project quality management tools

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I. Contents of project quality management tools

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Quality for Project Managers applies quality principles to project management itself, as well as

to the products and services resulting from projects. It brings to the forefront the essentials of

project quality management and its vital link to business success, with a focus on the tools and

essentials of effective quality management that work for your organization, regardless of your

industry. The course shows you how to integrate quality management concepts with project

management program to support your business success.

You’ll learn about the philosophy and principles of quality management and learn how to

translate these concepts into specific actions that are key to successful project quality efforts. The

course presents a five-step model for successfully planning project quality, a five-step model for

effectively assuring project quality and a quality-control toolkit, all of which you can

immediately apply to your work environment. With a strong emphasis on exercises, this course

gives you the opportunity to apply quality strategies and skills to real-world scenarios. You will

practice concepts, tools and techniques using modularized case studies that require immediate

and direct application of skills learned.

The strategies of quality management and continuous improvement dovetail with project

management concepts to increase your control over objectives, work and performance. Master

these proven methods and discover how quality greatly contributes to and enhances project

success.

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III. Quality management tools

1. Check sheet

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 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 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 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

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]

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