Review
In my last post, we continued our
discussion on what I believe are the 4 best tools for problem solving. I discussed the Pareto Chart and presented a simple example of how to construct
one. In today’s post we will look at the
third tool, the Causal Chain. Much of what I have discussed in this series, is taken from my newest
book, A Guide for Problem Solving, Prevention and
Making Effective Decisions - Roadmaps for Today and Tomorrow.
The
Causal Chain
The final tool we will be
discussing in this series of posts is the causal chain. When problems
occur, we know that a chain of events has taken place to alter the performance
of the process. We aren’t always certain as to what happened, so we need some kind
of tool or technique that will help us develop a theory as to what did happen.
One of the most effective tools available for accomplishing this is the causal
chain. Causal chains are step-wise evolutions of problem causes.
Each step in the causal chain represents an object in either a normal or
abnormal state. The object is placed on the line to the far right of the chain,
with the state it is in listed directly beneath it.
So, in the figure above, the object
in distress is the press and its state is that it has stopped. Although we
might use a cause-and-effect diagram to list the variety of reasons why the
press stopped, it does not explain the causal mechanism that actually caused
it.
In the figure above we see that a
press has stopped and we ask the question why. The press stopped because the
motor stopped. Why did the motor stop? Because the current has stopped flowing.
We continue asking why until we reach the end of our chain, and find that the
press stopped, because the motor stopped, because the current stopped, because
the pressure switch opened, because the air pressure was too low, because the
air compressor failed, because the oil level was too low, because of a gasket
that failed. We have now developed a potential theory as to why the press
stopped, and along the way we have identified objects or items (e.g., current,
oil level) that we can test to prove or disprove our theory. Each step is the
cause of the next step and the effect of the preceding step. That is, the
information on the step to the left is always the cause of the information on
the step to the right.
What if we have more than one
potential cause? How do we handle that situation? The answer is simple. We just
create additional, individual chains like the one in the figure above and place
them along the y-axis as in the figure below. This is an actual example from a
team that was working on a core machine that was malfunctioning. Here the team
brainstormed and came up with four different chains. Each individual chain is, in
reality, a brief theory to prove or disprove, as to how the core machine was
malfunctioning.
Ultimately, the team either
eliminated the chain as a potential cause through testing or developed action
items for each of the potential root causes. At the end of the day, the team
performed all of the actions in the gray shaded boxes, and the problem was not
only solved, but it was improved from its previous state.
Remember, the primary purpose of a
causal chain is to develop a stepwise chain of events that explains why a
particular performance shortfall exists. Once this is complete, hypotheses or
theories can be formulated as to why a problem exists. Causal chains are, in my
opinion, one of the simplest and effective, yet most underutilized tools
available for a team to use.
In my next post, we’ll start a new
blog post series on a different topic.
As always, if you have any questions for me, just send me an email and
I’ll answer it.
Bob Sproull
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