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Abstrak :
Penelitian ini bertujuan untuk merancang kerangka kerja inovasi disrupsi di industri otomotif dan memilih teknologi disruptif untuk menunjang inovasi disrupsi, sehingga perusahaan petahana maupun perusahaan otomotif lainnya dapat mengembangkan sebuah inovasi disrupsi untuk dapat tetap bersaing di pasar. Dalam merancang kerangka kerja inovasi disrupsi di industri otomotif, digunakan metode Delphi yang dikombinasikan dengan skala likert serta diskusi dengan para ahli. Pemilihan teknologi disruptif menggunakan metode Analytical Hierarchy Process (AHP) untuk menentukan kriteria dan metode Technique for Others Reference by Similarity to Ideal Solution (TOPSIS) untuk memilih alternatif. Hasil dari penelitian ini ialah kerangka kerja inovasi disrupsi di industri otomotif yang terdiri dari sembilan langkah, serta mobil listrik berkecepatan rendah sebagai alternatif yang dipilih untuk menunjang inovasi disrupsi. ......This study aims to design a framework for disruptive innovation in the automotive industry and choose disruptive technologies to support disruptive innovation so that incumbent companies and other automotive companies can develop disruptive innovation to be able to remain competitive in the market. In designing the framework for disruptive innovation in the automotive industry, the Delphi method combined with a Likert scale was used as well as discussions with experts. The selection of disruptive technology uses the Analytical Hierarchy Process (AHP) method to determine the criteria and method of the Technique for Others Reference by Similarity to Ideal Solution (TOPSIS) to select alternatives. The result of this study is a nine-step disruptive innovation framework in the automotive industry, as well as low-speed electric cars as alternatives chosen to support disruption innovation.

Abstrak :
ABSTRAK
Dalam melakukan pekerjaan sehari-hari personil bergantung pada pengetahuan personil lainnya dalam menyelesaikan masalah. Itu artinya mereka tidak bisa memecahkan masalahnya sendiri tanpa bantuan dari personil lain yang kompeten dan berpengetahuan dalam hal tertentu. Mengarnbil studi kasus pada divisi pemeliharaan alat berat, penelitian ini berkontribusi pada transfer pengetahuan tingkat individu antar member non-management level danbertujuan mendapatkan strategi manajemen pengetahuan. Kerangka Delphi-AHP-TOPSIS digunakan dalam penelitian ini yang terbagi dalam tiga tahap. Tahap pertama yaitu metode Delphi, dimana faktor-faktor penting transfer pengetahuan diidentifikasi. Tahap kedua yaitu Analytical Hierarchy Process (AHP) untuk membobotkan faktor tersebut. Dan tahap ketiga yai'ni Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) untuk menentukan prioritas strategi manajemen pengetahuan. Penelitian ini menghasilkan tiga strategi utama untuk member nonmanogement level diantaranya pelatihan dan penugasan/terlibat dalam proyek, membuat mekanisme sistem reward dan after action review. Dimana strategi personalisasi menjadi strategi yang paling disukai.

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ABSTRACT
In their day-to-day work, personnel rely on the knowledge of other personnel in solving problems. It means they could not solve their own problems without help from other persorurel who are competent and knowledgeable in certain cases. Drawing on a case study of heavy equipment maintenance division, this study contributes to knowledge transfer individual level among members of nonmanagement level and aims to formulate knowledge management strategy. Framework Delphi-AHP-TOPSIS was used in this study which divided into three stages. The first stage is Delphi method, where important factors of knowledge transfer were identified. The second stage is Analytical Hierarchy Process (AHP), where the weight of these factors obtained. And the third stage is Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to determine the priority of knowledge management strategy. This study found three major strategies for non-management level member such as training and assignment/ involved in the project, making mechanism of reward system and after-action reviews. Where the personalization strategy is the most preferred strategy.