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Pareto Analysis – Definition

Pareto Analysis Definition

Pareto Analysis is a method of decision making in business based on the 80/20 rule. The rule of Pareto analysis is that 80 percent of problems trail 20 percent of causes or alternatively 80 percent of a project’s advantages are attained with 20 percent of the work. Pareto analysis statistically isolates a defined amount of input factors as having the most influence on an undesirable or desirable outcome.

A Little More on What is Pareto Analysis

Pareto Analysis is used in decision-making as a statistical technique to select a defined number of functions that generate the most meaningful overall effect. It uses the 80/20 rule, also known as the Pareto Principle, which is the concept that in doing 20 percent of the work, you can produce 80 percent of the benefit of completing the whole task. In an example of quality improvement, most of the issues (80 percent) are generated by a few essential causes (20 percent). The Pareto analysis also refers to the vital few and the trivial many.

Joseph M. Juran was a Romanian-born American engineer and management consultant, who in the 1940s presented the idea and named it after Vilfredo Pareto, an Italian economist who observed that 20 percent of Italy’s population was responsible for 80 percent of the country’s income. Pareto later discovered that even in other countries the distribution was similar.

You can apply the 80/20 rule virtually anything:

  • 20 percent of defects in a system causes 80 percent of its problems.
  • 80 percent of complaints from customers arise from 20 percent of a company’s services and products.
  • 80 percent of schedule delays come from 20 percent of the conceivable reasons for the delays.
  • 80 percent of a company’s profit comes from 20 percent of its services and products.
  • 20 percent of the sales force generates 80 percent of the company’s revenues.

The Pareto Principle is applicable to many areas of quality control. It is the basis of the Pareto disagreed, which is used as a primary tool in Six Sigma and total quality control.

In PMBOK, which is the Project Management Body of Knowledge, Pareto ordering guides corrective action and helps a project team to take steps to correct issues that cause the highest number of defects.

There are eight steps to Pareto Analysis which allow you to identify the principal causes that need your attention:

  1. Put causes on the x-axis on a vertical bar chart. Put the number of occurrences (count) on the y-axis.
  2. The bar chart should be arranged with the causes in descending order relative to their importance. Put the cause that has the highest count first.
  3. For each cause, in descending order, calculate its cumulative count.
  4. In descending order, calculate the percentage of each cause’s cumulative count. For the calculation: {single cause count} / {total count of all causes}*100
  5. Make another chat with the calculated percentages on the y-axis in descending order in increments of 10 starting with 100 percent.
  6. On the x-axis, plot each cumulative count percentage of the causes.
  7. Connect the points to create a curve.
  8. At 80 percent point, draw a line on the y-axis that runs parallel to the x-axis. At the intersection point with the x-axis curve, drop the line. The point on the x-axis cuts apart the significant causes on the left or the vital few from the trivial many, which are the less important causes on the right.

References for Pareto Analysis

Academic Research on Pareto Analysis

Pareto analysis of critical success factors of total quality management: A literature review and analysis, Karuppusami, G., & Gandhinathan, R. (2006). The TQM magazine, 18(4), 372-385. This literature review aims to point out and suggest a list of a few vital CSFs or critical success factors of TQM or total quality management to benefit industries and researchers. The authors recognize there is a wealth of documented information about TQM but identifies how there has been little about the CSFs of TQM. They examine and list the CSFs based on a descending order of occurrence frequency. The review period of this literature is between 1989 and 2003. Pareto analysis is a quality tool used to arrange and sort the CSFs based on their order of criticality.

Pareto analysis in multiobjective optimization using the collinearity theorem and scaling method, Kasprzak, E. M., & Lewis, K. E. (2001). Structural and Multidisciplinary Optimization, 22(3), 208-218. This paper offers a technique to forecast the relating objective weighting design required to make arbitrary members of a Pareto solution set to reach optimalism. A polynomial explanation of the Pareto set is formed using high-performance computing and simulation. With the geometric relationships the designated member of the Pareto set had with the placement of the utopia point and the polynomial coefficients, the metrics of performance is determined that will cause the Pareto set member to become optimal. The technique is called a scaling method, and it is inspected with the use of a sample problem taken from the area of vehicle dynamics optimization. The paper also presents the relation of the collinearity theorem to the scaling method.

Safety At Sea–Applying Pareto Analysis, Ziarati, R. (2006, February). In Proceedings of World Maritime Technology Conference (WMTC 06), Queen Elizabeth Conference Centre(Vol. 94). A close examination of the analyses of casualty that especially focus on the accident causes makes it apparent that standards do not have the correct application, and it is clear that education and training programs completed by the seafarers implicated in action with the accidents are omitted. The IMO or International Maritime Organization has spent the last few years revising the crew standards, known as the International Convention on Standards of Training, Certification, and Watch-keeping for Seafarers or simply STCW. However, IMO is not able to see if there is compliance with the STCW’s requirements. This paper discusses a project funded by a major European Union that is promoting training and education for merchant navy officers of all ranks. Pareto analysis methodology is used to cross-reference the techniques in the project to discover the issues with the greatest possibility for improvements with the demonstration of their relative frequencies and magnitudes.

