Question 1

What is the no-estimates approach in Kanban?

Accepted Answer

The no-estimates approach replaces traditional time-based estimates with statistical forecasting based on historical data. Instead of guessing how long tasks will take, teams measure actual cycle time, lead time, and throughput to make probabilistic predictions about future delivery dates using Monte Carlo simulations.

Question 2

How do Kanban metrics improve project predictability?

Accepted Answer

Kanban flow metrics like cycle time, lead time, and throughput provide objective data about team performance. By tracking these metrics over time and analyzing trends, teams can make evidence-based forecasts without traditional estimates. This approach increases predictability while reducing estimation overhead.

Question 3

What are Monte Carlo simulations for project forecasting?

Accepted Answer

Monte Carlo simulations run thousands of scenarios using historical throughput data to predict likely completion dates. Instead of a single estimate, you get a range of probabilities (e.g., 85% confidence of completing by a certain date). This statistical approach provides more realistic forecasts than traditional estimation methods.

Question 4

How does flowban work with GitHub Projects?

Accepted Answer

flowban integrates directly with GitHub Projects to automatically track work items through your workflow. It measures when items move between columns, calculates flow metrics like cycle time and lead time, and uses this historical data to generate cumulative flow diagrams and run Monte Carlo simulations for forecasting.

Question 5

What is cycle time vs lead time in Kanban?

Accepted Answer

Cycle time measures how long work items actively spend in progress from commitment to completion. Lead time measures the total time from when a request first appears until delivery. Both metrics are essential for understanding workflow efficiency and making accurate delivery predictions without estimates.

Question 6

How can I predict project completion without estimates?

Accepted Answer

Use statistical forecasting based on historical throughput data. Track how many items your team completes per week, then run Monte Carlo simulations to predict completion dates with probability ranges. This data-driven approach is more accurate than traditional estimates and requires no time spent estimating.

Question 7

What is a Cumulative Flow Diagram (CFD)?

Accepted Answer

A Cumulative Flow Diagram visualizes work flowing through your process over time. Each colored band represents a workflow stage. CFDs help identify bottlenecks, measure work in progress, and spot trends in throughput and cycle time - all essential for no-estimates forecasting.

Question 8

Will using these metrics lead to more micromanagement by managers?

Accepted Answer

No, quite the opposite. Flow metrics measure system performance, not individual performance. They reveal bottlenecks and workflow issues that teams can address together. Transparent data actually reduces micromanagement because managers have objective insights into progress without constantly asking for status updates. Teams use these metrics for self-organization and continuous improvement - not managers for controlling individuals.