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[Sustainable Supply Chain Practices in Local Fashion Brands ]]>
<![CDATA[Kartika, Nabila]]>
<![CDATA[ Journal of Sustainability Industrial Engineering and Management System Vol. 3 No. 1 (2024): July - December ]]>
Publisher : <![CDATA[Omnia Tempus ]]>
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DOI: 10.56953/jsiems.v3i1.38
<![CDATA[This study investigates the sustainable supply chain practices of local fashion brands, focusing on operational dynamics, strategic integration, and their alignment with broader sustainable development goals. Amid rising global concern over the environmental and social impacts of the fashion industry, local brands in emerging economies such as Indonesia play an increasingly critical role. However, the disparity in awareness, resources, and institutional support among these enterprises necessitates a deeper understanding of how sustainability is interpreted and implemented in their supply chains. Employing a qualitative, literature-based research design, this study synthesizes findings from 45 peer-reviewed sources published between 2010 and 2024. Thematic content analysis examined key dimensions, including material sourcing, ethical labor practices, waste reduction, stakeholder engagement, and traceability. The findings reveal that while many local brands engage in eco-conscious practices and artisan-based production, implementation remains fragmented and context-dependent. Strategic integration of sustainability is often constrained by financial limitations, informal labor systems, and the absence of enabling policy frameworks. Nevertheless, local brands demonstrate agility, cultural rootedness, and innovation potential, positioning them as key drivers of change. The study contributes to theoretical discourses in sustainable supply chain management and offers practical implications for entrepreneurs, policymakers, and sustainability advocates. It concludes by emphasizing the importance of systemic support, stakeholder collaboration, and long-term strategic commitment to achieve scalable and inclusive sustainability in the local fashion sector.]]>
<![CDATA[Ethical Considerations in AI Deployment for Customer Profiling ]]>
<![CDATA[Firmansyah, Rendra]]>
<![CDATA[ Journal of Sustainability Industrial Engineering and Management System Vol. 3 No. 1 (2024): July - December ]]>
Publisher : <![CDATA[Omnia Tempus ]]>
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DOI: 10.56953/jsiems.v3i1.39
<![CDATA[This study investigates the ethical considerations in the deployment of artificial intelligence (AI) for customer profiling by employing a qualitative literature-based research methodology. With AI-driven profiling systems becoming central to consumer analytics, companies now have unprecedented capabilities to personalize interactions, segment audiences, and predict behavior. However, this technological progress is accompanied by pressing ethical concerns related to privacy, informed consent, algorithmic bias, transparency, and psychological manipulation. The research synthesizes insights from 45 scholarly articles, regulatory documents, and industry reports, applying qualitative document analysis to identify thematic patterns in ethical challenges and organizational responses. The findings reveal two major thematic domains: first, the emergence of ethical tensions in AI systems, including concerns over data commodification, opacity of algorithms, and discriminatory profiling practices; second, the varied and often fragmented organizational approaches to ethical governance, ranging from aspirational guidelines to practical gaps in implementation. While there is growing awareness of responsible AI principles—such as fairness, accountability, transparency, and explainability—many organizations continue to struggle with embedding these values into their AI lifecycle. This study contributes to the literature by offering a conceptual framework that bridges theoretical ethics and applied governance, and emphasizes the importance of sustained organizational commitment, participatory design, and ethical foresight. Ultimately, the research highlights the need for a paradigm shift in both academia and industry, where ethics in AI moves from peripheral compliance to core strategic practice.]]>
<![CDATA[Waste Minimization Culture in Community-Based Enterprises ]]>
<![CDATA[Wirandana, Raka]]>
<![CDATA[ Journal of Sustainability Industrial Engineering and Management System Vol. 3 No. 1 (2024): July - December ]]>
Publisher : <![CDATA[Omnia Tempus ]]>
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DOI: 10.56953/jsiems.v3i1.41
