quality control and spc

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OM2, Ch. 16 Quality Control and SPC

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QUALITY CONTROL AND SPC

CHAPTER 16

DAVID A. COLLIERAND

JAMES R. EVANS

OM2

2OM2, Ch. 16 Quality Control and SPC


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.

l e a r n i n g o u t c o m e s

Chapter 16 Learning Outcomes



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.

Chapter 16 Quality Control and SPC

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)?



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.




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.




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.




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.




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.




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.




Quality Control for Medical Prescriptions Poor doctor handwriting is the number one root cause of

medication errors. Often the wrong drug is prescribed, or the wrong dosage is used, or drug interactions cause adverse reactions. Mr. J. Lyle Bootman, the dean of the College of Pharmacy at the University of Arizona noted that “The economic consequences of medication errors are as costly as the entire cost of diabetes, and close to cancer and heart disease. It is a silent disease in America.” The Institute of Medicine estimates that a U.S. hospital patient is subject to at least one medication error daily. They estimate that more than 7,000 people die from medication errors every year. The solution is to streamline related processes, build quality control checks into every stage of each process, and use electronic prescription systems to eliminate handwritten prescriptions.




Statistical Process Control and Variation

Statistical process control (SPC) is a methodology for monitoring quality of manufacturing and service delivery processes to help identify and eliminate unwanted causes of variation.




Statistical Process Control and Variation• Common 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.




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.




Foundations of Statistical Process Control

• Stable system: a system governed only by common causes.

• In control: if no special causes affect the output of the process.

• Out of control: when special causes are present in the process.




Constructing Control ChartsSteps 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. Preparationa. Choose the metric to be monitored.b. Determine the basis, size, and

frequency of sampling.c. Set up the control chart.




Constructing Control Charts2. Data collection

a. Record the data.b. Calculate relevant statistics: averages,

ranges, proportions, and so on.c. Plot the statistics on the chart.

3. Determination of trial control limitsa. Draw the center line (process average)

on the chart.b. Compute the upper and lower control

limits.




Constructing Control Charts4. 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 toola. Continue data collection and plotting.b. Identify out-of-control situations and

take corrective action.6. Determination of process capability

using the control chart data





• A continuous metric 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 metric is one that is calculated from data that are counted.





• 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.





• 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.




[16.1]

[16.2]

[16.3]

Chapter 16 Constructing x-bar and R-Charts



Solved ProblemGoodman 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




Exhibit 16.1 Excel Template for Goodman Tire x-bar and R-Charts



Exhibit 16.2 R-Chart for Goodman Tire Example



Exhibit 16.3 x-Chart for Goodman Tire Example



Interpreting Patterns in Control ChartsA 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.




Interpreting Patterns in Control ChartsA 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




Exhibit Extra Illustration of Some Rules for Identifying Out-of-Control Conditions



[16.5]

[16.4]

[16.6]

Chapter 16 Constructing p-charts



Exhibit 16.4 Data and Calculations for p-Chart Solved Problem



Exhibit 16.5p-Chart for ZIP Code Reader Solved Problem with

Constant Sample Size



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.

Chapter 16 Constructing c-charts

[16.7]UCLc = c + 3√c

LCLc = c - 3√c



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.

Chapter 16 Constructing c-charts

[16.7]UCLc = c + 3√c

LCLc = c - 3√c



Exhibit 16.6

Machine Failure Data for c-Chart Solved Problem



Exhibit 16.7 c-Chart for Machine Failures



Control Chart Design

• Sample size: small sample size keeps costs lower; however, large sample sizes provide greater degrees of statistical accuracy in estimating the true state of control.

• Sampling 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.

Chapter 16 Control Chart Design



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.




IBMAt one IBM branch, pre-employment physical examinations took too long and taxed the medical staff assigned to conduct them. Such examinations are vital for assuring that employees can perform certain jobs without excess stress and that they pose no health threat to other employees. Therefore, the challenge IBM faced was to maintain the quality of the exam while reducing the time needed to perform it by identifying and eliminating waiting periods between the various parts of it. Preliminary control charts revealed that the average time required for the examination was 74 minutes, but the range varied greatly. New equipment and additional training of the medical staff were suggested as means of shortening the average time. Initial charts indicated that the process was out of control, but continued monitoring and process improvements lowered the average time to 40 minutes, and both the average and range were brought into statistical control with the help of x and R-charts.




Process Capability• Process capability is the natural variation

in a process that results from common causes.

Cp = (UTL – LTL) [16.9]

6σ Where:

UTL = upper tolerance limitLTL = lower tolerance limit σ = standard deviation of the

process (or an estimate based on the sample standard deviation, s)




Process Capability• Process capability is the natural

variation in a process that results from common causes.

• When Cp = 1, the natural variation is the same as the design specification width, as in Exhibit 16.8(b).

• When Cp < 1, a significant percentage of output will not conform to the specifications as in Exhibit 16.8(a).




Exhibit 16.8 Process Capability versus Design Specifications



Process Capability

• Cp > 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 off-center, such as in Exhibit 16.8(d), and still show an acceptable value of Cp.




Exhibit 16.8 Process Capability Versus Design Specifications



One-sided capability indices that consider off- centered processes

Cpu = (UTL – µ)/3σ [16.10]

Cpl = (µ – LTL)/3σ [16.11]

Cpk = min (Cpl, Cpu) [16.12]

whereUTL = upper tolerance limitLTL = 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)




Solved ProblemA 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.

Part A: What 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.

Part B: If specifications are 2.60 ± 0.25, how well can this process meet 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).




Exhibit 16.9 Comparison of Observed Variation and Design Specifications for

Solved Problem



Dean Door Corporation Case Study1. Interpret the data in Exhibit 16.13, establish a state of

statistical control, and evaluate the capability of the process to meet specifications.

2. What do the initial control charts tell you? Do any out-of-control conditions exist?

3. If the process is not in control, what might be the likely causes, based on the information that is available?

4. What is the process capability? What do the process capability indexes tell the company?

5. Is DDC facing a serious problem that it needs to address? How might the company eliminate the problems found by Walker Homes?


quality control and spc

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improved quality control

consistent good quality

simple control charts

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performance standard

learning outcomes

process capability indexes