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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 36 Documents
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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
Ergonomic Workstation Design for Reducing Worker Fatigue While Improving Manufacturing Occupational Safety Performance Standards Ansarullah Lawi; Cut Ita Erliana; Balqis Nurmauli Damanik; Willson Gustiawan; Irma Darmawati
Journal of Industrial Engineering and Management Vol 1, No 3 (2023)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/jaiem.v1i3.37

Abstract

Ergonomic workstation design is commonly expected to reduce physical exposure and support safer manufacturing work. This paper provides a reproducible descriptive analysis of supplied before-and-after ergonomic records from ten workstations and a separate paired body-map dataset from thirty respondents. The workstation file contains Rapid Entire Body Assessment (REBA) scores, categorical labels, musculoskeletal complaint counts, and reported output in units per hour. The body-map file contains ten region-specific numerical scores per respondent. Because the files have no worker-to-workstation linkage, no intervention description, no observation dates, no body-map scale definition, and no incident or audit data, they are analyzed as separate descriptive layers. The sum of supplied workstation REBA scores decreases from 96 to 41, a 57.29% reduction, while the supplied complaint count decreases from 105 to 24, a 77.14% reduction. Reported workstation output rises from 193 to 224 units per hour, a 16.06% increase. In the body-map layer, the summed supplied scores decrease from 743 to 551 across 300 paired region observations; every respondent has a lower total after score. These numerical patterns do not establish measured fatigue reduction, an implemented redesign, injury prevention, or improved occupational-safety performance. The study instead supplies an auditable baseline: data validations, formulae, English-language tables, and four non-duplicative figures that distinguish workstation and respondent evidence. The results identify workstations and body regions for subsequent field observation. A future intervention study should document the redesign, link workers to stations, define each scale, measure exposures and outcomes across a specified time window, and evaluate safety indicators independently. This preserves the applied ergonomics value of the source data without overstating what the records can demonstrate
Production-Loss Detection Using IoT-Enabled Real-Time Throughput Monitoring Across Automated High-Speed Packaging Manufacturing Lines Afferdhy Ariffien; Nurlaela Kumala Dewi; Indriyani Indriyani
Journal of Industrial Engineering and Management Vol 2, No 1 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/jaiem.v2i1.48

Abstract

High-speed beverage bottling lines lose a substantial share of their rated capacity to short, frequent, and easily overlooked disturbances that conventional end-of-shift reporting cannot capture. This paper reports the implementation and evaluation of an Internet of Things (IoT) real-time throughput-monitoring system on an automated bottling line at a beverage packaging plant in Pasuruan, Indonesia. The monitoring layer streams line speed, vibration, motor temperature, and air pressure from four sensor groups, flags micro-stoppages as they occur, and feeds a Pareto-based loss analysis that ranks the plant's chronic production losses by downtime and financial impact. The study analyzes 200 telemetry observations, an 80-event micro-stoppage log, four categorized loss streams, and 30 days of daily throughput records spanning a period before and after the monitoring rollout. Pareto analysis shows that micro-stoppages and speed reduction alone account for 67.9% of the 130.6 downtime hours and dominate the 391.8 million IDR of loss over the study window, identifying them as the vital few losses that warrant priority action. Telemetry profiling confirms that flagged micro-stoppages coincide with elevated vibration and motor temperature, giving operators an early physical signature to act on. After the IoT monitoring system was introduced, mean daily production loss fell from about 45,858 to 8,020 units, mean good output rose from 204,142 to 241,980 units per day, and mean capacity recovery improved from 81.7% to 96.8%, a gain of 15.1 percentage points sustained across the full post-implementation period. The results show that continuous IoT monitoring converts previously invisible micro-losses into actionable signals and delivers a large, measurable throughput improvement, providing a transferable loss-detection template for high-speed packaging operations
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
Manufacturing Facility Layout Optimization for Improv-ing Material Flow Efficiency Across Industrial Production Environments Effectively Rini Oktavera
Journal of Industrial Engineering and Management Vol 1, No 3 (2023)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/jaiem.v1i3.38

Abstract

Facility-layout decisions influence the physical path followed by materials, workers, and information in manufacturing systems. This study reports a reproducible descriptive assessment of supplied before-and-after distance evidence for an eight-department light-steel manufacturing setting. The source set contains two directed interdepartmental distance matrices and eight material-flow route records. The analysis deliberately treats these as separate evidence layers because every route record differs from the corresponding origin-destination matrix value. The matrix layer therefore characterizes the complete set of 56 non-diagonal directed department pairs, whereas the route layer characterizes only the listed material movements. For the matrix layer, the sum of directed distances decreases from 1,936 m to 1,124 m, a reduction of 812 m or 41.94%; all 56 pairs decrease. For the route layer, the listed total decreases from 3,778 m to 1,799 m, a saving of 1,979 m or 52.38%. The cutting-to-forming route contributes the largest listed saving, at 528 m or 26.68% of the route total. Route frequencies indicate that cutting has the greatest inbound burden, while raw-material storage has the greatest outbound burden. The paper provides a transparent calculation trail, four distinct source-derived visualizations, and a direct document-generation workflow. Its contribution is not an optimization-algorithm comparison or an implementation evaluation. Because the supplied data do not contain layout coordinates, department areas, handling costs, time studies, production output, or implementation records, the results must be read as descriptive distance evidence only. They identify priority movements and provide a defensible baseline for a subsequent layout-design study that can collect the missing engineering and operational variables
Bottleneck Diagnosis Using Real-Time OEE Analytics for Improving Throughput Across Multi-Stage Discrete Manufacturing Lines Aji Nurrohman; Yohan Indey; Sitti Rachmawati Yahya; Tuti Agustin
Journal of Industrial Engineering and Management Vol 2, No 1 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/jaiem.v2i1.44

