Project management quality management

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1. Project management quality management In this file, you can ref useful information about project management quality management such as project management quality managementforms, tools for project management quality management, project management quality managementstrategies If you need more assistant for project management quality management, please leave your comment at the end of file. Other useful material for project management quality management: I. Contents of project management quality management ================== Project quality management is all of the processes and activities needed to determine and achieve project quality. At its most basic level, quality means meeting the needs of customers. This is also known as "fit for use."But what does "quality" really mean? I like this simple definition of quality because its focus is where it should be, on the customer. This basic definition also implies that the requirements of the project have been met since the requirements should reflect the customer's needs if collected properly. Customer SatisfactionAs the project manager, there are three key quality management concepts that will help you deliver a high quality project... Prevention over Inspection Continuous Improvement 3 Key Quality Management Concepts Customer Satisfaction Customer satisfaction is a key measure of a project's quality. It's important to keep in mind that project quality management is concerned with both theproduct of the project and the 2. management of the project. If the customer doesn't feel the product produced by the project meets their needs or if the way the project was run didn't meet their expectations, then the customer is very likely to consider the project quality as poor, regardless of what the project manager or team thinks. As a result, not only is it important to make sure the project requirements are met, managing customer expectations is also a critical activity that you need to handle well for your project to succeed. Prevention over Inspection The Cost of Quality (COQ) includes money spent during the project to avoid failures and money spent during and after the project because of failures. These are known as the Cost of Conformance and the Cost of Nonconformance. Cost of ConformanceCost of Nonconformance Prevention Costs Training Document Processes Equipment Time To Do It Right Internal Failure Costs Rework Scrap Appraisal Costs Testing Destructive Testing Loss Inspections External Failure Costs Liabilities Warranty Work Lost Business The cost of preventing mistakes is usually much less than the cost of correcting them. Continuous Improvement Continuous improvement is a concept that exists in all of the major quality management approaches such as Six Sigma and Total Quality Management (TQM). In fact, it is a key aspect 3. of the last concept,prevention over inspection. Continuous improvement is simply the ongoing effort to improve your products, services, or processes over time. These improvements can be small, incremental changes or major, breakthrough type changes. From a project perspective, this concept can be applied by analyzing the issues that were encountered during the project for any lessons learned that you can apply to future projects. The goal is to avoid repeating the same issues in other projects. Quality Management for Projects Project Quality Management has three key processes that you should perform in your projects... Plan Quality Plan Quality involves identifying the quality requirements for both the project and the product and documenting how the project can show it is meeting the quality requirements. The outputs of this process include a Quality Management Plan, quality metrics, quality checklists and a Process Improvement Plan. Perform Quality Assurance Quality Assurance is used to verify that the project processes are sufficient so that if they are being adhered to the project deliverables will be of good quality. Process checklists and project audits are two methods used forproject quality assurance. Perform Quality Control Quality Control verifies that the product meets the quality requirements.Peer reviews and testing are two methods used to perform quality control. The results will determine if corrective action is needed. ================== III. Quality management tools 1. Check sheet 4. The check sheet is a form (document) used to collect data in real time at the location where the data is generated. The data it captures can be quantitative or qualitative. When the information is quantitative, the check sheet is sometimes called a tally sheet. The defining characteristic of a check sheet is that data are recorded by making marks ("checks") on it. A typical check sheet is divided into regions, and marks made in different regions have different significance. Data are read by observing the location and number of marks on the sheet. Check sheets typically employ a heading that answers the Five Ws: Who filled out the check sheet What was collected (what each check represents, an identifying batch or lot number) Where the collection took place (facility, room, apparatus) When the collection took place (hour, shift, day of the week) Why the data were collected 2. Control chart Control charts, also known as Shewhart charts (after Walter A. Shewhart) or process-behavior charts, in statistical process control are tools used to determine if a manufacturing or business process is in a state of statistical control. If analysis of the control chart indicates that the process is currently under control (i.e., is stable, with variation only coming from sources common to the process), then no corrections or changes to process control parameters are needed or desired. In addition, data from the process can be used to predict the future performance of the process. If the chart indicates that the monitored process is not in control, analysis of the chart can help determine the sources of variation, as this will 5. result in degraded process performance.