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Scenario for a Minor Customization Update Using ADM

This topic is part of Business Case Scenarios for Siebel ADM.

This topic gives one example of how Application Deployment Manager (ADM) can be used. You may use ADM differently, depending on your business model. The following scenario illustrates the use of ADM to deploy a simple change.

A Siebel administrator reviews the current production deployment on a daily basis and schedules an update to the deployment once a week. This weekly update includes minor changes and enhancements. Several users of the production application have requested the following:

  • Updates to three List of Values (LOVs) and the addition of five new LOVs
  • Updates to 10 responsibilities and views and the addition of two new responsibilities and views
  • Updates to 50 assignment rules and the addition of 10 new assignment rules

NOTE:  These minor updates and enhancements represent no new changes in business processes and do not require schema changes or compiled repository objects.

The Siebel administrator uses ADM to create one single ADM package containing the updates and enhancements. The ADM package contains the customizations data for each ADM data type. Starting with a refreshed test environment, the administrator deploys and activates the ADM package. In this scenario, the target Siebel Enterprise Server requires minimal or no downtime.

After reviewing the customizations updates in the test environment and verifying the correct application of the customizations, the administrator deploys and activates the ADM package to the production environment. The new and updated LOVs, responsibilities, views, and assignment rules are now available to the business users of the Siebel application, with minimal downtime.

This type of deployment can take place during working hours. However, it is always recommended that such changes be made when the system is offline or during periods of low load.

The actual length of time to deploy to the production environment depends on the volume of data being deployed and can be determined during the deployment to the test environment, factoring in the differences between the test environment and the production environment.

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