Tài liệu tiếng anh session 4 chapter 5 Managing quality

Tài liệu tiếng Anh Session 4 chapter 5 Managing quality, dành cho cao học.

Managing Quality

Chapter 5

What is Quality?

Quality

A term used by customers to

describe their general satisfaction

with a service or product.

Costs of Quality

• Prevention Costs

• Appraisal Costs

• Internal Failure Costs

• External Failure Costs

Ethics and Quality

• Balancing the traditional measures of quality performance and the overall benefits to society.

• Identifying deceptive business practices.

• Developing a culture around ethics.

• Training employees to understand how ethics interfaces with their jobs.

Total Quality Management

TQM

A philosophy that stresses

principles for achieving high

levels of process

performance and quality.

What is Six Sigma?

Six Sigma

A comprehensive and flexible system for achieving, sustaining, and maximizing business success by minimizing defects and variability in processes.

Six Sigma Approach

X X

X X X

X X

X X

X X X

X X

X X

Process average OK;

too much variation

Process variability OK;

process off target

Process

on target with low variability

Reduce spread

Center process

X

X

X

X X

X X X X

Six Sigma Improvement Model

Six Sigma Certification

• Master Black Belts

• Black Belts

• Green Belts

Acceptance Sampling

• Acceptance Sampling

– The application of statistical techniques to determine if a quantity of

material from a supplier should be accepted or rejected based on the

inspection or test of one or more samples

• Acceptable Quality Level

– A statement of the proportion of defective items that the buyer will accept

in a shipment.

Acceptance Sampling Interface

Firm A uses TQM or Six Sigma to achieve internal process performance

Supplier uses TQM or Six Sigma to achieve internal process performance

Supplier

Manufactures fan blades TARGET: Firm A’s specs

Accept motors?

Motor inspection

Blade inspection

Firm A

Manufacturers furnace fan motors TARGET: Buyer’s specs

Buyer

Manufactures furnaces

Statistical Process Control (SPC)

• SPC

The application of statistical techniques to determine whether a process is delivering what the customer wants.

• Performance Measurements

– Variables - Characteristics that can be measured.

– Attributes - Characteristics that can be counted.

• Sampling Plan

• Complete Inspection

– Inspect each product at each stage

n i

xi = observation of a quality characteristic (such as time)

n = total number of observations

Sampling Statistics

Standard deviation– The square root of the variance of a distribution

An estimate of the process standard deviation based on a sample is given by:

1

or

1

22

x

Sampling Statistics

1. The sample mean is the sum of the observations divided by the total number of observations.

n

x x

n i

xi = observation of a quality characteristic (such as time)

n = total number of observations

Sampling Statistics

2. The range is the difference between the largest observation in a sample and the smallest The

standard deviation is the square root of the variance of a distribution An estimate of the process standard deviation based on a sample is given by

1

2 2

Sampling Distribution

Effects of Assignable Cause Variation on the Process Distribution

Control Charts

• Time-ordered diagram used to determine whether observed variations are abnormal

– Mean

– Upper control limit

– Lower control limit

• Steps for a control chart

2. Plot statistics

3. Eliminate the cause, incorporate improvements

4. Repeat the procedure

Control Limits and Sampling Distribution

Samples

Assignable causes likely

UCL

Nominal

LCL

Nominal UCL

Nominal UCL

Nominal UCL

Nominal UCL

Control Chart Errors

Type I error –

Concluding that a process is out of control

when it is in control

Type II error – Concluding that a process is

in control when it is out of control

Control Chart Types

•

Variable Control Charts

R-Chart

UCLR = D4R and LCLR = D3R

Variable Control Charts

UCLx = x + A2R and LCLx = x – A2R

Calculating Control Chart Factors

Steps for x- and R-Charts

3. Use Table 5.1 to determine R-chart control limits.

4 Plot the sample ranges If all are in control, proceed to step 5 Otherwise, find the

assignable causes, correct them, and return to step 1.

