Designing The Estimation of The Need for Spare Parts and Inventory Policy on The D32 CP8 Compressor Machine

  • Herlambang Prasetyo Nugroho Faculty Industrial Engineering, Telkom University
  • Fransiskus Tatas Dwi Atmaji Faculty Industrial Engineering, Telkom University
  • Nopendri Nopendri Faculty Industrial Engineering, Telkom University
Abstract views: 4 , PDF downloads: 5
Keywords: compressor, maintenance, risk matrix, RCS, min-max stock

Abstract

Based on damage data owned by PT XYZ, the compressor engine that has a history of high damage is the D32 CP8 Compressor Engine. Critical components of the D32 CP 8 Compressor are determined using a risk matrix. The critical components selected from the D32 CP8 Compressor Engine are Screw Motor, Refrigerant Air Cooler, and Cylinder Bearing Oil Cooler. This study uses the Reliability Centered Spares (RCS), Min-Max Stock, and Reorder Point (ROP) methods. The data collection and processing results obtained the need for critical components in the next 1 year based on the MTTF critical component data. These calculations show that the value of the required spare parts for the Motor Screw, Refrigerant Air Conditioner, and Cylinder Bearing Oil Cooler in a year is 8 components. The minimum Stock of the Screw Motor is 3 components, the maximum stock is 8 components, ReOrder Point point is 4 components. The minimum stock of 4 component air coolers, maximum stock of 7 components, ReOrder Point when 3 components. The minimum stock of a Cylindrical Bearing Oil Cooler is 2 components, the maximum stock is 6 components, and the ReOrder Point is when there are 2 components.

Author Biographies

Fransiskus Tatas Dwi Atmaji, Faculty Industrial Engineering, Telkom University

Faculty Industrial Engineering, Telkom University

Nopendri Nopendri, Faculty Industrial Engineering, Telkom University

Faculty Industrial Engineering, Telkom University

References

C. F. Angelina, F. T. D. Atmaji, and B. Santosa, “Spare Part Requirement and Inventory Policy for Rovema’s 1 Machine using Reliability Centered Spare (RCS) and Min-Max Stock Methods,” in IOP Conference Series: Materials Science and Engineering, 2020, vol. 722, no. 1, p. 12017.

Q. Lv, X. Yu, H. Ma, J. Ye, W. Wu, and X. Wang, “Applications of machine learning to reciprocating compressor fault diagnosis: A review,” Processes, vol. 9, no. 6. 2021, doi: 10.3390/pr9060909.

G. Qi, Z. Zhu, K. Erqinhu, Y. Chen, Y. Chai, and J. Sun, “Fault-diagnosis for reciprocating compressors using big data and machine learning,” Simul. Model. Pract. Theory, vol. 80, 2018, doi: 10.1016/j.simpat.2017.10.005.

S. M. Hipple, H. Bonilla-Alvarado, P. Pezzini, L. Shadle, and K. M. Bryden, “Using machine learning tools to predict compressor Stall,” J. Energy Resour. Technol. Trans. ASME, vol. 142, no. 7, 2020, doi: 10.1115/1.4046458.

S. J. m. Tahir and E. AKIN, “Predictive Maintenance of Compressor Using ThingSpeak IoT Platform,” Int. J. Sci. Res. Manag., vol. 9, no. 06, 2021, doi: 10.18535/ijsrm/v9i06.ec01.

S. Qiu, X. Ming, M. Sallak, and J. Lu, “Joint optimization of production and condition-based maintenance scheduling for make-to-order manufacturing systems,” Comput. Ind. Eng., vol. 162, 2021, doi: 10.1016/j.cie.2021.107753.

V. Aulia, J. Alhilman, and S. Nurdinintya-Athari, “Proposed Maintenance Policy and Spare Part Management of Goss Universal Printing Machine with Reliability Centered Maintenance, Reliability Centered Spares, and Probabilistic Inventory Model,” in Proceeding of 9th International Seminar on Industrial Engineering and Management, 2016, pp. 81–86.

