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Online Student Guide

OpusWorks 2019, All Rights Reserved

Pareto Analysis

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Table of Contents

LEARNING OBJECTIVES ........................................................................................................................................ 4

INTRODUCTION ...................................................................................................................................................... 4 ORIGIN OF PARETO ANALYSIS PRINCIPLE ........................................................................................................................... 4 PARETO'S BASIC PRINCIPLE .................................................................................................................................................. 4 ORIGIN OF PARETO ANALYSIS PRINCIPLE ........................................................................................................................... 4 BUILDING A PARETO CHART .................................................................................................................................................. 5 APPLYING THE 80/20 RULE ................................................................................................................................................. 5

PARETO ANALYSIS & PARETO CHART ........................................................................................................... 5 PARETO ANALYSIS ................................................................................................................................................................... 5 PARETO CHART LAYOUT ........................................................................................................................................................ 6 REASONS TO USE THE PARETO CHART ................................................................................................................................ 6 WHY USE PARETO ANALYSIS AND A PARETO CHART?...................................................................................................... 6 PARETO CHARTS ARE VALUABLE TOOLS............................................................................................................................. 7

BUILDING PARETO CHART ................................................................................................................................. 7 BUILDING A PARETO CHART .................................................................................................................................................. 7 CUMULATIVE LINE................................................................................................................................................................... 7 TEAM PARETO ANALYSIS ....................................................................................................................................................... 9 TABULATING DATA ............................................................................................................................................................... 10 DATA ENTRY .......................................................................................................................................................................... 10 CUMULATIVE FREQUENCY ................................................................................................................................................... 10 CUMULATIVE PERCENTAGE ................................................................................................................................................. 11 WORKING AND SPREADSHEETS .......................................................................................................................................... 11 COMBINING CATEGORIES ..................................................................................................................................................... 12 PLOTTING THE PARETO CHART .......................................................................................................................................... 12 COMPLETING THE CHART..................................................................................................................................................... 13 RECOMMENDATIONS ............................................................................................................................................................. 13 TEAM RECOMMENDATIONS ................................................................................................................................................. 13 COST PER CATEGORY ............................................................................................................................................................ 13 PRIORITIZING BY COST ......................................................................................................................................................... 14 CHART INTERPRETATION ..................................................................................................................................................... 14 TEAM RECOMMENDATIONS ................................................................................................................................................. 15

SUMMARY .............................................................................................................................................................. 15

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Learning Objectives

Upon completion of this course, student will be able to: • Explain how to create a Pareto Chart, including a cumulative relative frequency line • Given data and a Pareto Chart, we will describe how to use a variable to weight the original data

and produce another Pareto Chart • Discuss how to use stratification methods to perform more in-depth Pareto analysis of the data • Explain how to interpret a Pareto Chart to make a business decision

Introduction

Origin of Pareto Analysis Principle

Pareto Analysis, and the resulting Pareto Chart, gets its name from Vilfredo Pareto, a 19th century economist who first developed the theory that a large share of wealth is owned by a small percentage of the population. From his study of wealth distribution, he developed the Pareto Principle, also known as the 80–20 rule and the law of the vital few. Pareto’s Principle states that for many events, issues, or problems, roughly 80 percent of the effects come from 20 percent of the causes.

Pareto's Basic Principle

Pareto’s basic principle translates well into Lean Six Sigma methodology and general problem solving. In terms of quality improvement, 80 percent of problems are produced by 20 percent of the key causes. This is sometimes referred to as the vital few and the trivial many. Use of this basic principle can help an organization identify the top 20 percent of causes that it needs to address in order to resolve 80 percent of its problems.

Origin of Pareto Analysis Principle

Once an organization identifies the top 20 percent of the causes, it can then zero in on the real root cause of the problem using more detail focused tools, such as the Ishikawa diagram or Fishbone Analysis. These tools are the topics of other specific modules. The Pareto Chart is a valuable tool that evolved from Pareto’s famous principle.

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Building a Pareto Chart

At the end of the year, the completed Pareto chart shows even more problems and new categories of problems too. In its most basic form, this simple bar chart is a Pareto chart.

Applying the 80/20 Rule

The 80/20 rule can be applied to virtually anything. Here are just a few examples: - 20 percent of a sales department’s salespersons generate 80 percent of that organization’s sales revenues. - 20 percent of the defects found in a product or system cause 80 percent of its problems or issues.

