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.
Articles
35 Documents
<![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.]]>
<![CDATA[A Qualitative Exploration of Predictive Maintenance Practices in Bali ]]>
<![CDATA[Wiratama, Bayu]]>
<![CDATA[ Journal of Sustainability Industrial Engineering and Management System Vol. 2 No. 2 (2024): January - June ]]>
Publisher : <![CDATA[Omnia Tempus ]]>
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DOI: 10.56953/jsiems.v2i2.32
<![CDATA[This study investigates the practices, challenges, and strategic implications of predictive maintenance (PdM) in the industrial sector of Bali, Indonesia, with a particular emphasis on how local organizations conceptualize, implement, and experience PdM within their socio-technical contexts. The research aims to bridge the knowledge gap between global technological paradigms and localized maintenance strategies by exploring the extent to which PdM has been integrated into organizational routines and infrastructure. Employing a qualitative research design grounded in the interpretive paradigm, the study adopts an exploratory case study approach. Data were collected through in-depth semi-structured interviews, document analysis, and site observations across multiple firms in the manufacturing, utility, and infrastructure sectors. Thematic analysis was conducted using NVivo 14, ensuring methodological rigor and triangulation of findings. The study reveals significant variations in organizational awareness, technological readiness, and human capital development related to PdM adoption. Key findings highlight the misalignment between technological investments and actual utilization, as well as the pivotal role of leadership and organizational culture in shaping implementation outcomes. Moreover, the research identifies infrastructural limitations, digital literacy gaps, and vendor dependencies as major constraints, especially for small and medium-sized enterprises. Notably, several firms demonstrated emerging alignment between PdM practices and sustainability goals, suggesting untapped potential for predictive strategies to contribute to broader environmental and operational performance. The study concludes that successful PdM implementation in Bali requires a synergistic combination of technical infrastructure, cultural transformation, and strategic alignment, supported by cross-sectoral collaboration and policy intervention. These insights contribute to the evolving discourse on smart maintenance in emerging economies and offer practical recommendations for industrial managers, policymakers, and technology providers.]]>
<![CDATA[Automation and Skill Shift: Understanding the Workplace Transformation ]]>
<![CDATA[Rahmadina, Tasya]]>
<![CDATA[ Journal of Sustainability Industrial Engineering and Management System Vol. 2 No. 2 (2024): January - June ]]>
Publisher : <![CDATA[Omnia Tempus ]]>
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DOI: 10.56953/jsiems.v2i2.33
<![CDATA[This study explores the evolving dynamics of workforce transformation amid accelerating automation and the ensuing shift in skill demands. In an era characterized by rapid technological advancement, automation is redefining not only job roles but also the parameters of employability across industries. The research adopts a qualitative, literature-based methodology grounded in interpretive analysis to examine the socio-technical implications of automation on the nature of work, particularly the emergence of new skill requirements and organizational adaptation strategies. By reviewing and synthesizing insights from approximately 60 scholarly sources published between 2010 and 2024, the study employs thematic content analysis to identify patterns related to job reconfiguration, skill shifts, and institutional responses. The findings reveal that automation primarily restructures rather than replaces jobs, reallocating routine tasks to machines while intensifying the cognitive and emotional demands placed on human workers. Moreover, the study underscores the growing importance of digital literacy, emotional intelligence, adaptability, and continuous learning as essential competencies in the modern workplace. Institutional readiness—manifested through policy reform, educational innovation, and proactive organizational strategy—is found to be a critical determinant of whether automation leads to empowerment or displacement. The research concludes that sustainable workforce transformation requires an integrated, human-centered approach that aligns technological deployment with inclusive and adaptive systems of education and employment. This study contributes to the broader discourse by offering a nuanced, context-sensitive understanding of how automation is currently reshaping labor markets, while advocating for ethical and inclusive frameworks to guide future transitions.]]>
<![CDATA[Cloud-Based ERP Implementation Challenges in Small Industries ]]>
<![CDATA[Handoko, Yusuf]]>
<![CDATA[ Journal of Sustainability Industrial Engineering and Management System Vol. 2 No. 2 (2024): January - June ]]>
Publisher : <![CDATA[Omnia Tempus ]]>
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DOI: 10.56953/jsiems.v2i2.34
