The Science of Kanban – Conclusions

This is the final part of a write-up of a talk I gave at a number of conferences last year. The previous post was about the science of economics

Scientific Management Revisited

Is scientific management still relevant for product development then? As I have already said, I believe it is, with the following clarifications. I am making a distinction between scientific management and Taylorism. Whereas scientific management is the general application of scientific approach to improving processes, Taylorism was his specific application to the manufacturing domain. Further, in more complex domains such as software and systems development, a key difference in application is that the workers, rather than the managers, should be the scientists, being closer to the details of the work.

Run Experiments

The used of a scientific approach in a complex domain requires running lots of experiments. The most well-known version is PDCA (“Plan, Do, Check, Act”) popularised by Deming and originally described by Shewhart. Another variation is “Check, Plan, Do”, promoted by John Seddon as more applicable to knowledge work because an understanding of the current situation is a better starting point, and Act is redundant because experiments are not run in isolation. John Boyd’s OODA loop takes the idea further by focussing even more on the present, and less on the past. Finally, Dave Snowden suggests “Safe To Fail” experiments as ways of probing a complex situation to understand how to evolve.

Whichever form of experiment is run, it is important to be able to measure the results, or impact, in order to know whether to continue and amplify the changes, or cease and dampen them. The key to a successful experiment is whether it completes and provides learning, not whether the results are the ones that were anticipated.

Start with Why

Knowing whether the results of an experiment are desirable means knowing what the desired impact, or outcome might be. One model to understand this is the Golden Circle, by Simon Sinek. The Golden Circle suggests starting with WHY you want to do something, then understanding HOW to go about achieving, and then deciding WHAT to do.

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Axes of Improvement

One set of generalisations about WHY to implement Kanban, which can inform experiments and provide a basis for scientific management is the following:

  • Productivity – how much value for money is being generated
  • Predictability – how reliable are forecasts
  • Responsiveness – how quickly can requests be delivered
  • Quality – how good is the work
  • Customer Satisfaction – how happy are customers
  • Employee Satisfaction – how happy are employees

The common theme across these measures is that they relate to outcome or impact, rather than output or activity. Science helps inform how we might influence these measures, and what levers we might adjust in order to do so.

Lean

In these posts I have described Kanban in terms of the sciences of people, process and economics. However, this can actually be generalised to describe Lean as applied to knowledge work, as opposed to the traditional definition of Toyata’s manufacturing principles. The differentiation is also a close match back to my original Kanban, Flow and Cadence triad.

  • Kanban maps to process, with the emphasis on eliminating delays and creating flow rather than eliminating waste.
  • Flow maps to economics, with the emphasis on maximising customer value rather than reducing cost.
  • Cadence loosely maps people and their capability, with the emphasis on investing in those who use the tools rather than the tools themselves.

References

The ideas in this article have been inspired by the following references:

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11 comments on “The Science of Kanban – Conclusions

  1. Pingback: Dew Drop – February 3, 2012 (#1,258) | Alvin Ashcraft's Morning Dew

  2. Hi Karl,

    There is a factual inaccuracy here. The shewhart cycle (PDCA). Was developed in the context of process engineering not within the “complexity domain” as you wrongly state. Shewhart had no concept of complexity, and instead applied a reductionist statistically deterministic approach inorder to predict and control the variability of quality within a Manufacturing process.

    Now knowledge work is not manufacturing. So the question needs to be asked whether process engineering approachesdevised in manufacturing apply?

    Dave snowden clear states not in this article.

    http://www.cognitive-edge.com/articledetails.php?articleid=40

    The ontology and epistemology where process engineering ideas are applicable are different from those of the complexity domain where social complexity ideas are deemed more appropriate. These domains are different requiring different techniques and skills.

    I keep stressing this difference since this failure in thinking as led to a number of ideas that have been tried and found wanting in the past. Much of the work of the CM-SEI is based on the misappropriation of process engineering ideas borrowed form manufacturing and applied to software development.

    The work by watts humphreys on earned value and statistical approaches to personal software development, are all products of this core mistake.

