Karl Scotland – Using Agile to Deliver Value
Archive for January, 2012
The Science of Kanban – People
Jan 31st
This is the second part of a write-up of a talk I gave at a number of conferences last year. The previous post was the Introduction.
Software and systems development is acknowledged to be knowledge work, performed by people with skills and expertise. This is the basis for the Software Craftsmanship movement, who are focussing on improving competence, “raising the bar of software development by practicing it and helping others learn the craft.” A kanban system should, therefore, accept the human condition by recognising sciences such as neuroscience and psychology.
Visualisation
Neuroscience helps us understand the need for strong visualisation in a kanban system. Creating visualisations takes advantage of the way we see with our brains, creating common, shared mental models and increasing the likelihood that the work and its status will be remembered and acted on effectively.
Vision trumps all our other senses because our brains spend 50% of the time processing visual input. Evidence of this can be found in an experiment run on wine-tasting experts in Bordeaux, France. Experienced wine-tasters use a specific vocabulary to describe white wine, and another to describe red wine. A group in Bordeaux were given a selection of wines to taste, where some of the white wines had an odourless and tasteless red dye added. These experts described the tainted white wine using red wine vocabulary because seeing the wine as red significantly influenced their judgement. The same experiment has apparently also been run on novice wine-tasters who were less fooled, showing the danger of how experts becoming too entrained in their thinking.
Visual processing further dominates our perception of the world because of the way our brain takes the different inputs from each eye. For each index card on a board, instead of seeing two, one from each eye, the brain deconstructs and reconstructs the two inputs, synthesising them into a single image by making up and filling the blanks based on our assumptions and experiences. Thus what we end up with is a mental model created by the brain, and the kanban board helps that mental model to be one that is shared between the team.
The more visual input there is, the more likely it is to be remembered, en effect known as the pictorial superiority effect (PSE). Tests have shown that about 65% of pictorial information can be remembered after 72 hours, compared to only 10% of oral information. Visualising work status and other dimensions on a kanban board can, therefore, increase the chances of that information having a positive influence on outcomes.
Multitasking
Neuroscience also explains one of the benefits of limiting WIP; that of reducing multitasking.
Multitasking, when it comes to paying attention, is a myth. The important detail here is that it is when it comes to paying attention. Clearly we can walk and talk at the same time, but if we have to concentrate on climbing over an obstacle we will invariably stop talking. The brain can only actively focus on one thing at a time and studies have shown that being interrupted by multitasking results in work taking 50% longer with 50% more errors. For example, drivers using mobile phone have a higher accident rate than anyone except very drunk drivers. In other words, multitasking in the office can be as bad as being drunk at work!
Other studies have shown that effectiveness drops off with more tasks. In “Managing New Product and Process Development: Text and Cases” by Clark and Wheelwright show that when someone is given a second task, their percentage of time on valuable work rises slightly because they are able to keep themselves busy when they are blocked. However, beyond that, with each additional task the time spent adding value reduces.
Gerald Weinberg suggests similar results in “Quality Software Management: Systems Thinking” when he reports that for each additional project undertaken, 20% of time is lost to context switching.
Learning
Recognising the way we deal with challenging situations, and how we can change, enables us to deal with the visibility and transparency that a kanban system creates in order for us to learn and improve.
As humans, we are natural explorers and learners. We evolved as the dominant species on the planet by constantly questioning and exploring our environment and trying out new ideas. However, when faced with difficult situations, we tend to react badly. Chris Argyris coined the term Action Science to describe how we act in these situations. The natural reaction is single loop learning, where we resort to our current strategies and techniques and try to implement them better. A more effective approach can be double loop learning, where we question our assumptions and mind-set in order to discover new strategies and techniques.
Another relevant model is Virginia Satir’s Change Model which describes how our performance dips into the valley of the ‘J-Curve’ while we adjust to a new way of being. Being aware of the dip, its depth, and our response it, can inform an appropriate approach to influencing change.
Next we’ll cover the science of process.
The Science of Kanban – Introduction
Jan 30th
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.
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:
Cynefin, Agile & Lean Mashups
Jan 10th
2012 certainly started with a bang for me (lets hope it doesn’t end with a bang!). After a relaxing Christmas and New Year, I was up at 6am on January 2nd to head for Almens in the Swiss Alps, and an intense few days with Simon Bennett, Steve Freeman, Joseph Pelrine and Dave Snowden. We gathered at Joseph’s house to discuss a common interest, namely how do we apply complexity science, and in particular the Cynefin framework, to Agile and Lean development.
Early on, Simon suggested the name CALM, and it stuck almost immediately. I like it for a couple of reasons. Mashups invokes the idea of “a creative combination or mixing of content from different sources”. That’s exactly what we want to do, and its the creative aspect that particularly appeals to me. A Cynefin, Agile and Lean Mashup will inevitably be created contextually. CALM also subtly counterbalances the XP extreme notion. While that’s not an intentional focus, I find it a mildly amusing reference.
My interest in Cynefin began back in around 2004 when Dave first spoke at XPDays London, and while back then I wasn’t smart enough to realise the full implications, fortunately others like Steve and Joseph were. I met Dave again at ScanAgile in 2009 and last year at the LeanSSC and ALE conferences. Simon also gave a great talk linking Scrum and Complexity more concretely at Agile2011, and I that’s when I finally figured out how Cynefin could match my interest in exploring the underlying theories behind Agile and Lean, and more specifically Kanban Thinking.
My personal goal for being involved in the meeting was to move Cynefin, and complexity science, from being something which is used as a justification, to something which provides meaningful explanation, and ultimately to new application. To keep the industry advancing, and to be able to apply Agile and Lean principles in increasingly challenging organisations, we need theory informed practices, as well as learning from our current success by evolving practice informed theory. In other words we need to take a scientific approach, which ties in nicely with my recent presentations on the Science of Kanban, which should make it to a blog post soon.
The primary outcome of the CALM meeting was the creation of CALMalpha. This is a two day residential conference to be held on the 16th and 17th of February 2012 at Wokefield Park in the United Kingdom. The alpha represents the notion that this is an initial safe-to-fail experiment where we hope to explore the subject in more detail, as we seek to find coherence, coalescence and convergence around what we do in the future.
More detail, including prices and booking information, can be found on the eventbrite page. I hope to see you there!





