CHAPTER 10

Project Quality Management

Organizations charter a project to meet a business need of the organization. The project quality management focuses on understanding this business objective and developing the methods and processes to assure the deliverables of the project meet this need. The project quality section of the project management plan defines the methods, tools, and processes for understanding the organization’s expectations, defining deliverable specifications that meet these expectations, the processes for tracking progress against these expectations and processes for making corrections when needed. To accomplish this responsibility, project managers need an array of tools, tech- niques, methods and processes and the ability to select the appropriate tool, techniques, etc. for their project profile.

10.1 Developing the Quality Section of the Project Management Plan

Learning Objective

1. Describe the components of the quality management plan.

Typically, the quality plan begins with quality assurance (QA) where the product or service specifi- cations are developed. The project team focuses on defining what they will measure to assure that the project deliverables meet customer requirements and specifications. Then the project manager determines the quality control (QC) methods. This typically includes the use of inspection and mea- surements of performance of the deliverables and making adjustments to meet requirements and specifications.

The project management team defines the tools, techniques, methods, and processes to iden- tify what is measured, how data is gathered and interpreted, and the process for making appropri- ate changes. The project quality plan addresses what to measure and how to measure it during the project to assure requirements are met (quality assurance). The project quality plan also addresses the tools and techniques the project team uses to measure quality during the project and make adjustments (quality control). The project manager frequently asks “How do we improve our processes for this project and for future projects?” (process improvement).

Building quality into a project requires determining how success will be measured and what data the project team will need to collect in order to monitor and adjust activities to conclude the project. This plan should include:

• Quality management overview, policies, and procedures

• Quality objectives

• Quality roles and responsibilities

• Project deliverables and quality specifications

• Quality metrics (what, when, and how)

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Quality

Degree to which a set of inherent characteristics fulfill requirements.

grade

Category of product or service requirements.

• Quality tools and techniques

• Process improvement

Key Takeaway

• The quality management plan consists of a quality assurance (QA) plan, a description of qual- ity control methods (QC) and a review to determine how the process can be improved.

Exercise

Quality Management PlanQuality Management Plan

Consider a project in which you have been involved where there was a quality management plan or where such a plan was missing. Describe the effect of having or not having such a plan.

10.2 Quality and Statistics

Learning Objectives

1. Define quality.

2. Define and explain statistics terms used in quality control.

3. Estimate the likelihood of samples falling within one, two, or three standard deviations of the mean given a normal distribution caused by random factors.

Definitions of Quality and Grade

Quality is a relative term, which means that something is of high or low quality compared to what it is required to be. The Project Management Institute defines quality as “the degree to which a set of inherent characteristics fulfill requirements.”[1] The requirements of a product or process can be categorized or given a grade. The quality is determined by how well something meets the require- ments of its grade. Consider the following examples.

Quality of Gasoline Grades

Petroleum refiners provide gasoline in several different grades based on the octane rating because higher octane ratings are suitable for higher compression engines. Gasoline must not be contaminated with dirt or water, and the actual performance of the fuel must be close to its octane rating. A shipment of low-grade gasoline graded as 87 octane that is free of water or other contaminants would be of high quality, while a shipment of high-grade 93 octane gas that is contaminated with dirt would be of low quality.

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statistics

Mathematical interpretation of numerical data.

control limits

Upper and lower extremes of allowable variation.

central limit theorem

Idea that if variation is caused by several random factors, they will generally cancel each other out and most measurements will be near the middle of the range of variation.

Quality of Furniture Packing in John’s Move

John has antique furniture that is in excellent condition that was left to him by his grandmother. The pieces are important to John for sentimental reasons and they are also valuable. John decides to hire movers (high-grade professionals) to load his furniture into the truck using appro- priate padding and restraints to prevent dents and scratches during the long trip to Atlanta and then to unload the truck in Atlanta. John’s standard for high quality is that no observable damage occurs to his large pieces of furniture, especially the antiques. If the furniture arrives in his new apartment without a single dent, scratch, or other damage, the activity will be of high quality.

John’s standard for packing his kitchen is lower. His dishes are old and cheap, so he decides to trust his inexperienced friends (low-grade amateurs) to help him pack his kitchen. If a few of the dishes or glassware are chipped or broken in the process, the savings in labor cost will more than make up for the loss, and the dishes can be easily replaced. If John has a few chipped dishes and a broken glass or two by the time he is unpacked in Atlanta, he will consider the kitchen packing to be of high quality.

