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The Problem: Complexity and Volatility of Business Logic
Companies have greatly improved the efficiency of their business processes by automating the flow of information, across distances and between systems. However, current improvements are limited, due to an inability to automate decision-making tasks.
Automation of decision-making tasks has been largely unsuccessful due to the complexity and volatility of the underlying business logic.
Figure 1 illustrates the problem, using the sample business process of mutual fund stock trading. The arrows indicate the information flow, and the circles indicate the activities, including both manual and automated. In this case, the Trade Execution or exchange activity is automated, while the other activities are manual. Trade Selection requires highly skilled and experienced employees. The decisions are complex and volatile, based upon numerous and varying, poorly documented criteria, most of which exist only in the heads of valued employees. Other activities, such as Trade Settlement, require less training. The decisions in Trade Settlement are based upon well-defined criteria, often documented in a policy manual.
The Solution: Corticon Business Rules Management Software
Corticon delivers a solution that offers dramatic improvements in business performance and agility. Corticon Business Rules Management enables companies to securely capture and model their business logic as interrelated sets of business rules, using a simple, drag-and-drop environment similar to a spreadsheet. In fact, the decision makers themselves participate directly in the Corticon business rules modeling process, capturing their best practices, policies, and procedures into formalized, executable software assets.
The business rule models are created and managed using the Corticon Business Rules Modeling Studio ("Corticon Studio"). Once defined, the rule models can be integrated into business applications as standards-based Web Services, using the Corticon Business Rules Server ("Corticon Server"). This combination helps to address the complexity and volatility inherent in the business logic of decision-making tasks.
Addressing complexity: In Corticon Studio, decision-making logic is represented as sets of interrelated business rules in a spreadsheet-like environment. Proprietary algorithms automatically identify conflict and incompleteness across the rule set, clarifying the logic interrelationships across the rules, and ensuring that exceptional circumstances are addressed. The models can then be tested against sample business scenarios, all without the assistance of programmers.
Once the models are loaded onto the Corticon Server, they are visible as Web Services, ready to be incorporated into existing business applications.
Addressing volatility: It seems that nearly the moment business rules are defined, they need to be changed. The problem is that a change to a single rule can break a whole application, due to the interdependencies across rules. When using the Corticon Studio, the interdependencies are clear, and business people can make changes safely, without the assistance of programmers. Furthermore, the changed models can be hot deployed into running systems, dramatically improving responsiveness.
Figure 2 illustrates the same trading process utilizing Corticon Business Rules Management.
Activities, such as Trade Settlement, in which all decisions are predefined, can be entirely automated. Other activities, such as Trade Selection, may require some degree of manual involvement. This is dependent on the preferences of the decision-making body. For example, stocks that meet all buy criteria may trigger an immediate, automated buy transaction; whereas stocks that meet only some criteria may trigger a notification and recommendation to a portfolio manager, who then approves or denies the trade. In both cases, Corticon Business Rules Management provides a complete audit trail for all transactions, tracking precisely which rules fired, in which order, for each trade. The decision-makers can then analyze the outcomes and rapidly adapt the decision-making logic to implement any changes in strategy or policy.
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