Journal of Sustainability Industrial Engineering and Management System
Journal of Sustainability Industrial Engineering and Management System is an interdisciplinary academic journal devoted to the publication of high-quality research and contributions to the industrial engineering and management community. The major focus are: - To collect and disseminate information on new and advanced developments in the field of industrial engineering and management; - To encourage further progress in engineering and management methodology and applications; - To cover the range of engineering and management development and usage in their use of managerial policies and strategies. Journal of Sustainability Industrial Engineering and Management System invites the submission of original, high-quality, theoretical, and application-oriented research; general surveys and critical reviews; educational or training articles, including case studies, in the field of industrial engineering and management. - Design and Manufacturing Engineering, - Facilities Engineering, Environment, and Energy - Production Systems, - Operations Research & Analysis, - Service Engineering, - Application of Artificial Intelligence in Industrial Engineering and Management, - Automation, Robotics, and Mechatronics, - Information and Communication Systems, - ICT for Collaborative Manufacturing, - Computational modelling, - Applied Statistics and Data Mining, - Quality and Reliability Engineering, - Human Factors, Ergonomics, and Safety, - Work Design and Measurement, - System Design and Engineering, - Organization and Human Resources, - Engineering Management, - Entrepreneurship and Innovation, - Inventory, Logistics, and Transportation, - Project Management, - Supply Chain Management, - Risk Management, - Asset Pricing Models and Portfolio Optimization, - Marketing and Commerce, - Investment, Finance, and Accounting, - Insurance Engineering and Management, - Media Engineering and Management, - Education and Practices in Industrial Engineering and Management, - Other Related Subject.
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<![CDATA[Analysis of Scooter Taxi User Satisfaction from the Aspect of Usability Through the Maxride Application in Makassar Using the Use Questionnaire Method ]]>
<![CDATA[Achmad, Muh Fachran Fahiran]]>;
<![CDATA[Saleh, Anis]]>;
<![CDATA[Hafid, Muhammad Fachry]]>
<![CDATA[ Journal of Sustainability Industrial Engineering and Management System Vol. 2 No. 1 (2023): July - December ]]>
Publisher : <![CDATA[Omnia Tempus ]]>
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DOI: 10.56953/jsiems.v2i1.22
<![CDATA[This study aims to analyze the usability of the Maxride ride-hailing application in Makassar, Indonesia, focusing on user satisfaction from a usability perspective. The research employed a quantitative approach using the USE Questionnaire method. Data was collected from 400 Maxride users in Makassar through an online survey. The questionnaire assessed four usability variables: Usefulness, Ease of Use, Ease of Learning, and Satisfaction.The overall usability score of the Maxride application was 65.60%, falling in the "feasible" category. While the app scored well in Usefulness (68.98%), Ease of Learning (74.06%), and Satisfaction (70.98%), it performed poorly in Ease of Use (48.38%), indicating significant room for improvement in this area.The study was limited to Makassar users and relied solely on quantitative data. As a cross-sectional study, it only captured user perceptions at a single point in time. These limitations suggest opportunities for future research with broader geographical scope, mixed methods, and longitudinal designs. The findings provide actionable insights for Maxride's development team to improve the app's user interface and functionality, particularly in terms of ease of use. Addressing these issues could enhance user satisfaction, retention, and potentially drive business growth.Improving the usability of ride-hailing apps like Maxride can contribute to better urban mobility solutions, potentially impacting transportation habits and quality of life in cities like Makassar.This study represents the first comprehensive usability analysis of the Maxride application, providing valuable insights for both the company and the broader ride-hailing industry in Indonesia. It establishes a baseline for future usability research in this sector and demonstrates the application of the USE Questionnaire method in evaluating mobile app usability.]]>
<![CDATA[Exploring Human-Machine Integration in Modern Manufacturing Environments ]]>
<![CDATA[Lazuardi, Aditya]]>
<![CDATA[ Journal of Sustainability Industrial Engineering and Management System Vol. 2 No. 1 (2023): July - December ]]>
Publisher : <![CDATA[Omnia Tempus ]]>
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DOI: 10.56953/jsiems.v2i1.26
<![CDATA[This research aims to explore the integration of human-machine collaboration in modern manufacturing environments, particularly focusing on the intersection of advanced technologies such as cyber-physical systems (CPS), artificial intelligence (AI), and collaborative robotics. The primary objective is to examine the role of human operators within these systems and to evaluate the challenges and opportunities that arise when human capabilities are combined with machine precision. A qualitative research methodology, structured as a systematic literature review, was employed to analyze and synthesize relevant academic studies, industry reports, and theoretical frameworks. The research delved into key theoretical models such as the Human-in-the-Loop (HITL) and Human-in-the-Mesh (HIM), which provide foundational perspectives on human involvement in decision-making processes within CPS. Additionally, the study explored cognitive ergonomics, the role of AI, and the psychological impacts of automation on human workers. Key findings include the importance of designing intuitive and adaptive human-machine interfaces to reduce cognitive load and enhance decision-making, as well as addressing the ethical implications of automation on job displacement and worker well-being. Furthermore, the integration of AI and collaborative robotics was found to improve operational efficiency, although human adaptability and continuous training remain crucial for successful implementation. The study concludes with a call for future research on the long-term impact of human-machine integration and the development of self-learning systems that can better collaborate with human operators.]]>
<![CDATA[Sustainable Facility Design: A Case Study on Energy-Efficient Plant Layouts ]]>
<![CDATA[Sekarwati, Nadira]]>
