Analisis Kesiapan Implementasi Kecerdasan Buatan Dalam Pemeliharaan Asset Menggunakan Technology Readiness Framework: Studi Kasus Pabrik Metal Stamping (Otomotif)

Wibowo, Daniswara Seto (2026) Analisis Kesiapan Implementasi Kecerdasan Buatan Dalam Pemeliharaan Asset Menggunakan Technology Readiness Framework: Studi Kasus Pabrik Metal Stamping (Otomotif). Other thesis, Institut Teknologi Sepuluh Nopember.

Text 5010221023-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only Download (4MB) | Request a copy

Abstract

Industri metal stamping otomotif beroperasi di bawah tekanan sistem pengiriman just-in-time yang ketat, sehingga keandalan mesin stamping press menjadi faktor kritis dalam menjaga kelangsungan produksi. Pabrik yang menjadi studi kasus penelitian ini masih bertumpu pada pemeliharaan berbasis waktu dan tindakan korektif reaktif tanpa pemantauan kondisi mesin secara kontinu, sehingga menimbulkan risiko unplanned downtime yang berdampak pada ketepatan pengiriman. Untuk mengatasi hal tersebut, penelitian ini menganalisis kesiapan implementasi kecerdasan buatan dalam pemeliharaan aset melalui dua aspek penilaian secara paralel. Aspek pertama menilai kesiapan infrastruktur teknologi menggunakan 31 kriteria functional entities ISO 23247-2:2021 melalui observasi, wawancara, dan studi dokumen. Aspek kedua mengukur kesiapan sumber daya manusia menggunakan Technology Readiness Index 2.0 yang dianalisis melalui statistik deskriptif, uji korelasi Spearman, dan uji Kruskal-Wallis, kemudian disegmentasikan ke dalam persona kesiapan dan diverifikasi melalui wawancara konfirmatori bersama manajemen produksi dan tim ICT. Hasil menunjukkan kesiapan infrastruktur teknologi berada pada skor rata-rata 2,58 dari skala 5 dengan kesenjangan 1,42 poin terhadap target Level 4 (Managed). Kesiapan sumber daya manusia berada pada kategori sedang dengan TRI Index 3,474, dengan dimensi Optimism dan Innovativeness yang paling dominan. Ditemukan perbedaan signifikan berdasarkan role pekerjaan dan tingkat pendidikan. Integrasi kedua aspek menempatkan perusahaan pada prioritas peningkatan infrastruktur dengan sumber daya manusia yang relatif siap, sehingga rekomendasi strategis disusun dalam tiga horizon waktu.
=======================================================================================================================================
The automotive metal stamping industry operates under the pressure of strict just-in-time delivery systems, making the reliability of stamping press machines a critical factor in maintaining production continuity. The factory examined as the case study in this research still relies on time-based maintenance and reactive corrective actions without continuous machine condition monitoring, which creates the risk of unplanned downtime that directly affects delivery accuracy. To address this issue, this research analyzes the readiness for implementing artificial intelligence in asset maintenance through two parallel assessment aspects. The first aspect evaluates technology infrastructure readiness using the 31 functional entity criteria of ISO 23247-2:2021 through field observation, interviews, and document study. The second aspect measures human resource readiness using the Technology Readiness Index 2.0, analyzed through descriptive statistics, the Spearman correlation test, and the Kruskal-Wallis test, then segmented into technology readiness personas and verified through confirmatory interviews with production management and the ICT team. The results show that technology infrastructure readiness is at an average score of 2.58 out of 5, with a gap of 1.42 points from the target of Level 4 (Managed). Human resource readiness falls into the medium category with a TRI Index of 3.474, with the Optimism and Innovativeness dimensions being the most dominant. Significant differences were found based on job role and education level. The integration of both aspects places the company in a position prioritizing infrastructure improvement with relatively ready human resources, so that strategic recommendations are arranged into three time horizons.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Condition Monitoring, kecerdasan buatan, ISO 23247:2021, TRI 2.0, Industri Otomotif, AI, Automotive Industry
Subjects:	T Technology > T Technology (General) T Technology > TS Manufactures > TS174 Maintainability (Engineering) . Reliability (Engineering)
Divisions:	Faculty of Industrial Technology and Systems Engineering (INDSYS) > Industrial Engineering > 26201-(S1) Undergraduate Thesis
Depositing User:	Daniswara Seto Wibowo
Date Deposited:	31 Jul 2026 02:27
Last Modified:	31 Jul 2026 02:27
URI:	http://repository.its.ac.id/id/eprint/140250

Actions (login required)

View Item