Using A Pareto Analysis To Tackle The Right Problems
Have you ever found yourself spending a lot of time working on a problem that turned out not to be such a problem after all? If your answer is “yes,” then you have missed “the law of the vital few and the trivial many” known more commonly as the “80/20 rule. ” In each organization, where there is much to do with limited resources, we must learn to effectively prioritize effort.
Executive Director, Center for Manufacturing Systems, New Jersey Institute of Technology
Have you ever found yourself spending a lot of time working on a problem that turned out not to be such a problem after all? If your answer is “yes,” then you have missed “the law of the vital few and the trivial many” known more commonly as the “80/20 rule.”
In each organization, where there is much to do with limited resources, we must learn to effectively prioritize effort. Unfortunately, in our daily pressure-packed manufacturing environments, it is sometimes difficult to take the time to prioritize. As a result, we typically address as many problems as we can. When fires flare up, we put them out and hope that they go away in the long run. Yet, if only we could find what is causing the worst fires, perhaps we could keep them from recurring.
Reason For CNC Lathe Rejection
||Number Of Occurrences||Percent||Cumulative Percent|
|Poor finish (tool lines, marks, etc)||8||40%||40%|
|Sharp edges or burrs||7||35%||75%|
|Bad ID thread||2||10%||85%|
|Bad OD thread||1||5%||90%|
A simple tool that can help us to prioritize is a Pareto analysis. A Pareto analysis will help identify specific areas where effort should be focused. Like any tool, a Pareto analysis starts with collecting data. Tools and techniques for collecting this data are varied and depend on technology employed in a company. In some cases, it is easiest to collect data manually, while in others, more automated systems may be in place to not only collect data, but conduct some type of data analysis as well.
To illustrate the use of a Pareto analysis, we will start with the problem of rejected parts being produced on a machine. It is likely that our practice would normally have been to address each rejection as it came up, fix it and move on to the next problem. It is also likely we would have given each rejection equal weight (and effort). However, if we apply some Pareto-like thinking, we may be better able to use our limited resources and implement permanent corrective action on the biggest or most recurring problems. We start by collecting data, such as what is shown in the table below.
The table includes data collected over a period of time and lists the reasons for the rejection, how frequently that particular rejection occurred, the frequency of that rejection expressed as a percent of all rejections and finally, a cumulative (running total) percent of all of the rejections.
A Pareto chart provides a clear picture of the cumulative total percentage of all the types of rejections (the steeper the curve, the larger the potential payback).
This graph clearly indicates that just two rejections (poor finish and sharp edges) represent 75 percent of all rejections. Although all rejections are indeed cause for concern, if we focus our efforts on permanently resolving only these two, overall rejections would be reduced 75 percent. That certainly would be a justifiable effort for the company’s limited resources.
Consider employing a Pareto analysis on some of your problems. (A Microsoft Excel spreadsheet—or similar—can be used to interpret the data and develop the charts.) Doing so will help you to focus your efforts in the right areas and make the best use of your limited resources.