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Siebel Configurator Performance Factors

Siebel Configurator performance has two aspects:

  • Customizable product loading time. 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.
  • Selection response time. 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 key performance factors that influence these times are as follows:

  • Use of Snapshot Mode caching. This feature caches customizable product models in memory, which significantly reduces the amount of time required to load customizable products for each new user. This feature is particularly useful for improving performance when a product line has a small number of large, complex customizable products. For more information on Snapshot Mode caching, see Product Administration Guide.
  • 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.
Deployment Planning Guide