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Kinnunen, Sini; Marttonen-Arola, Salla; Yla-Kujala, Antti; Karri, Timo; Ahonen, Toni; Valkokari, Pasi; Baglee, David (2016)
Publisher: Springer International Publishing Switzerland
Languages: English
Types: Part of book or chapter of book
Subjects: sub_mechanicalengineering
Large amounts of data are increasingly gathered in order to support de-cision making processes in asset management. The challenge is how best to utilise the large amounts of fragmented and unorganised data sets to benefit decision mak-ing, 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 deci-sion 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 man-agement 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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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