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 19^th 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 21^st 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.

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:

• People
• Process
• Economics
• Conclusions