Aulia Bening
Universitas Negeri Medan

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Analisis Hambatan Produksi dan Efektivitas Sistem Pemeliharaan Preventive Maintenance Pabrik PT. Nippon Indosari Corpindo Medan Khafi Puddin; Marito Ritonga; Viory Salsabila; Intan Jamilah; Dian Kartika Sari; Refi Adiyaksa Harahap; Aulia Bening
OPTIMAL Jurnal Ekonomi dan Manajemen Vol. 6 No. 1 (2026): Jurnal Ekonomi dan Manajemen
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/optimal.v6i1.9056

Abstract

Production disruptions remain a major challenge in the manufacturing industry, including at Indonesia's largest bread company, PT. Nippon Indosari Corpindo Tbk., which relies on a large-scale automated production system. Despite using modern technology and integrated management systems, the factory often faces technical obstacles that impact production efficiency and continuity. This study aims to analyze the factors that hinder the production process and evaluate the effectiveness of the preventive maintenance system at the Medan Factory of PT. Nippon Indosari Corpindo Tbk. This study uses a qualitative method with a case study approach through interviews, observations, and documentation of employees who are competent in the fields of production and maintenance. The results of the study show that the main obstacles stem from technical factors related to machinery, while human error and delays in raw materials are relatively minimal due to an automated work system and efficient raw material management. High dependence on machinery means that any damage has a direct impact on production continuity. The maintenance system implemented by the company consists of two layers, namely periodic preventive maintenance by technicians from Japan and corrective maintenance by company technicians who are on standby. Although effective in preventing major damage, disruptions still occur between maintenance periods, indicating the need to increase the frequency of maintenance, implement real-time machine condition monitoring, and integrate predictive maintenance to continuously improve production reliability and efficiency.