All models are wrong. By definition, they simplify reality. The map is not the territory, but sometimes it’s exactly what you need. Imagine if you could isolate the root causes of problems in your organisation and map their connections on a single page.
[Listen to audio version, read by David Hodes]
A Current Reality Tree (CRT) is a model of what exists in your system as examined in its present state. It seeks to take advantage of several features of systems and systems thinking. Firstly, everything’s connected, which implies that if you make a change to one part of the system, it will affect everything else. Secondly, there are very few reasons, at root, why a system behaves the way it does—indeed, there is sometimes only one. The idea of a single root cause for all subsequent effects is what attracted Goldratt to developing the CRT within his overall framework of the Theory of Constraints.
The CRT addresses the question ‘What to change?’ It is predicated on the idea that you have already defined your system boundary and have a clearly articulated goal. The CRT is, in some sense, a measure of how far off course you are, concerning your goal. Having a single, or very few, root causes allows you to understand what minimal intervention will have the most leveraged effects. In a way, the CRT is like a ‘five-why’ questioning tool or the Ishikawa (herringbone) diagram, but on steroids.
If we limit our example on this occasion to for-profit organisations, we can say that what all for-profits have in common is the goal to grow competitive returns for their shareholders, now and in the future. If earnings are lower than promised, or too much cash is tied up in investments without showing an acceptable return, then your organisation has a gap which you can analyse with the CRT as part of the Logical Thinking Process (LTP). The CRT is an example of those trees which use sufficient cause logic and the rigour of the logic checks to ensure that the reality model developed cleaves as close to the truth as is reasonable.
The tree is constructed from the top down but read from the bottom up. Let’s begin with a generic example of an independent business, or a strategic business unit of a larger concern. If their goal is, in Goldratt’s memorable phrase, to ‘make more money now and in the future’, then there are several reasons why they could be missing this goal—at least to the extent that they have to meet their shareholder expectations. Revenues may be below budget, margins less than expected, operating expense higher than planned, and inventory or investment too high relative to the hurdle rates of return. In a worst-case scenario, it could be that all of these drivers of profitability head in the wrong direction. How would we show this in our tree?
We start the tree with the big, red undesirable effect at the top. This is the statement which shows that our compass is pointing in the wrong direction. The longer we spend travelling in this direction, the worse the result will get. We read from the bottom left of the diagram: If [our margins are 15% lower than expected] then [our return on investment (ROI) fails to satisfy the demands of our shareholders]. It’s worthwhile interrogating these statements and the link between them using the logic checks from the Logical Thinking Process.
First, are the entities true? It’s good that the statement is specific, calling out 15% less than expected. But how do we know it is 15%? What do we mean by margins? Would that be gross or nett margins? How is each calculated and what, then, is the effect on the bottom line?
Second, we look at the arrow leading to the ROI failing to satisfy shareholder demands. Well, what are our shareholder demands, exactly? Is it the case, as illustrated in the diagram, and by the convention of the symbols used in the LTP, sufficient to cause the failure to satisfy the demands of our shareholders? In other words, if everything else was as planned, that is, the costs had not blown out and inventory was $10m lower, would we still have failed to satisfy our shareholder demands? What if costs had decreased in direct proportion to the drop in margins? And while we’re at it, what costs are we talking about? Is it the variable costs of inputs or the fixed costs of our operating expense?
“The only stupid question is the one that doesn’t get asked”
As we go to the right in the diagram, the statement claims that there is $10m too much inventory in the system. How do we know how much is too much? Perhaps there is a legitimate reason to hold more raw material if, for example, you are expecting a price increase and want to lock in a more favourable price. My aim here is not to solve the hypothetical problem, but rather to show the power of using logic and the process of the appropriately named Logical Thinking Process.
As an aside, I have consistently found this way of scrutinising the logic of this class of system dynamics valuable because it legitimises the asking of what we might sometimes characterise as ‘stupid questions’. I believe that the only stupid question is the one that doesn’t get asked. The logic checks provide a legitimate reason and a powerful rule-book for asking questions.
