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All Journal JITTER (Jurnal Ilmiah Teknologi Informasi Terapan)
Oktavian Endiyansyah
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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EVALUASI EKONOMIS PENGGANTIAN MESIN INDUSTRI DENGAN DYNAMIC PROGRAMMING: APLIKASI PADA CV CTR: ECONOMIC EVALUATION OF INDUSTRIAL MACHINE REPLACEMENT WITH DYNAMIC PROGRAMMING: APPLICATION TO CV CTR Fadly Rizky Dermawan; Dony Sastra Danuatmaja; Oktavian Endiyansyah; Febe Seren; Didit Damur Rochman
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 12 No. 1 (2025)
Publisher : Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33197/jitter.vol12.iss1.2025.3248

Abstract

Spinning machines play a crucial role in the production process. However, over time, increasing machine age and usage frequency lead to a decline in operational efficiency and a rise in operating costs. To optimize asset management, this study performs calculations by considering machine age, maintenance costs, salvage value, and annual operational revenue. This research aims to formulate an optimal replacement strategy for spinning machines at CV CTR using the Dynamic Programming approach. Data were collected through structured interviews and an internal document review, covering both technical and financial information. Calculations were carried out using a backward recursion method over a six-year planning horizon to evaluate various decision scenarios, including whether to retain or replace the machine. The results show that the best strategy involves replacing the machine in the first year, retaining it in the second year, replacing it again in the third year, and retaining it through the beginning of the sixth year. This strategy yields a maximum net income of Rp203,170,000 per year and is considered the most efficient compared to other alternatives. The study concludes that Dynamic Programming is effective in formulating data-driven and measurable machine replacement policies that support long-term operational cost efficiency.
Co-Authors Didit Damur Rochman Dony Sastra Danuatmaja Fadly Rizky Dermawan Febe Seren