AI curriculum
Course outline for Workflow Performance Engineering
Faster individual tasks do not necessarily improve the whole workflow. Measure flow, quality, and rework to identify constraints and test improvement hypotheses.
About Workflow Performance Engineering
Faster individual tasks do not necessarily improve the whole workflow. Measure flow, quality, and rework to identify constraints and test improvement hypotheses.
Workflow Performance Engineering Course Objectives
- Calculate cycle time and throughput from event records.
- Locate a bottleneck using flow and quality measures.
- Define an improvement experiment with guardrail metrics.
Pre-requisites
- Familiarity with an operational or software delivery process.
- Basic spreadsheet skills; coding is optional.
Lab Setup
- Computer with a spreadsheet or analysis notebook.
- Use anonymized or synthetic event records; no production monitoring access is needed.
Detailed Course Outline
Proposed modulesModule 1: Measuring performance
- Cycle time
- Throughput
- Cost
- Quality
Practical outcome: Calculate baseline process measures from sample records.
Module 2: Finding constraints
- Rework
- Bottlenecks
- Constraint analysis
Practical outcome: Identify a constraint and its downstream effects.
Module 3: Sustaining improvement
- DORA-style metrics
- Workflow observability
- Continuous improvement
Practical outcome: Select measures appropriate to the workflow rather than treating them as universal targets.
Practical exercise
- Analyze a workflow dataset and propose an improvement experiment with quality guardrails.
How we train
Contact us for full course details, including duration, delivery options and lab requirements.
Ask about exercises, instructor feedback and the prior knowledge you need. Tool-specific courses marked provisional may become modules in a broader course.
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