TY - GEN
T1 - Decision making situations define data requirements in fleet asset management
AU - Kinnunen, Sini-Kaisu
AU - Marttonen-Arola, Salla
AU - Ylä-Kujala, Antti
AU - Kärri, Timo
AU - Ahonen, Toni
AU - Valkokari, Pasi
AU - Baglee, David
PY - 2016/1/1
Y1 - 2016/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85042846177&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-27064-7_33
DO - 10.1007/978-3-319-27064-7_33
M3 - Conference article in proceedings
AN - SCOPUS:85042846177
SN - 978-3-319-27062-3
SN - 978-3-319-80065-3
T3 - Lecture Notes in Mechanical Engineering
SP - 357
EP - 364
BT - Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015)
A2 - Koskinen, Kari T.
A2 - Kortelainen, Helena
A2 - Aaltonen, Jussi
A2 - Uusitalo, Teuvo
A2 - Komonen, Kari
A2 - Mathew, Joseph
A2 - Laitinen, Jouko
PB - Springer
T2 - 10th World Congress on Engineering Asset Management, WCEAM 2015
Y2 - 28 September 2015 through 30 September 2015
ER -