<![CDATA[ Journal of Sustainability Industrial Engineering and Management System Vol. 2 No. 1 (2023): July - December ]]>
Publisher : <![CDATA[Omnia Tempus ]]>
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DOI: 10.56953/jsiems.v2i1.27
<![CDATA[This study aims to explore the role of sustainable facility design in enhancing energy efficiency within industrial environments, with a particular focus on plant layout configurations. Amid increasing environmental regulations and economic pressures, optimizing spatial arrangements in manufacturing facilities has emerged as a strategic pathway toward reducing energy consumption and supporting broader sustainability goals. Employing a qualitative literature-based research methodology, this study synthesizes findings from approximately 45 peer-reviewed articles, policy reports, and industrial case studies published between 2010 and 2024. The research adopts an interpretivist epistemology and applies thematic content analysis to identify key concepts, strategies, and barriers associated with energy-efficient facility layouts. Findings reveal that spatial configuration significantly affects energy performance, influencing variables such as lighting, HVAC demand, and material transport distances. Technological integration, particularly through digital simulation tools and Industry 4.0 technologies like IoT and digital twins, further enhances layout optimization by enabling real-time energy monitoring and adaptive control. The analysis also underscores that energy-efficient layouts generate economic co-benefits, including reduced utility costs and improved production throughput. However, implementation barriers persist, notably in small- and medium-sized enterprises due to financial constraints, technical knowledge gaps, and organizational resistance to change. The study concludes that energy-efficient facility layouts are not only feasible but essential for sustainable manufacturing, offering a synergistic solution that aligns operational efficiency with environmental stewardship. It advocates for proactive, data-driven layout planning supported by cross-functional collaboration and policy incentives.]]>
<![CDATA[Energy Utilization Behavior in Small Manufacturing Enterprises ]]>
<![CDATA[Ramadani, Tariq]]>
<![CDATA[ Journal of Sustainability Industrial Engineering and Management System Vol. 2 No. 1 (2023): July - December ]]>
Publisher : <![CDATA[Omnia Tempus ]]>
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DOI: 10.56953/jsiems.v2i1.29
<![CDATA[This study explores the behavioral dimensions of energy utilization within small manufacturing enterprises (SMEs), an often-overlooked sector in industrial energy policy and sustainability research. Recognizing that SMEs collectively contribute significantly to national energy consumption yet frequently operate with limited energy awareness, this study investigates how organizational behavior, managerial perceptions, and operational routines influence energy efficiency outcomes. Employing a qualitative methodology grounded in literature-based analysis, the research synthesizes findings from over 45 peer-reviewed journal articles, institutional reports, and case studies published between 2010 and 2024. Thematic content analysis was applied to extract key patterns and constructs related to SMEs' energy behavior, focusing on cognitive, structural, and cultural drivers. The findings reveal that while technical solutions such as energy-efficient technologies are available, their adoption is often hindered by behavioral constraints, including low managerial commitment, lack of employee engagement, and entrenched operational habits. Furthermore, institutional support mechanisms, such as energy audit programs and incentive schemes, have shown limited effectiveness due to their misalignment with SME capacities and realities. The study highlights that sustainable energy practices in SMEs require more than technology upgrades; they demand a shift in organizational mindset supported by behavioral interventions, capacity building, and localized policy design. By emphasizing the socio-organizational context of energy use, the study contributes to a growing body of interdisciplinary research and provides practical insights for policymakers, industry leaders, and sustainability advocates aiming to promote energy-conscious behavior in the small manufacturing sector.]]>
<![CDATA[Organizational Readiness for AI Adoption in Indonesian Manufacturing SMEs ]]>
<![CDATA[Maharani, Rizka]]>
<![CDATA[ Journal of Sustainability Industrial Engineering and Management System Vol. 2 No. 1 (2023): July - December ]]>
Publisher : <![CDATA[Omnia Tempus ]]>
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DOI: 10.56953/jsiems.v2i1.30
<![CDATA[This study aims to explore and analyze the organizational readiness of Indonesian manufacturing Small and Medium-sized Enterprises (SMEs) in adopting Artificial Intelligence (AI) technologies. Given the pivotal role of SMEs in the national economy and their increasing exposure to digital transformation pressures, understanding their internal preparedness to integrate AI is both timely and essential. Employing a qualitative research methodology grounded in an extensive literature-based analysis, the study synthesizes empirical and conceptual findings from 45 peer-reviewed sources published between 2010 and 2024. The research adopts an interpretivist epistemological stance and applies thematic content analysis to uncover recurring patterns related to AI readiness across strategic, infrastructural, human, and institutional domains. The findings reveal that Indonesian manufacturing SMEs exhibit uneven levels of AI readiness, with significant gaps in strategic alignment, digital infrastructure, human capital development, and leadership commitment. Notably, cultural resistance and limited access to ecosystemic support further hinder sustainable AI adoption. However, the study also identifies emerging examples of collaborative innovation, particularly among SMEs engaged with universities, tech providers, and government initiatives. These cases illustrate the potential of context-sensitive readiness strategies tailored to Indonesia’s industrial landscape. The research contributes to the literature by integrating AI adoption frameworks with a nuanced understanding of local SME dynamics and offers actionable insights for business leaders and policymakers. Ultimately, this study calls for a multidimensional, continuous, and ecosystem-driven approach to AI readiness to ensure inclusive and sustainable digital transformation within Indonesia’s manufacturing sector.]]>
