The TameFlow Chronologist

Genesis and Evolution of the TameFlow Approach

Actionable Agile Metrics Review - Part 10: Forecasting and Analytics

This is the tenth episode examining Dan Vacanti’s book Actionable Agile Metrics for Predictability, An Introduction. Previously we discovered the following peculiarities about Service Level Agreements and Classes of Service:

  • Different types of work receive different treatment or service.
  • A Class of Service is a policy that determines the pulling sequence of committed work
  • The decision about which Class of Service applies should be done only when a work item is first pulled.
  • Even minor changes to pull policies can have huge impact on Cycle Time distributions.
  • Policies induce self-inflicted variability!
  • A FIFO queue is the most effective pull policy.
  • If the nature of your process disallows FIFO queuing, you should strive to change the process to support FIFO pulling as much as possible.
  • The best way to handle variability and yet maintain high predictability is to deliberately build excess capacity — that is, slack — into the process.
  • There will be a strong incentive to expedite all items.
  • Expedition will effectively stop all work on standard items.
  • Classes of Service are really speculative guesses about business value.
  • The highest-priority Class of Service will damage every other item put on hold!
  • Things should be done as fast as possible, without interferences in their flow through the process.
  • Classes of Service are considered as an institutionalized violation of Little’s Law.
  • Once the process is predictable, chances are you won’t ever need Classes of Service.
  • Expedition and preferential treatment is recognized as a source of conflict which undermines TameFlow’s Unity of Purpose.

Now we will discover Dan Vacanti’s ideas about Forecasting, the Monte Carlo Method and how to Get Started with Flow Metrics and Analytics.

Actionable Agile Metrics Review - Part 9: Pull Policies

This is the ninth episode examining Dan Vacanti’s book Actionable Agile Metrics for Predictability, An Introduction. Previously we discovered the following peculiarities about Service Level Agreements:

  • A delivery commitment should be expressed as date ranges with confidence levels.
  • Improvement can be measured as a decrease in the percentile values, and in the spreads between them.
  • Only few data samples are necessary to determine a reliable confidence level.
  • The closer you are to a stable process, the less data points you need.
  • A Service Level Agreement can be determined only by analyzing your Cycle Time data.
  • You can have different Service Level Agreements for different kinds of work
  • Percentile values can be used for sizing work items.
  • Percentile lines can be used as intervention triggers.
  • Service Level Agreements can and should be used in place of planning and estimation.

Now we will discover Dan Vacanti’s ideas about Classes of Service (CoS) and Pull Policies. This is arguably the most important chapter in his book.

Actionable Agile Metrics Review - Part 8: Service Level Agreements

Welcome to the eighth installment about Dan Vacanti’s book Actionable Agile Metrics for Predictability, An Introduction. In the previous post on Scatterplots we discovered the following highlights:

  • A Scatterplot is not a Control Chart.
  • Cycle Time data is not normally distributed.
  • Percentile Lines can be drawn independently of the data’s distribution.
  • Scatterplots give a temporal view and can uncover trends over time.
  • Histograms give an idea about the shape of the data’s distribution.
  • The shapes of Scatterplots are really a reflection of organizational policies.
  • Some of the most common shapes are: Triangle, Clusters and Gaps.
  • You cannot identify special/common causes simply by looking at a Scatterplot.
  • It is really important to figure out if any variability is self-imposed rather than being out of control.
  • It pays to identify Internal and External Variability.

In this episode we will see what Dan Vacanti has to say about Service Level Agreements (SLA).

