Implementing KPI Trending in OBIEE
Introduction
Business Managers are frequently provided with BI reports designed to support their decision making
process. These analyses typically include highly visual graphs showing how the company performs over
time against a set of predefined indicators, aka KPIs.
However, to build a report under OBIEE showing the historic trend of different KPIs is not as simple as it
may seem.
Real case scenario
KPI trending for Procurement
Suppose that a procurement manager needs to track the evolution of the following indicators:


Number of Winning RFQ Responses
Number of Tenders Under Process
Before going ahead with the solution, a few details about the RFQ business process will be explained:



The first stage of The Request for Quotation process starts when the buying company opens a
tender for acquiring a product/service.
In the second stage, suppliers can start bidding for that specific product/service by sending their
RFQ responses.
Once the buyer decides which is the best option, the winning RFQ response is approved and the
buying company places an order containing the terms agreed upon.
The following report would cover what the procurement manager requires:
Problem description
The main problem lies in the fact that the time dimension to be used in the trending analysis needs to
be related to the dates that define the different KPIs:
1. The number of winning RFQ responses has to be shown according to the Tender Award Date.
2. For the Number of Tenders under Process, we’ll have to count the number of RFQs that are
included in the date range defined between Tender Enter Date and Tender Close Date.
At this stage it is important to identify the specified aggregation for the two KPIs. For clarification
purposes, let’s take a look at the company results in January 2012:
o
Winning RFQ responses
Setting a sum so the aggregation rule will bring the expected results:
o
Tenders Under Process
In this case, the last value of the period will lead to the results we need:
In the next few lines, we will explain how to conform these two dates and be able to report on a
single time dimension analysis.
Solution
1. Create a new time dimension, i.e. Dim_Date_PRC_Trending
2. Join it to the fact table within the physical layer and set a 1:1 relation.
3. Link the two tables in the business layer
4. Create two measures. One based on count distinct and the other based on the last period value:
o Number of RFQs (sum) -> RFQ # (sum)
o
Number of RFQs (last period) -> RFQ # (last period)
5. Create the KPIs as new logical columns in the related fact table
6. Set the formula of the two columns as it follows. This is the place where the join condition with
the Trending date will be specified:
Number of Winning RFQ responses
filter ( "HR"."Fact RFQ"."RFQ # (sum)" using
"HR"."Dim RFQ"."Tender APP Type" = 'ITT' and "HR"."Dim RFQ"."Tender Award Date - Full Date" = "HR"."Dim Date
PRC Trending"."Date" )
Number of Tenders Under Process
filter ( "HR"."Fact RFQ"."RFQ # (last period)" using
("HR"."Dim RFQ"."Tender Enter Date" <= "HR"."Dim Date PRC Trending"."Date" )
and
("HR"."Dim RFQ"."Tender Close Date" is null or "HR"."Dim RFQ"." Tender Close Date " > "HR"."Dim Date PRC
Trending"."Date" )
and
"HR"."Dim RFQ"."Tender APP Type" ='ITT'
and
"HR"."Dim RFQ"."Tender Status" <> 'CANCEL'
)