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Dahlan Abdullah
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INDONESIA
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 14 Documents
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AI-Based Production Planning for Enhancing Manufacturing Resource Allocation Through Intelligent Decision Support Systems Mutasar Mutasar; Chaeroen Niesa
Journal of Industrial Engineering and Management Vol 1, No 2 (2023)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/jaiem.v1i2.32

Abstract

Production planning is a critical manufacturing function that requires balancing production targets, equipment availability, workforce capacity, and material expenditure under continuously changing operational conditions. Artificial intelligence (AI)-based decision support systems (DSS) have emerged as promising tools for improving planning accuracy and resource allocation by enabling data-driven production decisions. This study presents a descriptive evaluation of production performance and departmental resource allocation before and after the implementation of an AI-based DSS in a manufacturing facility in Aceh, Indonesia. A before-and-after research design was applied using 24 monthly production records, comprising 12 months of conventional planning in 2023 and 12 months of AI-supported planning in 2024. The analysis compares key operational indicators, including production targets, realized output, production achievement, machine utilization, workforce allocation, raw material costs, departmental allocation shares, and operational efficiency. Results indicate that mean realized production increased from 812.25 tons to 984.67 tons per month, while the average production achievement rate improved from 83.96% to 95.89%. Mean machine utilization increased from 69.92% to 87.49%, reflecting more effective use of manufacturing resources. At the same time, average monthly raw material expenditure decreased from IDR 575.91 million to IDR 548.09 million, indicating improved cost efficiency. Across seven production departments, mean operational efficiency increased by 18.51 percentage points, rising from 69.05% to 87.56% after AI-based DSS implementation. Because the comparison is based on two consecutive calendar years without a parallel control facility or randomized intervention, the findings should be interpreted as descriptive operational evidence rather than causal proof of AI effectiveness. This study contributes a practical evaluation framework for assessing AI-supported production planning by integrating production performance, resource utilization, cost efficiency, and departmental allocation into a comprehensive decision-support model for continuous improvement in manufacturing operations
Sustainable Logistics Network Optimization for Reducing Distribution Costs Across Regional Manufacturing Supply Operations Efficiently Nurlaela Kumala Dewi
Journal of Industrial Engineering and Management Vol 1, No 2 (2023)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/jaiem.v1i2.21

Abstract

Distribution networks must simultaneously optimize transport costs, travel time, service performance, vehicle utilization, fuel consumption, and carbon emissions to achieve sustainable logistics operations. Balancing these interrelated performance dimensions remains a significant challenge, particularly in regional distribution systems where operational data are often heterogeneous and limited. This study presents a descriptive evaluation of sustainable logistics performance using two complementary academic datasets representing a regional logistics context connecting Aceh and North Sumatra, Indonesia. The first dataset comprises ten paired conventional and optimized route scenarios, while the second consists of twelve-monthly operational observations including shipment volume, total logistics cost, on-time delivery performance, carbon emissions, fuel consumption, and vehicle load factor. Comparative analysis of the paired route scenarios indicates that total transportation costs decreased from IDR 12,933.0 thousand to IDR 10,437.0 thousand, representing a 19.30% reduction. Reported carbon emissions declined from 3,024.8 kg to 2,147.0 kg (29.02%), while total travel time decreased from 59.5 hours to 49.6 hours (16.64%). The monthly operational dataset records 2,583 shipments, with an average on-time delivery rate of 87.26%, mean carbon emissions of 21.35 tons, average fuel consumption of 12,542 liters, and a mean vehicle load factor of 71.53%. Because the paired route scenarios and monthly operational records do not share a common optimization protocol, deployment timeline, or route frequency, they are analyzed independently to avoid unsupported causal interpretations. The findings therefore identify favorable operational patterns rather than causal effects of network optimization. This study contributes a transparent evaluation framework that integrates route-level cost, emissions, and travel-time indicators with monthly service, fuel-efficiency, and loading-performance metrics, providing a practical decision-support approach for sustainable logistics management and future performance benchmarking
Smart Warehouse Operations for Enhancing Storage Accuracy Through Intelligent Real-Time Inventory Monitoring Technologies Continuously Afferdhy Ariffien
Journal of Industrial Engineering and Management Vol 1, No 2 (2023)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/jaiem.v1i2.22

