Improve predictability in software 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. Daily&Scrum
Sprint&
Review
Retrospective
Sprint&Planning Backlog&Grooming
Burndown
charts

7. Story
Point

14. 1. Define a Kanban system

22. 2. Define Work in Progress

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

29. 4. Visualise ‘Cycle time’ using Scatterplots

43. 5. Introduce a ‘Replenishment’ ceremony

51. 6. Monitor ‘Aging WIP items’

53. 7. Introduce WIP limiters across all states

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

68. A bit of
theory

69. “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

71. 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.

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

73. Protect Assumptions
as
Process Policies

74. 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

75. 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

76. 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

77. 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

78. 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

79. FLOW MANAGED
FORECASTING on SOLID GROUNDS
PREDICTABLE SYSTEM

80. When will it be done?

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

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

83. 9. Forecasting - using Monte Carlo simulations

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

85. 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

88. 10. One Page reports

91. Date
Driven
Development

92. Data
Driven
Development

93. Effortless
Data
Driven
Development

94. Effortlessly
Predictable
Development

95. Thank you!
@theppito