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Dahlan Abdullah
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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 3 (2023)" : 5 Documents clear
Knowledge Management Strategies for Enhancing Industrial Innovation Through Organizational Learning and Digital Col-laboration Platforms Santi Susanti; Fisla Wirda; Kraugusteeliana Kraugusteeliana; Arlis Dewi Kuraesin
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.v1i3.35

Abstract

Knowledge management in manufacturing depends on the visibility of practical knowledge, its retention, and its use in operational decisions. This study provides a descriptive account of an anonymized manufacturing survey containing 80 individual responses and 25 supplied instrument-result rows. The survey includes four individual knowledge-management ratings, three innovation-related ratings, one digital-collaboration rating, job satisfaction, respondent characteristics, and tenure. Item means are reported separately on the supplied 1–5 scale; no multi-item composite, association test, or causal model is constructed. Knowledge creation had the highest knowledge-management item mean (3.63), followed by storage (3.55), application (3.39), and sharing (3.29). Organizational innovation had the highest related item mean (3.68), while process innovation was 3.33. Digital collaboration averaged 3.59. The 80 respondents represented 12 departments; Maintenance and PPIC each contributed 10 respondents. The supplied instrument file reports 25 items as valid and reliable, with r calculated values from 0.430 to 0.885 against a 0.220 threshold and supplied Cronbach alpha values from 0.723 to 0.911. Four source-derived figures show item ratings, knowledge-management response distributions, department representation, and item-level validity results. The study contributes a reproducible reporting approach for operational knowledge-management surveys that separates descriptive evidence from claims about performance, innovation, or implementation effects.
Artificial Intelligence-Based Supply Chain Resilience for Strengthening Manufacturing Performance During Global Operational Disruptions Effectively Nurlaela Kumala Dewi; Afferdhy Ariffien; Santi Susanti; Arlis Dewi Kuraesin
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.v1i3.36

Abstract

Supply-chain resilience requires an organization to detect disruptions, coordinate a response, and restore service while preserving decision traceability. This study descriptively analyzes academic consumer-goods manufacturing records containing 12 quarterly supply-chain-performance observations from 2022 to 2024 and eight paired risk assessments. The performance series compares four conventional-period observations with eight artificial-intelligence-enhanced observations; the risk dataset compares probability, impact, and risk-priority-number values before and after an AI-enhanced mitigation label. Mean supply-chain-performance-index score increased from 62.77 to 85.06. Mean disruption count decreased from 7.50 to 3.38 per quarter, and mean recovery time decreased from 12.22 to 4.88 days. Mean fill rate increased from 84.98% to 93.02%, while mean perfect-order rate increased from 76.43% to 90.05%. Inventory days declined from 39.25 to 18.50, and supplier lead-time variance declined from 6.10 to 1.62 days. Total risk-priority number across eight categories decreased from 351 to 124. The two datasets are analyzed separately because the records do not identify a common intervention protocol, algorithm, control group, or matched operational conditions. The findings therefore describe differences associated with the supplied period and mitigation labels rather than a causal estimate of artificial-intelligence impact. The study contributes a reproducible reporting format that links quarterly resilience indicators, paired risk-priority analysis, and a practical review matrix for supply-chain governance
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
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
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

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