5. Calculate x for each sample.

Steps for x- and R-Charts

6. Use Table 5.1 to determine x-chart control limits

7 Plot the sample means If all are in control, the process is in statistical control

Continue to take samples and monitor the process If any are out of control, find the assignable causes, correct them, and return to step 1

Example 5.1

The management of West Allis Industries is concerned about the production of a special metal screw used by several of the company’s largest customers The diameter of the screw is critical to the customers Data from five samples appear in the accompanying table The sample size is 4 Is the process in statistical control?

Example 5.1

Process variability is in statistical control

Example 5.1

Compute the mean for each sample and the control limits

0.5027 + 0.729(0.0021) = 0.5042 in.

0.5027 – 0.729(0.0021) = 0.5012 in.

Example 5.1

Process average is NOT in statistical control.

An Alternate Form

If the standard deviation of the process distribution is known, another form of

the x-chart may be used:

Example 5.2

For Sunny Dale Bank the time required to serve customers at the drive-by window is an important quality factor in competing with other banks in the city

After several weeks of sampling, two successive samples came in at 3.70 and 3.68 minutes,

respectively Is the customer service process in statistical control?

Example 5.2

For Sunny Dale Bank the time required to serve customers at the drive-by window is an important quality factor in competing with other banks in the city

Is the customer service process in statistical control?

Application 5.1

Webster Chemical Company produces mastics and caulking for the construction industry The product is blended in large mixers and then pumped into tubes and capped.

Webster is concerned whether the filling process for tubes of caulking is in statistical control The process should

be centered on 8 ounces per tube Several samples of eight tubes are taken and each tube is weighed in ounces

Control Charts for Attributes

• p-charts are used to control the proportion defective

• Sampling involves yes/no decisions so the underlying distribution is the binomial distribution

• The standard deviation is

Example 5.3

account numbers recorded Each week a random sample of 2,500

deposits is taken and the number of incorrect account numbers is

recorded

• Using three-sigma control limits, which will provide a Type I error of 0.26 percent, is the booking process out of statistical control?

Example 5.3

147 12(2,500)

= = 0.0049

p =

Total defectives Total number of observations

UCLp = p + zσp LCLp = p – zσp

= 0.0049 + 3(0.0014) = 0.0091

= 0.0049 – 3(0.0014) = 0.0007

Calculate the sample proportion defective and plot each sample proportion defective on the chart

Example 5.3

Fraction Defective

Sample

Mean UCL

The process is NOT in statistical control

Application 5.2

A sticky scale brings Webster’s attention to whether caulking tubes are being properly capped If a significant proportion of the tubes aren’t being sealed, Webster is placing their customers in a messy situation Tubes are packaged in large boxes of 144 Several boxes are inspected and the following numbers of leaking tubes are found:

number Total

tubes leaky

of number Total

0.025

=

−

= σ +

UCL

= σ

Control Charts for Attributes

• c-charts count the number of defects per unit of service encounter

• The underlying distribution is the Poisson distribution

UCLc = c + z√c and LCLc = c – z√c

Example 5.4

The Woodland Paper Company produces paper for the newspaper industry As a final step in the process, the paper passes through a machine that measures various product quality characteristics When the paper production process is in control, it averages 20 defects per roll.

a. Set up a control chart for the number of defects per roll For this example, use two-sigma control limits.

b Five rolls had the following number of defects: 16, 21, 17, 22, and 24, respectively The sixth roll, using pulp from a different supplier, had 5 defects Is the paper production process in control?

Example 5.4

The process is technically out of control due to Sample 6 However, Sample 6 shows that the new supplier is

a good one

b.