For most people, the term quality also implies good value—getting your money’s worth. For example, even low-grade products should still work as expected, be safe to use, and last a reasonable amount of time.

Statistics Terminology

Determining how well products meet grade requirements is done by taking measurements and then interpreting those measurements. Statistics—the mathematical interpretation of numerical data—is useful when interpreting large numbers of measurements and is used to determine how well the product meets a specification when the same product is made repeatedly. Measurements made on samples of the product must be between control limits—the upper and lower extremes of allowable variation—and it is up to management to design a process that will consistently pro- duce products between those limits.

Setting Control Limits in Gasoline Production

A petroleum refinery produces large quantities of fuel in several grades. Samples of the fuels are extracted and measured at regular intervals. If a fuel is supposed to have an 87 octane perfor- mance, samples of the fuel should produce test results that are close to that value. Many of the samples will have scores that are different from 87. The differences are due to random factors that are difficult or expensive to control. Most of the samples should be close to the 87 rating and none of them should be too far off. The manufacturer has grades of 85 and 89, so they decide that none of the samples of the 87 octane fuel should be less than 86 or higher than 88.

If a process is designed to produce a product of a certain size or other measured characteristic, it is impossible to control all the small factors that can cause the product to differ slightly from the desired measurement. Some of these factors will produce products that have measurements that are larger than desired and some will have the opposite effect. If several random factors are affect- ing the process, they tend to offset each other most of the time, and the most common results are near the middle of the range. This idea is called the central limit theorem.

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bins

Equal size ranges into which measurements are sorted to obtain a frequency distribution.

frequency distribution

The number of measurements that fall into defined bins.

normal distribution

Frequency distribution that looks like a bell and is the result of offsetting random variations.

mean

Average found by summing the values and dividing by the number of values.

standard deviation

A type of average that takes into account positive and negative values where a sample is used instead of all the measurements. To calculate the standard deviation, first the difference between each value and the mean is squared. The squared values are summed and divided by the number of values minus one. The square root of this value is the standard deviation, also known as the Sample Standard Deviation.

If the range of possible measurement values is divided equally into subdivisions called bins, the measurements can be sorted, and the number of measurements that fall into each bin can be counted. The result is a frequency distribution that shows how many measurements fall into each bin. If the effects that are causing the differences are random and tend to offset each other, the fre- quency distribution is called a normal distribution, which resembles the shape of a bell with edges that flare out. The edges of a theoretical normal distribution curve get very close to zero but do not reach zero.

Normal Distribution of Gasoline Samples

A refinery’s quality control manager measures many samples of 87 octane gasoline, sorts the measurements by their octane rating into bins that are 0.1 octane wide, and then counts the number of measurements in each bin. Then she creates a frequency distribution chart of the data, as shown in Figure 10.1.

If the measurements of product samples are distributed equally above and below the center of the distribution as they are in Figure 10.1, the average of those measurements is also the center value that is called the mean and is represented in formulas by the lowercase Greek letter µ (pro- nounced mu). The average difference of the measurements from the central value, using samples, is called the sample standard deviation or just the standard deviation. The first step in calculating the standard deviation is subtracting each measurement from the central value and then squaring that difference. (Recall from your mathematics courses that squaring a number is multiplying it by itself and that the result is always positive.) The next step is to sum these squared values and divide by the number of values minus one. The last step is to take the square root. The result can be thought of as an average difference. (If you had used the usual method of taking an average, the positive and negative numbers would have summed to zero.) Mathematicians represent the stan- dard deviation with the lowercase Greek letter σ (pronounced sigma). If all the elements of a group are measured, it is called the standard deviation of the population and the second step does not use a minus one. The standard deviation is a descriptive statistic that indicates how narrow or broad the distribution of samples is from the mean.

FIGURE 10.1 Normal Distribution of Measurements of Gasoline Samples The chart shows that the most common measurements of octane rating are close to 87 and that the other measurements are distributed equally above and below 87. The shape of the distribution chart supports the central limit theorem’s assumption that the factors that are affecting the octane rating are random and tend to offset each other, which is indicated by the symmetric shape. This distribution is a classic example of a normal distribution. The quality control manager notices that none of the measurements are above 88 or below 86 so they are within control limits and concludes that the process is working satisfactorily.

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68-95-99.7 rule

Approximate percentages of measurements that are within one, two, and three standard deviations of the mean.