Rethinking Pareto analysis: maintenance applications of logarithmic scatterplots, Knights, P. F. (2001). Journal of Quality in Maintenance Engineering, 7(4), 252-263. To determine maintenance priorities, Pareto histograms are used often to rank equipment failure codes based on their relative cost or downtime input. However, they do not easily allow for the identification of the dominant variable with impact on repair and downtime costs, particularly the frequency of failure, mean repair and mean downtime cost related to each failure code. Log scatterplots will enable identification of failure frequency that takes up comparably little downtime or repair cost but contributes to disturbances in operation that end up in causing production losses. This paper presents a practical application of log scatterplots by many Chilean mining companies and equipment suppliers.

Pareto analysis of total quality management factors critical to success for service industries, Talib, F., Rahman, Z., & Qureshi, M. N. (2010). Talib, F., Rahman, Z. and Qureshi, MN (2010). International Journal of Quality Research (IJQR), Center for Quality, University of Podgorica Montenegro and University of Kragujevac, Serbia, 4. TQM or total quality management is an approach to management that works to always improve the performance of services, processes, and products to outdo customer expectation. A major factor that is partly responsible for the success of TQM has to be identified to reach goals. The factors are critical success factors or CSFs. This study aims to point out and suggest a list of CSFs that are the vital few so that practitioners of service industries and researchers can benefit. The results of the analysis will aid in the successful application of total quality management programs in organizations.

Pareto analysis vis-à-vis balance space approach in multiobjective global optimization, Galperin, E. A. (1997). Journal of Optimization Theory and Applications, 93(3), 533-545. There is much debate regarding the balance space approach previously introduced that considered balance vectors and balance number and the later development with consideration of balance sets and balance points. Recently Pareto analysis was used as another method of multi-objective optimization in an attempt to identify the balance space approach. This paper compares Pareto analysis with the balance space approach to show the connection and the differences of both methods.

Applied digital library project management: using Pareto analysis to determine task importance rankings, Cervone, H. F. (2009). OCLC Systems & Services: International digital library perspectives, 25(2), 76-81. The aim of this paper is to explain and illustrate Pareto analysis as a technique useful in spotting out and addressing the factors with the most influence in the success of a digital library project.

Pareto analysis of supply chain contracts under satisficing objectives, He, X., & Khouja, M. (2011). European Journal of Operational Research, 214(1), 53-66. Pressure has increased on firms to improve the performance of supply chain coordination. We analyze the efficiency of Push, Pull, and Advance-purchase discount (APD) agreements in a manufacturer-retailer supply chain where one firm or both of them have a goal of optimizing the likelihood of obtaining a target profit. The authors define the operational modes of the supply chain that came as a result and the possible conflicts over the favored contracts beneath the Push, Pull, and APD contracts. When each firm adopts the first satisfactory option that comes, a difference arises over the preferred contract when the manufacturer has a zealous profit target or the retailer has a low target for profit. The writers show that altered buy-back and profit assurance contracts can offer major Pareto improvement over APD or Push agreements when the manufacturer is risk-neutral but a revenue-sharing contract cannot.

Pareto analysis for the selection of classifier ensembles, Dos Santos, E. M., Sabourin, R., & Maupin, P. (2008, July). In Proceedings of the 10th annual conference on Genetic and evolutionary computation (pp. 681-688). ACM. The overproduce-and-choose method includes the creation of a large sample of candidate classifiers and its purpose is to examine various candidate ensembles so as to choose the best performing option. By using the group’s error rate, the authors are successful at achieving the primary aim in Pattern Recognition and Machine Learning to find discover high-performance predictors. To test the relationship between the key search criteria used in the overproduce-and-choose strategy, the authors apply two Pareto front spread quality measures. The results of the experiments showed that conflicting multi-objective optimization issues were not created when the ensemble size and diversity were combined. When ensemble size or diversity is combined with the generalization error rate, the performances of the solutions are greater.

Supercharging your Pareto analysis, Stevenson, W. J. (2000). Quality Progress, 33(10), 51. A growing number of businesses are giving their employees training in quality and pushing them to use quality tools. There must be care taken when using these tools because of the risk that they may be used inappropriately. One of the basic tools of quality is Pareto analysis. The Pareto charts are easy to make and understand. They can offer significant insights for process improvement and problem-solving. This article attempts to clear up the circumstances when a frequency-based approach is applicable and offers suggestions for how to approach issues when it’s not appropriate.

Pareto analysis based on records, Doostparast, M., & Balakrishnan, N. (2013). Statistics, 47(5), 1075-1089. This paper come up with optimal statistical methods involving point and interval estimation and the most effective tests based on data obtained from a two-parameter Pareto model. The authors use a data set on the yearly wages of production-line workers in a prominent industrial firm to illustrate the application of the proposed procedures.

Critical success factors of supply chain management: a literature survey and Pareto analysis, Ab Talib, M. S., Abdul Hamid, A. B., & Thoo, A. C. (2015). EuroMed Journal of Business, 10(2), 234-263. This paper reviews existing literature on critical success factors related to supply chain management. The authors also examine the frequency of occurrences of all possible critical success factors in the literature of supply chain management. They also point out the vital few and use many of those factors that correspond across the supply chain management field.

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