One way we could accomplish this is to mask the data using pre-defined aggregation tables and then use them with the cube, but we really donot want to pre-define the aggregations and are looking at using the MDX. Also on the OLAP cube it allows adding all the measures using MDX as shown below in one of the calculated Members. SUM(([DIMENSION_1].[HIERACHY_1],[DIMENSION_2].[HIERACHY_2]),GFI_M1+CE_M1+NE_M1+NLE_M1+RTI_M1+NF_M1 ) , FORMAT_STRING="BEST15.", SOLVE_ORDER=3 But the problem is comparing these multiple measures to get the max / min across dimensions on the Cube. I used the SAS Information Map Studio to define a test measure which calculates the maximum of all the measures using MDX as MAX((<<DIMENSION1.HIERACHY_1>>,<<DIMENSION2.HIERACHY_2>>), max(measures.members)) But that only gives me the maximum of all the measures used in the query, the problem now starts with how to loop across all the measures to compare their values with the above calculate maximum measure across the dimensions and hierarchies and to calculate the second largest value of all the measures.. Any Help would be greatly appreciated. Thanks SAPPER
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