Proposed Knowledge Management System for the Time Efficient in R&D Department of PT Automotive Lumina

The automotive industry has grown post-Covid 19, positively impacting related sectors like car lighting. Due to high demand, PT Automotive Lumina, a lamp manufacturer specializing in OEM car lights, needs better time management. Responding to RFQs for new car models poses significant challenges due to delays caused by dependence on Juoku Technology. To tackle this, the company initiated an independent R&D process, but the need for more effective knowledge management worsens the situation, affecting competitiveness in a rapidly changing market.

This research addresses two key questions: What Knowledge Management system can be proposed for time efficiency in the R&D Department of PT Automotive Lumina? and How to implement the suggested Knowledge Management system for time efficiency in the R&D Department of PT Automotive Lumina? Qualitative research methods were employed to study these questions, including interviews with key stakeholders. Analysis revealed eight sub-causes contributing to the observed problems, including a lack of skilled personnel, ineffective knowledge management, reliance on Juoku’s R&D schedule, rapid technological advancements, inefficient processes, limited decision-making authority, limited testing equipment in the R&D department, and most importantly, the absence of a knowledge management system. Several solutions to these challenges are presented based on the SECI, PPT framework, and Core, Advanced, and Innovative knowledge framework. Direct training, knowledge capture, and collaboration tools are among the planned knowledge management programs at PT Automotive Lumina. Preparation procedures were conducted in November and December 2023, with the proposed solution implementation set to begin in February 2024. Following deployment, knowledge management activities will be evaluated, and necessary modifications will be made for future improvements. PT Automotive Lumina aims to enhance time efficiency and support independent R&D through improved knowledge management procedures.