OM CHAPTER 16
QUALITY CONTROL AND SPC DAVID A. COLLIER AND JAMES R. EVANS
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Learning Outcomes
learning outcomes LO1 Describe quality control system and key issues in manufacturing and service.
LO2 Explain types of variation and the role of statistical process control.
LO3 Describe how to construct and interpret simple
control charts for both continuous and discrete data.
LO4 Describe practical issues in implementing SPC. LO5 Explain process capability and calculate process capability indexes.
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
arriott has become infamous for its obsessively detailed standard operating procedures (SOPs), which result in hotels that travelers either love for their consistent good quality or hate for their bland uniformity. “This is a company that has more controls, more systems, and more procedural manuals than anyone— except the government,” says one industry veteran. “And they actually comply with them.” Housekeepers work with a 114-point checklist. One SOP: Server knocks three times. After knocking, the associate should immediately identify themselves in a clear voice, saying, “Room Service!” The guest’s name is never mentioned outside the door. Although people love to make fun of such procedures, they are a serious part of Marriott’s business, and SOPs are designed to protect the brand. Recently, Marriott has removed some of the rigid guidelines for owners of hotels it manages, empowering them to make some of their own decisions on details.
What do you think? What opportunities for improved quality control or
use of SOPs can you think of at your college or university (e.g., bookstore, cafeteria)? OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Quality Control Systems
The task of quality control is to ensure that a good or service conforms to specifications and meets customer requirements by monitoring and measuring processes and making any necessary adjustments to maintain a specified level of performance.
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Quality Control Systems Quality Control Systems have three components: 1. A performance standard or goal, 2. A means of measuring actual performance, and 3. Comparison of actual performance with the standard to form the basis for corrective action.
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
1:10:100 Rule: If a defect or service error is identified and corrected in the design stage, it might cost $1 to fix. If it is first detected during the production process, it might cost $10 to fix. However, if the defect is not discovered until it reaches the customer, it might cost $100 to correct.
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Quality at the source means the people responsible for the work control the quality of their processes by identifying and correcting any defects or errors when they first are recognized or occur.
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Quality Control Practices in Manufacturing • Supplier Certification and Management: ensures conformance to requirements before value-adding operations begin. • In-process control: ensures that defective outputs do not leave the process and prevents defects in the first place. • Finished goods control: verifies that product meets customer requirements.
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Quality Control Practices in Services • Prevent sources of errors and mistakes in the first place by using poka-yoke approaches. • Customer satisfaction measurement with actionable results (responses that are tied directly to key business processes). • Many quality control tools and practices apply to both goods and services.
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Statistical Process Control and Variation
Statistical process control (SP C) is a methodology for monitoring quality of manufacturing and service delivery processes to help identify and eliminate unwanted causes of variation.
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Statistical Process Control and Variation • Com m on cause variation is the result of
complex interactions of variations in materials, tools, machines, information, workers, and the environment.
• Common cause variation accounts for 80 to 95 percent of the observed variation in a process. • Only management has the power to change systems and infrastructure that cause common cause variation. OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Statistical Process Control and Variation • Special (assignable) cause variation arises
from external sources that are not inherent in the process, appear sporadically, and disrupt the random pattern of common causes.
• Special cause variation accounts for 15 to 20 percent of observed variation. • Front-line employees and supervisors have the power to identify and solve special causes of variation. OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Foundations of Statistical Process Control • Stable system : a system governed only by common causes. • I n control: if no special causes affect the
output of the process.
• Out of control: when special causes are
present in the process.
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Constructing Control Charts Steps 1 through 4 focus on setting up an initial chart; in step 5, the charts are used for ongoing monitoring; and finally, in step 6, the data are used for process capability analysis.
1. P reparation a. Choose the metric to be monitored. b. Determine the basis, size, and frequency of sampling. c. Set up the control chart. OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Constructing Control Charts 2. Data collection a. Record the data. b. Calculate relevant statistics: averages, ranges, proportions, and so on. c. Plot the statistics on the chart.
3. Determ ination of trial control lim its a. Draw the center line (process average) on the chart. b. Compute the upper and lower control limits. OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Constructing Control Charts 4. Analysis and interpretation a. Investigate the chart for lack of control. b. Eliminate out-of-control points. c. Recompute control limits if necessary.
5. Use as a problem -solving tool a. Continue data collection and plotting. b. Identify out-of-control situations and take corrective action. 6. Determ ination of process capability using the control chart data OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Foundations of Statistical Process Control • A continuous m etric is one that is
calculated from data that are measured as the degree of conformance to a specification on a continuous scale of measurement.
• A discrete m etric is one that is
calculated from data that are counted.
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Foundations of Statistical Process Control • SPC uses control charts, run charts to which two horizontal lines, called control limits, are added: the upper control limit (UCL) and lower control limit (LCL). • Control limits are chosen statistically to provide a high probability (generally greater than 0.99) that points will fall between these limits if the process is in control.
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Foundations of Statistical Process Control • As a problem-solving tool, control charts allow employees to identify quality problems as they occur. Of course, control charts alone cannot determine the source of the problem.