- 80 percent of an organization’s customer complaints arise from 20 percent of their products or services. - 80 percent of schedule delays result from 20 percent of delay causes. - 20 percent of an organization’s products or services account for 80 percent of its profit. Can you think of more examples?

Pareto Analysis & Pareto Chart

Pareto Analysis

Now that you understand the origin of Pareto analysis and the Pareto Chart, let’s take a closer look at how these tools are used today. Pareto analysis is a statistical tool or technique used to identify, rank, and prioritize issues relative to the cause of a specific problem, or a variation within a project, process, or organization. Used during problem solving and continuous improvement activities, it is very effective at helping teams decide which key issues to address first.

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Pareto Chart Layout

Here you see a Pareto Chart, which consists of a series of bars. The height of each bar reflects the frequency or impact of problems. Notice that the bars are arranged (or ranked) in descending order of height, from left to right. The rank and height of the bars on the left portion of the chart identify elements or issues that appear to be relatively more significant than those on the right.

Reasons to use the Pareto Chart

There are many reasons for using the Pareto Chart, including the following: 1. It breaks down a big problem into smaller, more manageable issues. 2. It identifies the most significant issue factors and defines where to focus improvement efforts, thus providing better use of limited resources. 3. It ranks and arranges data according to priority or importance. By doing so, it allows the user to use data, rather than perception, to determine which problems are most important. Factual, data driven information is a key prerequisite of any process improvement project.

Why use Pareto Analysis and a Pareto Chart?

In summary, Pareto analysis and the Pareto Chart help develop answers to the following questions: - What are the largest and most significant issues facing a team or business? - What 20 percent of problem sources are causing 80 percent of the problems? - Where should an improvement team focus its efforts to achieve the greatest improvement results?

Use of the Pareto Chart is not limited to problem solving. It can also be used to set priorities for many other practical applications. Click on the Pareto chart to see other applications. When you are finished select the Next button to continue.

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Pareto Charts are Valuable Tools

Although the Pareto Chart is a rather simple bar chart, it can be an extremely valuable tool. When used properly, it acts as a guide to help select opportunities for process improvement; it helps teams prioritize those opportunities; and it directs teams to make good financial decisions when fixing problems.

Building Pareto Chart

Building a Pareto Chart

Building a Pareto Chart is easy. To demonstrate, we will walk through the basic steps. Let’s begin by looking at the earlier example of a restaurant that had a lot of problems during a one-year time frame. Our task is to construct a Pareto Chart to help identify and solve its problems.

Recall that the restaurant collected customer feedback for a one-year period, and the complaints were counted and listed in order from the most frequently occurring complaint to the least. The bars on this chart reflect the data collected. For example, we see that Parking Problems was the most frequent complaint, and Long Wait was the least frequent. The actual count, or measure, for each complaint is listed in the table on the right.

Cumulative Line

For some applications, a basic Pareto Chart, like the one on the previous slide, may provide enough information to make a decision. However, in many applications some additional analysis may be required. When evaluating more complex chart examples, the most significant issues may not be so obvious. To help clarify where to focus improvement activities, we add a line to the chart. This line represents the cumulative percentage of the problems or categories. To illustrate, let’s continue with our restaurant example.

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First, we need to add another scale for percentage. This scale is added to the right side of the Pareto Chart, with a range from 0 to 100 percent. We have determined that there’s a total of 257 customer complaints. The first category, Parking Problems, had 112 complaints. This category alone accounts for

43.6 percent of the complaints. So, at the top right hand corner of the first bar, we plot a point at 43.6 percent, using the right hand scale. The cumulative number of problems for the first two categories is 185; 112 for Parking Problems plus 73 for Rude Waiters. The cumulative percentage for the first two columns is approximately 72 percent. Now that we have this data, we plot the point, connect it with a line to the previous plot point, and continue this for all the categories.

Now look at the line. As you can see, using the chart with this cumulative percentage plot makes it easy to see that the first three categories account for almost 92 percent of the complaints. If those three problems can be fixed, the restaurant will have many more happy customers.

In our example, fixing the three major problems identified would be ideal. However, it may not be practical or possible to do so. Lack of available space or cost could prohibit the owner from easily fixing the parking problem. On the other hand, continuing to allow waiters to be rude is a very serious issue that is easy to fix.

