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Showing posts from July, 2015

Beyond Control Charts and Cumulative Flow Diagrams

Control Charts (CCs) and Cumulative Flow Diagrams (CFDs) are powerful ways to display information about a flow system, such as a Scrum or Kanban development process. Unfortunately the very fact that the charts display so much information means that it is often difficult to extract specific information from them. That is why it's useful to also plot some of the key attributes of the systems on their own - this allows us to look at these aspects specifically, alongside the rawer view of the data that you get from CCs and CFDs.

The graphic on the right shows a number of diagrams all of which were derived from very simple data about each item that flowed through this system:
when it arrived into the system; when it departed the system; andwhether the item was "delivered" or "discarded".Note: I use the term "discard" here as a general term to include an exit from the system at any point in the system and for any reason. It includes aborting/abandoning the …

Postscript on Throughput and Delivery Rate

Most people use Throughput and Delivery Rate in Kanban systems as synonyms - including myself up to this point. I've changed my view however.

The canonical form of Little's Law in Kanban is as follows:
Delivery Rate = WiP / Lead Time    [ande] even though these days I more frequently express it this way:
Throughput = WiP / TiP Surely these two ways of writing the equation are entirely equivalent aren't they? Well maybe not.
Note: All these terms are defined in my Glossary Proposal (which has recently been updated to include the definition of Throughput). Feedback, comments and references to publications using the terms defined are welcomed and encouraged. Throughput is the term +Daniel Vacanti uses (among many others), particularly in his excellent new book Actionable Agile Metrics [vaca], and it got me thinking about one of the problems with using Delivery Rate: what about the items which are not delivered but are discarded? If there are a significant number of these Little&…

Little's Inequality

The interesting thing about Little's Law, in spite of its very general applicability, and in certain cases mathematical certainty, there are many cases when it simply does not hold true. In those cases it's not so much Little's Law as Little's Inequality. Could the fact that specific instances of data do not follow Little's Law actually give us useful information about our Kanban and Scrum systems and point to the right kind of interventions to manage and improve their flow? That's what this blog is about.


In 1961 John Little published his proof of this general queuing theory equation [litt]:
L=λWL is the average number of items in the queue, λ is the average arrival rate, and W the average wait time Since that time Little's Law has found numerous applications in the study of general flow systems from telecommunications to manufacturing, including in Kanban systems. Because throughput or delivery rate is the more significant attribute for management of such …