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Constructing x-bar and R-Charts
[16.1]
[16.2]
[16.3]
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Solved Problem: Goodman Tire and Rubber Company • Goodman Tire periodically tests its tires for tread wear under simulated road conditions using x- and R-charts. • Company collects twenty samples, each containing three radial tires from different shifts over several days of operations. • x-bar Control Limits: UCL = 31.88 + 1.02(10.8) = 42.9 LCL = 31.88 – 1.02(10.8) = 20.8 OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Exhibit 16.1
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
Excel Template for Goodman Tire x-bar and R-Charts
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Exhibit 16.2
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
R-Chart for Goodman Tire Example
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Exhibit 16.3
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
x-Chart for Goodman Tire Example
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Chapter 16 Quality Control and SPC
Interpreting Patterns in Control Charts A process is said to be “in control” when the control chart has the following characteristics: 1. No points are outside the control limits (the traditional and most popular SPC chart guideline). 2. The number of points above and below the center line is about the same. 3. The points seem to fall randomly above and below the center line. 4. Most points, but not all, are near the center line, and only a few are close to the control limits. OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Interpreting Patterns in Control Charts A more in-depth understanding of SPC charts includes evaluating the patterns in the sample data using guidelines, such as: • 8 points in a row above or below the center line • 10 of 11 consecutive points above or below the center line • 12 of 14 consecutive points above or below the center line • 2 of 3 consecutive points in the outer one-third region between the center line and one of the control limits • 4 of 5 consecutive points in the outer two-thirds region between the center line and one of the control limits OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Exhibit Extra
Illustration of Some Rules for Identifying Out-of-Control Conditions
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Constructing p-charts
[16.4]
[16.5]
[16.6]
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Exhibit 16.4
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
Data and Calculations for p-Chart Solved Problem
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Exhibit 16.5
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
p-Chart for ZIP Code Reader Solved Problem with Constant Sample Size
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Chapter 16 Constructing c-charts
Constructing c-charts • Where p-chart monitors the proportion of nonconforming items, a c-chart monitors the “number of nonconformances” per unit (i.e., a count of the number of defects, errors, failures, etc.). • Example: one customer’s purchase order may have several errors, such as wrong items, order quantity, or wrong price.
UCLc = c + 3 √ c LCLc = c - 3 √ c OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
[16.7]
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Chapter 16 Constructing c-charts
Constructing c-charts • These charts are used extensively in service organizations. • To use a c-chart, the size of the sampling unit or the number of opportunities for errors remains constant. • Examples of c-chart applications: a fender or windshield on a certain automobile model, ceramic coffee cups all of same size and shape, etc.
UCLc = c + 3 √ c LCLc = c - 3 √ c OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
[16.7] 32
Exhibit 16.6 Machine Failure Data for c-Chart Solved Problem
The number of machine failures over a 25-day period.
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Exhibit 16.7
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
c-Chart for Machine Failures
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Chapter 16 Control Chart Design
Control Chart Design • Sam ple size: small sample size keeps costs lower; however, large sample sizes provide greater degrees of statistical accuracy in estimating the true state of control. • Sam pling frequency: samples should be close enough to provide an opportunity to detect changes in process characteristics as soon as possible and reduce the chances of producing a large amount of nonconforming output. OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Other Practical Issues in SPC Implementation • SPC is a useful methodology for processes that operate at a low sigma level (less than or equal to 3-sigma). • However, when the rate of defects is extremely low, standard control limits are not so effective. • For processes with a high sigma level (greater than 3-sigma), few defects will be discovered even with large sample sizes. OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Process Capability • P rocess capability is the natural variation in a
process that results from common causes. Cp = (UTL – LTL) 6σ
[16.9]
Where: UTL = upper tolerance limit LTL = lower tolerance limit σ = standard deviation of the process (or an estimate based on the sample standard deviation, s) OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Quality Control and SPC
Process Capability • P rocess capability is the natural variation in a
process that results from common causes.
• When C p = 1, the natural variation is the same as the design specification width, as in Exhibit 16.8(b). • When C p < 1, a significant percentage of output will not conform to the specifications as in Exhibit 16.8(a).
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Exhibit 16.8
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
Process Capability versus Design Specifications
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Chapter 16 Quality Control and SPC
Process Capability • C p > 1, indicates good capability as in Exhibit 16.8(c); in fact, many firms require Cp values of 1.66 or greater from their suppliers, which equates to a tolerance range of about 10 standard deviations. • The value of Cp does not depend on the mean of the process; thus, a process may be offcenter, such as in Exhibit 16.8(d), and still show an acceptable value of Cp. OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Exhibit 16.8
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
Process Capability Versus Design Specifications
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Chapter 16 Quality Control and SPC
One-sided capability indices that consider offcentered processes
C pu = (UTL – µ)/3σ C pl = (µ – LTL)/3σ C pk = Min (Cpl, Cpu)
[16.10] [16.11] [16.12]
where UTL = upper tolerance limit LTL = lower tolerance limit µ = the mean performance of the process σ = standard deviation of the process (or an estimate based on the sample standard deviation, s) OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Chapter 16 Solved Problem
A controlled process shows an overall mean of 2.50 and an average range of 0.42. Samples of size 4 were used to construct the control charts.
P art A: W hat is the process capability? From Appendix B, d2 = 2.059, σ = R/d2 = 0.42/2.059 = 0.20. Thus, the process capability is 2.50 ± 3(.020), or 1.90 to 3.10.
P art B: I f specifications are 2.60 ± 0.25, how w ell can this process m eet them ? Because the specification range is 2.35 to 2.85 with a target of 2.60, we may conclude that the observed natural variation exceeds the specifications by a large amount. In addition, the process is off-center (see Exhibit 16.9). OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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Exhibit 16.9
Comparison of Observed Variation and Design Specifications for Solved Problem
OM, Ch. 16 Quality Control and SPC ©2009 South-Western, a part of Cengage Learning
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