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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 5 Documents
 1 
Search results for , issue "vol 1, no 4 (2023)" : 5 Documents clear
AI-Driven Quality Management for Enhancing Manufacturing Product Consistency Through Intelligent Process Monitoring Technologies Continuously Defi Irwansyah; Indriyani Indriyani; Rini Oktavera; Fathan Mubina Dewadi
Journal of Industrial Engineering and Management Vol 1, No 4 (2023)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/jaiem.v1i4.41

Abstract

Manufacturing quality management depends on timely detection of nonconformities, reliable process measurement, and corrective action before defective units advance through production. This study presents a descriptive comparison of manual visual inspection and AI-vision inspection records from a machine-component factory. The supplied dataset covers five production lines over twelve months, comprising six manual-inspection months and six AI-vision months, together with a before-and-after defect-category dataset. The analysis validates 60 line-month records and calculates weighted defect density, percentage reduction in defect density, mean detection-time reduction, first-pass-yield change, and defect-category reduction. During the manual period, 363,845 units and 5,562 defects produced a weighted defect density of 15,286.73 PPM. During the AI-vision period, 357,506 units and 1,195 defects produced 3,342.60 PPM. The observed comparison corresponds to a 78.13% lower weighted defect density, an 87.51% shorter mean detection time, and a 4.28-percentage-point higher mean first-pass yield. Every production line recorded a lower weighted defect density in the AI-vision period. The before-and-after defect-category records show the largest percentage reduction for surface cracks, 86.57%, followed by dimensional nonconformity, 85.86%. Four source-derived figures distinguish line-level defect-density changes, monthly quality trends across the inspection transition, defect-category shifts, and the association between detection time and defect density. The study does not establish that the inspection technology alone caused the observed differences because the records are an observational pre/post comparison without controls for product mix, operator effects, or other concurrent changes. Its contribution is a transparent quality-management evidence base for identifying where deeper process validation is needed
Reverse Logistics Implementation for Recovering Industrial Products Through Sustainable Circular Resource Management Practices Successfully Afferdhy Ariffien; Nurlaela Kumala Dewi; Fathan Mubina Dewadi; Cut Ita Erliana
Journal of Industrial Engineering and Management Vol 1, No 4 (2023)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/jaiem.v1i4.42

Abstract

Industrial return streams can create disposal pressure when returned products, used packaging, production residues, and end-of-life components are handled as waste rather than as recoverable resources. This study describes the implementation performance of reverse logistics for an academic packaging-factory case in the Medan industrial area. The assessment uses eight product-category records and twelve-monthly circular-resource records supplied for the case. The analysis measures return volume, recovery rate, recovery cost, recovery value, net benefit, return-on-investment (ROI), waste diversion, material reuse, carbon saved, recovery revenue, and carbon intensity. The category analysis covers 161.70 tons of returned material, with recovery cost of IDR 151.44 million, recovery value of IDR 266.51 million, and net benefit of IDR 115.07 million. Used packaging generated the largest net benefit, IDR 35.61 million, whereas production residue was the only loss-making category at minus IDR 1.04 million. The monthly data show 239.41 tonnes of waste diverted over the year, a rise in material reuse from 33.44% in January to 41.67% in December, and the highest recovery revenue of IDR 20.32 million in December. Four complementary source-derived figures distinguish category-level financial contributions, operational recovery profiles, monthly circular performance, and the association between waste diversion and carbon saving. The results indicate where the case can retain value through resale, recycling, refurbishment, remanufacturing, and controlled disposal. The study is descriptive; it does not estimate causal effects or generalize beyond the supplied case records. Its contribution is a transparent operational evidence base that connects economic and environmental indicators for reverse-logistics decisions
Intelligent Supplier Evaluation Framework for Improving Procurement Decisions Across Manufacturing Organizations Using Multi-Criteria Analysis Aji Nurrohman; Alfry Aristo Jansen Sinlae; Fathan Mubina Dewadi Dewadi; Sitti Rachmawati Yahya
Journal of Industrial Engineering and Management Vol 1, No 4 (2023)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/jaiem.v1i4.43

