BPM Ecosystems Need Simulation and Optimization - Page 3

Mar 27, 2007

Marcia Gulesian

With an ESB, the enterprise has the ability to scale seamlessly from a small number of initial containers acting as aggregation points, to a multi-level network of localized integration points.

But, new platforms can quickly become a victim of their own success in that once rolled out most enterprises become wholly dependent on them, making it nearly impossible to schedule down-time for maintenance or enhancements. Any downtime can quickly impact sales, or increase costs.

The ability to manage the cost of deploying changes is key to any solution, and it is here the ESB excels again. The use of lightweight distributed containers means that new services can readily be deployed “on-the-fly” to remote nodes from a central server without any downtime.

It also means the use of a central message bus ensures that where services are taken offline, messages relating to active business processes will be held on a queue until processing restarts.

Older solutions, for example hub and spoke engines, may find a role as integration solutions within a business unit but an ESB is the natural solution to tie together business processes that span the enterprise and need to leverage components, in a transacted and secure way, often with different architectures and implemented in different technologies.

Standardization will allow heterogeneous components (including BPEL engines) to be plugged into different vendor's ESBs. ESB changes the economics of integration, enabling the rapid introduction of SOA disciplines with the potential for significant technical and economic benefits.

Simulation and Optimization

Your BPM process should include understanding risk in order to enable decisions that are not only most likely to succeed, but also highly beneficial. As a result, business performance management today requires predictive analytic applications that enable businesses to make decisions that maximize success while mitigating risk and understanding uncertainty.

Using modeling and simulation techniques, enables companies to gain insight into the range of possible business planning outcomes, quantify the likelihood and impact of those outcomes, and make decisions that balance risk and reward. The result is decisions of higher value.

The need for simulation optimization arises frequently in practice, given that most real world systems are too complex to analyze by trial and error. A growing number of business process management software vendors are offering simulation capabilities to extend their modeling functions and enhance their analytical proficiencies.

Simulation is positioned as a means to evaluate the impact of process changes and new processes in a model environment through the creation of “what-if” scenarios. Simulation is promoted to enable examination and testing of decisions prior to actually making them in the “real” environment. Since simulation approximates reality, it also permits the inclusion of uncertainty and variability into the forecasts of process performance.

Once a simulation model has been developed to represent a system or process, you may want to find a configuration that is best, according to some performance measure, among a set of possible choices.

For simple processes, finding the best configuration may be done by trial and error or enumeration of all possible configurations. When processes are complex, and the configuration depends on a number of strategic choices, the trial and error approach can be applied with only very limited success. In these cases, you may want to use an optimization tool to guide the search for the best configuration.

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