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There are two ways to use analytic techniques to improve business rules. Firstly you can use data mining techniques.  This involves using data driven analysis to identify more profitable rules and strategies. With data mining you can use historical data to see what segments of a population respond best to what actions or what values predict members of which group and turn this into business rules. With optimization techniques, multiple data variables, predictive models, business objectives and constraints to produce optimized decision strategies or “decision models”.  With optimization, decision models can determine the most successful rule sets, models and thresholds to reach explicit business goals and meets constraints. Secondly you can embed more sophisticated predictive analytic models into rules-based decisioning. Predictive analytic models improve the precision and targeting of decisions based on forcasts of future customer behavior and other metrics.  These models work within business rulesets to segment customers for more targeted...

In many cases, a corporation’s business rules are defined in manuals or other documents, or possibly not defined formally at all—many organizations rely on business managers to interpret company policies as business rules. This can be inefficient and lead to inconsistencies in the application of the rules. Business Rules Management Systems are an alternative approach. The rules can then be applied efficiently and consistently across all touch points that use them. Business Rules Management Systems (BRMS) help companies develop and update business applications software more quickly and gives business experts greater control and power to make smart business decisions. With a BRMS, the total cost of ownership for managing business applications software is lowered by reducing programming requirements and delays in implementing operational rules. Allows non-technical business managers and analysts to maintain application logic as rules in production systems without being dependent on technical resources.This reduces application modification cycle time and...

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The California DMV recently replaced the portion of a legacy system that dealt with license fee calculation with a business rules implementation. This was the one part of the old legacy system that had variable business rules  - it required changes whenever the legislature changed the law. Now the California Legislature, like most, can't resist tinkering with the license fee calculations.  This meant that the system must constantly adapt to new rules about licensing, even though most of what it does stays the same. By using business rules technology for this one component the DMV was able to dramatically reduce how long it took to get changes into production and eliminate much of the cost of these changes. The use of business rules to manage this kind of highly volatile component is one of the classic first uses in an organization. If you want to try these rules out yourself, you can. Check out the Vehicle Registration Fee Calculator or read about the awards this system won - Intelligent Enterprise RealWare Award and Application Development Trends...

I am going to post a ton of examples here over time. To be getting on with, The Norwegian police department does shift assignments for their personnel based on experience, union rules, etc. A company in Japan used rules to control security clearances in a “smart office building.” A truck manufacturer uses rules to determine what kinds of parts can be placed on the basic assembly frame and where they should be positioned for the assembly process. Rules look for fraud, they spot people who haven’t filed tax returns, they react to automatic diagnostics on manufactured parts, they help insurers crawl through a ship’s hold while inspecting it for seaworthiness (and so on ad infinitum).

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