Building and Using Domain-Specific Libraries of Decision Models

Our customers frequently build not one but multiple decision models for their business domains like property and casualty insurance, loan origination, medical guidelines, etc. After building several decision models, they already have a quite rich glossary and various decision tables that essentially cover their business domain. So, it gives them a good foundation to build a library of relatively small decision models which can be used to assemble more complex decision models. Sometimes they even add domain-specific decision tables and supporting Java classes. This PDF document uses well-known loan origination problems (described in the Chapter 11 of the DMN specification) to explain how to build and utilize a library of decision models. Link

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My New Year Letter to OpenRules Customers

Dear Customers,

I’m Jacob Feldman, the CTO of OpenRules, Inc. I wanted to reach out to wish you a Happy New Year and thank you for your ongoing support of OpenRules. 2018 was a very successful year for OpenRules. We essentially advanced our decision engine to support a new decision modeling approach. We also received the Business Rules Excellence Award. For those of you who have already experienced the latest OpenRules Release 7.0.0, I hope you are seeing the benefits of the work we did during the last few years to improve the OpenRules robustness, reliability and simplicity of use. Continue reading

Which changes we may expect in 2019

I was asked by BPM.com to share my thoughts of what to expect in 2019. Digital Decisioning and DMN will continue to play an essential role in BPM. I can see two major trends in this development:

  1. Simplification. Representation of decision logic within business processes will be de-facto standardized using mainly simple DMN concepts such as decision tables and avoiding complex programming concepts. The simplified approaches such as “Goal-Oriented Decision Modeling” supported by OpenRules will continue to prevail in development of decision models incorporated into real-world business process models.
  2. Addressing Complex Decision Optimization Problems. So far, human decision modelers were forced to describe exactly HOW to find a decision by handling all possible combinations of business factors using business rules with multiple exceptions on top of exceptions. More powerful decision engines will allow decision modelers to concentrate on WHAT instead of HOW and will automatically determine multiple feasible decisions and select the optimal decision.

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