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Journal of Industrial Engineering and Management
Published by Universitas Malikussaleh
ISSN : -     EISSN : 29855683     DOI : https://doi.org/10.52088
Core Subject : Science, Agriculture, Social, Engineering,
Agriculture, Biological Sciences & Forestry Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering
The aim of the Journal of Industrial Engineering and Management is to publish theoretical and empirical articles that are aimed to contrast and extend existing theories and build new theories that contribute to advance our understanding of phenomena related with industrial engineering and industrial management in organizations, from the perspectives of Production Planning/Scheduling/Inventory, Logistics/Supply Chain, Quality Management, Operations Management, and Operational Research. The contributions can adopt confirmatory (quantitative) or explanatory (mainly qualitative) methodological approaches. Theoretical essays that enhance the building or extension of theoretical approaches are also welcome. JAIEM selects the articles to be published with a double-blind peer review system, following the practices of good scholarly journals. JAIEM is published quarterly (online and printed) following an open-access policy. Online publication reduces publishing costs and makes reviewing and editing more agile. JAIEM defends that open-access publishing fosters the advancement of scientific knowledge, making it available to everyone. Main topics of interest but not limited to: Supply chain Lean manufacturing Operations improvement Innovation management in operations Operations in service industry Operational Research Total Quality Management Innovation in Engineering/Management Education Total Productive Maintenance How to manage workforce in operations Logistic in general Information Technology Chemical Engineering Mechanical Engineering Ergonomics Productions Electrical Engineering Information System Informatics Renewable Energy Engineering Civil Engineering Architecture Human Resource Management Entrepreneurship Banking management Industrial Management Digital Management HRD Management
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 1 Documents
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Search results for , issue "Vol 1, No 2 (2023)" : 1 Documents clear
AI-Based Industrial Management for Enhancing Operational Manufacturing Processes of Medical Bed Parts via AI-Driven Quality Gholampoor, Hadi
Journal of Industrial Engineering and Management Vol 1, No 2 (2023)
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/jaiem.v1i2.18

Abstract

In the realm of medical equipment manufacturing, ensuring the quality of each component is crucial due to the direct impact on patient safety and product reliability. This study introduces a novel application of machine learning within industrial management to enhance the operational manufacturing processes of medical bed parts. Utilizing a Random Forest classifier, we developed a predictive model based on five critical features collected during the manufacturing process: the physical dimensions of Length, Width, Height, Weight of the parts, and the operator involved in manual grinding. The classifier aimed to predict whether each part would be defective or accepted before assembly, potentially revolutionizing the traditional quality control approach by reducing dependency on post-manufacturing inspections and minimizing human error. The model was trained on a dataset of 500 parts, with a class distribution reflecting a significant imbalance between defected and accepted pieces. Despite this, the classifier achieved a high accuracy of 97.0% on the test set, demonstrating robustness and reliability in predicting part quality. Feature importance analysis revealed that while physical attributes like Weight and Height significantly influenced predictions, operator variability also played a crucial role, indicating areas for operational improvement through training and standardization. This research highlights how integrating AI into industrial manufacturing processes can significantly enhance efficiency, reduce waste, and ensure higher standards of quality control, setting a precedent for future applications in similar high-stakes manufacturing environments

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