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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The Science of Kanban – Introduction

This is a write-up of a talk I gave at a number of conferences last year. I have split it into 5 parts.

Abstract

Science is the building and organising of knowledge into testable explanations and predictions about the world. Kanban is an approach which leverages many scientific discoveries to enable improved flow, value and capability. This article will explore some of science behind kanban, focussing on mathematics and brain science in particular, in order to explain the benefits of studying a system, sharing and limiting it, sensing its performance and learning in order to improve it. Readers will gain a deeper understanding of why and how kanban systems work so that they can apply the theory to their own team and organisation’s practices.

Introduction

Background

When I first started talking and writing about Kanban I was trying to articulate that Kanban is more than just using a card-wall. The kanban board is the visible mechanics of the system, but the goal is achieve a flow of value, and while time-boxes become optional, a cadence is required to understand capability. I referred to this triad of Kanban, Flow and Cadence as KFC (the irony being that fried chicken is not at all lean!) and that blog post from October 2008 remains the most popular I have written. While my language and thinking has evolved since then, I have realised that as I learn more about the science behind Kanban, much of it still maps back to those three core elements.

Kanban Thinking

This article will not describe how to design a Kanban system, but explores some of the science behind Kanban Thinking, an approach to creating a contextually appropriate solution.

Kanban Thinking is a systemic approach which places an overall emphasis on achieving flow, delivering value and building capability. The primary activities are studying, sharing, limiting, sensing and learning, and thus Kanban Thinking is itself a scientific approach.

Scientific Management

Frederick Winslow Taylor is generally credited with the development of Scientific Management in the late 19th Century by applying a scientific approach to improving manufacturing processes and publishing “The Principles of Scientific Management” in 1911. Given that we are now in the 21st Century, how relevant is scientific management to us today for software and systems development? Scientific management is considered to have become obsolete in the 1930s, yet I believe we can still apply science to understanding why and how differences in productivity exist. Scientific theory can be used to inform the practices we use, while our experiential practice can also inform and evolve the scientific theory.

Cynefin

Dave Snowden’s Cynefin model is a good example of balanced theory and practice. Cynefin suggests that there are different domains, and that we should act appropriately for each one. Thus depending on our understanding of the current context, we should apply scientific theory differently, and implement alternative practices appropriately. Thus scientific management can still be relevant for software and systems development if we apply a scientific approach contextually.

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The manufacturing context was probably complicated at worst, with elements possible being simple. Thus Taylor’s approach to scientific management, with best and good practice, was appropriate. However, software development and knowledge work is often complex, so the appropriate approach is to allow emergent practice, using what Snowden calls probe-sense-respond.

Making an Impact

In a complex domain, not being able to predict or repeat cause and effect does not mean that a situation cannot be improved. It is still possible to understand the current state, and current performance, and known whether things are improving. Rather than simply reacting to the current state or attempting to predict or plan for a future state, having anticipatory awareness of the current state, with a view to exploring its evolutionary potential, allows the application of continuous experimentation to sense whether we are making an impact by improving outcomes for both the business and for the people.

I’ll cover some of the sciences that can be used to make an impact in the following future posts:

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