F. B. B. Nasution and N. E. N. Bazin, “Creating model with system breakdown structure for system dynamics,” Indones. J. Electr. Eng. Comput. Sci., vol. 6, no. 2, 2017, doi: 10.11591/ijeecs.v6.i2.pp447-456.

O. A. Makinde, K. Mpofu, B. I. Ramatsetse, M. K. Adeyeri, and S. P. Ayodeji, “A maintenance system model for optimal reconfigurable vibrating screen management,” J. Ind. Eng. Int., vol. 14, no. 3, 2018, doi: 10.1007/s40092-017-0241-7.

I. Hussain, S. U. Rahman, A. Zaheer, and S. Saleem, “Integrating factors influencing consumers’ halal products purchase: Application of theory of reasoned action,” J. Int. Food Agribus. Mark., vol. 28, no. 1, 2016, doi: 10.1080/08974438.2015.1006973.

M. Rucco, F. Giannini, K. Lupinetti, and M. Monti, “A methodology for part classification with supervised machine learning,” Artif. Intell. Eng. Des. Anal. Manuf. AIEDAM, vol. 33, no. 1, 2019, doi: 10.1017/S0890060418000197.

K. K. Sudheesh, G. Asha, and K. M. Jagathnath Krishna, “On the mean time to failure of an age-replacement model in discrete time,” Commun. Stat. - Theory Methods, vol. 50, no. 11, 2021, doi: 10.1080/03610926.2019.1672742.

N. Yunus, M. Othman, and Z. M. Hanapi, “Mean time to failure analysis in shuffle exchange systems,” Int. J. Eng. Adv. Technol., vol. 9, no. 1, 2019, doi: 10.35940/ijeat.A2644.109119.

R. Florin, A. G. Zadeh, P. Ghazizadeh, and S. Olariu, “Towards Approximating the Mean Time to Failure in Vehicular Clouds,” IEEE Trans. Intell. Transp. Syst., vol. 19, no. 7, 2018, doi: 10.1109/TITS.2017.2710353.

A. P. Kinanthi, D. Herlina, and F. A. Mahardika, “Analisis Pengendalian Persediaan Bahan Baku Menggunakan Metode Min-Max (Studi Kasus PT.Djitsoe Indonesia Tobacco),” PERFORMA Media Ilm. Tek. Ind., vol. 15, no. 2, 2016.

T. Iqbal, D. Aprizal, and M. Wali, “Aplikasi Manajemen Persediaan Barang Berbasis Economic Order Quantity (EOQ),” J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), vol. 1, no. 1, pp. 48–60, 2017.

T. Lukmana and D. T. Yulianti, “Penerapan Metode EOQ dan ROP (Studi Kasus: PD. Baru),” J. Tek. Inform. dan Sist. Inf., vol. 1, no. 3, 2015.

G. G. Kencana, “Analisis Perencanaan dan Pengendalian Persediaan Obat Antibiotik di RSUD Cicalengka Tahun 2014,” J. Adm. Rumah Sakit Indones., vol. 3, no. 1, 2018.

A. D. Susanto and H. H. Azwir, “Perencanaan Perawatan Pada Unit Kompresor Tipe Screw Dengan Metode RCM di Industri Otomotif,” J. Ilm. Tek. Ind., vol. 17, no. 1, 2018, doi: 10.23917/jiti.v17i1.5380.

B. F. Zakaria, M. A. Murti, and A. S. Wibowo, “Sistem Pemantauan Kompresor Udara Berbasis Internet Of Things,” eProceedings Eng., vol. 7, no. 1, 2020.

Sugiyono, metode penelitian kualntitaltif, kuallitaltif,daln R&D. Jakarta: Alfabeta, 2017.

PlumX Metrics

Published
2023-01-26