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All Journal Nusantara Hasana Journal Mount Hope Global Economic Journal JUPARITA: Jurnal Pariwisata Tawangmangu VISI SAKTI: Jurnal Industri Kreatif dan Inovatif
Uning Heri Gagarin
Universitas Efarina
Agriculture, Biological Sciences & Forestry Arts Humanities Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Nursing Social Sciences

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STRATEGI PORTOFOLIO KORPORAT: DIVERSIFIKASI VS. FOKUS UNTUK KEBERLANJUTAN USAHA PADA PERUSAHAAN MANUFAKTUR Uning Heri Gagarin; Yeni Trisna Purba; Yohanis Rumbiak
Jurnal Industri Kreatif dan Inovatif Vol. 3 No. 2 (2025): Pengembangan Komunikasi Visual dan Komunikasi Digital
Publisher : Institut Teknologi dan Bisnis Kristen Bukit Pengharapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61696/visisakti.v3i2.1048

Abstract

This study examines corporate portfolio strategies in manufacturing firms by comparing two main approaches—focus strategy and diversification strategy—and assessing the role of sustainability through ESG-based diversification. The research aims to (1) compare the effectiveness of focus versus diversification, (2) analyze their impacts on financial performance and risk management, and (3) explore how sustainability-oriented diversification can generate competitive advantage and support long-term growth. The study applies a mixed-methods approach, combining quantitative analysis using key financial performance indicators (e.g., ROA, ROE, revenue growth, diversification index, and debt ratio) with qualitative case studies. The findings indicate that focus strategy generally delivers better market performance, with an average shareholder return (rTSR) of 2.3% compared to 1.6% for more diversified firms. Nevertheless, diversification remains important for spreading risk and sustaining growth stability. ESG-based diversification further strengthens innovation, mitigates regulatory and reputational risks, and enables access to new markets that increasingly demand environmentally friendly products. The study recommends an integrated approach: strengthen core competencies first, then pursue measured diversification into related segments while integrating ESG principles, enabling manufacturing companies to achieve business sustainability amid global uncertainty.
INTEGRASI AI/ML UNTUK MITIGASI RISIKO OPERASIONAL DI PERUSAHAAN INDUSTRI BEI Uning Heri Gagarin; Jumadiah Wardati; Lidia Simanjuntak
Jurnal Industri Kreatif dan Inovatif Vol. 3 No. 1 (2025): Desain Komunikasi Visual
Publisher : Institut Teknologi dan Bisnis Kristen Bukit Pengharapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61696/visisakti.v3i1.1058

Abstract

Digital technology has introduced major disruption to operational risk management, particularly for industrial companies listed on the Indonesia Stock Exchange (BEI). The growing complexity of operations increases the difficulty of identifying and mitigating risks such as process failures, human error, outdated systems, and external disruptions—factors that can threaten business continuity and cause significant losses. This study examines how the integration of Artificial Intelligence (AI) and Machine Learning (ML) can be applied strategically to improve the accuracy, speed, and efficiency of operational risk mitigation in BEI-listed industrial firms. The main focus includes the benefits of AI/ML implementation: predictive analytics for risk identification, real-time monitoring and anomaly detection, automation of routine tasks to reduce human error, efficiency gains and cost savings, as well as improved regulatory compliance and data security. It also addresses effective implementation strategies such as starting with low-risk pilot projects, fostering cross-department collaboration and workforce training, establishing strong governance and oversight (human-in-the-loop), improving data quality and mitigating algorithmic bias, and aligning the approach with regulatory requirements and security standards. The paper further presents practical examples including predictive maintenance, sentiment analysis for reputation risk mitigation, and AI/ML-based automation for compliance reporting and auditing. Overall, the study concludes that AI/ML is not an instant solution, but a transformative approach requiring careful planning, high-quality data, integration with existing systems, and organizational readiness through training and robust governance to minimize operational risk, enhance efficiency, and maintain competitiveness.
Co-Authors Elia Agus Victoria Evlin Anariska Sebayang Frans Favo Purba S Hendri Mayanta Jumadiah Wardati Lidia Simanjuntak Obet Hendra Berutu Yeni Trisna Purba Yohanis Rumbiak