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All Journal Metode : Jurnal Teknik Industri Indonesian Journal of Innovation Studies
Sari Wulandari, Indah Apriliana
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice

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ANALISIS EFEKTIVITAS MESIN BATCHING PLANT MENGGUNAKAN OVERALL EQUIPMENT EFFECTIVENESS DAN ANALYTICAL HIERARCHY PROCESS sisiliani, fitria trisna; Sari Wulandari, Indah Apriliana; Sukmono, Tedjo; Marodiyah, Inggit
Metode : Jurnal Teknik Industri Vol. 10 No. 2 (2024): Jurnal Metode
Publisher : Universitas Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33506/mt.v10i2.3372

Abstract

In the face of fierce global competition, construction industry companies such as PT XYZ require efficient production machinery to increase productivity. This study aims to evaluate the effectiveness of batching plant machines at PT XYZ using the Overall Equipment Effectiveness (OEE) method and identify improvements through Analytical Hierarchy Process (AHP). The batching plant machine often experiences downtime, especially on line five, which hampers production performance. The OEE method was used to measure three main aspects: availability, performance, and quality, with an average OEE result of 91.34%, which indicates good performance but still below the company's internal standards. Fishbone diagram analysis identified the main causal factors of downtime, while AHP was used to prioritize improvements. The AHP results showed that developing a Standard Operating Procedure (SOP) was the best strategy to reduce downtime, with a weight of 39.8% and consistency of 2%. This research suggests the implementation of SOPs to improve machine effectiveness and maintain optimal productivity at PT XYZ.
Neural Network Sales Revolution Outperforms Exponential Smoothing: Revolusi Penjualan Jaringan Syaraf Mengungguli Perataan Eksponensial Sari, Safia Meilia; Sari Wulandari, Indah Apriliana
Indonesian Journal of Innovation Studies Vol. 25 No. 3 (2024): July
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v25i3.1178

Abstract

This research addresses the challenges faced by food manufacturing companies, focusing on UD. XYZ as a case study. With fluctuating sales levels causing raw material buildup and shortages, the study proposes an improved sales forecasting method to enhance raw material control. By comparing Artificial Neural Network (ANN) and Double Exponential Smoothing Holts, the research aims to optimize inventory management and production processes. Results indicate ANN's superiority over Holts, with an accuracy rate of 0.118 compared to 11.639. The ANN model accurately forecasts sales for the upcoming twelve-month period, highlighting a decline from July 2023 to May 2024. Implementing advanced forecasting methods can mitigate raw material-related risks and enhance operational efficiency for companies like UD. XYZ. Highlight: Enhanced sales prediction methods crucial for inventory planning. Artificial Neural Network outperforms traditional forecasting techniques. Improved forecasting mitigates raw material shortages and excesses. Keywoard: Sales forecasting, Artificial Neural Network, Raw material control, Inventory management, Production optimization.
Co-Authors Marodiyah, Inggit Sari, Safia Meilia sisiliani, fitria trisna Tedjo Sukmono