Actionable Agile Metrics Review - Part 7: Scatterplots

Here is the seventh post about Dan Vacanti’s book Actionable Agile Metrics for Predictability, An Introduction. In the previous installment we got to know Dan’s thought about Flow Debt and learned the following:

  • If the Average Cycle Time is not constant over time then predictability is at risk.
  • Watch out for when the Approximate Average Cycle time becomes greater than the actual average Cycle Time: you are incurring in Flow Debt.
  • Flow Debt is what you will incur and have to pay off later whenever you decide to expedite work.
  • Flow Debt happens whenever work-items age artificially because of some interference in their natural flow through the process.
  • The degree to which interferences have a negative impact on predictability depend on their frequency of occurrence and on their handling policies.
  • Even well intended polices might cause Flow Debt!
  • Paying off Flow Debt will also damage predictability!
  • The length of a horizontal line on a Cumulative Flow Diagram is not an exact time. It is not even an exact average time. It is an APPROXIMATE average time.

Now we will find out what Dan teaches about Cycle Time Scatterplots which he describes in chapters 10 and 11.

Actionable Agile Metrics Review - Part 6: Flow Debt

This is the sixth post about Dan Vacanti’s book Actionable Agile Metrics for Predictability, An Introduction. In the previous post we learned about Conservation of Flow. The key points were:

  • If items enter into a process faster than they exit, the process will become overloaded and collapse.
  • In order not to overload the process you simply need to control how much work is allowed to enter it across the arrival point.
  • The average arrival rate should be approximately equal to the average departure rate.
  • When the process is balanced, the top and bottom lines on the Cumulative Flow Diagram become parallel.
  • Getting a balanced process is the single most important step towards predictability; and how WIP is limited is less important than actually doing it.
  • In advanced applications of Tameflow, the amount of work that is allowed to enter into the process is limited to the amount of work that can be handled by the constraint.
  • The arrival point is the point where work is effectively committed to.
  • After stepping across the point of commitment, there is no returning back.
  • Any backlog maintenance work, which is typically done in Agile methods, can be considered as waste and be discarded entirely.
  • All and any prioritization is done only when capacity is available and only to the extent that can be handled by that capacity.
  • There is a commitment to conserving flow.
  • The commitment to conservation of flow allows you to express expected Cycle Time for delivery as ranges with a probability distribution.
  • Flow disruptions should always be taken as an opportunity to ask: Why did it happen?
  • Items that are prematurely discarded from the process, should never be removed from the data.
  • The state of the process should be taken into consideration when making prioritization and pull decisions.

In this installment we will examine one of the most interesting chapters of Dan Vacanti’s book. It is about the very useful idea about understanding, detecting and acting on Flow Debt.

Actionable Agile Metrics Review - Part 5: Conservation of Flow

This is the fifth post about Dan Vacanti’s book Actionable Agile Metrics for Predictability, An Introduction and how it relates to TameFlow. In the previous installment about Cumulative Flow Diagrams we saw:

  • By representing the “Done” states, the waiting time between any successive steps of the process is captured and portrayed in the Cumulative Flow Diagram.
  • Any violation of the assumption of Little’s Law will become visible on the Cumulative Flow Diagram.
  • You must keep track of the times of arrivals and departures of the single work-items into and out of each state of the process.
  • You must avoid depicting backlogs and projections on a Cumulative Flow Diagram.
  • A MMR is a work package that has been truly committed to.
  • With an MMR, the Cumulative Flow Diagram will look like an S-Curve, due to work being started from zero at the beginning, and then going back to zero at the end.
  • With MMRs you must take into account that the average Cycle Time will be skewed because of the initial batch transfer.
  • The notions of Cycle Time skewing and of the S-Curve effect need to be fully understood and considered if you are using MMRs.
  • With actionable agile metrics, you can run experiments with your process and see what gives the best measured outcome in your context.
  • The horizontal difference between any two lines represents the Approximate Average Cycle Time.
  • Typical patterns and shapes that may develop on a Cumulative Flow Diagram reveal common flow problems and process dysfunctions.
  • The purpose of a Cumulative Flow Diagram is to trigger the right questions about the process, trigger them sooner, and suggest improvement actions.
  • Cumulative Flow Diagrams should not be used to identify bottlenecks.