Next, we must decide which specific cause to address. As we review our Pareto Chart in more detail, we see that although prioritizing problems by the number of occurrences was informative, we need more information before we can make this decision. There may be underlying issues that we have not identified by evaluating the chart alone. To clarify this point, consider the following situation: If an improvement team is making a recommendation to management, it would be extremely helpful to review the benefits of fixing each problem, relative to the investment cost required for each fix. Providing this cost data would help management make the right decision.

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Team Pareto Analysis

Now let’s look at another example of Pareto analysis. Here we have a process improvement team from a call center. The team was given a project charter to improve customer satisfaction. In this example, the customer service office receives calls from clients who have submitted claims for processing. The representative’s ability to service the customer depends on accurate and timely information from the online computer system. Recently, the management team received several letters from key clients who were disappointed in customer service response. The management team established an improvement team, and gave it a project charter to significantly improve the office’s ability to serve the customer.

In this customer service office calls come in from clients who have submitted claims for processing. The representatives ability to service the customer depends on accurate and timely information from the on-line computer system. Team Leader: Team, our mission, if we decide to accept it, is to improve our ability to serve the customer. Now, recently our executives have received a couple of letters from key clients that have been disappointed in our service.

Team Member 1: Well it's not our fault, it’s the system’s fault. Team Member 2: Yea, the procedures are inadequate. Team Member 1: Well I mean the computer system, not our system. Team Leader: Look. This is not a fault finding mission. No one is placing the blame. We need to understand our customer's expectations and find out why we are not meeting them. And both of you made a good point, our internal processes may be outdated and the computer system could be a part of the problem. Either way, we need to find out what's wrong and find a way to fix it. Team Member 2: I recommend that we use this time to do some brainstorming and identify some of the obvious types of problems we may be aware of. Then we can take a survey of the customer complaints and categorize them by type. Team Leader: Sounds like a plan to me. I’ll be the scribe. Let's go.

Team Leader: OK. Here’s the summarized list of categories we've agreed to. Now Tom, you enlist some help, gather the necessary customer complaint data and any additional information you need. Team Member 1: No problem. I should be able to do that by next week. Team Leader: Good. Now the team has completed the first step of the process by identifying categories for the data being analyzed. Next, to further understand and verify the accuracy of its data, the team decides to analyze a random 10 percent sample of received

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calls. The data it collects during this sample is very important, and the team will use it as part of its final analysis. The results are tabulated on the next slide.

Tabulating Data

To analyze the calls received, the team summarizes the data by category. It then tabulates the number of times each problem category occurred during its random sampling.

Data Entry

Continuing with its Pareto analysis, the team’s next step is to enter the data on a spreadsheet. Doing this makes it easier to plot the Pareto Chart. In addition to tabulating the number of occurrences, the team adds a column for the cumulative frequency so that it can calculate the cumulative percentage.

Cumulative Frequency

The cumulative frequency for a category is simply the sum of the frequencies for all categories up to and including the given category. In this example, for the first category, Account Code Table, the cumulative frequency is 42. Which number from the first row, labeled Frequency, would you add to 42 to calculate the cumulative frequency for the second category, Supporting Documentation? Select your answer from the choices at the bottom. The cumulative frequency for the third category, Phone Systems, is 78, which is the sum of 64 and 14. Now let’s fill in the cumulative frequencies for the remaining categories.

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Cumulative Percentage

Once the team has calculated the cumulative frequencies, it can calculate the cumulative percentages for each category.

The cumulative percentage for a category is the cumulative frequency for that category divided by the total number of observations.

The calculation for the cumulative percentage for the first category, Account Code Tables, is shown here. What is the cumulative percentage for the second category, Supporting Documentation?

Working and Spreadsheets

Spreadsheet programs make it easy to do repetitive calculations in tables. Once the team knows the formula for one cell, it simply copies it for all the others to complete the calculations.

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Combining Categories

The team has now completed the data for frequency, cumulative frequency, and cumulative percentage. Next, it reviews the table and decides to combine the “trivial many” into a separate category. To do this, it combines the last four columns into one, and labels this new category “Other.”

As you see here, this new category turns out to have a larger frequency count than the Staffing category. For this reason, combining the columns could raise concern. However, it really is not a problem as long as the team makes sure to fully explain the “Other” column when presenting the final data. In this example, the team could just as well decide to keep all the original categories, rather than combining them into one.

