The document discusses a #noestimates approach to improving predictability in agile delivery projects using a Kanban system and key metrics like cycle time, throughput, and work in progress. It emphasizes the importance of adhering to assumptions from Little’s Law for maintaining process predictability and introduces Monte Carlo simulations for forecasting project timelines. The overall aim is to enhance flow management and resource efficiency in project delivery.

1. Improving predictability by
managing flow
A #NoEstimates approach in an agile delivery project
@theppito

2. When will it be done?

3. DDD

4. Date
Driven
Development

5. Burndown
charts

7. Story
Point

13. 1. Define a Kanban system

19. 2. Define Work in Progress

23. 3. Measure ‘Cycle time’,’Throughput’ and ‘WIP’

26. 4. Visualise ‘Cycle time’ using Scatterplots

34. 5. Introduce a ‘Replenishment’ ceremony

42. 6. Monitor ‘Aging WIP items’

44. 7. Introduce WIP limiters across all states

45. 8. Use Cumulative Flow Diagrams to validate impact of our actions

52. A bit of theory

53. “We are dumbing down knowledge to
information and elevating
‘it works for me so I will do it again’
to a status that is inauthentic at best,
dangerous at worst”
Dave Snowden

55. Professor of Marketing at MIT
Management Sloan School.
He has published seminal papers in
operations research methodology, traffic
signal control, decision support systems,
and especially marketing.
In operations research he is best known
for his proof of the queuing formula
L = λW, commonly known as Little's Law.
Dr. John Little

56. Expressing Little’s Law
Arrival rate
L = λW
Departure rate
CT= WIP/tH
Avg. Cycle
time
Avg. Work
in Progress
Avg.
Throughput
Avg. Items
in Queue
Avg. Items
arriving
Avg. Wait
time

57. Departure rate
CT= WIP/tH
Expressing Little’s Law
Avg. Cycle
time
Avg. Work
in Progress
Avg.
Throughput

58. Little’s Law assumptions
1. The average of Arrival Rate should equal the average of
Departure Rate (Throughput).
2. All work that is started will eventually be completed and
exit the system.
3. The amount of WIP should be roughly the same at the
beginning and at the end of the time interval chosen for
calculation.
4. The average age of WIP is neither increasing nor
decreasing.
5. Cycle Time, WIP and Throughput must be all measured
using consistent units.

59. Every time an assumption of
Little’s Law is violated the process
becomes less predictable.
Every time.

60. Protect Assumptions
as
Process Policies

61. 1. We will start new work about the same time we finish
old work.
2. We will make every reasonable effort to finish all
work that is started.
3. If work becomes blocked we will do everything
possible to unblock it as expeditiously as possible.
4. We will monitor our policies around the order in
which we pull items through so that work items do
not sit and age unnecessarily.
Assumptions as Process Policies

62. FLOW MANAGED
FORECASTING on SOLID GROUNDS
PREDICTABLE SYSTEM

63. When will it be done?

64. 1. Number of items
2. Start date 15/09/2017
How long will it take?

65. 2. Target end date 21/11/2017
How many items can we build?
1. Start date 12/06/2017

66. 9. Forecasting - using Monte Carlo simulations

67. Monte Carlo Simulation

68. MC Simulation
• Define domain of possible inputs
• Generate inputs randomly from domain
• Perform a computation
• Aggregate result
[source: Wikipedia]
Statistical sampling based simulation

69. 1
Domain of
possible inputs
Daily throughput
2
Generate
inputs
randomly from
domain
Select randomly a throughput
3
Perform a
computation
Use the Throughput and
deduct from the number of
items to complete
Increase the end date by one
4 Stop if …
… remaining number of items
is 0 or negative value
5
Aggregate
results
Calculate % of completions

71. 10. One Page reports

74. Date
Driven
Development

75. DatA
Driven
Development

76. Effortless
DatA
Driven
Development

77. Effortlessly
Predictable
Development

79. Thank you!
@theppito