Abstract

Warehouse operations require accurate inventory visibility, reliable location tracking, efficient order picking, and continuous monitoring to ensure operational efficiency and uninterrupted customer service. The growing adoption of Internet of Things (IoT) technologies provides opportunities to enhance warehouse performance through real-time monitoring and data-driven decision support. This study presents a descriptive evaluation of smart warehouse operations using two complementary academic datasets representing a food-industry distribution center in Lhokseumawe, Indonesia. The first dataset compares warehouse performance under conventional manual monitoring and IoT-enabled monitoring across six storage zones. The second dataset summarizes 30 days of warehouse operations, including inbound and outbound transactions, tracked stock-keeping units (SKUs), environmental conditions, sensor alerts, and system downtime. Comparative analysis of the storage-zone dataset indicates that mean location accuracy increased from 82.43% under manual monitoring to 97.51% with IoT-based monitoring, representing an improvement of 15.09 percentage points. Mean order-picking time decreased from 14.52 minutes to 6.88 minutes, while the reported picking error rate declined from 6.30% to 0.96%. The daily operational dataset records 2,397 inbound and 2,069 outbound transactions, an average of 2,006.77 tracked SKUs, 64 sensor alerts, and 98 minutes of reported system downtime during the observation period. Because the storage-zone comparison and the daily operational records do not share a common implementation protocol, deployment timeline, or control warehouse, they are analyzed independently to avoid unsupported causal conclusions. The findings identify consistent operational patterns that integrate location accuracy, picking efficiency, transaction flow, environmental monitoring, sensor alerts, and downtime into a unified performance review framework. This study contributes a transparent evaluation model for assessing smart warehouse monitoring systems, providing a practical decision-support approach for warehouse performance assessment while avoiding causal claims beyond the available evidence
Digital Inventory Management for Improving Raw Material Availability Across Modern Manufacturing Production Facilities Efficiently Nensi Lapotulo; Lily Yuntina
Journal of Industrial Engineering and Management Vol 1, No 2 (2023)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/jaiem.v1i2.23

Abstract

Effective raw-material inventory management is critical for maintaining uninterrupted production, reducing operational costs, and ensuring manufacturing efficiency. Achieving these objectives requires accurate inventory records, timely replenishment, and inventory policies that account for demand variability and supplier lead times. This study presents a descriptive evaluation of digital inventory management using two complementary datasets obtained from a fertilizer manufacturing facility in North Aceh, Indonesia. The first dataset compares inventory performance before and after digitalization for fifteen critical raw materials by examining stockout frequency and inventory record accuracy. The second dataset summarizes twelve months of post-digitalization operations, including active stock-keeping units (SKUs), inventory value, inventory turnover, stock accuracy, and monthly stockout incidents. Comparative analysis of the material-level dataset shows that stockout events decreased from 74 under manual inventory management to 15 following digitalization, representing a descriptive reduction of 79.73%. Mean inventory record accuracy improved from 80.26% to 96.57%, an increase of 16.31 percentage points. The monthly operational dataset reports an average stock accuracy of 91.91%, a mean inventory turnover ratio of 4.43, and 15 stockout events across the twelve-month observation period, with six months recording no stockout occurrences. Because the before-and-after material comparison and the post-digitalization monthly performance series do not share a common implementation timeline, control facility, or standardized deployment protocol, the datasets are analyzed independently to avoid unsupported causal inference. The findings therefore identify favorable trends in inventory availability, record accuracy, and operational performance rather than demonstrating causal effects of digitalization. This study contributes a transparent evaluation framework integrating material-level availability, monthly inventory performance, lead-time segmentation, and implementation-governance considerations, providing a practical decision-support approach for reviewing digital inventory management and supporting continuous improvement in manufacturing supply-chain operations

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