[1] A process that is stable but operating outside of desired (specification) limits (e.g., scrap rates may be in statistical control but above desired limits) needs to be improved through a deliberate effort to understand the causes of current performance and fundamentally improve the process. The control chart is one of the seven basic tools of quality control.[3] Typically control charts are used for time-series data, though they can be used for data that have logical comparability (i.e. you want to compare samples that were taken all at the same time, or the performance of different individuals), however the type of chart used to do this requires consideration. 3. Pareto chart A Pareto chart, named after Vilfredo Pareto, is a type of chart that contains both bars and a line graph, where individual values are represented in descending order by bars, and the cumulative total is represented by the line. The left vertical axis is the frequency of occurrence, but it can alternatively represent cost or another important unit of measure. The right vertical axis is the cumulative percentage of the total number of occurrences, total cost, or total of the particular unit of measure. Because the reasons are in decreasing order, the cumulative function is a concave function. To take the example above, in order to lower the amount of late arrivals by 78%, it is sufficient to solve the first three issues. The purpose of the Pareto chart is to highlight the most important among a (typically large) set of factors. In quality control, it often represents the most common sources of defects, the highest occurring type of defect, or the most frequent reasons for customer complaints, and so on. Wilkinson (2006) devised an 6. algorithm for producing statistically based acceptance limits (similar to confidence intervals) for each bar in the Pareto chart. 4. Scatter plot Method A scatter plot, scatterplot, or scattergraph is a type of mathematical diagram using Cartesian coordinates to display values for two variables for a set of data. The data is displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis.[2] This kind of plot is also called a scatter chart, scattergram, scatter diagram,[3] or scatter graph. A scatter plot is used when a variable exists that is under the control of the experimenter. If a parameter exists that is systematically incremented and/or decremented by the other, it is called the control parameter or independent variable and is customarily plotted along the horizontal axis. The measured or dependent variable is customarily plotted along the vertical axis. If no dependent variable exists, either type of variable can be plotted on either axis and a scatter plot will illustrate only the degree of correlation (not causation) between two variables. A scatter plot can suggest various kinds of correlations between variables with a certain confidence interval. For example, weight and height, weight would be on x axis and height would be on the y axis. Correlations may be positive (rising), negative (falling), or null (uncorrelated). If the pattern of dots slopes from lower left to upper right, it suggests a positive correlation between the variables being studied. If the pattern of dots slopes from upper left to lower right, it suggests a negative correlation. A line of best fit (alternatively called 'trendline') can be drawn in order to study the correlation between the variables. An equation for the correlation between the variables can be determined by established best-fit procedures. For a linear correlation, the best-fit procedure is known as linear 7. regression and is guaranteed to generate a correct solution in a finite time. No universal best-fit procedure is guaranteed to generate a correct solution for arbitrary relationships. A scatter plot is also very useful when we wish to see how two comparable data sets agree with each other. In this case, an identity line, i.e., a y=x line, or an 1:1 line, is often drawn as a reference. The more the two data sets agree, the more the scatters tend to concentrate in the vicinity of the identity line; if the two data sets are numerically identical, the scatters fall on the identity line exactly. 5.Ishikawa diagram Ishikawa diagrams (also called fishbone diagrams, herringbone diagrams, cause-and-effect diagrams, or Fishikawa) are causal diagrams created by Kaoru Ishikawa (1968) that show the causes of a specific event.[1][2] Common uses of the Ishikawa diagram are product design and quality defect prevention, to identify potential factors causing an overall effect. Each cause or reason for imperfection is a source of variation. Causes are usually grouped into major categories to identify these sources of variation. The categories typically include People: Anyone involved with the process Methods: How the process is performed and the specific requirements for doing it, such as policies, procedures, rules, regulations and laws Machines: Any equipment, computers, tools, etc. required to accomplish the job Materials: Raw materials, parts, pens, paper, etc. used to produce the final product Measurements: Data generated from the process that are used to evaluate its quality Environment: The conditions, such as location, time, temperature, and culture in which the process operates 6. Histogram method 8. A histogram is a graphical representation of the distribution of data. It is an estimate of the probability distribution of a continuous variable (quantitative variable) and was first introduced by Karl Pearson.[1] To construct a histogram, the first step is to "bin" the range of values -- that is, divide the entire range of values into a series of small intervals -- and then count how many values fall into each interval. A rectangle is drawn with height proportional to the count and width equal to the bin size, so that rectangles abut each other. A histogram may also be normalized displaying relative frequencies. It then shows the proportion of cases that fall into each of several categories, with the sum of the heights equaling 1. The bins are usually specified as consecutive, non-overlapping intervals of a variable. The bins (intervals) must be adjacent, and usually equal size.[2] The rectangles of a histogram are drawn so that they touch each other to indicate that the original variable is continuous.[3] III. 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