DESIGN OF PERFORMANCE MANAGEMENT SYSTEMS FOR MARINE TRANSPORTATION SYSTEM IN PERTAMINA

• Main Author: Yunik Tri Wedayanti, Putu
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/38779
• Institution: Institut Teknologi Bandung

PERTAMINA is a stated owned company that has business in producing and distributing oil and gas. On the one hand, PERTAMINA is required to pursue financial benefits. On the other hand, PERTAMINA also received mandates from the government to provide public services in the form of energy distribution to all areas across Indonesia. These special characteristics require a contextual Performance Management System (PMS). However, the results of literature studies reveal that the numbers of PMS frameworks that can be applied to companies with characteristics similar to PERTAMINA are quite limited. The aim of the research is to design a PMS that is appropriate with PERTAMINA’s unique characteristics. Considering that several popular PMS frameworks like the Balance Score Card (BSC), SCOR, and Integrated Performance Management System (IPMS) were more frequently applied in single-role companies, this research utilises the PMS framework developed by Irfani, et al., as this framework is specifically designed for multiple-role companies like PERTAMINA. To design the PMS for PERTAMINA, this study follows the five-stage PMS, designed as suggested by Irfani, et al. In the first stage, analysis of PERTAMINA’s external environments such as that of the stakeholders, political, economic, social, technological, environmental, legal, competitors, supplier, substitute products, business rivalry, and customer factors were carried out. Several tools like stakeholder analysis, PESTEL, and Porter’s five forces models are used at this stage. In the second stage, the existing PMS that is currently used in PERTAMINA will be assessed, especially regarding its alignment to PERTAMINA’s strategies and characteristics. The data were collected by distributing questionnaires to respondents from ten divisions related to the marine transportation system at PERTAMINA. This stage covers four types of data analysis, including alignment analysis, congruence analysis, consensus analysis, and confusion analysis. In the third stage, the key performance indicators that are important and appropriate for PERTAMINA’s business were selected. After the key indicators have been successfully selected, in the fourth stage, the causal relationships among performance dimensions and performance indicators will be analysed using the DEMATEL method. This stage is deemed necessary, since the causal relationships between performance measures are expected to minimise the conflicts between the duties as profit generators and public service providers, conducted by PERTAMINA. The results of the study indicate that the existing PMS of PERTAMINA is not fully aligned with the organisation’s strategy. The alignment analysis results show that the existing PMS has not emphasised the infrastructure aspects of the marine transportation system, although the reliability aspect is emphasised in the organisational strategy. In addition, in the existing PMS, there are still several gaps and false alarms. Following up with these findings, several new key indicators that were perceived as important and appropriate for PERTAMINA were successfully selected by implementing the mean analysis, covariance analysis, and Cronbach’s Alpha analysis. Furthermore, the relationships between several dimensions of performance and performance measures have been successfully modelled. These causal relationships have helped decision-makers in PERTAMINA to comprehensively analyse how the marine transportation system at PERTAMINA performs. Although this research has made a new contribution, both for PERTAMINA and for academia, it is only conducted in one division related to the marine transportation system. To find out the generalisability of the PMS that has been proposed, this research can be replicated and furthered by applying it to other contexts. Furthermore, statistical tests to determine the impact of the new PMS implementation on the actual system’s performance also need to be carried out.