Abstract

Supplier evaluation must balance commercial, operational, and capability considerations before a procurement decision is made. This study analyzes academic evaluation records for 12 suppliers serving an automotive-component factory. Seven criteria are available: price, quality, delivery, flexibility, after-sales service, production capacity, and financial stability. The study uses the supplied analytic-hierarchy-process weights to reproduce the supplier scorecard and rank alternatives through a weighted-sum calculation. Quality has the highest weight (0.25), followed by price (0.20) and delivery (0.18); these three criteria account for 0.63 of the total decision weight. The reported consistency-ratio values range from 0.028 to 0.063. Supplier H ranks first with an AHP-weighted score of 83.99, followed by Supplier F (82.89) and Supplier C (82.32); Supplier K ranks twelfth with 67.15. The source total scores are verified as weighted sums of the seven criterion scores. Although the accompanying documentation describes an AHP-TOPSIS approach, the available data does not include criterion directions, ideal solutions, distance measures, or closeness coefficients. The paper therefore reports a transparent AHP-weight ranking rather than attributing the supplied scores to TOPSIS. The contribution is a reproducible procurement review framework that links criterion priorities, supplier profiles, ranking results, and differentiated follow-up actions without making unsupported performance or causal claims
Predictive Maintenance Implementation for Reducing Indus-trial Equipment Downtime Using Machine Learning Predic-tive Analytics Models Alfry Aristo Jansen Sinlae; Fathan Mubina Dewadi; Fegie Yoanti Wattimena
Journal of Industrial Engineering and Management Vol 1, No 4 (2023)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/jaiem.v1i4.39

Abstract

Predictive maintenance aims to improve maintenance timing by converting condition signals into information that supports inspection, intervention, and scheduling decisions. This study provides a reproducible descriptive analysis of the two supplied files for an industrial-equipment monitoring exercise: 150 daily observations from five machines during 1-30 June 2024 and five supplied model-benchmark rows. The sensor file contains vibration, temperature, pressure, current, operating hours, a maintenance-alert prediction, and a same-day recorded status. An alert is treated as positive when its label is “maintenance required,” and a recorded breakdown is treated as positive for the descriptive comparison. The resulting same-day confusion matrix is TP = 6, FP = 10, TN = 134, and FN = 0. Accuracy is 93.33%, precision is 37.50%, recall is 100.00%, F1 is 54.55%, and the false-alarm rate is 6.94%. The high recall is accompanied by ten alerts on records marked normally, which constrains the operational interpretation of accuracy alone. The supplied benchmark table reports five algorithms, with LSTM having the highest listed accuracy and F1 score but also the longest listed training time; these values are reported as supplied and are not recomputed from the sensor file. Four source-derived figures examine alert timing, vibration-temperature patterns, the same-day outcome matrix, and the supplied benchmark trade-offs. The study does not train, tune, split, or test a new model, and it does not observe maintenance actions, downtime duration, or a causal comparison period. Accordingly, the results describe alignment between same-day labels in the supplied records; they do not prove that a deployed model reduced industrial equipment downtime. The contribution is a transparent reporting template that links sensor evidence, alert burden, and model-selection trade-offs while preserving this evidence boundary
Reliability Engineering Framework for Improving Manufacturing System Availability Through Failure Prevention and Performance Enhancement Strategies Ansarullah Lawi; Fathan Mubina Dewadi; Indriyani Indriyani
Journal of Industrial Engineering and Management Vol 1, No 4 (2023)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/jaiem.v1i4.40

Abstract

Manufacturing availability depends on how quickly failure-prone assets are identified, how consistently their failure data are defined, and how maintenance actions are connected to operational consequences. This study develops a descriptive reliability-engineering framework for a ten-equipment manufacturing dataset and an eight-component failure mode and effects analysis (FMEA) register. The framework integrates availability, mean time between failures (MTBF), mean time to repair (MTTR), failure rate, reported annual breakdowns, repair cost, and risk priority number (RPN). The results show a mean recorded availability of 99.02%, 117 reported annual breakdowns, and reported repair cost of IDR 495.7 million. The injection molding machine has the highest observed failure rate at 1.718 failures per 1,000 h, while the robotic welding machine has the lowest availability at 98.38%. Bearing overheating has the highest FMEA RPN of 192; hydraulic-seal leakage and gearbox wear also exceed the stated RPN 150 intervention threshold. The framework prioritizes targeted preventive actions, condition monitoring, and review of detection controls rather than claiming a causal improvement from the descriptive data. It offers a reproducible way to connect equipment records with component-level risk decisions and to monitor whether planned interventions subsequently change availability, failure exposure, and repair burden

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