Abstract

A multi-stage discrete manufacturing line performs only as well as its constraining workstation, yet many plants still locate that constraint through periodic manual reports that arrive too late to guide daily action. This study analyzes 60 days of field operational data from a five-station precision automotive-component line, comprising 600 daily-shift overall-equipment-effectiveness (OEE) records, 120 machine-breakdown events, a 45-element cycle-time study, and six months of throughput records before and after a real-time diagnosis practice. The objective is to demonstrate a reproducible diagnostic procedure that decomposes OEE into availability, performance, and quality, locates the constraint, and separates reliability losses from inherent process time. The welding workstation is the persistent constraint, with a mean OEE of 56.54% against roughly 83% at the other four stations, and it concentrates 91 of 120 breakdown events and 91.5% of total downtime. The standard-time study places welding within the 147.9-168.8 second designed-workload range, while its observed cycle time and breakdown burden are disproportionate, which is consistent with a reliability-driven constraint. Between the conventional and real-time periods, mean line OEE rose from 63.62% to 82.12%, scrap rate fell from 3.99% to 1.23%, and good output increased from 24,666 to 32,279 units per month. Because the two periods span different calendar months without a controlled trial, the results are reported as descriptive associations rather than causal effects. The contribution is a transparent bottleneck-diagnosis and review structure that links OEE decomposition, downtime concentration, and throughput monitoring for shop-floor decision making
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
Intelligent Industrial Energy Management for Reducing Manufacturing Power Consumption Through Continuous Energy Performance Optimization Herlina Herlina; Indriyani Indriyani
Journal of Industrial Engineering and Management Vol 1, No 3 (2023)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/jaiem.v1i3.34

Abstract

Industrial energy review requires measurements that keep total electricity use, production, demand, and area-level observations distinct. This study descriptively analyzes two supplied academic files: 12 monthly electricity and production records and eight factory-area before/after records. The monthly file totals 3,141,001 kWh and 13,218 tonnes of production. Dividing those totals produces an annual descriptive ratio of 237.631 kWh/ton. Supplied monthly specific energy consumption (SEC) ranges from 165.3 kWh/ton in September to 320.8 kWh/ton in October; the maximum supplied peak demand is 645 kW. The separate area file reports 480,411 kWh before and 392,084 kWh after, with reported savings of 88,327 kWh, or 18.386% of the before total. Factory lighting has the largest reported absolute saving, 20,843 kWh, and the largest reported percentage, 27%. Four source-derived figures display monthly energy and production, monthly SEC with the annual ratio reference, paired area electricity use, and reported percentage savings. Supplied cost fields are retained separately within their source files and are not combined or used for payback calculations. The records do not identify dates, intervention details, measurement boundaries, operating hours, weather, tariff or cost-period basis, emission factors, or comparable controls. Results therefore describe the supplied values and labels only; they do not estimate implementation effects, causal savings, emissions reductions, or optimization outcomes
Assembly Line Balancing Using Digital Twin Simulation for Reducing Cycle-Time Imbalance in Human-Centric Manufacturing Nensi Lapotulo; Irma Darmawati; Sitti Rachmawati Yahya; Makmur Harun
Journal of Industrial Engineering and Management Vol 2, No 1 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/jaiem.v2i1.45

Abstract

Cycle-time variation can constrain the practical performance of human-centric assembly lines when individual workstations operate above or below the planning target. This study examines a digital-twin simulation workflow for assembly-line balancing using a time study of 24 task elements, pre-balance workstation records from eight stations, 20 algorithm scenarios, and 60 post-balance validation observations. The task elements total 769.3 s, and the pre-balance reference takt is 90.0 s. The mean observed station cycles range from 88.0 s at ST05 to 98.5 s at ST07, with ST03 also above the takt reference at 97.6 s. Across the simulation records, mean line efficiencies are 70.08% for manual balancing, 75.88% for ranked positional weight, 81.77% for COMSOAL, 86.33% for the genetic algorithm, and 92.25% for digital-twin simulation. The 60 validation observations have mean line efficiency of 94.06%, smoothness index of 4.42, daily output of 365.42 units, takt compliance of 97.57%, and work-in-process accumulation of 16.3 units. These findings are descriptive summaries of the supplied operating records, not causal estimates. The proposed workflow combines measured task data, scenario comparison, human review, and validation feedback to support transparent balancing decisions
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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