Jurnal Teknologi dan Manajemen

IMPROVING THE PERFORMANCE OF THE 476 CUTTING MACHINE AT PT RACHMAT PERDANA ADHIMETAL PLANT DELTA WITH OVERALL EQUIPMENT EFFECTIVENESS (OEE)

2024-11-15T06:08:00+00:00

PT Rachmat Perdana Adhimetal is one of the manufacturing industries engaged in the manufacture of automotive and non-automotive components in the Delta industrial area, Cikarang, Bekasi Regency. In the implementation of the production process on the cutting line, the company experienced a breakdown problem. Of the 8 available cutting machines, there is 1 machine, the 476 cutting machine which has the highest breakdown frequency. To overcome these problems, data related to operating time, output and downtime is collected, followed by data processing using Overall Equipment Effectiveness (OEE) which can describe the efficiency of the machine. The results of data processing showed an OEE value of 68% of the standard value of 85%. Furthermore, an analysis was carried out with six big Losses, the results showed that there were 2 largest Losses, reduce speed Losses with a value of 14.90% and breakdown losses with a value of 12.65%. Followed by a cause analysis using a causal diagram to find out the factors that affect engine performance from machines and humans. To determine the priority of repairs made on the 476 cutting machine, an analysis was carried out using Failure Mode and Effect Analysis (FMEA). Priority for repairs based on the value of RPN 120 in the type of failure there is no wind pressure regulator with the repair of installing air speed adjuster/faucet, the value of RPN 102 in the type of failure of maintenance staff does not understand the daily checksheet with the improvement of the socialization of the daily checksheet to the maintenance staff, the value of RPN 54 in the type of failure 1/3 of the wind pressure is divided into the air sprayer and the value of RPN 21 in the type of failure leader does not understand the SOP for moving materials at the beginning of the set up with improvement of socialization of SOPs for moving materials at the beginning of the set up to the leader. The OEE value after the improvement showed a value of 77%, an increase of 9% from before the improvement.

FSN Analysis of Spare Parts Based On Turnover Ratio In PT XYZ Maintenance Warehouse

2025-02-13T05:49:17+00:00

The primary function of the warehouse is to store raw materials, semi-finished goods, finished products, and spare parts. The Maintenance Division of PT XYZ does not have a policy on theplacement of spare parts. There are two warehouses for storing spare parts. The distance of the second warehouse is 470 meters from the first warehouse. Although the second warehouse isfarther away, operators frequently use it to store spare parts. It causes the flow of spare parts to be less than optimal, especially if the spare parts are needed suddenly. This study aims to design a policy for grouping spare parts in two warehouses using the FSN (Fast-Slow Non-Moving) method based on the turnover ratio. FSN analysis is a classification method based on the frequency of use. The calculation results show that the maintenance division can group spare parts into three groups to minimize the distance for taking spare parts. The FSN analysis states that 83.9% of the 1045 electric spare parts and 79.5% of the 39 FC production spare parts are in the non-moving category. Meanwhile, as many as 51.8% of the 2670 mechanical spare parts are in the slow-movingcategory. Managerial decisions dictate that electrical and FC production parts are placed in the second warehouse, while mechanical parts are in the first warehouse. By implementing FSN, companies can achieve operational efficiency and can increase the reliability of the overall maintenance system.

The Reduction of Paint Defects Through DMAIC Approach to Improve Product Quality in the Automotive Industry

2025-02-21T02:04:35+00:00

Indonesia is one of the largest four-wheeled vehicle producers in ASEAN and has experienced rapid growth in the automotive sector. The challenge currently faced by the automotive industry is how to continuously improve product quality and reduce waste in the production process in order to remain competitive amidst the intensifying market competition. Lean Six Sigma is a systematic, data-driven methodology that combines two effective business improvement approaches to enhance product quality and reduce waste. This study applies the Lean Six Sigma framework with the DMAIC methodology to improve process quality and reduce defects in the four-wheeled vehicle assembly line. The case study was conducted at a four-wheeled vehicle manufacturer in the GIIC industrial area in Indonesia. The research began by identifying waste through value stream mapping and collecting defect data over 20 working days in October 2020. The results of the implementation showed a reduction in product defects from 2004 DPMO to 1304 DPMO, along with an improvement in the company's sigma level from 4.37 to 4.53, indicating significant improvements in both product quality and process efficiency.