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All Journal AGRISAINTIFIKA Jurnal Ilmu-ilmu Pertanian Agroindustrial Technology Journal Journal of Industrial System Engineering and Management Integrasi: Jurnal Ilmiah Teknik Industri
Negara, Palmadi Putri Surya
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Transportation

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Journal : Journal of Industrial System Engineering and Management

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Integrating Digitalization in Supply Chain Performance Evaluation of the Zinc Industry through the Analytic Network Process (ANP) Fole, Asrul; Safitri, Khoerun Nisa; Aini, Nurul; Negara, Palmadi Putri Surya
Journal of Industrial System Engineering and Management Vol. 4 No. 2 (2025): Journal of Industrial System Engineering and Management (Edisi November)
Publisher : PT. SAFARI AMALIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56882/jisem.v4i2.55

Abstract

The zinc industry faces significant challenges in improving supply chain performance due to complex production, distribution, and quality control systems, as well as uneven digitalization that limits real-time monitoring and decision-making. This study develops a digitalization-based supply chain performance evaluation model using the Analytic Network Process (ANP) to determine priority weights of key performance indicators (KPIs). Data were collected from 15 industry experts with 15–20 years of experience. The analysis shows that Reliability (0.32) and Cost Efficiency (0.26) are the most influential dimensions, followed by Responsiveness (0.19), Flexibility (0.13), and Asset Management (0.10). The highest-priority KPIs are On-Time Delivery (0.0518) and Order Fulfillment Rate (0.0506), while Capacity Utilization Ratio (0.0033) has the lowest weight. Integrating digital systems such as ERP, IoT, and MES enhances real-time visibility, data accuracy, and responsiveness—reducing production delays by 18%, improving logistics cost efficiency by 12%, and increasing delivery reliability by 20%. The ANP-based model thus provides an objective, data-driven foundation for continuous performance improvement and strategic decision-making.
Co-Authors Agung Dwicahyo Agustin Rani Nurfadila Agustino, Salindra Aini Nurul Asrul Fole Bayu Wahyudi Khoerun Nisa Safitri Mahmud, Ahya Varian, Ahmad