Kurs informasjon: IBM WebSphere Lombardi Edition V7.1 Process Optimization (Remote Classroom)

NOTE: THIS IS AN INSTRUCTOR-LED ONLINE COURSE. PLEASE DO NOT MAKE TRAVEL ARRANGEMENTS FOR THIS COURSE.

This one-day, instructor-led course teaches core product skills and best practice methods required to analyze and improve business processes using IBM WebSphere Lombardi Edition. Students learn how to:

• Leverage Lombardi to define and track metrics such as Key Performance Indicators (KPIs)
• Evaluate the performance of existing business processes to identify bottlenecks and inefficiencies using historical data inside the Optimizer

This course utilizes a collaborative learning environment, with hands-on demonstrations, exercises to reinforce concepts and check understanding, and labs embedded in each of the course units.

This is a very interactive course, where students work in teams to perform lab exercises. Students should have a high fidelity stereo PC headset with a noise cancelling microphone.

For information on other related WebSphere courses, visit the WebSphere Education Training Paths Web site:

http://www.ibm.com/software/websphere/education/paths/

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This intermediate course is designed for business analysts and project team members responsible for analyzing and improving process performance.

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Students should have knowledge of BPM concepts and experience with Lombardi process modeling.

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• Define optimization within the BPM life cycle
• Describe the benefits of process optimization
• Identify parts of the Optimizer
• Define key metrics (KPIs and SLAs) for optimization
• Use the Lombardi Simulator to validate process changes and analyze cycle times and costs
• Identify the tradeoffs and costs related to adding or changing resources dedicated to a process
• Model potential process improvements
• Analyze simulated data in relation to key metrics
• List tasks to set up a process for historical analysis
• Use the Optimizer to view historical process performance
• Analyze historical data in relation to key metrics

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• Course introduction
• Process optimization concepts
• Simulating and improving processes
• Analyzing historical data
• Course summary

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