We will now continue to see what Dan has to teach in chapter 7, 8 and 9 which are all about Conservation of Flow

Actionable Agile Metrics Review - Part 4: Cumulative Flow Diagrams

This is the fourth post about Dan Vacanti’s book Actionable Agile Metrics for Predictability, An Introduction and how it relates to TameFlow. The most interesting points found in chapter 3 were:

  • You really need to understand Little’s Law and when it is applicable.
  • In order to get work done faster, you need to work on less stuff.
  • Little’s Law is exact between any two time points when WIP is zero.
  • Little’s Law can be applied exactly between the start and end points of a Minimum Marketable Release (MMR).
  • In a continuous flow process, care must be taken to guarantee conservation of flow and to keep the process in a stable state.
  • One can consider the assumptions of Little’s Law as process policies
  • When process policies warrant the assumptions of Little’s Law, the entire process becomes more predictable.
  • Process policies ultimately determine the performance of your process.
  • Use Little’s Law and the underlying assumptions as guidance as to how to design and govern your process.
  • Even with segmentation and categorizations of work in progress (WIP), Little’s Law still applies, both for the segments as well as for the aggregate of WIP.
  • Work items do NOT have to be of the same size in order for Little’s Law to apply.
  • Little’s Law describes the past, and it should not be used to predict the future.
  • Predictability is more about having a system that performs according to expectation, rather than making exact forecasts.

Now we will look at what Dan has to teach with regards to Cumulative Flow Diagrams, which he describes in chapters 4, 5 and 6.

Actionable Agile Metrics Review - Part 3: Little’s Law

This is the third post about Dan Vacanti’s book Actionable Agile Metrics for Predictability, An Introduction and how it relates to TameFlow. So far, the key findings are:

  • Unpredictability stems from poor flow.
  • Predictability depends not only on what you measure but also on what you do.
  • Actionable agile metrics are fundamental to TameFlow as they support the two fundamental patterns of Unity of Purpose and Community of Trust.
  • It is critical to have clarity in defining the start and end points of the process.
  • Clarity in definitions and agreement on terms is positively constructive of the essential TameFlow pattern of Unity of Purpose.
  • TameFlow works only with a pull-system, because push-systems inherently prevent the cultivation of Unity of Purpose.
  • It is of essence to focus on the Cycle Time (actual elapsed time), the time it takes Work in Progress to move through the process.
  • Cycle Time can be considered as a proxy of Operating Expense (in the context of Throughput Accounting and Financial Flow).

Now we will examine the next chapter in Dan’s book. It is all about Little’s Law.

Actionable Agile Metrics Review - Part 1: Flow and Predictability

Dan Vacanti, well known in the Kanban community, has just published the book Actionable Agile Metrics for Predictability, An Introduction. This is an amazing book which is very much relevant to TameFlow. I would have no hesitation to recommend this book as mandatory reading to anybody who is interested in TameFlow.

In fact, Dan Vacanti’s earlier blog posts and presentations explained a lot about Flow and Little’s Law. They had a deep influence on my own understanding about the topic and on how I evolved the way TameFlow deals with Operational Flow.

(Note: Operational Flow is one of the four flows that are part of TameFlow; the other three are: Financial Flow, Informational Flow and Psychological Flow).

Now (early March, 2015) Dan has collected, expanded and published his thoughts in this book. As soon as I skimmed through the table of contents, I knew this was a book I just had to read — so much of it resonated directly with my thoughts about how to manage (operational) flow.

Already the opening paragraph of the preface will come through as a shock for most people involved in managing knowledge work, who might be formally trained in project management or software engineering management. It reads like this:

Your process is unpredictable. What you may not realize, though, is that you are the one responsible for making it that way. But that is not necessarily your fault. You have been taught to collect the wrong metrics, implement the wrong policies, and make the wrong decisions.

The implication here is that many professionals will have to exercise a deep reflective introspection to understand why they engage in management practices that are really counterproductive. Dan’s description is colorful:

You, in effect, initiate a denial of service attack on yourself