Standard Deviation of Gasoline Samples

The refinery’s quality control manager uses the standard deviation function in his spreadsheet program to find the standard deviation of the sample measurements and finds that for his data, the standard deviation is 0.3 octane. She marks the range on the frequency distribution chart to show the values that fall within one sigma (standard deviation) on either side of the mean. See the figure below.

FIGURE 10.2 Most of the measurements are within 0.3 octane of 87.

For normal distributions, about 68.3 percent of the measurements fall within one standard deviation on either side of the mean. This is a useful rule of thumb for analyzing some types of data. If the variation between measurements is caused by random factors that result in a normal distribution and someone tells you the mean and the standard deviation, you know that a little over two-thirds of the measurements are within a standard deviation on either side of the mean. Because of the shape of the curve, the number of measurements within two standard deviations is 95.4 percent, and the number of measurements within three standard deviations is 99.7 percent. For example, if someone said the average (mean) height for adult men in the United States is 5 feet 10 inches (70 inches) and the standard deviation is about 3 inches, you would know that 68 percent of the men in the United States are between five feet seven inches (67 inches) and six feet one inch (73 inches) in height. You would also know that about 95 percent of the adult men in the United States were between five feet four inches and six feet four inches tall, and that almost all of them (99.7 percent) are between five feet one inches and six feet seven inches tall. These figures are referred to as the 68-95-99.7 rule.

Almost All Samples of Gasoline are Within Three STD

The refinery’s quality control manager marks the ranges included within two and three standard deviations, as shown below.

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FIGURE 10.3 The 68-95-99.7 Rule

Some products must have less variability than others to meet their purpose. For example, if one machine drills a hole and another machine shapes a rod that will slide through the hole, it might be very important to be sure that if the smallest hole was ever matched with the widest rod, that the rod would still fit. Three standard deviations from the control limits might be fine for some products but not for others. In general, if the mean is six standard deviations from both control limits, the likelihood of a part exceeding the control limits from random variation is practically zero (2 in 1,000,000,000). Refer to Figure 10.4.

FIGURE 10.4 Meaning of Sigma Levels

A Step Project Improves Quality of Gasoline

A new refinery process is installed that produces fuels with less variability. The refinery’s quality control manager takes a new set of samples and charts a new frequency distribution diagram, as shown below.

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FIGURE 10.5 Smaller Standard Deviation

The refinery’s quality control manager calculates that the new standard deviation is 0.2 octane. From this, she can use the 68-95-99.7 rule to estimate that 68.3 percent of the fuel produced will be between 86.8 and 87.2 and that 99.7 percent will be between 86.4 and 87.6 octane. A short- hand way of describing this amount of control is to say that it is a five-sigma production system, which refers to the five standard deviations between the mean and the control limit on each side.

Key Takeaways

• Quality is the degree to which a product or service fulfills requirements and provides value for its price.

• Statistics is the mathematical interpretation of numerical data, and several statistical terms are used in quality control. Control limits are the boundaries of acceptable variation.

• If random factors cause variation, they will tend to cancel each other out—the central limit theorem. The central point in the distribution is the mean, which is represented by the Greek letter mu, µ. If you choose intervals called bins and count the number of samples that fall into each interval, the result is a frequency distribution. If you chart the distribution and the factors that cause variation are random, the frequency distribution is a normal distribution, which looks bell shaped.

• The center of the normal distribution is called the mean, and the average variation is cal- culated in a special way that finds the average of the squares of the differences between samples and the mean and then takes the square root. This average difference is called the standard deviation, which is represented by the Greek letter sigma, σ.

• About 68 percent of the samples are within one standard deviation, 95.4 percent are within two, and 99.7 percent are within three.

Exercises

1. According to the PMI, quality is the degree to which a set of inherent characteristics fulfill ___________.

2. The upper and lower extremes of acceptable variation from the mean are called the __________ limits.

3. The odds that a sample’s measurement will be within one standard deviation of the mean is ____ percent.

4. How is quality related to grade?

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5. If the measurements in a frequency distribution chart are grouped near the mean in normal distribution, what does that imply about the causes of the variation?

6. If you have a set of sample data and you had to calculate the standard deviation, what are the steps?

7. If a set of sample measurements has a mean of 100, a normal distribution, a standard devi- ation of 2, and control limits of 94 and 106, what percentage of the samples are expected to be between 94 and 106? Explain your answer.

