metrics

Improving Predictability - Average Age

In a previous post I’ve introduced the four assumptions behind Little’s Law and discussed the first two assumptions in detail. If you haven’t read those previous posts I encourage you to go back to understand the background. As a reminder, the four assumptions are listed below.

Improving Predictability - All work must finish

In a previous post I introduced the four assumptions behind Little’s Law and the idea that they are critical to understanding and improving your system’s predictability. We’ve also already discussed the first assumption regarding the equality of average arrival and departure rates. If you haven’t read those previous posts I encourage you to go back to understand the background. As a reminder, the four assumptions are listed below.

Improving Predictability - Average Arrival and Departure Rates

In a previous post I introduced the four assumptions behind Little’s Law and the idea that they are critical to understanding and improving your system’s predictability. If you haven’t read that post I encourage you to go back to understand the background. As a reminder, the four assumptions are listed below.

Improving Predictability

Little’s Law is an equation that frequently appears in discussions of Kanban systems. While initially formulated as a part of queueing theory to describe the length of time people would spend in stores it has since been applied to many other contexts from manufacturing to knowledge work (particularly Kanban for the purposes of today’s conversation).

Determining cycle time from an online system

This post is aimed at a fairly niche audience so if you aren’t trying to make sense of poor data out of some ticketing system then you might want to skip this one.

Improve the work, not the metrics

One of the key practices supporting continuous improvement is making your work, and how you do the work, visible. This starts by tracking the progress of that work in a highly visual way, often by using a kanban board. Once that work is being tracked we can begin to gather that data and start to gain insights into where our biggest opportunities for improvement are, often by using the metrics defined in The Three Flow Metrics (Plus One).

The three flow metrics (plus one)

Some of the best indicators of team performance are the flow of both new information into the team and of value out of the team. If we can improve visibility into these indicators, and therefore the opportunities for the team to improve the way they work, it becomes possible for the team to support their organization in ways they couldn’t before. There are three standard metrics that are core to understanding the effectiveness of any flow-based system. The relationship between the three metrics is defined by Little’s Law. When applied to the systems used to enable knowledge work the law is usually restated in terms of Throughput, Work In Progress (WIP), and Cycle Time.

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predictability

Improving Predictability - Average Age

In a previous post I’ve introduced the four assumptions behind Little’s Law and discussed the first two assumptions in detail. If you haven’t read those previous posts I encourage you to go back to understand the background. As a reminder, the four assumptions are listed below.

Improving Predictability - All work must finish

In a previous post I introduced the four assumptions behind Little’s Law and the idea that they are critical to understanding and improving your system’s predictability. We’ve also already discussed the first assumption regarding the equality of average arrival and departure rates. If you haven’t read those previous posts I encourage you to go back to understand the background. As a reminder, the four assumptions are listed below.

Improving Predictability - Average Arrival and Departure Rates

In a previous post I introduced the four assumptions behind Little’s Law and the idea that they are critical to understanding and improving your system’s predictability. If you haven’t read that post I encourage you to go back to understand the background. As a reminder, the four assumptions are listed below.

Improving Predictability

Little’s Law is an equation that frequently appears in discussions of Kanban systems. While initially formulated as a part of queueing theory to describe the length of time people would spend in stores it has since been applied to many other contexts from manufacturing to knowledge work (particularly Kanban for the purposes of today’s conversation).

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waste

The cost of interruptions, and how to reduce it

Interruptions can be a significant source of waste. By their nature, interruptions cause a context switch as we lose track of what we had been working on to focus on the interruption. There is a significant cost to that context switch as it takes time and effort away from the task at hand. There is also a real impact on quality as mistakes are far more likely to happen when we’re distracted.

Waste: Psychological Distress

One of the more subtle forms of waste is psychological distress. When we are afraid or anxious, our sympathetic nervous system activates to prepare us for one of fight, flight or freeze. All good responses in a survival situation.

Understanding waste in the system

In Kanban, we are always trying to optimize for efficiency, effectiveness and predictability. Waste in the system is something that hurts all three of these objectives and is something we want to remove or reduce wherever possible.

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improvement

“We tried Kanban and it didn’t work”

I sometimes run across teams that say “we tried kanban and it didn’t work”. When I hear this, I’m always genuinely curious and ask for more details about what they’d done and what specifically didn’t work for them.

Improve the work, not the metrics

One of the key practices supporting continuous improvement is making your work, and how you do the work, visible. This starts by tracking the progress of that work in a highly visual way, often by using a kanban board. Once that work is being tracked we can begin to gather that data and start to gain insights into where our biggest opportunities for improvement are, often by using the metrics defined in The Three Flow Metrics (Plus One).

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WIP

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cycletime

Determining cycle time from an online system

This post is aimed at a fairly niche audience so if you aren’t trying to make sense of poor data out of some ticketing system then you might want to skip this one.

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standup

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visualize

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