Plotting the Pareto Chart

The team’s next step is to construct the Pareto Chart. To begin, the team selects a title for the chart, labels each category, and decides to plot both the number of occurrences and a cumulative percentage line. Since one measure is a count, and the other is a percent, two separate Y-axis scales are required. By common convention, the left side of a Pareto Chart is used for the frequency scale, and the right side is used for the cumulative percentage.

Here, it is important to note that the two Y-axis scales should be proportional. That is, a frequency count of 105 on the left hand Y-axis scale should correspond to 100 percent on the other scale. The diagram located below the data tabulation spreadsheet illustrates this key point.

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Completing the Chart

Next, the team uses the frequency values to plot the height of the bar for each category. It then uses the cumulative percentage value to plot a point at the top of each bar. Finally, it connects each successive point to plot the cumulative percentage curve. Its Pareto Chart is now complete and ready to review.

Recommendations

At first glance of this Pareto Chart, one may conclude that the team should start its improvement activity with the “Account Code Table” problem. Which way would you vote? While the team would like to solve all the problems, resources like time and money are constraining factors. In order to be absolutely sure that it works on the most critical problems, the team must collect more information.

Team Recommendations

Even though the problem with the Account Code Table occurred the most often, the team feels its impact is relatively small compared to some of the other problems. The consensus is that each category probably has a different cost impact, affects the customer differently, and that some cost more to rectify than others. To find out, the team decides to gather some financial data in order to weight the categories according to cost and impact.

Cost Per Category

Working with the finance and the technical staff, the team develops an estimate for Cost per Occurrence. Although it knows that some categories may contain intangible costs, it decides not to consider them for this cost estimate. The team analyzes all the original problem categories by breaking down the “Other” category into the original problems. It does this because a category that was among the “trivial many” for the number of occurrences may turn out to have a large impact on the business whenever it occurs. The team then tabulates the total Monthly Cost for each category. It does this by multiplying the Frequency (or number of occurrences) by the estimated Cost per Occurrence. For example, to find the Monthly Cost for the first category, Account Code Table, the team simply multiplies 42 by 5.2, which equals 218. Next, we will show you how the Pareto Chart uses these values to prioritize and rank the categories.

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Prioritizing by Cost

Now, the team creates a new Pareto Chart, ranked by monthly cost. As it did with the frequency chart, the team combines the last four categories by rank into a new "Other“ category. It then rearranges the columns according to the new cost ranking and re-plots the chart. Notice that the ranking of the categories by monthly cost is different than the ranking by frequency.

Frequently, there will be a change in priority for a category when costs are considered. Pareto Charts often have a greater impact on decision makers when problems are presented in terms of their cost or impact to the business. Let’s take a look at the new results.

Chart Interpretation

When reviewing the new Pareto Chart, the team notices that the first two categories account for a cumulative cost of $596. Consider this question: Can the team now tell Management that if the first two categories are fixed and the problems are eliminated, the cost savings will be $596 per month? If you answered “No,” you are correct. The team can’t really say there will be a savings of $596 per month because data for this Pareto Analysis was based upon a 10 percent sample of all problems. To estimate the total savings to the business, it would have to extrapolate from the sample data to the entire population. The team can provide a better answer to the question, however, by computing the cumulative percentage for costs. Once it has done this, it can then determine how much of the total monthly costs are accounted for by the first two categories. To do this, the team divides 596, the cumulative monthly cost for the first two categories, by 1,170, the cumulative total cost from the sample. To convert this number into a percentage number, it multiplies the answer, 0.509, by 100. Now, the team can tell Management that 50.9 percent of the total monthly costs are accounted for by the first two categories.

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Team Recommendations

In its final analysis, the team decides to compute the ROI, or return on investment. To do this, it looks at the cost of each action recommended to rectify the problems, and analyzes them relative to the savings. Recommended actions include rewriting support documentation, installing new phone software, hiring additional part-time staffing, and fixing Account Code Tables. The ROI data, along with any budget or capital constraints, could very well influence management’s final decision on which course of action to take.

Summary

Pareto analysis and the Pareto Chart help develop answers to the following questions: - What are the largest and most significant issues facing a team or organization? - What 20 percent of problem sources are causing 80 percent of the problems? - Where should an improvement team focus its efforts to achieve the greatest improvement results? By helping the team identify the most important problems and focus on the task at hand, the Pareto Chart enhances team cooperation. In summary, Pareto analysis is a very simple but powerful technique that can be used for solving problems and improving processes.