    For a description of an approach to software development that is sympathitic to social complexity I would advise your readers to take a look here.

    http://www.cognitive-edge.com/articledetails.php?articleid=70

    We’ve discussed this many times. It surprises me that you still insist on propagating this thesis, without stating that your ideas are at least contentious :)

    It feels like you are trying to square a circle. Circles are round and squares are.. Well square :)

    Let me know next time you plan to attend XTC and we can discuss this some more over a Beer.

    Regards,

    Paul.

    • Hi Paul,

      I don’t disagree with you (except that I didn’t claim PDCA was developed in a complex domain!). I reference Cynefin in the Introduction to this series (did you read that?) so I’m surprised you think this is contentious. Why do you think that is?

      Karl

      • Hi Karl,

        It was this section I had in mind.

        “The used of a scientific approach in a complex domain requires running lots of experiments. The most well-known version is PDCA (“Plan, Do, Check, Act”) popularised by Deming and originally described by Shewhart.”

        Perhaps you want to reword it?

  3. Hi Karl,

    Noticed another factual error. Scientific management is Taylorism. Taylor and another guy (his name slips me) invented it.

    Scientific management started in manufacturing. It wasn’t until General Motors in the 30’s I think, that scientific Management was more widely applied.

    GM came up with the idea of treating business adminstration (white collar work) as a production line too. Applying the scientific management ideas of divide and conquer, specialisation of roles, experts, time and motion, etc.

    It is what has lead to the cost acounting and the siloed departmental budgets and accounting practices we know today, along with all the associated pathologies :)

    I think you are raising some interesting questions here, well worth exploring.

    Scientific managment has been a great success and is what has given us our industrial age, raising the standard of living of millions of people. We are know moving into an information age and the relevant question is whether scientific management is the right vehicle to drive progress over the next 100 years the way it has over the last 100 years?

    Many believe that we have become too singuarly reliant on scientific management with its focus on control, and need to look to more balanced, people centric, collaborative approaches in the future. Looking past industrial metaphor seeking inspiration from our medivial craftsmanship past.

    A worthwhile discussion, but we need to get the historical facts right to begin with.

    http://en.wikipedia.org/wiki/Scientific_management

    Paul.

    • Hi Paul

      From the wikipedia page you referenced, Taylor’s scientific management “was one of the earliest attempts to apply science to the engineering of processes and to management”. My view is that we can still “apply science to the engineering of processes and to management” in complex domains including software and systems development. Different sciences and different techniques, but still an application of science. Lean Startup is a great example of a scientific approach to product development.

      Do you believe science has no place at all?

      Karl

      • Hi Karl,

        Of course there is a place for science. There is a place for Art too. The suggestion (by many) is that we’ve got the balance wrong.

        It’s the assumptions of scientific management that are in question,dividing work into doing and managing, dehumanising something that humans have done in an holistically fashion from time began.

        As for lean Startups, Steve Jobs always said that customers don’t know what they want, so the idea that all there is to product innovation is building stuff and seeing whether the public like it is open to question IMHO. We didn’t get the iPhone or the iPad that way. Infact the original GUI research at Xerox Parc wasn’t done that way either.

        Not saying it hasn’t got its place. We need science sure, but we need Art also.

        Fancy writing a series on the Art of Kanban?

        Now that would be interesting.

        P.

  4. Paul

    I think the idea of separating work from it’s context goes further back than GM. The logistical arm of the Prussian army in the C18th is where the modern story starts I think.

    I understand the initial premise was “How can we separate morality from work” and it turned out bureaucracy was an effective meme for this.

  5. Thanks Craig,

    I know the roots of scientific management go deep, preceeding Taylor. I also had an inkling that the origins lay in the Military. Didn’t know the original premise though. Interesting…

    Cheers,

    P.

  6. The article about scientific management is itself very unscientific, too many corrections required to begin here.

    One minor point the check act plan do is from Brian Joiner 4th generation management . Suggest you read it. It’s the original source.

    • Hi Stephen

      Would love to hear your view in more detail. Maybe offline? Is it the whole series, or just the references to scientific management?

      Thanks for the reminder about Joiner – its on my list :)

      Karl

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