Jurnal Sains dan Aplikasi Keilmuan Teknik Industri (SAKTI)

Transformation Toward Slow Fashion: A Literature Synthesis on the Ecological and Social Impacts of Fast Fashion

Yoel Santo Andrianus Sormin, Yoga Matin Albar (Author) — Tue, 23 Dec 2025 00:00:00 +0000

This study synthesizes recent literature on the ecological and social impacts of fast fashion and explores slow fashion as a sustainable alternative. A Systematic Literature Review (SLR) was conducted following the PRISMA protocol, analyzing peer-reviewed articles published between 2014 and 2024 and indexed in Scopus, ScienceDirect, and Google Scholar. The findings indicate that fast fashion contributes approximately 10% of global carbon emissions, generates substantial volumes of textile and microplastic waste, and consumes excessive amounts of water. From a social perspective, the industry is strongly associated with labor exploitation, wage inequality, unsafe working conditions, and violations of workers’ rights, disproportionately affecting women in developing countries. As a response to these challenges, slow fashion emphasizes ethical production, conscious consumption, and product durability, with adoption influenced by consumers’ moral awareness and self-identity. This study contributes to sustainable fashion scholarship by integrating previously fragmented ecological and social evidence into a unified synthesis, identifying key research gaps, and proposing a conceptual framework to support the transition toward slow fashion. These contributions enhance academic understanding of the interconnected environmental and social consequences of fast fashion while offering strategic insights for researchers, practitioners, and policymakers seeking to advance sustainability within the global fashion industry.

Penerapan FMEA untuk Analisis Penyebab Reject pada Produk Under Bracket HK2SO di PT XYZ

Revina Oktaviani, Devina Putri, Dwita Suastiyanti (Author) — Tue, 23 Dec 2025 00:00:00 +0000

Competition in the manufacturing industry requires companies to produce high-quality products with a low reject rate. PT XYZ faces a high level of rejects in the Under Bracket HK2SO component, resulting in increased production costs and decreased efficiency. This study aims to identify the main causes of product defects and determine improvement priorities using the Failure Mode and Effect Analysis (FMEA) method. Data were collected over a 14-week period through process observation, operator interviews, and quality report analysis. The results show that three dominant defect types—dent, underfill, and trimming—contribute to the majority of rejects, with dent having the highest Risk Priority Number (RPN) of 320. These failure modes are influenced by improper handling procedures, suboptimal equipment conditions, and unstable product positioning on the jig. Based on the FMEA analysis, corrective actions were proposed, including installing soft-pads on trays, adding locator pins to jigs, controlling billet temperature, calibrating hammer pressure, and providing operator training. Implementation of these improvements is projected to reduce RPN values by 30–50% and decrease overall reject rates by 30–45% within 1–3 months. These findings contribute to strengthening quality control in forging processes and may serve as a reference for similar industries. Future studies are recommended to integrate FMEA with statistical methods such as Statistical Process Control (SPC) or risk-weighting techniques like AHP to enhance evaluation accuracy and the sustainability of quality improvements.

Quality Control Analysis of the Coating Process at PT ABC Using Statistical Quality Control (SQC) and FMEA

Halwa Annisa Khoiri, Wilis Herlin Aryani (Author) — Tue, 23 Dec 2025 00:00:00 +0000

PT ABC is a manufacturing company operating in the construction and fabrication sector. One of the projects currently undertaken by PT ABC is the manufacture of 12 sets of off-screen bars. In this process, the coating stage shows a high defect rate, accounting for 35.1% of total defects. This study aims to analyze the quality control process at the coating stage using Statistical Quality Control (SQC) and Failure Mode and Effects Analysis (FMEA). The SQC tools applied include control charts, Pareto diagrams, and fishbone diagrams. SQC is used to identify dominant defect types and their contributing factors, followed by FMEA to determine risk priorities using the Risk Priority Number (RPN) approach. The integration of these two methods provides a comprehensive framework for quality improvement. The results show that the two dominant defects are mottling and dust spray. Based on the RPN calculations, the highest RPN value is 280, caused by inconsistent spraying at corner areas leading to mottling defects. This is followed by excessive spraying distance in dust spray defects with an RPN of 252. Other significant causes are related to human factors, namely operators failing to clean the workpiece surface in dust spray defects and lack of operator attentiveness in mottling defects, both with an RPN value of 210. Based on these findings, several improvement recommendations are proposed, including operator retraining, standardization of spraying distance and angle, and the implementation of surface cleaning procedures prior to the coating process.

