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Duration 14 hours
Course Outline
1. Introduction to Predictability in Agile Delivery
- The significance of predictability in operational contexts
- Limitations inherent in traditional estimation methods
- Distinguishing between forecasting and estimating
- Foundational principles of flow-based delivery
- Course goals and expected learning outcomes
2. Understanding Flow in Agile Systems
- Defining workflow
- Visualizing work processes through Kanban
- Categorizing types of work items
- Identifying distinct workflow stages
- Regulating work in progress
- Attributes of a stable flow system
3. Core Flow Metrics
- Work in Progress (WIP)
- Cycle Time
- Lead Time
- Throughput
- Work Item Age
- Service Level Expectation (SLE)
- Interrelationships among flow metrics
- Selecting relevant and meaningful metrics
4. Applying Little's Law
- Comprehending Little's Law
- Examining assumptions and limitations
- Implementing Little's Law for Agile teams
- Estimating capacity via flow metrics
- Practical examples and exercises
5. Flow Analytics
- Overview of flow analytics
- Cumulative Flow Diagrams (CFDs)
- Scatterplots
- Histograms
- Run Charts
- Control Charts
- Detecting bottlenecks and variability
- Interpreting trends and patterns
6. Forecasting with Monte Carlo Simulation
- Principles of probabilistic forecasting
- Mechanisms of Monte Carlo simulation
- Forecasting completion dates
- Forecasting multiple work items
- Confidence intervals
- Understanding probability distributions
- Practical forecasting exercises
7. Measuring and Managing Risk
- Sources of delivery risk
- Quantifying uncertainty
- Assessing forecast confidence levels
- Risk-based decision making
- Scenario analysis
- Communicating uncertainty to stakeholders
8. Improving Flow and Process Performance
- Identifying bottlenecks
- Reducing work in progress
- Managing variability
- Improving throughput
- Optimizing workflow policies
- Continuous improvement using metrics
9. Collecting and Managing Flow Data
- Essential data collection requirements
- Sources of Agile data
- Analyzing historical data
- Data quality considerations
- Minimum data required for forecasting
- Avoiding common measurement mistakes
10. Using Agile Tools for Metrics
- Extracting metrics from Agile management tools
- Visualizing flow metrics
- Creating dashboards
- Automating reports
- Monitoring team performance
- Best practices for reporting
11. Communicating Forecasts Effectively
- Presenting probabilistic forecasts
- Explaining confidence levels
- Communicating risks to stakeholders
- Supporting management decisions
- Setting realistic delivery expectations
12. Applying Predictability Metrics in Practice
- Forecasting user stories
- Forecasting features and epics
- Release planning
- Capacity planning
- Portfolio forecasting
- Case studies and practical examples
13. Building a Metrics-Driven Culture
- Encouraging data-driven decision making
- Avoiding metric misuse
- Creating transparency
- Continuous improvement practices
- Establishing meaningful KPIs
14. Hands-on Workshop and Summary
- Building a forecasting model from historical data
- Creating flow analytics dashboards
- Running Monte Carlo simulations
- Interpreting forecasting results
- Identifying improvement opportunities
- Review of key concepts
- Questions and answers
- Next steps and recommended resources
Requirements
No prerequisites.
Testimonials (4)
Really enjoyed the exercises and putting agile principles into practice
Cahlum Richardson - Essex County Council
Course - Certified Digital & Agile in Government™ (CDAG™) Foundation
The training was practical and straight forward
Mutu Bengui - EMIS - Empresa Interbancaria de Servicos, S.A
Course - Agile Product Management - Growth Marketing
workshops, practical cases
Joanna Nowak - LKQ Polska Sp. z o. o.
Course - Introduction to Agile Testing
team exercises
