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Actionable Agile Metrics for Predictability: An Introduction Paperback – 4 Mar 2015
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About the Author
Daniel Vacanti has more than 20 years focusing on Lean and Agile practices. In 2007, he helped to develop the Kanban Method for knowledge work. He managed the world’s first project implementation of Kanban that year, and has been conducting Kanban training, coaching, and consulting ever since—including extensive work for several Fortune 100 companies. Most recently, Daniel founded ActionableAgile™ which provides industry leading predictive analytics tools and services to any flow-based process. Daniel holds a MBA and regularly teaches a class on lean principles for software management at the University of California Berkeley.
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It goes through CFDs, cycle time, WIP, throughput - and explains in great details how to use them, all the traps to avoid, and all the value you can get from them!
The chapters on Little's Law are amazing: after hearing it mentioned so much in Kanban it's great to see its implications in action.
The author has a clear and direct style, even when he's going through advanced content he makes it very easy to understand.
I only wish he had gone into more details on forecasting and monte-carlo simulations, but I guess that might be coming in a future book.
Most helpful customer reviews on Amazon.com
One thing that I miss in the book is advice how to use the metrics in the Complex domain (per the Cynefin framework).
Metrics should be used in context. We see manufacturing models being applied to a non-manufacturing process such as software development. The notion that we need to drive variability out of the process is expressed in Lean Manufacturing and unfortunately it is leaking into the Agile software development. We see people constructing Statistical Process Control charts for Lead Time to see if the development process is under control. It is the belief that if we just eliminate variability the work will flow smoothly and all our problems will be over.
Eliminating variability eliminates innovation because it makes everybody risk averse. We must understand the specific conditions that produce variability and manage our development process to thrive in the presence of variability. Measuring development progress on the assumption of order is a fundamental error.
I highly recommend this book to all software development professionals!
The unwitting consequence is that this does not make teams change the way they work to keep their queues stable. The real world workflow of many types of teams cannot help but take work out of flow and perform all sorts of machinations with items in their queues. Consequently, the take away is that the value of CFDs is simply greatly reduced. The truth about the characteristics of Little's Law will not have a significant remedial effect on how people work.