Application 5.3

At Webster Chemical, lumps in the caulking compound could cause difficulties in dispensing a smooth bead from the tube Even when the process is in control, there will still be an average of 4 lumps per tube of caulk Testing for the presence of lumps destroys the product, so Webster takes random samples The following are results of the study:

= c

c c z

UCL

= σ

2 9 0 5 6 1 4 6 4 0 5 6

= + + + + + + + + + + +

2

( ) 2 8 2

( ) 2 0 2

Process Capability

Upper specification

Lower specification

Nominal value

(a) Process is capable

Process distribution

Process Capability

Upper specification

Lower specification

Nominal value

(b) Process is not capable

Process distribution

Process Capability

Lower specification

Mean

Upper specification

Nominal value

Six sigma

Four sigma

Two sigma

Process Capability Index

• Measures how well a process is centered and whether the variability is

Process Capability Ratio

• A test to see if the process variability is capable of producing output within a product’s specifications

Cp =

Upper specification – Lower specification

6σ

Example 5.5

• The intensive care unit lab process has an

average turnaround time of 26.2 minutes and a

standard deviation of 1.35 minutes

• The nominal value for this service is 25 minutes

Application 5.4

Webster Chemical’s nominal weight for filling tubes of caulk is 8.00 ounces ± 0.60 ounces The target process capability ratio is 1.33 , signifying that management wants 4-sigma performance The current distribution of the filling process is centered on

8.054 ounces with a standard deviation of 0.192 ounces Compute the process

capability index and process capability ratio to assess whether the filling process is capable and set properly.

The value of Cp is less than the target for four-sigma quality

Therefore we conclude that the process variability must be addressed first, and then the process should

be retested

Quality Loss Function

International Quality

Documentation Standards

• ISO 9001:2008 – Quality Standards

• ISO 14000:2004 – Environmental Management Standards

• ISO 26000:2010 – Social Responsibility Guidelines

Solved Problem 1

The R-chart control limits are

570), which is outside the UCL for the R-chart Since the process variability is out of control, it is meaningless

to test for the process average using the current estimate for R A search for assignable causes inducing

excessive variability must be conducted.

Solved Problem 2

a Based on these historical data, set up a p-chart using z = 3.

b Samples for the next four days showed the following:

Sample Number of Defective Records

Solved Problem 2

SOLUTION

sample of 7,500 [or 30(250)] Therefore, the central line of the chart is

= 0.04 300

z p

p

− +

z p

0.04(0.96) 3

=

0.003 250

0.04(0.96) 3

=

Samples for Thursday and Friday are out of control The supervisor should look for the problem and, upon identifying it, take corrective action.

Solved Problem 3

The Minnow County Highway Safety Department monitors accidents at the intersection of Routes 123 and 14 Accidents at the intersection have averaged three per month

a. Which type of control chart should be used? Construct a control chart with three

sigma control limits.

b Last month, seven accidents occurred at the intersection Is this sufficient evidence to justify a claim that something has changed at the intersection?

Solved Problem 3

SOLUTION

a The safety department cannot determine the number of accidents that did not occur, so it has no way to

compute a proportion defective at the intersection Therefore, the administrators must use a c-chart for which

There cannot be a negative number of accidents, so the LCL in this case is adjusted to zero.

causes are present and that the increase in accidents was due to chance.

UCLc = c + z c LCLc = c – z c

= 3 + 3 3 = 8.20

= 3 – 3 3 = –2.196

Solved Problem 4

Pioneer Chicken advertises “lite” chicken with 30 percent fewer calories (The pieces are

33 percent smaller.) The process average distribution for “lite” chicken breasts is 420 calories, with a standard deviation of the population of 25 calories Pioneer randomly takes samples of six chicken breasts to measure calorie content.

a. Design an x-chart using the process standard deviation.

b The product design calls for the average chicken breast to contain 400 ± 100 calories Calculate the process capability index (target = 1.33) and the process capability ratio Interpret the results.

Solved Problem 4

Because the process capability ratio is 1.33, the process should be able to produce the product reliably within specifications However, the process capability index is 1.07, so the current process is not centered properly for four-sigma performance The mean of the process distribution is too close to the upper specification.

The process capability ratio is

b The process capability index is

Cpk = Minimum of ,

= Minimum of = 1.60, = 1.07 420 – 300

3(25)

500 – 420 3(25)

Cp =

Upper specification – Lower specification

500 – 300 6(25)

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