<![CDATA[This study investigates the multifaceted challenges faced by small industries in implementing cloud-based Enterprise Resource Planning (ERP) systems, aiming to provide a comprehensive understanding of the technical, organizational, and human-centric barriers that hinder adoption. Employing a qualitative research design, the study adopts an interpretivist approach through a systematic literature review of 45 peer-reviewed articles published between 2010 and 2024. Data were analyzed thematically using ATLAS.ti, enabling the identification of key implementation challenges and contextual factors shaping ERP adoption in resource-constrained industrial environments. The findings reveal three primary categories of obstacles: infrastructure and technological deficiencies, organizational unreadiness coupled with resistance to change, and significant human resource and knowledge gaps. Specifically, the study finds that inadequate digital infrastructure, cultural inertia, and the absence of sustained training programs frequently result in project delays, underutilized systems, and operational inefficiencies. Moreover, cloud-based ERP systems' success depends heavily on contextual alignment with the firm’s internal capabilities, strategic planning processes, and commitment to long-term digital transformation. These insights contribute to existing theoretical models such as the Technology-Organization-Environment (TOE) framework by highlighting the role of sustainability-oriented digital strategies and adaptive implementation practices. The study recommends collaborative interventions among policymakers, vendors, and industry associations to address structural disparities and to foster inclusive technological ecosystems. The research contributes both theoretically and practically to the discourse on ERP adoption, particularly within emerging markets where small industries remain foundational to economic development.]]>
<![CDATA[Organizational Challenges in Quality Assurance among Food Startups ]]>
<![CDATA[Lestiani, Amira]]>
<![CDATA[ Journal of Sustainability Industrial Engineering and Management System Vol. 2 No. 2 (2024): January - June ]]>
Publisher : <![CDATA[Omnia Tempus ]]>
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DOI: 10.56953/jsiems.v2i2.35
<![CDATA[This study explores the organizational challenges in quality assurance (QA) encountered by food startups operating within resource-constrained and rapidly evolving business environments. As consumer expectations for food safety and regulatory compliance intensify, startups face significant pressure to institutionalize effective QA systems. However, their informal structures, limited capital, and lack of specialized expertise often result in ad hoc quality practices that threaten operational integrity and customer trust. The objective of this research is to examine how internal organizational dynamics—such as leadership commitment, resource allocation, staff competencies, and learning capacity—affect QA implementation in food startups. Employing a qualitative methodology through literature-based analysis, the study systematically synthesizes academic articles, case studies, and regulatory reports to identify recurring themes and structural impediments. Findings reveal four major organizational challenges: weak quality culture and leadership, inadequate infrastructure and human resources, complex regulatory landscapes, and limited scalability of QA systems. These challenges are shown to interact in mutually reinforcing ways, exacerbating quality risks as startups grow. The study also uncovers a critical gap in quality learning mechanisms that prevents startups from evolving toward maturity. The results underscore the need for tailored QA frameworks and policy support to foster sustainable quality practices. The study contributes to the theoretical understanding of QA in emerging business contexts and offers managerial insights for founders, incubators, and regulators aiming to strengthen quality foundations in the food startup ecosystem.]]>
<![CDATA[Adapting Statistical Thinking in Traditional Manufacturing Contexts ]]>
<![CDATA[Nugroho, Fajar]]>
<![CDATA[ Journal of Sustainability Industrial Engineering and Management System Vol. 2 No. 2 (2024): January - June ]]>
Publisher : <![CDATA[Omnia Tempus ]]>
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DOI: 10.56953/jsiems.v2i2.36
<![CDATA[This study explores the adaptation of statistical thinking in traditional manufacturing contexts, emphasizing its significance as a cognitive and cultural framework for improving quality, reducing process variation, and fostering data-informed decision-making. While statistical thinking has become a cornerstone in modern manufacturing systems, its integration into traditional environments—dominated by legacy processes, experiential judgment, and minimal technological infrastructure—remains limited. The purpose of this research is to analyze the current landscape of statistical awareness in such settings and to identify both enablers and barriers to its broader adoption. Employing a qualitative methodology based on an integrative literature review, this study synthesizes evidence from peer-reviewed articles, industry case studies, and theoretical frameworks published between 2000 and 2024. Thematic analysis reveals a persistent gap in statistical literacy among employees, cultural resistance among leaders, and technological limitations that impede implementation. However, emerging practices—such as contextualized training, policy interventions, and leadership-driven cultural change—offer promising pathways for sustainable integration. The findings contribute to both academic and managerial discourse by reframing statistical thinking as a holistic organizational capability rather than a technical function. This research advocates for a strategic and inclusive approach that combines education, infrastructure, and leadership to foster statistical maturity in resource-constrained manufacturing environments. In doing so, it supports long-term resilience, continuous improvement, and competitiveness in the face of global industrial transformation.]]>
