Tools Techniques Pareto Charts Essay Example For Students

Tools Techniques Pareto Charts Essay Tools Techniques Praetor Charts As a decision-making tool, the Praetor chart provides facts and insights necessary for setting priorities. Vilified Praetor was an Italian economist credited with establishing what is now widely known as the Praetor Principle. It is also known as the 80/20 Rule (sigmas, 2006). When Praetor discovered the principle in 1906, he established that 80% of the land in Italy was owned by of the population. Later, Praetor discovered his principle was valid in other parts of his elite, such as gardening. For example, 80% of his garden peas were produced by of the peapod. The 80/20 Rule is not literal. The ratios may vary. Rather than an even to ratio the exact percentage may be to 18%, or to 22%. However as a Rule of thumb it is common practice to refer to an 80% to 20% ratio. On their website showing examples of the Praetor charts and the 80/20 Rule, sigmas provides several examples of common applications for the 80/20 Rule of process defects arise from 20% of the process issues. Of your sales force produces Of your company revenues. Of delays in schedule arise from 20% of the possible causes of the delays. Of customer complaints arise from 20% Of your products or services. Robbers in order to aid in decision making. However paraphrased; a Praetor chart is a simple management tool with broad business applications. Praetor charts organize and display information to show the relative importance of various problems. It is essentially a special form of a vertical bar chart that puts items in order from the highest to the lowest relative to another measu rable quantity such as frequency, cost, or time. Placing the items in descending order of frequency makes it easy to discern Robbers that are tot greatest importance or those causes that appear to account for most of the variation. We will write a custom essay on Tools Techniques Pareto Charts specifically for you for only $16.38 $13.9/page Order now Thus, a Praetor chart helps individuals or teams to focus their efforts where they can have the greatest potential impact. Praetor charts are useful in establishing priorities by showing which are the most critical problems to be tackled or causes to be addressed. Comparing Praetor charts of a given situation over time can also determine whether an implemented solution reduced the relative frequency or cost of that problem or cause. Trends can be Observed. A Praetor Chart is basically a vertical bar graph showing problems in a prioritize order, so it can be determined Which problems should be tackled first. When making decisions it is often useful to make Praetor Charts of data collected over a set time period. The first Step would be to list the problems identified for a particular problem. Data is collected for the variable elements of the units to be measured and displayed. New or existing data are grouped by consistent units of measure. The attributes to be charted are arranged so as to fall under one category only. Units of measure are labeled and displayed on the left vertical axis. The categories are labeled and displayed on the horizontal axis. Categories are plotted according to frequency, starting from the vertical axis using the highest numbers first. Categories that appear indifferently are grouped under other to avoid confusion, The Coast Guard Process Improvement Guide (2006) cautions measurement units can significantly affect a Praetor chart. The same units of measure must be used. They should be clearly marked. Also the other category, if used, should be no more than 25% of the data. When to use a Praetor Chart Praetor charts are typically used to prioritize competing or conflicting problems, so that resources are allocated to the most significant areas. .ucdf564660424751250955f15e2e13609 , .ucdf564660424751250955f15e2e13609 .postImageUrl , .ucdf564660424751250955f15e2e13609 .centered-text-area { min-height: 80px; position: relative; } .ucdf564660424751250955f15e2e13609 , .ucdf564660424751250955f15e2e13609:hover , .ucdf564660424751250955f15e2e13609:visited , .ucdf564660424751250955f15e2e13609:active { border:0!important; } .ucdf564660424751250955f15e2e13609 .clearfix:after { content: ""; display: table; clear: both; } .ucdf564660424751250955f15e2e13609 { display: block; transition: background-color 250ms; webkit-transition: background-color 250ms; width: 100%; opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #95A5A6; } .ucdf564660424751250955f15e2e13609:active , .ucdf564660424751250955f15e2e13609:hover { opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #2C3E50; } .ucdf564660424751250955f15e2e13609 .centered-text-area { width: 100%; position: relative ; } .ucdf564660424751250955f15e2e13609 .ctaText { border-bottom: 0 solid #fff; color: #2980B9; font-size: 16px; font-weight: bold; margin: 0; padding: 0; text-decoration: underline; } .ucdf564660424751250955f15e2e13609 .postTitle { color: #FFFFFF; font-size: 16px; font-weight: 