Root Cause Analysis of Rule System Execution – One Step forward towards to Intelligent Decision Making
In this small blog entry I will introduce a research project if mine. I will introduce a use case, a model without using too much mathematics and will explain my next steps. I always appreciate comments, further suggestions and I hope to connect with other people from academia and business, who are working on the same problem.
The Need for Root Cause Analysis
We are using BRFplus to check business objects like insurance claims in automated processes. Sometimes those insurance claims are very complex – think of special care for example. The rule systems are quite huge and can be implemented in BRFplus using decision tables having some hundred rows.
The result of the rule set execution is a set of conspicuous features that have to be controlled by officials in a workflow based task management. This can be complex if the result of BRFplus execution consists of dozens of tasks. And in many cases it is possible that the execution of one task leads to a recalculation of the business objects which closes other tasks.
To optimize the working process of an official it is necessary to find the root cause and create and prioritize the tasks. But root cause analysis can be useful in dialog processes as well: the complex complex result of rule system execution can be presented in a way that the SAP user who works on that insurance case can understand easily what the problem is. This speeds up the whole work process and can also increase the service quality of an insurance company.
Please note that looking for root causes is different from rule system trace. I don’t want to show how a rule system execution was performed – I want to explain the behavior.
As I explained above we are looking for root cause. Sometimes a simple set of facts (represented my master or application data) can explain dozens of suspicious features: one special insurance policy is missing, some medical treatments are restricted to the age of the insurant and so on.
Finding explanations is a task for logical abduction – which was introduced by the philosopher and mathematician Peirce. Those methods can be applied in systems but also medical diagnosis. So this is what Dr. House is doing in his show.
I don’t want to go into detail into the topic but you should be aware of the following:
- Explanation is usually more complex then proving.
- Often we are looking for “simple” solutions. This is challenging since it is hard to define simplicity. And you can also question this approach: sometime the “simple” explanation is not correct what you can see in the show of Dr. House from time to time J
- The concept of simplicity leads to non-monotonic logic which is different compared to first order logic that we use in the foundations of set theory and so (modern) mathematics.
My little Contribution
When working with decision tables I found the standard approach of mathematical logic no very effective. As a consequence I tried to find out how a business object like an insurance claim has to be altered that many conspicuous features vanish.
This is not very difficult and leads to well understood combinatorial problems like set covering. Nevertheless I decided it to write it down in terms of mathematical logic although other formulations like Boolean functions are possible, too.
If you are interested you can read a draft version here. But please be warned, it is only a draft and I tested the approach with random data. I already got valuable feedback from SAP Mentor colleague Peter Langner which still has to be included. He also mentioned the concept of information flows which I have to
read and understand.
At the moment I am thinking of a BRFplus implementation of my approach. I think I will have to try out whether BRFplus API and or execution trace are sufficient for it. But I would also like to have the feedback from scientists who are experts in this area – I only consider myself as a beginner. May be we could also write a paper. In the past I worked together with one colleague from SAP UK who is also researcher at the University of Belfast. It contains a mathematical model that helps to understand the behavior of rule systems working with ambiguous data. Our paper was accepted KES-IDT-15 conference about intelligent decision making.
For me the participation was one highlight of the year and I learned a lot. The most important learning was that my mathematical skills became a little bit rusty so I appreciate any help. I am convinced the Decision Making and Decision Making Technologies is one of most promising and interesting areas and cornerstone of Next Generation ERP Systems. Today Enterprise Resource Planning is not so smart but with technologies like DSM and HANA SAP provides edge technologies. It is our task to use them to develop business applications that use these technologies to create amazing new solutions. I appreciate any help from students with skills in optimization and artificial intelligence.