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All Journal Journal of Industrial Engineering Management Industrika : Jurnal Ilmiah Teknik Industri
Momon S, Ade
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Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Industrial & Manufacturing Engineering

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COMPARATIVE ANALYSIS OF MOVING AVERAGE AND DOUBLE EXPONENTIAL SMOOTHING METHODS FOR FORECASTING ASTM A252 GR 2 PIPE DEMAND AT PT XYZ Agustin, Ardita Dwi; Momon S, Ade; Suseno, Agustian; Maulidin, Wildan Fatchan
Journal of Industrial Engineering Management Vol 9, No 3 (2024): Journal of Industrial Engineering and Management
Publisher : Center for Study and Journal Management FTI UMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33536/jiem.v9i3.1897

Abstract

Raw material inventory planning is a crucial aspect in the manufacturing industry to ensure smooth production and cost efficiency. However, PT XYZ has not implemented a forecasting method in its raw material planning system, so that procurement decisions are still reactive to actual demand. This study aims to analyze and compare forecasting methods using Double Exponential Smoothing (DES) and Moving Average (MA) to determine the most accurate method in projecting raw material needs for Non-API spec pipe products, type ASTM A252 GR 2 at KT 24 PT XYZ. The data used is historical demand data, which is then analyzed using POM-QM for Windows software. The results of the analysis show that the Moving Average method with a two-month period (MA-2) has the smallest Mean Squared Error (MSE), which is 182067, and a Mean Absolute Percentage Error (MAPE) value of 1.24%, which indicates a higher level of accuracy than other methods. Thus, the MA-2 method is recommended to be implemented in PT XYZ's raw material planning system to improve production efficiency and reduce the risk of excess or shortage of stock. For further research, it is recommended to develop a forecasting model by considering external factors such as market trends and seasonality, and integrating machine learning or hybrid forecasting methods to improve prediction accuracy. In addition, the implementation of an Enterprise Resource Planning (ERP)-based system with a forecasting module can also be a solution for long-term planning efficiency.
Analisis Pengendalian Kualitas Produk Cacat Menggunakan Metode Lean Six Sigma dengan Konsep DMAIC di PT. PQR Wicaksono, Rizky Adib; Momon S, Ade; Wahyudin, Wahyudin; Malik Ij, Falih Abdul; Setyanto, M. Ulfi Akhsan
Industrika : Jurnal Ilmiah Teknik Industri Vol. 10 No. 1 (2026): Industrika: Jurnal Ilmiah Teknik Industri
Publisher : Fakultas Teknik Universitas Tulang Bawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37090/s4abxz34

Abstract

A high defect rate is a critical issue in the manufacturing industry as it affects efficiency and product quality. PT. PQR still experiences defects in the production of Part Member Sub Assy FR Under RH. This study aims to analyze defect levels, identify dominant defects, and propose improvements using the Lean Six Sigma approach with the DMAIC method. A descriptive quantitative method was employed using secondary production and defect data from June to October 2023. The results show that the dominant defects are Part Gap (79%) and Welding CO Blow Hole (21%). The highest DPMO value is 27,617.6, with the lowest sigma level of 3.417 occurring in July, indicating that the process capability is not yet optimal. The defects are mainly caused by human, material, machine, and environmental factors. It is concluded that Lean Six Sigma with the DMAIC method is effective as a systematic framework to improve quality and reduce defect rates. Keywords: DMAIC, Manufacturing Industry, Lean Six Sigma, Quality Control, Defective Product.
Co-Authors Agustian Suseno Agustin, Ardita Dwi Malik Ij, Falih Abdul Maulidin, Wildan Fatchan Setyanto, M. Ulfi Akhsan Wahyudin Wahyudin Wicaksono, Rizky Adib