AN INTEGRATIVE ANALYSIS OF AVIATION RISK MANAGEMENT PERFORMANCE PROFILE TO LEVERAGE DATA-DRIVEN SAFETY MANAGEMENT FOR BUSINESS COMPETITIVE ADVANTAGE: A CASE STUDY OF TRAVIRA AIR AVIATION SAFETY MANAGEMENT SYSTEM IMPLEMENTATION

The downturn in the oil and gas industry had led the producers to severe cuts in expenditure over exploration, production and maintenance activities globally, thereby adversely affecting the air charter services market. Responding to this condition, all air charter companies as a service provider in...

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Bibliographic Details
Main Author: Ihsan Salim, Muhammad
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/47583
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Institution: Institut Teknologi Bandung
Language: Indonesia
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Summary:The downturn in the oil and gas industry had led the producers to severe cuts in expenditure over exploration, production and maintenance activities globally, thereby adversely affecting the air charter services market. Responding to this condition, all air charter companies as a service provider in the industry are following with the low-cost strategy. However, one of the features in the Oil & Gas industry that is valued the most despite of the downturn fluctuation is the performance of Air Operator’s Safety Management System (SMS). This final project is composed with the objective to solve the problem on the effort to find a method of safety data analysis which can provide a reliable basis for communicating the risk management results to stakeholders in a consistent and comparable manner. The misleading information which is coming from invalid safety data analysis method may cause poor managerial decision in communicating safety priorities and evaluating the effectiveness of risk mitigation measures. Moreover, this problem will discredit the SMS process and lower the company business competitive advantage. In order to solve the problems above, an integrative approach is developed to transform large amounts of safety data collected from the SMS activities into useful information that supports effective decision making. Case study was performed based on Travira Air SMS implementation data during the period of 1 January 2019 until 20 April 2020. ARMS Methodology of Event Risk Classification is used as the Risk Assessment methodology which capable to provide Quantitative Risk Index approach to analyse the safety data. Whilst, the Flight Safety Foundation BARS Bow tie schematic diagram is used as a frame of reference for accident causation model. By combining those two methods we can conduct the integrative risk assessment of the various safety data which can show the Risk Management Performance Profile of the company based on demonstrated performance and be continuously improved with experience. Based on the case study performed, several results can be highlighted as an important point in this final project. Quantitative risk indexes as the result can be used as a reference for understanding how the accidents occurs and monitor the effectiveness of preventive barriers or recovery measures. By having an integrative approach to safety data analysis, through the application of Risk Management Performance Profile, the company can leverage its data-driven Safety Management to gain the business competitive advantage. In addition, it will help Safety Manager to correctly identify the safety hotspots and find a best fit solution to safety management system related problems. It also provides credible evidence to stakeholders and management to make effective decision based on informed data and sufficient analysis. A simple 4 (four) steps of implementation plan are proposed to be followed in order to make this Risk Management Performance Profile Model can be implemented and give a real positive impact to the company. The steps consist of Organisation Assessment, Process Implementation Planning, Process Execution and Evaluation of the process. Further research in terms of application of the model such as the development of data analysis method, other indicators, and correlation analysis between safety data attributes can be explored further to gain the most benefit from this model.