Measuring The Pig In The Python

Data collection and analysis are critical in process improvement. Data shows progress in process improvement over time and serves as a barometer of process effectiveness and efficiency.

Measuring a straightforward process that takes seconds, minutes, or hours is fairly easy because you can witness the start and the stop of the process. These processes exist everywhere from manufacturing, call centers, service centers, health care, and even back-office administrative processes.

However, there are many administrative processes that take days, weeks, months, and even years that are not easily measured. Let us use the example of a human resources recruiting process. The process has a definite start and stop, but it can take weeks or months to occur.

When you are trying to improve these types of processes, data collection and analysis can be rather tricky. One single instance of a process like this is often called a “tranche,” which means, “A portion of…” The term tranche is often used when talking about money, but it is synonymous with words like segment, piece, heart, subdivision, share, percentage, and proportion.

One tranche – one instance – of a long (multi-day) process is what Landon Jones coined as, “The pig in the python.” Jones used the term to address the “baby boom” after the Second World War, but it is a good representation of one instance of something progressing through a long process.

Why is this important?

When trying to improve a long process, the data measurement, collection, and analysis are much harder. If you are trying to capture the end-to-end process cycle, the data can be very elusive.

Today, if you pull data on this type of process, it will look like the process is getting better and faster closer to the current date. This is because the closer you get to today the shorter the process times are presented, while there are still several that are still open and being worked on – moving through the python.

This makes measuring the pig in the python more challenging.

How do you measure the pig in the python?

First, you select a start time where everything that started in the process has already ended. Then, that becomes your snapshot of the process. Essentially this becomes your measurable tranche.

This subset of all your data is representative of the population of the process as a whole. Thus, you end up looking at tranches of data sets week over week or month over month.

This means that it will take longer to see the impact of any changes to the process. This is because you have to continue to take these snapshots of what is happening in the process.

The concept of tranches and the pig in the python is very important to remember when working with longer administrative processes.

 

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