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Performance Factors for Siebel Configurator

In planning Siebel Configurator server-based deployments, or in troubleshooting performance for existing deployments, you must consider several key factors that determine or influence performance.

Subsequent sections provide information and guidelines to help you achieve and maintain optimal performance and scalability.

Performance contexts to consider include response times for:

  • Loading customizable products. This is the time elapsed from the moment a user clicks Customize in a quote or order until the user interface for the customizable product has been loaded and displayed to the user.
  • Responding to user selections. This is the time elapsed from the moment a selection is made by the user until Siebel Configurator returns a response such as an update to the customizable product or a conflict message.

The factors below, particularly customizable product size and complexity, are relevant in both of these contexts.

Some of the key performance factors for server-based deployments of Siebel Configurator include:

  • Number of concurrent configuration users. The number of concurrent users who access customizable product models. This figure will be some percentage of the total number of concurrent users on the AOM.

    More specifically, you would be concerned with the total number of configuration sessions per hour, and the average length of those sessions.

  • Size and complexity of product models. The total size and complexity of each customizable product model, particularly where multiple hierarchical levels, many constraints, and a complex user interface are defined.

    A major potential performance factor is custom scripting attached to update events on applicable business components, such as Quote, Quote Item, Quote Item Attribute, Order, Order Item, and Order Item Attribute.

  • Number of product models. The number of customizable product models accessed by users. It is assumed that each user accesses no more than one customizable product model at one time. A given group of concurrent users may access multiple models, however, each of which must be separately cached.
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