600; margin: 0; padding: 0; width: 100%; } .ucdf564660424751250955f15e2e13609 .ctaButton { background-color: #7F8C8D!important; color: #2980B9; border: none; border-radius: 3px; box-shadow: none; font-size: 14px; font-weight: bold; line-height: 26px; moz-border-radius: 3px; text-align: center; text-decoration: none; text-shadow: none; width: 80px; min-height: 80px; background: url(https://artscolumbia.org/wp-content/plugins/intelly-related-posts/assets/images/simple-arrow.png)no-repeat; position: absolute; right: 0; top: 0; } .ucdf564660424751250955f15e2e13609:hover .ctaButton { background-color: #34495E!important; } .ucdf564660424751250955f15e2e13609 .centered-text { display: table; height: 80px; padding-left : 18px; top: 0; } .ucdf564660424751250955f15e2e13609 .ucdf564660424751250955f15e2e13609-content { display: table-cell; margin: 0; padding: 0; padding-right: 108px; position: relative; vertical-align: middle; width: 100%; } .ucdf564660424751250955f15e2e13609:after { content: ""; display: block; clear: both; } READ: Kilauea Hawaii EssayThey can be used to determine which of several classifications have the most value or cost associated with them. An example would be the number Of people using the various outdoor Tams versus each of the indoor teller locations. Another example would be the number Of times an employee or group Of employees were tardy and/or absent. The important limitations are that the data must be in arms Of either counts or costs. The data can not be in terms that cant be added, such as percent yields or error rates. Count data is also referred to as attribute data. Typically, a person will count the number of times a condition is observed in a given sample from the process. It is different from measurement data in its resolution. Attribute data has less resolution, since there would be a count only if something occurs rather than measuring the event being observed. For example, attributes data for absenteeism might include the number of times an employee was late for work. Whereas variables data for the same process might be the measurement of the number of minutes the employee reported late for work. Consequently, attributes data generally provides less information than measurement (variables) data would for the same process, Therefore, for attributes data, it would generally not be able to predict if the process is trending towards an undesirable state, since it is already in this condition, Praetor analysis is one way to determine the major causes of particular problems. While it has mostly been used by quality assurance people and others n the quality movement, Praetor analysis is also useful for organizational development. Typically, Praetor analysis is used both to begin problem solving and to identify root causes of problems. The root cause being the basic underlying issue causing the problem. This is opposed to the apparent issue which may in itself, be caused by something else. To clarify by example, replacing a defective voltage regulator Which is allowing batteries to be damaged, rather than simply replacing the batteries. Praetor charts are useful because most problems tend to come from one or NON processes or components, rather Han from a large number of causes. As described earlier, a Praetor chart is simply a histogram, where the horizontal axis shows categories (process or material problems); the vertical Y axis shows the number or proportion of incidents. The vertical axis shows the cumulative percentage of incidents. Each bar in the graph shows the proportion of errors caused by each issue or process. The hard part of constructing Praetor charts is generally collecting the information to be used in the chart. Categories of information must be established, along with their incidence. They generally are set up in descending order, so that the most common issue or process shows up first. The categories should be specific enough to be actionable. If no clear cause appears, one can change the categories to see if new conclusions are possible. To review, Praetor charts are a decision making tool used to identify elements of a problem. The first step is to identify the group of items that are to be charted and sorted, Then create a few major groupings, Identify the unit of measurement. All items on the chart must be measurable by this unit, i. E. Number of days absent. Determine the time frame or period of measurement. Measure the items accurately. Sort the items into size order with the largest measure positions closest to the vertical axis. A large number Of smaller units can be lumped into a special category named other to avoid complexity. Plot the vertical bars With the largest bar on the left side. Then interpret the results and make a decision based on the results. Should there be no clear highest bar another Praetor chart can be created using a different set of measurements or items. Common sense should be utilized when interpreting Praetor charts because sororities the highest bars are tot always the best action items.