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All Journal Jurnal Manajemen Transportasi & Logistik (JMTRANSLOG) Metta : Jurnal Ilmu Multidisiplin Management Studies and Business Journal
Nugroho, Cahyo Adi
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Kinerja Angkutan Barang pada jalur Kereta Api di Sumatera Selatan Nugroho, Cahyo Adi; Pribadi, Ocky Soelistyo; Soekirman, Atong; Hidayat, Masjarul; Mariana, Sandrina
Jurnal Manajemen Transportasi & Logistik (JMTRANSLOG) Vol. 11 No. 2 (2024): Juli
Publisher : Institut Transportasi dan Logistik Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54324/j.mtl.v11i2.1405

Abstract

The study aims to determine the impact of the infrastructure and maintenance quality on On Time Performance to the company’s freight transportation railroad track performance in South Sumatra. The data of the research was collected from a population of 30 coal mining companies operating in the southern part of Sumatra. This research used Path Analysis as the data analysis techniques. The conclusion states that there is a significant positive direct and indirect influence of the infrastructure facilities and maintenance quality variable on the company’s goods transportation performance through OTP. In addition, the company’s freight transportation performance contributes directly to OTP. Based on the research findings, the company’s freight transportation performance can be maximized by improving the rolling stocks quality and maintenance through On Time Performance. The Path Analysis results show that the infrastructure maintenance directly contributes to OTP of 21.17 percent and contributes to the freight transportation performance of 17.04 percent.
Pendekatan Manajemen Modern Strategi Efektif untuk Meningkatkan Produktivitas Tim Napitupulu, Rohani Lestari; Avian, Zakhi Bailatul Nur; Nugroho, Cahyo Adi; Pamuji, Slamet; Kasmin, Kasmin
Metta : Jurnal Ilmu Multidisiplin Vol. 4 No. 4 (2024)
Publisher : Jayapangus Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37329/metta.v4i4.3659

Abstract

This research discusses effective strategies for increasing team productivity through modern management approaches. In an increasingly complex and dynamic era, organizations are faced with the challenge of maximizing team performance to remain competitive. This research identifies various management methods that can be applied to create a more productive and collaborative work environment. Through case studies and literature analysis, we explore techniques such as open communication, efficient time management, and the application of digital technologies in team collaboration. The research results show that an inclusive and adaptive management approach contributes significantly to increasing team member motivation and engagement. Methods such as Agile and a results-based approach have been proven to significantly increase productivity and job satisfaction. By involving team members in the decision-making process and providing constructive feedback, organizations can build a positive and innovative work culture. This not only encourages creativity, but also creates a sense of belonging among employees. This research concludes that modern management strategies that focus on collaboration, effective communication and the use of technology can be the key to increasing team productivity. It is hoped that these findings can provide guidance for organizational leaders in formulating managerial policies that are more effective and responsive to team needs in the future. This research also invites further discussion about the importance of management adaptation in facing changing work dynamics and evolving challenges.
Enhancing Quality Management through Advanced Statistical Techniques Nugroho, Cahyo Adi; Perdana, Janatika Putra; Tirtoadisuryo, Dendy; Rachmat, Asep Ferry; Erlangga, Irwan Syah
Management Studies and Business Journal (PRODUCTIVITY) Vol. 1 No. 9 (2024): Management Studies and Business Journal (PRODUCTIVITY)
Publisher : Penelitian dan Pengembangan Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62207/vbrcgz16

Abstract

Production environments with high variability present challenges in maintaining quality consistency, which are difficult to address using traditional approaches. This research aims to evaluate the impact of machine learning (ML)-based optimization on long-term quality management in industrial sectors that experience high production fluctuations. Using a systematic literature review approach with the PRISMA method, this research analyzes 18 studies related to the implementation of ML in quality process optimization. Results show that ML significantly supports product stability, defect reduction, and sustainable operational efficiency. The implications of this research strengthen the application of ML as a relevant and effective method for improving long-term quality in dynamic production environments.
Co-Authors Avian, Zakhi Bailatul Nur Erlangga, Irwan Syah Hidayat, Masjarul Kasmin, Kasmin Mariana, Sandrina Ocky Soelistyo Pribadi, Ocky Soelistyo Pamuji, Slamet Perdana, Janatika Putra Rachmat, Asep Ferry Rohani Lestari Napitupulu Soekirman, Atong Tirtoadisuryo, Dendy