Penentuan Umur Dump Truck dengan Metode Optimal Replacement Interval di CV. X

Yuli Setiawannie; Nita Marikena; Sindi Sania Putri

doi:10.35447/jitekh.v9i2.422

Yuli Setiawannie Universitas Potensi Utama
Nita Marikena Universitas Potensi Utama
Sindi Sania Putri Universitas Potensi Utama

DOI: https://doi.org/10.35447/jitekh.v9i2.422

Abstract

Every asset in the operation of certain services, transportation or services will experience a change in each period of time. Replacement is made on an asset that is damaged, operating costs are too high, or a change in technology makes an equipment unusable. CV. X is a company engaged in providing services for transportation fleets, one of which is dump trucks. Today, companies are often faced with complex replacement problems and only apply the depreciation method to determine the life of their equipment. This study aims to propose determining the age of dump trucks with optimal replacement intervals and to find out the hourly usage costs incurred by the company at the optimal replacement age. This method uses the hourly cost as the basis for determining the optimal replacement life. The research data is in the form of secondary company data consisting of maintenance data such as vehicle purchase price data, fuel consumption, lubricant consumption, labor wages, annual operating hours, productivity, rental rate, interest rate value, and income tax rate. The parameters are in 1000 hour intervals. The optimal replacement life for dump trucks is in the interval of 30,000 hours – 40,000 hours or 12-16 years. The hourly usage cost at the optimal replacement life interval with the productivity approach is $159.74 and the hourly usage cost with the rental rate approach is $104.64.

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References

[1] Y. Y. Prawiro, “Penentuan Interval Waktu Penggantian Komponen Kritis Pada Mesin Volpack Menggunakan Metode Age Replacement,” J. Tek. Ind., vol. 16, no. 2, p. 92, 2017, doi: 10.22219/jtiumm.vol16.no2.92-100.
[2] O. Z. Lin and H. Miyauchi, “Optimal Replacement Time of Electrical Components Based on Constant-Interval Replacement Model: Equipment Inspection Method and Weibull Analysis,” Energy Power Eng., vol. 09, no. 04, pp. 475–485, 2017, doi: 10.4236/epe.2017.94b053.
[3] J. Boudart and M. Figliozzi, “Key variables affecting decisions of bus replacement age and total costs,” Transp. Res. Rec., no. 2274, pp. 109–113, 2012, doi: 10.3141/2274-12.
[4] V. Omoke, O. R. Nwaogbe, A. Pius, S. M. Ayam, and H. A. Hemli, “Analytical Study of Fleet Management and Vehicle Replacement Model : Evidence from the Nigerian National Petroleum Co- operation Headquarter Abuja,” vol. 2, no. 1, pp. 1–19, 2019.
[5] A. Redmer, “Vehicle replacement planning in freight transportation companies,” Eur. J. Oper. Res., no. January 2005, pp. 1–7, 2005.
[6] S. Munuhwa, M. Chibaro, J. Kanyepe, and M. Tukuta, “International Journal of Research in Education Humanities and Commerce Optimising Vehicle Fleet Replacement and Disposal for Small to Medium Transport CompaniesvIN International Journal of Research in Education Humanities and Commerce,” vol. 01, no. 01, pp. 44–55, 2020.
[7] X. Zhao, K. N. Al-Khalifa, A. Magid Hamouda, and T. Nakagawa, “Age replacement models: A summary with new perspectives and methods,” Reliab. Eng. Syst. Saf., vol. 161, no. January, pp. 95–105, 2017, doi: 10.1016/j.ress.2017.01.011.
[8] D. Nurock and C. Porteous, “Methodology to determine the optimal replacement age of mobile mining machines,” Third Int. Platin. Conf. ‘Platinum Transform., pp. 297–306, 2008.
[9] W. Feng and M. Figliozzi, “Vehicle technologies and bus fleet replacement optimization: Problem properties and sensitivity analysis utilizing real-world data,” Public Transp., vol. 6, no. 1–2, pp. 137–157, 2014, doi: 10.1007/s12469-014-0086-z.
[10] A. Adhiutama, R. Darmawan, and A. Fadhila, “Total Productive Maintenance on the Airbus Part Manufacturing,” J. Bisnis dan Manaj., vol. 21, no. 1, pp. 3–15, 2020, doi: 10.24198/jbm.v21i1.280.