<![CDATA[This study explores the cultural dimensions of waste minimization within community-based enterprises (CBEs), emphasizing their role as agents of grassroots sustainability. As environmental degradation escalates globally, CBEs—small, locally grounded organizations with social and environmental missions—have become pivotal in promoting sustainable practices through localized waste reduction behaviors. Despite the increasing relevance of CBEs in sustainability discourses, a gap remains in understanding how cultural values, leadership, education, and institutional reinforcement shape waste minimization practices. To address this, the study adopts a qualitative literature-based methodology, synthesizing findings from academic journals, policy reports, and empirical case studies published between 2010 and 2024. Using an inductive thematic approach, four key dimensions emerged: the influence of collective environmental values and community identity, the role of leadership and institutional reinforcement, the effectiveness of participatory education in behavioral change, and the integration of CBEs into long-term sustainability frameworks. The findings reveal that waste minimization culture in CBEs is driven by locally embedded norms and collective agency, reinforced by trust-based governance and peer influence. Furthermore, educational engagement and alignment with global agendas such as the Sustainable Development Goals (SDGs) enhance the scalability and durability of these practices. The study concludes that waste minimization should not be viewed solely as a technical or managerial function, but as a deeply cultural process rooted in community identity and social structure. These insights have both theoretical and managerial implications for scaling sustainability efforts in low-resource settings.]]>
<![CDATA[Human Perceptions of AI Reliability in Quality Control ]]>
<![CDATA[Maulidya, Intan]]>
<![CDATA[ Journal of Sustainability Industrial Engineering and Management System Vol. 3 No. 1 (2024): July - December ]]>
Publisher : <![CDATA[Omnia Tempus ]]>
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DOI: 10.56953/jsiems.v3i1.42
<![CDATA[In response to the growing application of artificial intelligence (AI) in industrial quality control (QC), this study explores how human users perceive the reliability of AI systems in manufacturing environments. While the technical capabilities of AI—including high-speed defect detection and pattern recognition—are well-documented, the human dimension of trust and perceived system reliability remains underexplored. Adopting a qualitative literature-based approach grounded in interpretivist methodology, this research systematically analyzes academic publications, empirical case studies, and theoretical contributions from fields such as human-computer interaction, industrial engineering, and cognitive psychology. Through thematic analysis of 75 peer-reviewed articles published between 2010 and 2024, the study identifies key factors that influence how reliability is perceived, including consistency, explainability, interface design, organizational culture, and user training. The findings suggest that perceived AI reliability is a dynamic, context-dependent construct shaped by both system attributes and the sociotechnical environment in which the AI operates. Specifically, the presence of transparent feedback mechanisms and adaptive explanations significantly enhances trust, while opaque decision-making processes and poor user alignment can erode perceived reliability even when actual performance is high. The study concludes by offering theoretical implications for human-AI interaction models and managerial strategies for effective AI deployment in quality assurance workflows. Ultimately, it underscores the need for human-centered AI design that aligns technological efficiency with psychological credibility and organizational readiness, thus paving the way for sustainable integration of AI in industrial quality control.]]>
<![CDATA[Systemic Risk Management in Small Manufacturing Networks ]]>
<![CDATA[Radhitya, Zaky]]>
<![CDATA[ Journal of Sustainability Industrial Engineering and Management System Vol. 3 No. 1 (2024): July - December ]]>
Publisher : <![CDATA[Omnia Tempus ]]>
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DOI: 10.56953/jsiems.v3i1.43
<![CDATA[In an increasingly volatile industrial landscape, the management of systemic risk has become a critical concern, particularly for small manufacturing networks (SMNs) composed predominantly of interconnected small and medium-sized enterprises (SMEs). These networks are vulnerable to cascading disruptions due to structural interdependencies, limited redundancy, and constrained institutional support. This study aims to investigate the nature of systemic risk in SMNs, assess existing mitigation strategies, and explore the role of collaboration and digital innovation in enhancing resilience. Employing a qualitative research methodology based on literature review, the study synthesizes insights from 65 peer-reviewed academic sources published between 2010 and 2024. The research is structured around four analytical themes: the triggers of systemic risk, current mitigation practices, network-based collaboration, and the impact of digitalization and policy innovation. The findings reveal that while SMNs have developed adaptive mechanisms such as supplier diversification and lean production buffers, these remain insufficient without coordinated inter-firm governance and technological integration. The study also highlights the paradoxical role of digital tools, which both mitigate and introduce new systemic risks, especially in resource-constrained environments. Furthermore, institutional frameworks and collaborative governance structures are identified as key enablers of systemic resilience. The study contributes theoretically by expanding the discourse on systems thinking and resilience engineering within SME networks and offers managerial implications for embedding risk management into digital and relational infrastructures. The conclusions advocate for targeted policies, inclusive platforms, and training programs to co-create resilient, adaptive, and sustainable manufacturing ecosystems.]]>
