Empirical Process Control

Empirical Process Modeling

Ken Schwaber and others talk of Empirical Process Control ideas as being key to understanding Scrum.  I think this is good sense.

Mr. Schwaber got these ideas from Babtunde Ogunnaike and W. Harmon Ray, who wrote the process bible: Process Dynamics, Modeling and Control.  A big ole book, mainly about chemical processes.

We are talking about how to build new products.  How to get business results, in the form of new products.  Innovation.  Some of you don’t even want to call it a ‘process’.

But to process geeks, if you build something in a half-way regular way, then you are building things, even if fairly irregularly, using a process.

Ken Schwaber uses this (to the Scrum world) famous quote from Ogunnaike and Ray’s book:

It is typical to adopt the defined (theoretical)
modeling approach when the underlying
mechanisms by which a process operates are
reasonably well understood. When the process
is too complicated for the defined approach, the
empirical approach is the appropriate choice.”

In very simple terms, we Agile folks take ‘defined’ to mean ‘waterfall’, roughly as defined by the famous waterfall article by Dr. Winston Royce.

Two things must be said about ‘waterfall’.  First, Dr. Royce defines and shows lots of feedback loops, and most people, when they speak of waterfall, do not mean that.  Or they mean that those feedback loops (in practice at least) are very weak and work poorly.

Second, Dr. Royce calls for builders to ‘build it once, throw it away, and build it again (correctly).’  This is virtually never done in real life.  So, we people talk of waterfall they are usually NOT including this idea.

By ’empirical’, I take that to mean, very simply, Scrum Empirical Process, as defined too quickly in the Scrum Guide by Jeff Sutherland and Ken Schwaber.  The two key words are inspect (key: what the process produces) and adapt (make changes to the inputs of the process steps so that the revised version is likely to be ‘good enough’).

So, how do we connect the dots?

A later question is: have we connected them fairly, usefully, and with as much rigor as possible?  A final question: is there any more light that ‘process control’ ideas can give us, to enable us to do our innovation/new product development work better?

Here are what I think of as the basics of Empirical process Control, as applicable and useful to understanding Scrum better.  The two basic methods or approaches (defined vs empirical).

In fact, I may be adding and subtracting what others have said to me.

It is a very simple theory or set of ideas.  Even though it is simple, it is still (IMO) useful.

In the simplest model, process control consists of a flow across three ‘elements’.
1. Inputs
2. The black box (‘the process’)
3. Output

If 1 and 2 are both ‘in control’ and highly reliable, then 3 is likely to be reliable.  Ceteris paribus.  These are the conditions for a defined (waterfall) approach.

At the other ‘end’, if both 1 and 2 are ‘not in control’ or unreliable, then 3 is, by the definition of this model, unreliable, as so Empirical Process Control is recommended. (Unless there is some other element that magically makes it reliable.)  These are the conditions for an empirical (process control) approach.

What does the Empirical Process approach mean?

a. We inspect 3 often. This is only common sense — when 3 is unreliable one naturally wants to inspect it more often, with the ‘best possible’ eyes, expecting it often, not to be what we want. In fact, we inspect each piece of output.
b. When 3 is not what we want, if we can, we pull it back to the beginning, and run it through again in Empirical Process.  And try to ‘adapt’ either 1 or 2, to make them temporarily more reliable.  And pray that 3 becomes — after we run it through again — actually what we want.

We are assuming for now 3 (the widget) can be run through again, usefully. Of course, this may not always be the case.  For software, this is generally true.  For some physical products, this may be a bad option.

Now, the empirical approach is terrible, obviously. If one (the ‘God’ of the process) had any sense at all, one would change things so that 1 and 2 were both highly reliable.  And then 3 would become reliable.  That is, one would change things so that one could use the defined approach.

But, what we are saying with new product innovation with human abeings, is that we never have 1 or 2 in a reliable state.
We are not God, and there is too much stuff hitting the fan.  From every direction.
So, while we might control the inputs and the black box for 3 minutes, after about 3 minutes things are unreliable again.  Sadly.  So we are always stuck using an empirical process.

But at least we understand the Empirical Process Control we do have.

Personally, I consider human beings highly unreliable (do you prefer inconsistent?) inputs to any Empirical Process. We humans often compare ourselves to machines, but in fact we are highly unreliable.  Now, innovation and creativity are ‘the unexpected’.  So, in innovation ‘unreliability’ is actually a good thing or can be.  So, humans aren’t that bad after all.

This very simple theory or paradigm seems very real and accurate to me. It makes sense, to me, of the mess that we are in. The tar pit, as Fred Brooks calls it.

I think this is basically what Tunde Ogunnaike and Harmon Ray meant.  When they compared ‘defined’ and ’empirical’.

Now, some questions this very simple theory does not answer.
1. What if only 1 or 2 is ‘unreliable’?  (And the other one is reliable.)
2. How unreliable do 1 or 2 need to be before one uses an empirical approach (as we call it)?  One imagines that, at very low levels of ‘variation’ in 1 and 2, …that the ‘defined’ approach would still work or be better.
(As a practical matter for software development, I find that 1 and 2 are so ‘not in control’ that this question becomes moot.)
3. How do you know there is not another ‘element’?
4. What if you can’t adapt ‘enough’ (on 1 or 2)?
(Logically, in the simple case, one stops working or trying to produce anything, since 3 is highly likely to be wrong.  Unless one can live with totally random success.)


We hope these comments about empirical process control help you understand Scrum better and use it more effectively.


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