The document discusses the impact of Tasktop Viz on engineering and operations, highlighting a significant increase in defect work and flow inefficiencies observed during March and April 2020. It notes the introduction of an APM tool for anomaly detection and emphasizes the need to analyze ServiceNow incidents and make tech debt visible. Additionally, the document captures how shelter-at-home orders affected productivity, leading to increased wait times and decreased flow efficiency, while suggesting further investigation for improvements.

1. © 2020 Tasktop

2. © 2020 Tasktop
Presenters
Dr. Mik Kersten
CEO & Founder
Tasktop
Jim Colson
Vice President & Chief Technology Officer
Cubic Corporation

3. © 2020 Tasktop
The Impact of
Tasktop Viz
Stories from the real world

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Providing Visibility
across Engineering
and Operations -
Transportation
S1 S2 S3
Transportation

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Steep increase in Defect work
What are we seeing here?
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1. Steep increase in defect
(red) work on March 22

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S1 S2 S3
2. ServiceNow incidents and
problems added to metrics
1. Steep increase in defect (red)
work on March 22

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S1 S2 S3
3. Dev (focused on features)
and Ops (focused on
defects) are seen together,
but why are we seeing all
that red?
2. ServiceNow incidents and
problems added to metrics
1. Steep increase in defect (red)
work on March 22

8. © 2020 Tasktop
4. An APM that identifies
anomalies and opens and
resolves issues using the
ServiceNow connector was
introduced
3. Dev (focused on features) and
Ops (focused on defects) are
seen together, but why are we
seeing all that red?
2. ServiceNow incidents and
problems added to metrics
1. Steep increase in defect (red)
work on March 22
S1 S2 S3
Next step is to analyze
ServiceNow work and to make
tech debt work visible and
actionable

9. © 2020 Tasktop
“We used this visibility to further
optimize Development and
Operations.”
-- Jim Colson, CTO, Cubic
41
S1 S2 S3
4. Next step is to analyze
ServiceNow work and to make
tech debt work visible and
actionable
3. Dev (focused on features) and Ops
(focused on defects) are seen
together
2. ServiceNow incidents and
problems added to metrics
1. Steep increase in defect (red)
work on March 22

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Surfacing WIP –
Mobile App
Transportation
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1. Flow Load is much higher
than predicted based on
Flow Velocity and Flow Time
• Avg daily Flow Velocity: 2
• Avg Flow Time: 6.6 days
• Predicted Flow Load: 2 x 6.6 = 13.2
• Actual Flow Load: 107
Weekly Avg Flow Velocity: 14
Daily Avg Flow Velocity: 14/7=2
Actual Flow Load: 107
Predicted Flow Load: 13
S1 S2 S3

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2. Flow Load is stagnant,
indicating that only a portion
of WIP is moving to
completion
1. Flow Load is much higher than
predicted based on Flow
Velocity and Flow Time
S1 S2 S3

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3. To drive this effort to
completion, there is 2
months of work to complete
2. Flow Load is stagnant, indicating
that only a small current of WIP
is moving to completion
1. Flow Load is much higher than
predicted based on Flow
Velocity and Flow Time
S1 S2 S3

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3. To drive this effort to
completion, there is 2
months of work to complete
Where is work waiting?
2. Flow Load is stagnant, indicating
that only a small current of WIP
is moving to completion
1. Flow Load is much higher than
predicted based on Flow
Velocity and Flow Time
S1 S2 S3
107/2 = 53.5 days

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• Completed work is stalled in
“Ready for Test”
• User stories are “Not Ready” or
“In Review”
Where is work waiting?
3. To drive this effort to
completion, there is 2
months of work to complete
2. Flow Load is stagnant, indicating
that only a small current of WIP
is moving to completion
1. Flow Load is much higher than
predicted based on Flow
Velocity and Flow Time
S1 S2 S3

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“We’ve been focusing on sprint
burn-up and burn-down charts. This
is helping us see the picture of how
to address the total WIP in the
overall system.”
-- Mobile Product Lead, Cubic
2. Flow Load is stagnant, indicating
that only a small current of WIP is
moving to completion
1. Actual Flow Velocity is lower than
what Flow Time would predict
3. To drive this effort to completion,
there is 2 months of work to
complete
S1 S2 S3

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The impact of
‘Shelter at Home’
on productivity -
Transportation
Engineering
Transportation
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S1 S2 S3
1. Wait time (light green)
increased significantly in
April after Shelter at Home
orders

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S1 S2 S3
1. Wait time (light green)
increased significantly in April
after Shelter at Home orders
2. Flow Efficiency decreased in
April as well

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S1 S2 S3
Flow Load is decreasing
• The team took the initiative to
groom their backlog which will
assist in the flow of business
value going forward
1. Wait time (light green)
increased significantly in April
after Shelter at Home orders
2. Flow Efficiency decreased in
April as well
3. A month after orders were
provided, Flow Load began
to decrease from end of
April onward

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S1 S2 S3
1. Wait time (light green)
increased significantly in April
after Shelter at Home orders
2. Flow Efficiency decreased in
April as well
4. These changes should be
investigated for
improvement outcomes to
apply to a wider group
3. A month after orders were
provided, Flow Load began to
decrease from end of April
onward

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S1 S2 S3
1. Wait time (light green) increased
significantly in April after Shelter at
Home orders
2. Flow Efficiency decreased in April
as well
4. These changes should be
monitored for improvement
outcomes to apply to a wider
group
3. A month after orders were
provided, Flow Load began to
decrease from end of April onward
“We are pleased thus far with what
we are learning through Tasktop Viz
and the Flow Metrics.”
-- Jim Colson, CTO, Cubic

23. © 2020 Tasktop
Request a
Tasktop Viz
Demo
go.tasktop.com/viz-demo-request

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Q&A