Pareto Analysis - The Statistical technique

Pareto Analysis is a statistical technique in decision-making used for the selection of a limited number of tasks that produce significant overall effect. It uses the Pareto Principle (also known as the 80/20 rule) the idea that by doing 20% of the work you can generate 80% of the benefit of doing the entire job.

 

Pareto Analysis: 

 

When faced with a range of issues, it is often difficult to know which to work on first. To resolve this problem, the most useful thing to do is to apply Pareto's rule. It can be described as the 80/20 rule applied to quality-control.  The 80/20 rule was originally formalized by Wilfredo Pareto, after studying the distribution of wealth.  He noticed that about 80% of wealth was held by about 20% of the population. Several years later, Joseph Juan applied the principle to quality-control, and Pareto Analysis was born. Pareto Analysis essentially states that 80% of quality problems in the end product or service are caused by 20% of the problems in the production or service processes. Once these problems are identified, the 20% that are causing 80% of the problems can be addressed and remedied, thus efficiently obtaining quality.

It can be used in a technical sense to try and improve a process by eliminating defects.  It can be used in a human resources to try and find the time wasters in different work environment.  It can be used in finding out what the biggest hurdle may be to achieving a goal.

Use of Pareto Analysis:

An example of where one might use a Pareto Analysis might be if you were running a restaurant. Approximately 20% of the menu items would account for 80% of the profit taken in by the restaurant.  By using a Pareto Analysis, the restaurateur would know which menu items to focus his business around. In the manufacturing of clothing if one monitores the returns of clothing with a Pareto Analysis, the manufacturer would be able to find the 20% of the root causes behind 80% of the returns.  A third example can be seen in the semiconductor industry. Again, a manufacturing process will be looked at but this time the Pareto Analysis will be used inline to determine defect causes during inspection.  Using a Pareto Analysis, engineering can decide which defects warrant the most attention, cut costs, and improve the end result.

 A Pareto chart has the following objectives:

• -  Separate the few major problems from the many possible problems so you can focus your improvement    efforts.
• -  Arrange data according to priority or importance.
• - Determine which problems are most important using data, not perceptions.

Benefits of Pareto Analysis

Pareto diagrams:

• Solves efficiently a problem by the identification and the hierarchization, according to their importance, of the main causes of the faults.
• Sets the priorities for many practical applications. Some examples are: process improvement efforts for increased unit readiness, customer needs, suppliers, investment opportunities.
• Shows where to focus efforts.
• Allows better use of limited resources.

A Pareto Diagram is a good tool to use when the process investigated produces data that are broken down into categories and you can count the number of times each category occurs. A Pareto diagram puts data in a hierarchical order, which allows the most significant problems to be corrected first. The Pareto analysis technique is used primarily to identify and evaluate nonconformity, although it can summarize all types of data. It is the perhaps the diagram most often used in management presentations.

The Pareto chart

A Pareto chart is a graphical representation that displays data in order of priority. It can be a powerful tool for identifying the relative importance of causes, most of which arise from only a few of the processes, hence the 80:20 rule. Pareto Analysis is used to focus problem solving activities, so that areas creating most of the issues and difficulties are addressed first.

How to Use It

In conducting a Pareto Analysis, the first phase is concerned with identifying possible causes of inferior quality.  This can be done through brainstorming, focus groups, surveys, or any other method appropriate to the given business.  The goal is to obtain actionable items that result in inferior quality.  For example, if I manufacture glass windows, and some of them must be returned due to glass chips and cracks, I may identify the following four possible causes of the glass inconsistencies (inferior quality): poor production process, mishandling at the factory, faulty packaging, and problems in transit.  Each of these items can be acted upon, and in our situation, we will assume they are truly possible causes of inferior quality.  Once the actionable items are identified, we can move on to phase two.
The second phase is comprised of picking an appropriate time period over which we would like to conduct our analysis and then conducting the assessment.  The goal here is to obtain a statistical sampling that is representative of the time period over which we are trying to improve quality.  Some quality-control measures may be intentionally applied to seasonal, biannual, or some other specified time period, depending on the business.  

Some businesses may care about the quality and increased investment of obtaining that quality at certain times of the year, but not at others.  The objective is to make sure that the measured time period accurately represents the time period over which the quality-control measures will be enacted.  Once the time period is chosen, the quality problems are tallied under the causes of inferior quality that were identified in the first phase.  In our example, each time we received a return for the reason of ‘faulty packaging,’ we would add one to the tally for that cause.  Each time we incurred an inconsistency for the reason of ‘mishandling at the factory,’ we would add one to the tally for that cause.  

This process would continue until our predefined time period had elapsed, after which we would subtotal the results and move on to the third phase.
Phase three is summarizing and graphing the results obtained in the previous phases.  

After subtotaling the numbers for each of the causes of inferior quality, those numbers are summed to obtain the total number of defects.  Then, in order to acquire the percentage of each cause in relation to the total number of defects, each subtotaled number is divided by the total number of defects and multiplied by 100.  

After these percentages are obtained, they can be graphed in a histogram, with the causes of poor quality listed on the x-axis, and the percentages of their occurrence listed on the y-axis.  The causes are listed from left to right, with the most often occurring cause listed on the furthest left, the next most often occurring cause listed next to it, and so on.  

Finally, in order to make the histogram more easily interpret able, a cumulative line graph can be placed over the existing bars.  This histogram is called a Pareto Chart.  Now we are ready for phase four.

The fourth and final phase is concerned with interpreting and applying the graphed results.  The overlaid line graph helps us in this process, as it shows the percentage of the total defects that we dispose of as we perform the actionable items, from left to right.  

This is where the 80/20 rule comes into play, as you will most often notice that about 80% of the defective products are caused by about 20% of the possible defect causes.  When actually implementing solutions, however, it is recommended that after the leftmost quality problem is dealt with, that another Pareto Analysis is conducted before moving on to the other identified quality problems.  

The reason for this is that the percentages for each of the remaining quality problems may shift disproportionately as the production or service process is changed in implementing the quality-control measures on the initial quality problem.  This caution may not always be necessary, but should be realized by the person conducting the analysis.

Pareto Analysis in Excel

Pareto charts are often used in quality control to display most common reasons for failure, customer complaints or product defects.

The principle behind pareto charts is called as pareto principle or more commonly the 80-20 rule
1) Once we have the values for each cause, we can easily calculate cumulative percentages. We will also require a dummy series to display the “cutoff %” in the Pareto chart.

 2) Make a column chart using cause importance data i.e., the data values in column 2 and the fields in column 1.
3) Add the cumulative % to the Pareto Chart as a line.
4- Move the cumulative % line to secondary axis.

5) Add the cut-off % to the Pareto chart.


Now, our basic Pareto analysis in excel is ready.