Large amounts of data are increasingly gathered in order to support decision making processes in asset management. The challenge is how best to utilise the large amounts of fragmented and unorganised data sets to benefit decision making, also at fleet level. It is therefore important to be able to utilize and combine all the relevant data, both technical and economic, to create new business knowledge to support effective decision making especially within diverse situations. It is also important to acknowledge that different types of data are required in different decision making context. A review of the literature has shown that decision making situations are usually categorized according to the decision making levels, namely strategic, tactical and operational. In addition, they can be classified according to the amount of time used in decision making. For example, two situations can be compared: 1) optimization decision where a large amount of time and consideration is used to determine an optimum solution, and 2) decisions that need to be made instantly. Fleet management of industrial assets suffers from a lack of asset management strategies in order to ensure the correct data is collected, analysed and used to inform critical business decisions with regard to fleet management. In this paper we categorize the decision making process within certain situation and propose a new framework to identify fleet decision making situations.

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Nuclear power company plant maintenance has been conservative since it was born. Before we adopted DREAMS system, most of PM documents were handled with paperwork and we spent much time to perform plant maintenance. Last year KHNP constructed and implemented the computerized PM system, which was part of SAP system named DREAMS, and it made KHNP cut down expenses, save time, decrease trouble and optimize work intervals. To make PM system much better, we are trying to import equipment reliability process and we have a plan to initiate it within a year. Therefore the purpose of this paper is not only introducing an advanced methodology of plant maintenance, but showing the optimized PM system (equipment reliability process), considering safety and cost-effectiveness simultaneously.

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