How to use Cumulative Flow Diagrams and Monte Carlo simulations to manage feature flow and forecast delivery

1. How to use Cumulative Flow Diagrams and Monte
Carlo simulations to manage feature flow and forecast
delivery
Probabilistic forecasting

2. Carl Starendal
SAFe SPCT, trainer, mentor and sometimes coach
We Are Movement, Sweden

3. Forecasting delivery is a hard problem to solve

4. When are these X features done?

5. Program Predictability can’t really tell us when…

6. ?
How big is it?
4
hours
6
Iterations
60 MPH
30
SP/Iteration
240
miles
180 Story
points
+
Feature size in story points + historical program velocity then?

7. story points aggregation
and historical velocity
is a good heuristic

8. .. but it does not account for
the dark matter in our universe
.. the delays, the waiting time

9. Queuing .. is most of the delay and waiting

10. http://www.traffic-simulation.de/roadworks.html

11. queuing delta is a leading
indicator of cycle time changes

12. the system queuing state
is the stable state of the system

13. to forecast delivery we must see and
understand queues in the system

14. The Cumulative Flow Diagram (CFD)

16. The Program Kanban Board

17. The Cumulative Flow Diagram (CFD)

18. Reading Cumulative Flow Diagrams
0
10
20
30
40
50
60
70
80
PI1 - I1 PI1 - I2 PI1 - I3 PI1 - I4 PI1 - I5 PI2 - I1 PI2 - I2 PI2 - I3 PI2 - I4 PI2 - I5 PI3- I1 PI3- I2 PI3- I3 PI3- I4 PI3- I5
Features
Done Implementing Backlog Analysis Funnel
Funnel
Backlog Implementing
Analysis
Done
WIP

19. Reading Cumulative Flow Diagrams
0
10
20
30
40
50
60
70
80
PI1 - I1 PI1 - I2 PI1 - I3 PI1 - I4 PI1 - I5 PI2 - I1 PI2 - I2 PI2 - I3 PI2 - I4 PI2 - I5 PI3- I1 PI3- I2 PI3- I3 PI3- I4 PI3- I5
Features
Done Implementing Backlog Analysis Funnel
Funnel
Backlog Implementing
Analysis
Done
Cycle time

20. Reading Cumulative Flow Diagrams
0
10
20
30
40
50
60
70
80
PI1 - I1 PI1 - I2 PI1 - I3 PI1 - I4 PI1 - I5 PI2 - I1 PI2 - I2 PI2 - I3 PI2 - I4 PI2 - I5 PI3- I1 PI3- I2 PI3- I3 PI3- I4 PI3- I5
Features
Done Implementing Backlog Analysis Funnel
Funnel
Backlog Implementing
Analysis
Done
Throughput

21. An example..

22. 0
20
40
60
80
100
120
PI1 - I1 PI1 - I2 PI1 - I3 PI1 - I4 PI1 - I5 PI2 - I1 PI2 - I2 PI2 - I3 PI2 - I4 PI2 - I5 PI3- I1 PI3- I2 PI3- I3 PI3- I4 PI3- I5
Features
Done Implementing Backlog Analysis Funnel
Backlog
Implementing
Analysis
Done
Funnel

23. takeaways

24. CFDs shows system
dynamics and can help you
see leading indicators

25. manage emerging queues
to keep the system predictable

26. ok, but what about
that forecasting then?

27. Let’s try a probabilistic approach

28. When are these X features done?

29. How many have we completed per PI?

30. The Program Kanban Board

31. The Program Kanban Board

32. historical program throughput
is a powerful data-point

33. 5
FEATURES
3
FEATURES
8
FEATURES
15
FEATURES
6
FEATURES
PI 1 PI 2 PI 3 PI 4 PI 5
Average throughput = 37/5 = 7,4 Features/PI

34. however .. averages are tricky ..

35. Monte Carlo Simulations!

36. Let’s run a simulation live in Excel!

37. takeaways

38. taking a probabilistic approach for
forecasting feature delivery is key
for the RTE and PM roles

39. add program throughput to
your program metrics
to help with forecasting
feature delivery

40. use the program Kanban
board and the CFD to manage
flow and queues

41. run monte carlo simulations
on top of your historical data
to move beyond reasoning only
about averages

42. think in probabilities and bets
when forecasting

43. Carl Starendal
We Are Movement: wearemovement.se
TWITTER: @cstarendal
LINKEDIN: cstarendal
INSTAGRAM: carl_starendal
FACEBOOK: carlstarendalswe