2015). Construction of house of quality for new product planning: A 2-tuple fuzzy linguistic approach. Computers in Industry, 73, 117-127. https://doi.org/10.1016/j.compind.2015.07.008

Lin, L., Luo, B., & Zhong, S. S. (2017). Development and application of maintenance decision-making support system for aircraft fleet. Advances in Engineering Software, 114, 192-207. https://doi.org/10.1016/j.advengsoft.2017.07.001

Madureira, S., Flores-Colen, I., de Brito, J., & Pereira, C. (2017). Maintenance planning of facades in current buildings. Construction and Building Materials, 147, 790-802. https://doi.org/10.1016/j.conbuildmat.2017.04.195

Moghimi, V., Jusan, M. B. M., Izadpanahi, P., & Mahdinejad, J. (2017). Incorporating user values into housing design through indirect user participation using MEC-QFD model. Journal of Building Engineering, 9, 76-83. https://doi.org/10.1016/j.jobe.2016.11.012

Mwanza, B. G., & Mbohwa, C. (2015). Design of a total productive maintenance model for effective implementation: Case study of a chemical manufacturing company. Procedia Manufacturing, 4, 461-470. https://doi.org/10.1016/j.promfg.2015.11.063

Nakajima, S. (1984). Introduction to TPM. Cambridge: Productivity Press, Inc.

Park, S., Won, J. J., Yoon, J., Kim, K. H., & Han, T. (2016). A tiny hypervisor-based trusted geolocation framework with minimized TPM operations. Journal of Systems and Software, 122, 202-214. https://doi.org/10.1016/j.jss.2016.09.026

Popoff, A., & Millet, D. (2017). Sustainable life cycle design using constraint satisfaction problems and Quality Function Deployment. Procedia CIRP, 61, 75-80. https://doi.org/10.1016/j.procir.2016.11.147

Pramod, V. R., Devadasan, S. R., Muthu, S., Jagathyraj, V. P., & Dhakshina Moorthy, G. (2006). Integrating TPM and QFD for improving quality in maintenance engineering. Journal of Quality in

Maintenance Engineering, 12(2), 150-171. https://doi.org/10.1108/13552510610667174

Purba, H. H., Prayogo, H., Wibowo, R., Pradipta, Y., & Aisyah, S. (2017). Increasing the thermal comfort, ergonomics and safety of helmet by using of Quality Function Deployment method: A case study in Indonesia. Journal of Scientific and Engineering Research, 4(7), 184-192.

Rajesh, G., & Malliga, P. (2013). Supplier selection based on AHP QFD methodology. Procedia Engineering, 64, 1283-1292. https://doi.org/10.1016/j.proeng.2013.09.209

Schillo, R. S., Isabelle, D. A., & Shakiba, A. (2017). Linking advanced biofuels policies with stakeholder interests: A method building on Quality Function Deployment. Energy Policy, 100, 126-137. https://

doi.org/10.1016/j.enpol.2016.09.056

Shen, C. C. (2015). Discussion on key successful factors of TPM in enterprises. Journal of Applied Research and Technology, 13(3), 425-427. https://doi.org/10.1016/j.jart.2015.05.002

Singh, M., & Narwal, M. S. (2017). Measurement of Overall Equipment Effectiveness (OEE) of a manufacturing industry: An effective lean tool. International Journal of Recent Trends in Engineering and Research, 3(5), 268-275. https://doi.org/10.23883/IJRTER.2017.3222.WCT1O

Downloads

Published

2018-06-30

Issue

Section

Articles
Abstract 4981  .
PDF downloaded 802  .