Finding Sources of Variation

Typically, the most suspect processes or process steps for introducing variation are manual or judgment oriented in nature.  For example, if an individual applies personal judgment within a process you would expect to see bias or higher variation in the process output. Automated processes will typically have more consistent performance and lower variation.

One of the best ways to find these manual or judgment steps in a process is through the use of a process map. As a process is mapped, decision points are represented as diamonds. This becomes the first place to look for variation.

When mapping a process, information from both the process owners and the Six Sigma team’s observations are used. There are situations where the process as described by the process owners is really from the “as we think it is” or “as it should be” world. The Six Sigma team that falls into this trap is doomed. In these cases, the process owners may know the standardized process, but chose to not follow it.

On a project I worked a few years back, there was too much variation in the concentration of petcoke being blended with the coal burned in a business’s boilers.  The result was either too much petcoke, resulting in a violation of environmental parameters, or too little petcoke which increased the cost of operation.

The fuel feed system was comprised of 2 conveyer belts that fed a third conveyer belt. One of the feeder belts fed coal and the other fed petcoke. The third belt, called the silo belt, fed the boilers.  The concentration of petcoke loaded to the boilers was controlled by the belt speed of each of the first two belts.

Additionally, the silo feed belt was designed to start empty and, as a result, was the last belt to be shut off so that it would be empty when stopped. The two feed belts were designed to be started empty or full.

The process of starting up the fuel feed system was to start the silo feed belt first, the coal feed belt second and the petcoke feed belt last. All belts were to be empty when started. The shutdown process required that the petcoke belt be shut down first, after it was empty. The Coal feed belt was to be shut down second, when it was empty.  The silo feed belt was to shut down last when it was empty.

The first step in the team’s analysis of the variation issues was to compare each shift’s start-up and shutdown processes. The Six Sigma Team did not take for granted that all shifts were compliant with the standardized start-up and shutdown processes, since the computer systems allowed them to change the order of startup and shut down.

What we found was that one of the four shifts shut down the coal and petcoke belts full, and then shut down the silo feed belt when empty. When starting the system up again, this shift would start the silo feed belt first and then both the coal and petcoke belts simultaneously. This shift had a low variation in petcoke concentration (within variation). Much lower, in fact, than all the shifts put together (between variation).

Another of the shifts would first shut down the petcoke feed belt full, then the coal belt full, then the silo belt when empty. On start-up they would start the silo feed belt first, then coal feed belt, then the petcoke feed. This group had the highest within variation in petcoke concentration, but still lower than the between variation of all the shifts put together.

The other two shifts followed the standardized process of start-up and shutdown. Their within variation in petcoke concentration was higher than the first shift, but lower than the second shift.

What the team had found, so far, was that two of the four shifts did not follow the standardized process of operation. Even so, the variation within each shift was within tolerance. The team then matched up emissions logs with the petcoke feed logs over a three week period. What they found was that the interaction of the different shifts created significant swings in petcoke concentration.  This “between” variation turned out to be the root cause of the variation problem.

The solution was to get all sifts to follow the same process. A team meeting with representatives of each shift resulted in an agreement to follow the first shift’s start-up and shutdown processes.  This became the standardized process for the plant’s fuel feed.

The Six Sigma team monitored the fuel feed process for four weeks after the agreement to measure the results. They found all shifts in compliance with the standardized process and a very low variation in petcoke concentration.  The low variation allowed the plant Operations group to incrementally increase the petcoke concentration and thereby reduce the plant’s operating cost.

One conclusion that the Six Sigma team made was that compliance with standardized processes is higher when the process owners were part of the dialog that creates the standardization. Processes are processes, but people are people. Processes are developed to serve the process owners (people) not the other way around.

Another conclusion was that communication between groups needed to be improved. The different groups need to understand why standardization is necessary and they need to know how to recover from unforeseen process upsets. In this case, what is the standardized process for start-up and shutdown when system maintenance required a different shutdown condition than normal? This latter situation required an expansion of the process management plan to include all pre start-up and per shutdown scenarios.

2 thoughts on “Finding Sources of Variation

  1. Greetings! I’ve been reading your web site for a while now and finally got the bravery to go ahead and give you a shout out from Atascocita Tx! Just wanted to mention keep up the fantastic job!

Leave a Reply