Get in Touch

Course Outline

1. Introduction to Predictability in Agile Delivery

  • Importance of delivery predictability
  • Limitations of traditional estimation methods
  • Distinction between forecasting and estimating for government operations
  • Foundational principles of flow-based delivery
  • Course objectives and expected learning outcomes

2. Understanding Flow in Agile Systems

  • Definition of workflow processes
  • Visualization techniques using Kanban boards
  • Categorization of work item types
  • Identification of workflow stages
  • Control of work in progress (WIP)
  • 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)
  • Interdependencies among flow metrics
  • Selection of actionable metrics

4. Applying Little's Law

  • Conceptual framework of Little's Law
  • Operational assumptions and constraints
  • Application to Agile team dynamics
  • Capacity estimation using flow metrics for government projects
  • Practical application examples and exercises

5. Flow Analytics

  • Overview of flow analytics methodologies
  • Cumulative Flow Diagrams (CFDs)
  • Scatterplot analysis
  • Histogram distributions
  • Run Charts
  • Control Charts
  • Identification of bottlenecks and process variability
  • Analysis of trends and patterns

6. Forecasting with Monte Carlo Simulation

  • Principles of probabilistic forecasting
  • Efficacy of Monte Carlo simulation techniques
  • Projection of completion dates
  • Simulation for multiple work items
  • Utilization of confidence intervals
  • Analysis of probability distributions
  • Practical forecasting simulations

7. Measuring and Managing Risk

  • Identification of delivery risk sources
  • Quantification of uncertainty factors
  • Evaluation of forecast confidence levels
  • Decision-making frameworks based on risk assessment
  • Scenario analysis techniques
  • Effective communication of uncertainty to stakeholders for government accountability

8. Improving Flow and Process Performance

  • Identification of operational bottlenecks
  • Strategies for reducing work in progress
  • Management of process variability
  • Enhancement of throughput capacity
  • Optimization of workflow policies
  • Continuous improvement through metric analysis

9. Collecting and Managing Flow Data

  • Criteria for essential data collection
  • Identification of Agile data sources
  • Extraction and analysis of historical data
  • Data integrity and quality assurance
  • Minimum data requirements for accurate forecasting
  • Mitigation of common measurement errors

10. Using Agile Tools for Metrics

  • Data extraction from Agile management platforms
  • Visualization of flow metrics for transparency
  • Development of performance dashboards
  • Automation of reporting processes
  • Monitoring of team performance indicators
  • Adherence to best practices for government reporting

11. Communicating Forecasts Effectively

  • Presentation of probabilistic forecasts to leadership
  • Clarification of confidence levels and implications
  • Disclosure of risks to stakeholders for informed governance
  • Support for executive decision-making processes
  • Establishment of realistic delivery expectations

12. Applying Predictability Metrics in Practice

  • Forecasting for user stories and requirements
  • Forecasting for features and epics
  • Release planning strategies
  • Capacity planning methodologies
  • Portfolio-level forecasting for government programs
  • Analysis of case studies and practical applications

13. Building a Metrics-Driven Culture

  • Promotion of data-driven decision-making practices
  • Prevention of metric manipulation and misuse
  • Establishment of organizational transparency
  • Implementation of continuous improvement cycles
  • Definition of relevant key performance indicators (KPIs)

14. Hands-on Workshop and Summary

  • Construction of forecasting models using historical data
  • Development of flow analytics dashboards for monitoring
  • Execution of Monte Carlo simulations
  • Interpretation of forecasting outcomes
  • Identification of actionable improvement opportunities
  • Review of core course concepts
  • Interactive question and answer session
  • Determination of next steps and recommended resources for government practitioners

Requirements

None.

 14 Hours

Number of participants


Price per participant

Testimonials (4)

Upcoming Courses

Related Categories