Analysis of Overall Equipment Effectiveness and Six Big Losses for Performance Improvement

Tue, 23 Dec 2025 00:00:00 +0000

This study examines machine performance inefficiencies in a digital printing production system by applying the Overall Equipment Effectiveness (OEE) method to the Versant 3100i digital printing machine at CV XYZ. The objective of this research is to evaluate machine effectiveness, identify dominant sources of production losses, and analyze their root causes to support operational performance improvement. An integrated analytical approach is employed by combining OEE measurement, Six Big Losses analysis, and Fishbone Diagram techniques to systematically diagnose machine performance issues. The results show that the average OEE value of the Versant 3100i machine is 82.92%, which remains below the international benchmark of 85%, indicating that machine performance has not yet reached an optimal level. Analysis of the Six Big Losses reveals that Reduced Speed is the most significant contributor to performance loss, accounting for 36.24% of total losses. Further analysis using a Fishbone Diagram indicates that Reduced Speed is mainly caused by human-related factors and the absence of standardized Standard Operating Procedures (SOPs). Based on these findings, targeted improvement strategies are proposed, including structured operator training, the implementation of standardized operating procedures, and the strengthening of quality control mechanisms. This study demonstrates that the integrated use of OEE, Six Big Losses, and Fishbone Diagram analysis is effective in identifying priority improvement areas and formulating practical improvement strategies. The proposed approach can be applied by manufacturing companies to enhance machine effectiveness and operational efficiency, particularly in digital printing production environments.

Quality Improvement Analysis of X Brand Tube Packaging Using Six Sigma and Failure Mode and Effects Analysis (FMEA)

Tue, 23 Dec 2025 00:00:00 +0000

PT XYZ is a manufacturing company producing cosmetic products, including 180 ml lotion packaged in tubes. From January to July 2024, the average defect rate reached 0.82%, exceeding the company’s maximum tolerance limit of 0.8%. This study aims to evaluate the sigma level, identify the root causes of defects, and propose improvement strategies to enhance packaging quality. The research applies the Six Sigma methodology integrated with Failure Mode and Effects Analysis (FMEA) using the DMAIC (Define, Measure, Analyze, Improve, Control) framework. The results identify four dominant defect types: unsealed sealing, damaged caps, damaged batch codes, and dented containers. The initial sigma level of 3.27 indicates suboptimal process performance. Based on the FMEA results, the main causes of defects include worn machine components and the absence of standardized work instructions. Improvement actions proposed and implemented include replacing critical machine parts, adjusting machine operating parameters, and installing a leakage tester. Following the implementation of these improvements, the sigma level increased to 4.30, and the average defect rate decreased by 96.65%, demonstrating a significant enhancement in the quality of the tube packaging process.

Improving the Effectiveness of Ampoule Filling Machines at PT X Using Total Productive Maintenance (TPM) and Overall Equipment Effectiveness (OEE)

Mufidah Erngganis Devia Rohmah, Sunday Noya, Novenda Kartika Putrianto (Author) — Tue, 23 Dec 2025 00:00:00 +0000

This study aims to improve the effectiveness of ampoule filling machines at PT X, one of Indonesia’s leading pharmaceutical companies, through the implementation of Total Productive Maintenance (TPM) and Overall Equipment Effectiveness (OEE) measurement. The research is motivated by a decline in machine performance observed between January and August 2024, characterized by low production output, high downtime, and elevated defect rates. The study adopts a quantitative research approach, utilizing both primary and secondary data collected from 30 production batches through field observations, interviews, and documentation. OEE is evaluated based on its three main components—availability, performance, and quality—while production inefficiencies are identified using the Six Big Losses framework. Root cause analysis is conducted using a fishbone diagram, and improvement recommendations are developed in alignment with relevant TPM pillars. The implemented improvement actions include replacing frequently malfunctioning swing conveyor sensors and redesigning ampoule storage to reduce the risk of ampoule breakage caused by limited output space. The evaluation results indicate a significant improvement in OEE values following implementation, accompanied by reductions in downtime and product defects. These findings demonstrate that the integrated application of TPM and OEE evaluation is effective in optimizing machine performance and enhancing production efficiency in the sterile pharmaceutical manufacturing industry.