Using Statistical MeasuresUsing Statistical Measures

Choose two groups of people or items that have a measurable characteristic that can be compared, such as the height of adult males and females. Describe the distribution of the mea- surements by stating whether you think the groups have a relatively small or large standard deviation and whether the distributions overlap (e.g., some women are taller than some men even though the mean height for men is greater than the mean height for women). Demonstrate that you know how to use the following terms correctly in context:

• Normal distribution

• Standard deviation

• Mean

10.3 Development of Quality as a Competitive Advantage

Learning Objectives

1. Describe the historical events and forces that led up to today’s emphasis on quality as a competitive requirement.

2. Describe quality awards in Japan and the United States.

3. Describe quality programs and standards such as TQM, Six Sigma, and ISO 9000.

4. Describe and calculate the cost of quality.

Quality management is an approach to work that has become increasingly important as global cooperation and competition have increased. A review of the history of quality management explains why it is so important to companies and why clients often require projects to document their processes to satisfy quality standards.

Statistical Control Before World War II

Prior to the late 1700s, products such as firearms and clocks were made as individual works where the parts were adjusted to each other so they could work together. If a part broke, a new one had to be made by hand to fit. In 1790 in France, Honoré Blanc demonstrated that he could make musket parts so nearly identical that a musket could be assembled from bins of parts chosen at random.[2]

The practice of making parts to a high level of accuracy in their dimensions and finishes made the parts interchangeable. The use of interchangeable parts became the founding principle of assem- bly line manufacturing to produce all manner of goods, from sewing machines to automobiles. The

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quality control (QC)

Management of production standards through statistical interpretation of random product measurements.

chance cause

Variation due to random events.

assignable cause

Variations that can be attributed to a particular event or influence.

control chart

Scatter chart with time on the horizontal axis and measurement scale on the vertical axis. It also displays the mean and control limits. It may mark two standard deviations from the mean.

run chart

Chart of measurements that shows variations as the process progresses in time.

manufacturers of firearms and weapons were often the leaders in improving quality because reli- able and safe operation of weapons and their rapid repair is a matter of life and death.

Statistical Control in the United States During World War II

During World War II, factories were converted from manufacturing consumer goods to weapons. War plants had to make large numbers of parts as fast as possible while doing it safely for the work- ers and for the service members who used them. Important improvements in quality control (QC)—the management of production standards through statistical interpretation of random product measurements, which emphasizes consistency and accuracy—were made during this period. A key figure in the history of quality management who was an important person in the war effort was Walter Shewhart at Bell Telephone Laboratories. Shewhart recognized that real processes seldom behaved like theoretical random distributions and tended to change with time. He separated causes of variation into two categories: chance cause and assignable cause. Chance causes could be ignored if they did not cause too much variation, and trying to eliminate them often made the problem worse, but assignable causes could be fixed. To help distinguish between variations caused by random events and trends that indicated assignable causes, Shewhart intro- duced the control chart, which is also known as a type of run chart because data are collected while the process is running. A control chart has time on the bottom axis and a plot of sample mea- surements. The mean, upper control limit, lower control limit, and warning lines that are two sigma from the mean are indicated by horizontal lines.

Control Chart Shows Production Variation of Gasoline

The refinery quality control manager takes samples each day of the 87 octane gasoline for twenty days and charts the data on a control chart, as shown below.

FIGURE 10.6 Control Chart Displaying Variations Due to Chance Causes

She recognizes that the highest and lowest measurements are not part of a trend and are prob- ably due to chance causes. However, the control chart from the next twenty days, as shown below, indicates an upward trend that might be due to an assignable cause. She alerts the process manager to let him know that there is a problem that needs to be fixed before the prod- uct exceeds the upper control limit. This might indicate the need to initiate a project to fix the problem.

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Deming prize

Award for quality in Japan named after W. Edwards Deming, an American.

FIGURE 10.7 Control Chart Displaying Variations That Might Be Due to an Assignable Cause

Deming and Postwar Japan

The most influential person in modern quality control was an American who was a hero in Japan but virtually unknown in the United States. W. Edwards Deming worked with Shewhart at Bell Labs and helped apply Shewhart’s ideas to American manufacturing processes during World War II. Following the war, American factories returned to the production of consumer goods. Many of the other major manufacturing centers in the world had been damaged by bombing during the war and took time to recover. Without the safety needs of wartime and with little competition, qual- ity control was not a high priority for American companies.[3] Management in the United States focused on increasing production to meet demand and lowering costs to increase profits.

After the war, while the United States occupied Japan, Deming was asked by the U.S. Depart- ment of the Army to assist with the statistics of the 1950 census in Japan. Kenichi Koyanagi, the man

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