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.
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.
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.
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.
The ideas in this article have been inspired by the following references:
- Kanban, David J. Anderson
- The Principles of Product Development Flow, Donald G. Reinertsen
- Brain Rules, John Medina
- Software by Numbers, Mark Denne & Jane Cleland-Huang