What happens, then, when we take a deeper dive? We’re striving to get to the root cause of the problem. The reason for that is quite sound. If we try and solve the problem at the level shown in the first tree, we don’t have much leverage, because we haven’t formed a hypothesis as to why these high-level entities exist. We need to go deeper with each of them in their turn and ask the ‘why?’ question, again and again. As per the unfolding logic of the CRT below, when we ask ‘why’ questions, we start to see some deeper truths. Reading the entities in the same way as before: if[entity at the base of the arrow] then [entity at the tip of the arrow] we can keep digging until, at the end of our analysis, we have hit bedrock.
In the central trunk of the tree is an ellipse with an ‘and’ binding the two arrows feeding into the entity immediately above it. This logical connector makes the claim that both entities bound by the ellipse must be present to cause the stated effect—in the language of the LTP, these are concurrent causes. Thus, if [our suppliers hit us with a 10% price increase] and [we had several unplanned breakdowns], then [costs are 10% higher than budgeted]. If you take away either of the entities bound by the ellipse, the effect at the tip of the arrows should go away. Thus, by the logic stated in the tree, whether you solve for the supplier increase or the unplanned breakdowns, either one will eliminate the cost problem without having to do anything about the other.
How likely is it that this would be the case? If the difference in the computation of the cost was 10% higher than budgeted, how could only one of those factors account for the difference? And again, what costs are we talking about? The supplier increase is most likely going to affect variable costs. In contrast, the unplanned breakdowns may have meant having to endure an increase in operating expense due to the payment of some hefty overtime bills to make up for lost production.
Once again, as we dig down, we find that the questions we ask are getting more powerful. What do we mean our competitors went to market with crazy offers? Were we competing just on price, or were they able to offer shorter turnaround time on orders and more reliable delivery, which the customer valued more than price alone? Why did the suppliers hit us with a 10% price increase? Were we late in ordering and thus had to pay the penalty for expediting orders? And why then were we late in ordering? What does it say about our people, processes and systems that we can’t, in a timely fashion, plan the work and then work the plan? And, have these same factors of people, processes and systems led to our production lead times being twice as long as planned, with all its consequence for inventory getting stuck in the supply chain?
A current reality tree is a beautiful tool for focusing a team on what matters. It can serve as a platform for what I call requisite conversations—those conversations we have to have, if we are to get unstuck from our deepest and most systemic blockages. By finding those few root causes amongst the complex, interacting chains of cause and effect, we know where to focus our efforts for high-leverage outcomes. It’s essential when having those requisite conversations that one is in an active-listening mode—empathetic to how, in most cases, it’s the system which produces the aberrant performance, not the people within it.
“Executives are so busy safeguarding today’s results that they neglect the work needed to secure their vision of the future.”
I’ll leave you with an example, which I have abstracted from a real CRT developed for a client. I show the full tree, albeit in miniaturised form to preserve confidentiality. I ask that you look at the entity in the middle of the tree, which I have placed within a red circle. For our purposes, it’s not important what the specifics are above the entity I have called out, but it’s worth noting that many of them emanate from that single cause in the middle of the diagram.
The entity says: ‘A significant amount of effort is reactive, unproductive and of poor quality, requiring rework’. Had we stopped there, we might have thought we needed a cultural intervention to address the behaviours associated with reactivity, low productivity and poor quality. However, as we dug deeper, you can see what we found when we reached bedrock.
In the many years I have been using these critical thinking tools, the chain of cause and effect at the bottom of this tree is amongst the most common and can be considered archetypal. The executives are so busy safeguarding today’s results that they neglect the work needed to secure their vision of the future.
Now that you have some understanding of the logic checks, why not take the thinking process out for a drive? See if you agree with my hypothesis of where the place/s of highest leverage is for the design of an intervention if you chase the tree down to its root.
Then take a situation within your own organisation and start gathering causes and effects that will let you make your own powerful hypothesis about how to improve. Remember, as the statistician George Box noted, ‘All models are wrong, but some are more useful than others.’
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[Background image: Tree on the hill, Gilly Stewart on Unsplash]
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