Business operating management discussion

Yuzhi Zhao

chapter16.pptx

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Quality Control and SPC

COLLIER/EVANS

Operations + Supply Chain Management

LEARNING OUTCOMES

Describe quality control systems and key issues in manufacturing and service

Explain types of variation and the role of statistical process control (SPC)

Describe how to construct and interpret simple control charts for both continuous and discrete data

Describe practical issues in implementing SPC

Explain process capability, and calculate process capability indexes

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Quality Control

LO 16-1

Ensures that a good or service conforms to specifications

Meets customer requirements by:

Monitoring and measuring processes

Making any necessary adjustments to maintain a specified level of performance

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Quality Control Systems Components

LO 16-1

Performance standard or goal

Means of measuring actual performance

Comparison of actual performance with the standard to form the basis for corrective action

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1:10:100 Rule

LO 16-1

Defect or service error:

If identified and corrected in the design stage, could be fixed for $1

If detected during the production process, could be fixed for $10

If not discovered until it reaches the customer, could cost $100 to fix it

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Quality at the Source

LO 16-1

People responsible for the work control the quality of their processes by:

Identifying and correcting any defects or errors when they are first recognized or occur

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Quality Control Practices in Manufacturing

LO 16-1

Supplier certification and management

Ensure the integrity of incoming materials

In-process control

Prevents defects

Ensures that defective outputs do not leave the process

Finished-goods control

Verifies that a product meets customer requirements

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Quality Control Practices in Services

LO 16-1

Help prevent sources of errors and mistakes by:

Using the poka-yoke approach

Hiring and training service providers in service management skills

Measuring customer satisfaction

Using the net promoter score (NPS) metric that gives a difference in the percentage of promoters and detractors

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Statistical Process Control and Variation

LO 16-2

Statistical process control (SPC)

Methodology for monitoring the quality of manufacturing and service-delivery processes

Helps identify and eliminate unwanted causes of variation

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Common Cause Variation

LO 16-2

Result of complex interactions of variations in materials, tools, machines, information, workers, and the environment

Accounts for 80 to 95 percent of the observed variation in a process

Reduced only if better technology, process design, or training is provided

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Special (or Assignable) Cause Variation

LO 16-2

Arises from external sources that:

Are not inherent in the process

Appear sporadically

Disrupt random pattern of common causes

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Special Cause Variation

LO 16-2

Depends on the nature of the output of the processes

Results from:

Overadjusting a process that is already in control

In control: Output is not affected by special causes

Failing to correct a process that is out of control

Out of control: Output is affected by special causes

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Construction and Use of Control Charts, Part 1

LO 16-3

Preparation

Choose the metric to be monitored and controlled

Determine the sample size and frequency of sampling

Set up the control chart

Data collection

Record the data

Calculate relevant statistics

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Construction and Use of Control Charts, Part 2

LO 16-3

Plot the statistics on the chart

Determination of trial control limits

Draw the center line (process average) on the chart

Compute the upper and lower control limits

Analysis and interpretation

Investigate the chart for lack of control

Eliminate out-of-control points

Recompute control limits if necessary

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Construction and Use of Control Charts, Part 3

LO 16-3

Use charts as a problem-solving tool

Continue data collection and plotting

Identify out-of-control situations, and take corrective action

Determination of process capability using the control chart data

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Types of Metrics

LO 16-3

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Constructing - and R-Charts, Part 1

LO 16-3

k - Number of samples

n - Sample size

Overall mean ()

Average range ()

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Constructing - and R-Charts, Part 2

LO 16-3

Average range and average mean are used to compute upper and lower control limits (UCL and LCL) for charts

UCLR = D4

LCLR = D3

UCL =

LCL =

Where

D3, D4, and A2 are constants that depend on the sample size, n

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Characteristics of a Control Chart for a Process under Control

LO 16-3

No points are outside the control limits

Number of points above and below the center line is about the same

Points seem to fall randomly above and below the center line

Most points are near the center line, and only a few are close to the control limits

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Rules for Identifying a Shift in a Process, Part 1

LO 16-3

8 points in a row are above or below the center line

10 of 11 consecutive points are above or below the center line

12 of 14 consecutive points are above or below the center line

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Rules for Identifying a Shift in a Process, Part 2

LO 16-3

2 of 3 consecutive points are in the outer one-third region between the center line and one of the control limits

4 of 5 consecutive points are in the outer two-thirds region between the center line and one of the control limits

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p-Charts, Part 1

LO 16-3

Known as fraction nonconforming or fraction defective chart

Monitor the proportion of nonconforming items (p) found in a sample

Average fraction nonconforming for a group of k samples ()

Where yi represents the number nonconforming in a samplei

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p-Charts, Part 2

LO 16-3

Where is the average fraction nonconforming for the group of k samples

Control limits

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c-Charts

LO 16-3

Control the total number of nonconformances per unit

Size of the sampling unit or number of opportunities for errors should be constant

Control limits

Where is the average number of nonconformances/unit

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Practical Issues in SPC Implementation, Part 1

LO 16-4

Sample size

Small samples lead to lower costs

Large samples

Provide greater degrees of statistical accuracy in estimating the true state of control

Allow smaller changes in process characteristics to be detected with higher probability

Not economical when taken on a frequent basis

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Practical Issues in SPC Implementation, Part 2

LO 16-4

Sampling frequency

Samples should be close enough to provide an opportunity to detect changes in process characteristics

Reduces the chances of producing a large amount of nonconforming output

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Application of SPC Charts for Six Sigma Processes

LO 16-4

SPC is a useful methodology for processes that operate at three sigma level or less

Few defects will be discovered even with large sample sizes for processes with a high sigma level

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Process Capability

LO 16-5

Natural variation in a process that results from common causes

Process capability study

Carefully designed to yield specific information about the performance of a process under specified operating conditions

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Exhibit 16.4

Process Capability versus Design Specifications

LO 15-5

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Process Capability Index (Cp), Part 1

LO 16-5

Relationship between the natural variation and specifications is quantified by a measure

Where

USL - Upper specification limit

LSL - Lower specification limit

σ - Standard deviation of the process

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Process Capability Index (Cp), Part 2

LO 16-5

When Cp = 1, the observed variation is the same as the design specification width

When Cp < 1, a significant percentage of output will not conform to the specifications

When Cp > 1, nearly all the output will conform to the specifications

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Process Capability Index (Cp), Part 3

LO 16-5

Value of Cp does not depend on the mean of the process

Processes may be off-center and still show an acceptable value of Cp

One-sided process capability indexes

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KEY TERMS

Quality control

Quality at the source

Statistical process control (SPC)

Common cause variation

Special (or assignable) cause variation

In control

Out of control

Continuous metric

Discrete metric

Process capability

Process capability study

Process capability index

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SUMMARY

Statistical process control (SPC) is used for monitoring quality of manufacturing and service-delivery processes

Control charts help analyse the desirable quality characteristic of a process

Process capability study yields information about the performance of a process under specified operating conditions

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4LTR Press

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