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Transforming Information Systems into Intelligent Socio-Technical Ecosystems via Machine Learning and Explainable AI: A Systematic Review Misti Jahrani Putri; Martanto Martanto; Fathurrohman Fathurrohman; Edi Tohidi; Umi Hayati
Immortalis Journal of Interdisciplinary Studies Vol. 2 No. 3 (2026): July - September
Publisher : PT. Caesarindo Triloka Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67307/ijis.v2i3.128

Abstract

Rapid developments in Machine Learning (ML) and Data Analytics (DA) are reshaping Information Systems (IS) from transactional tools into intelligent, socio-technical ecosystems. However, existing literature remains fragmented between purely algorithmic focus and organizational adoption. This Systematic Literature Review (SLR) synthesizes 48 high-impact Scopus-indexed studies published between 2020 and 2026, following the PRISMA 2020 framework, to map the architectural and organizational integration of ML and DA in IS transformation. Our findings reveal a paradigm shift across five dominant thematic clusters: predictive forecasting, intelligent automation, big data integration, Explainable AI (XAI), and Human-AI collaboration. Rather than pure automation, the literature strongly underscores a transition toward Human-Centered AI, where model interpretability and socio-technical governance are critical to user trust and system performance. Furthermore, we identify core deployment bottlenecks—specifically regarding algorithmic transparency, cross-system interoperability, data privacy, and ethical governance. This study contributes a novel conceptual framework illustrating the interplay between technical ML capabilities, cross-cutting enablers, and organizational value creation, offering actionable guidelines for designing sustainable, transparent, and adaptive AI-driven IS.
Optimizing Supply Chain Management through ERP Odoo Implementation: A Systematic Literature Review Naufal Febriano; Martanto Martanto; Ade Irma Purnama Sari; Rudi Kurniawan
Immortalis Journal of Interdisciplinary Studies Vol. 2 No. 3 (2026): July - September
Publisher : PT. Caesarindo Triloka Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67307/ijis.v2i3.141

Abstract

The growing complexity of supply chain operations and the acceleration of digital transformation have heightened organizational demand for integrated enterprise systems, yet the specific contribution of Odoo-based ERP to supply chain optimization remains fragmented across the literature and underexplored as a dedicated research focus. Enterprise Resource Planning (ERP) systems have emerged as indispensable technological infrastructures for coordinating business processes and optimizing supply chain performance, and among contemporary ERP solutions, Odoo has attracted notable interest due to its open-source architecture, modular flexibility, and scalability. This study aims to synthesize the scattered evidence on how Odoo-based ERP contributes to supply chain integration, operational efficiency, and Industry 4.0-enabled transformation, and to distill the critical success factors that shape its adoption. A Systematic Literature Review (SLR) methodology following the PRISMA 2020 framework was employed, synthesizing 41 Scopus-indexed journal articles published between 2021 and 2025 across four analytical themes. The synthesis shows that ERP systems consistently strengthen supply chain integration, process automation, and operational visibility, and that these benefits are amplified when ERP is combined with AI, IoT, Blockchain, Cloud Computing, and Big Data Analytics; at the same time, the realization of these benefits is conditional on organizational readiness, leadership commitment, employee competencies, and structured change management rather than on technology adoption alone. By consolidating this fragmented evidence, the review offers a synthesized understanding of Odoo's role in SCM optimization and evidence-based guidance applicable to Shegia Grup and comparable open-source ERP-adopting organizations.