The Constraint Unblocker


#2 of jobs that should exist but don’t in most IT departments (#1 was The Application Portfolio Manager).

What’s a constraint?

From Wikipedia:

The theory of constraints (TOC)[1] is an overall management philosophy introduced by Eliyahu M. Goldratt in his 1984 book titled The Goal

It’s the idea that in a manufacturing process there will be a constraint (or bottleneck) and that:

  • there’s no point in doing any optimisation work before the constraint, because that will just make work in progress stack up even quicker
  • there’s no point in doing any optimisation work after the constraint, because the work in progress is still stuck upstream

TOC drives us towards a singular purpose – identify the present constraint and fix it.

Of course this becomes a game of ‘Whac-A-Mole‘, just as soon as one constraint is dealt with another lies waiting to be discovered. But it’s an excellent way of ensuring that time, money and other resources are focused in the right place, and the starting point for continuous improvement that takes advantage of incremental gains.

The constraint unblocker

Is an individual who’s empowered to work across an organisation identifying its constraints and leading the efforts to fix them.

James Hamilton

One of my industry heroes is Amazon’s constraint unblocker – James Hamilton[2]. He has:

  • Reinvented data centre cooling (and many other aspects of data centre design)
  • Reinvented servers
  • Reinvented storage
  • Reinvented networks
  • Modified power switching equipment

Take a look at his AWS Innovation at Scale presentation for some depth, or the Wired article Why Amazon Hired a Car Mechanic to Run Its Cloud Empire.

The consequence of that list above shouldn’t be underestimated. Where Hamilton (and his like at Google, Facebook etc.) have led, the entire industry has followed.

The impact of that list shouldn’t also imply that there’s no point in doing this elsewhere. This approach isn’t just the preserve of hyperscale operators. All IT shops have their constraints, and so all IT shops should have a leader who’s focused on unblocking them.

TOC and DevOps

There’s a close relationship between TOC and DevOps. The Goal inspired The Phoenix Project and the ‘3 DevOps Ways’ of Flow, Feedback and Continuous Learning by Experimentation are all about dealing with constraints.

That isn’t however to say that organisations doing DevOps have everything covered. The 3 ways make sure that constraints are addressed in the context of a single continuous delivery pipeline for a single product, but as soon as there’s more than one product there’s most likely a global constraint that can’t be dealt with at a local level.

Amazon may be doing DevOps up and down the organisation, and they very effectively organise themselves into ‘2 pizza‘ teams ‘working backwards‘ building micro services to power their ever expanding service portfolio. But they still need James and his team working top down to get the big roadblocks out of their way as they spend $Billions scaling their infrastructure.

Data (science) required

Notionally this stuff was easy with manufacturing. Look down on the factory floor and you can see the workstation where the work in progress is stacking up. Pop down there and figure out how to fix it.

Of course the reality was much messier than that, which is why Goldratt quickly found himself revising The Goal, and a whole consulting industry sprang up around TOC. But with software we have to acknowledge from the outset that we’re not going to see work in progress physically piling up; and beyond DevOps it’s entirely possible that the constraint may have little to do with ‘work in progress’.

Thus in IT we need data to find our constraints, and we usually need that same data (or more) to inform the model-hypothesise-experiment process that determines what to do about a constraint. In my own work (that we now brand Bionix) that’s why we start with the data science team and their analytics.

Why bother?

My personal observations of TOC in action over the past few years have generally found a 20% improvement in efficiency/effectiveness on the first iteration. That’s not moving the decimal disruption, but that’s a realistic first approximation of what’s achievable in a six week cycle.

Of course because this is Whac-A-Mole you never get the same pay off again. The next iteration might be 15%, then 12%, then 9% and quickly off into the weeds. But stack those gains on top of each other and you’re quickly into completely different territory.


As we can see from Amazon even the best organisations have constraints, and they can benefit from having a leader focused on identifying and fixing them. That way they can achieve continuous improvement and the fruits of incremental gains across the organisation, and not just in a product silo.


[1] I find it somewhat frustrating that ‘theory’ is used here as it makes the approach seem ‘academic’ and thus easily dismissed by those claiming that they only care about practical outcomes.
[2] James starred in my ScotCloud keynote last year “Our problems are easy“.

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