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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 2, no 1 (2024)" : 5 Documents clear
Demand-Driven Takt-Time Synchronization for Stabilizing Mixed-Model Output Across Fluctuating Consumer-Goods Manufacturing Production Lines Johni S Pasaribu; Mohammad H. Holle; Sitti Rachmawati Yahya; Reni Koibur
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.v2i1.46

Abstract

Consumer goods manufacturers must adapt production rhythm to demand that shifts daily between normal, high-season, and promotional-surge patterns while operating mixed-model lines with frequent changeovers. This study examines 88 sequential production days of demand and takt-time records, 60 changeover events across three product variants, and 12 months of synchronization key-performance indicators from a fast-moving consumer-goods plant. The objective is to describe the operational profile of a demand-driven takt-time synchronization practice relative to a conventional push approach. Mean daily demand ranges from 2,675 to 4,091 units, producing calculated takt times between 6.13 and 10.30 seconds. Of 60 recorded changeover events against a 15-minute SMED target, only five meet the target. In the demand-driven period, mean schedule adherence rises from 79.5% to 96.5%, overtime drops from 26.1 to 7.0 hours per month, and output variance decreases from 16.9% to 3.3% compared with the conventional-push period. Because the two periods cover different calendar months without a controlled experiment, the results are reported as descriptive associations rather than causal effects. The contribution is a transparent reporting structure that links demand variability, takt-time response, changeover constraint, and synchronization indicators for production-planning review
Work-in-Process Control Using Digital e-Kanban for Reducing Floor Congestion in Metal Fabrication Manufacturing Workshops Rini Oktavera; Cut Ita Erliana; Balqis Nurmauli Damanik; Sitti Rachmawati Yahya
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.v2i1.47

Abstract

Work-in-process (WIP) accumulation on a fabrication floor consumes physical space, extends queue time, and raises inventory holding cost, yet many metal fabrication workshops still coordinate material movement with paper travelers rather than a system that records replenishment timing directly. This study examines four operational datasets from a precision metal fabrication workshop: 720 hourly WIP-inventory records across six process bays, 150 digital e-Kanban card transactions with replenishment lead time and service-level status, 100 job-order lead-time records across four batch sizes, and 24 weekly records of WIP value, holding cost, and floor-congestion incidents spanning a period before and after e-Kanban implementation. The objective is to describe how WIP accumulates across the fabrication line, how the digital e-Kanban system performs against its replenishment service target, how job lead time is composed of queue and processing time across batch sizes, and how the weekly cost and congestion indicators differ between the two management phases. The press-brake and welding bays carry roughly two and a half times the mean floor occupancy of the other four bays, at 67.0% and 67.5% respectively against 25.8% to 26.4% elsewhere. Of 150 e-Kanban card transactions, 40 (26.7%) exceed the destination bay's replenishment target, with a mean lead time of 21.96 minutes against 14.44 minutes for cards that meet the target. Job lead time is dominated by queue time at every batch size, ranging from 47.3% of total lead time for 200-unit batches to 51.8% for 500-unit batches. Between the two weekly phases, mean total WIP value falls from 1,048.95 to 337.79 million IDR, mean holding cost falls from 26.22 to 8.45 million IDR, and mean floor-congestion incidents fall from 7.33 to 0.33 per week. Because the two phases span different calendar weeks without a controlled trial, these differences are reported as descriptive associations rather than causal effects. The contribution is a transparent reporting structure that connects bay-level WIP accumulation, e-Kanban replenishment performance, batch-level lead-time composition, and weekly cost outcomes for shop-floor inventory-control review
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
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
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

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