#TITLE_ALTERNATIVE#

• Main Author: PUTRI UTAMI (NIM : 13413060), DESY
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/21671
• Institution: Institut Teknologi Bandung

PT Pertamina EP Asset 3 is one of the regional subsidiaries of PT Pertamina EP which is a subsidiary of PT Pertamina (Persero). This company deals in the upstream sector management, including exploration and exploitation of oil, gas and geothermal and is responsible for the fulfillment of domestic energy needs. <br /> <br /> <br /> The increase of world oil consumption, including Indonesia, prompted PT Pertamina EP to improve the performance of its productions. However, the phenomenon of the declining world oil prices has become a huge impact on the decrease in revenues earned by PT Pertamina EP Asset 3. The realization of total oil and gas sales revenues in 2016 reached 2,495 million USD decreased by 15% compared to total revenue in 2015 that reached 2,924 million USD. This has direct impacts on the savings made by PT Pertamina EP Asset 3 such as the efficiency of spare parts inventory. This savings turned out to affect the occurrence of spare parts understock that could disrupt the oil and gas exploration. This problems indicate that the company requires an efficient inventory policy but still consider to the level of service availability materials. <br /> <br /> <br /> The characteristics of the spare parts demand are intermittent and lumpy which have a very scare demand frequencies. It makes the use of theoretical distribution assumptions to represent demand patterns for those materials are difficult to do. The reference model used to solve this problem is the periodic review control model (T, S) based on group probability distribution formulated by Razi & Tarn (2003). This model was chosen because it is appropriate to represent the characteristics of spare parts inventory in PT Pertamina EP Asset 3 which is large enough to be divided into several groups. With this concept, probability distribution for every material’s demand was gained from their group probability distribution. <br /> <br /> <br /> The proposed inventory policy were designed for 33 materials that create 6 groups. After calculating the order-up-to-level (S) value for each material, implementing the proposed inventory policy gives better performances. Based on Of the 18 comparable materials, this policy could decrease 14.119% of the total inventory cost or Rp15.314.665.763 total saving and increase 3.77% of the average service level. From 94.68% to 98.45%.