PROPOSED SOLUTION TO OPTIMIZE FUEL BLENDING PROCESS THROUGH KNOWLEDGE MANAGEMENT INITIATIVES - CASE STUDY AT PT PERTAMINA (PERSERO)

• Main Author: Gifry, Rizwan
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/53485
• Institution: Institut Teknologi Bandung

In Pertamina’s fuel distribution process, especially for the Pertamax type which has RON 92, the fuel distributed to the fuel terminal is carried out through the import process from abroad or from Pertamina's refineries. The fuel supplied to the fuel terminal can be in the form of fuel that has been mixed with additives and dyes or which has not been mixed. For unmixed fuel, additives and / or dyes will be blended at Pertamina's fuel terminal. The additive / dyes mixing process is done manually by inserting a number of additives / dyes into the tank. Thus, the manual mixing is not met to the standard and not optimal, resulting in non-standard quality of fuel sold to consumers. Based on these problems, in this final project a research was conducted using a combination of qualitative methods with structured interviews and focus group discussions to obtain data and information that will be included in the concept of knowledge management. The implementation of knowledge management implementation strategy uses the 8 steps developed by Tjakraatmadja (2017) to obtain knowledge management strategies and knowledge management initiatives. Furthermore, a focus group discussion was conducted to determine the main priority of the knowledge management initiative. Furthermore, from the knowledge management initiative, a comparison is made with the implementation of the innovation program that has been carried out at Pertamina and the result is a similarity between the knowledge management initiative's priorities and the existing implementation. Then with the efficiency and savings obtained from the implementation of the innovation program, it is recommended that the KM initiative be replicated for other locations in order to obtain projections of investment cost efficiency and raw material cost savings.