This document discusses actionable agile metrics including work in progress, cycle time, and throughput. It defines each metric and explains why they are useful for understanding process stability, predictability, and improvements. Visualizations like cumulative flow diagrams and scatter plots of cycle times can help teams identify patterns and anomalies to investigate. Analyzing metrics over time through run charts also supports monitoring trends and capacity planning. The key message is that these quantitative metrics can trigger process improvements when used to learn rather than assess teams.

1. ACTIONABLE AGILE METRICS
Rajesh Viswanathan
Lean Agile Coach, Mentor & Consultant
https://www.linkedin.com/in/rajeshviswa/
https://twitter.com/rajeshviswa
https://medium.com/@rajeshviswa

2. © Rajesh Viswanathan 2
“Metrics must use …
→ To understand, ask questions, learn & improve.
→ NOT to compare / assess / punish the teams. “

3. © Rajesh Viswanathan
ACTIONABLE METRICS
1. Work in Progress → Number of work items that we are working at any given time.
2. Cycle Time → How long it takes each of those work items to get through our process?
3. Throughput → How many of those work items complete per unit of time?
Why these are actionable metrics?
→ Support understanding stability & predictability of the underlying process.
→ They can trigger actions & specific interventions that can improve the underlying process &
its predictability.
→ Unlike abstract story points, velocity, man hours etc, these metrics speak the language of
the customers.
→ “When is it ready?”
→ “How much do I get?”
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Work items can be anything
such as Epics, Features,
Stories, Defects, Production
tickets etc.

4. © Rajesh Viswanathan 4
BACKLOG SELECT REFINE DEVELOP VALIDATE ACCEPT DONE
WIP
WIP
WIP WIP
WIP
WIP WIP
WIP
WIP
DOING DONE
WORK IN PROGRESS
DOING DONE
WIP
DOING DONE
WIP
Throughput
Cycle Time
Work enters the
Kanban system
Work leaves the
Kanban system

5. © Rajesh Viswanathan
CUMULATIVE FLOW DIAGRAM
▪ Visualization of the flow of work (WIP) through a process.
▪ Offer massive amount of information at just a glance.
▪ Provide both quantitative & qualitative about system performance.
▪ Violations of key assumptions of stable / predictable system is easily visible in CFD.
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CFD PATTERNS
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UNSTABLE
PROCESS
STABLE / PREDICTABLE
PROCESS
SHORTER CYCLE
TIME
INCREASED
THROUGHPUT
1. CFD lines are not parallel → Unstable system.
2. Parallel CFD → Process stability.
3. CFD gets narrower → Process improvement.
4. CFD gets narrower & parallel → Process stability &
improvement.
5. CFD gets steeper → Capability improvement.

7. © Rajesh Viswanathan
SAMPLE USE OF CFD
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What to look for? Comments
Mismatched arrivals & departures → Indication of an unstable system.
Flat Lines over long periods of time → Indication of zero arrivals / departures.
→ No value is delivered to customer / downstream step.
→ External dependencies.
Stair steps → Indication of cadence based process such Scrum.
→ Batch transfers (due to regular cadence or not).
Bulging Bands → Explosion of WIP. May be a push from upstream or block in downstream.
Disappearing Bands → Indication of upstream variability.
→ Frequent skipping of some workflow steps.
S Curve → Inefficiency in workflow & less predictable process.
State wise cycle times → “Conditioning flow” [Items to be worked are selected based on the best chances of
success they might have.]
Balanced process → The top & bottom lines on the CFD become parallel.
Flow debt → Artificially reducing the cycle time of some work items.
Violation of Little Law’s
assumptions
→ Any violations make a process unstable & unpredictable.
Purpose of analyzing CFD is to learn, so ask questions.

8. © Rajesh Viswanathan
CYCLE TIME
Cycle Time
▪ Amount of elapsed time a work item spends in as work in progress.
▪ Measure of efficiency of a process.
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9. © Rajesh Viswanathan
CYCLE TIME SCATTERPLOT
▪ Visualization of cycle times of completed work items for a given period.
▪ Improve your team performance.
▪ Spot bottlenecks at a glance.
▪ Forecast completion dates.
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Shape of scatterplots are really a reflection
of team policies. Any anomaly should
trigger questions about what might be
causing the data to look as it does.

10. © Rajesh Viswanathan
SAMPLE USE OF CYCLE TIME SCATTERPLOT
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What to look for? Comments
Extreme outliers → Dependency outside the team
→ Encourage blocker cluster analysis & root cause analysis.
Percentiles (@50th, 70th, 85th, 95th ) → Determine the estimated completion time of a single work item with the given probability.
→ Establish SLA for different kinds of work.
→ Sizing work items.
→ Monitor aging of work items (With the help of WIP aging report).
Spread → Indicates process stability / predictability.
→ Higher spread or randomness means less stable / predictable the process.
Patterns → Gaps – Batch transfers, external blockers, holidays / vacations, team working on
multiple types of works without any capacity planning.
→ Clustering – Mandatory overtime, large work items, unforeseen problems
→ Triangle pattern – Starting more than completing. Cycle time increases over time. Too
much WIP. Aging of work items beyond control. High flow debt.
Cycle time trend → If the cycle time trend is not constant over time, predictability is at risk.
Process improvement → Decreasing percentile values means faster delivery.
→ Spreads means variability & reducing variability means improving predictability.

11. © Rajesh Viswanathan
THROUGHPUT
▪ Amount of work delivered over a certain period such as weekly or monthly.
▪ Indication of team’s capacity to deliver work items.
▪ Enables you to make data driven decisions about future performance.
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12. © Rajesh Viswanathan
THROUGHPUT RUN CHART
▪ Shows actual throughput on a daily or weekly or monthly basis.
▪ Monitor how much work you deliver.
▪ Track how throughput trends build over time.
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SAMPLE USE OF THROUGHPUT RUN CHART
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What to look for? Comments
Randomness or spread → Close the values (tight clustering), more stable & consistent the process.
Trend & Moving Average → Show whether the productivity is increasing or decreasing over a time.
→ Can compare actual vs average.

14. Thank you
Rajesh Viswanathan
vsrajeshvs@gmail.com