Presented by:

Dr. Husam Arman

Quality management: SPC - I

Quality Control (QC)

Control – the activity of ensuring conformance to requirements and taking corrective action when necessary to correct problems

Importance Daily management of processes Prerequisite to longer-term improvements

Designing the QC System

Quality Policy and Quality Manual Contract management, design control and

purchasing Process control, inspection and testing Corrective action and continual improvement Controlling inspection, measuring and test

equipment (metrology, measurement system analysis and calibration)

Records, documentation and audits

Inspection/Testing Points

Receiving inspectionIn-process inspectionFinal inspection

Receiving Inspection

Spot check procedures100 percent inspectionAcceptance sampling

Acceptance Sampling

Lot received for inspection

Sample selected and analyzed

Results compared with acceptance criteria

Accept the lot

Send to production or to customer

Reject the lot

Decide on disposition

Pros and Cons of Acceptance Sampling

Arguments for: Provides an

assessment of risk Inexpensive and

suited for destructive testing

Requires less time than other approaches

Requires less handling

Reduces inspector fatigue

Arguments against: Does not make sense

for stable processes Only detects poor

quality; does not help to prevent it

Is non-value-added Does not help

suppliers improve

In-Process Inspection

What to inspect? Key quality characteristics that are related to

cost or quality (customer requirements) Where to inspect?

Key processes, especially high-cost and value-added

How much to inspect? All, nothing, or a sample

Human Factors in Inspection

Inspection should never be means of assuring quality. The purpose of inspection should be to gather information to understand and improve the processes that produce products and services.

Measurement system components

Equipment or gage Type of gage

Attribute: go-no go, vision systems(part present or not present)

Variable: calipers, probe, coordinate measurement machines

Unit of measurement Operator and operating instructions

Measurement error

Measurement error is considered to be the difference between a value measured and the true value.

Examples of Gauges

Metrology - Science of Measurement

Accuracy - closeness of agreement between an observed value and a standard

Precision - closeness of agreement between randomly selected individual measurements

Measurement accuracy and precision

Calibration

Calibration - comparing a measurement device or system to one having a known relationship to national standards

Types of measurement variation

Accuracy

Stability

Reproducibility

Repeatability

AccuracyDifference between the true average and the observed average.(True average may be obtained by using a more precise measuring tool)

True average

Observedaverage

Accuracy

Stability

Time 1 Time 2

The difference in the average of at least 2 sets of measurements obtained with a gage over time.

Stability

Reproducibility

Operator B

Operator A

Operator C

True Average

Variation in average of measurements made by different operators using the same gage measuring the same part.

RepeatabilityRepeatability is the variability of the measurements obtained by one person while measuring the same item repeatedly. This is also known as the inherent precision of the measurement equipment.

True Average

Observed Average

Repeatability

Which one is more repeatable?

How do we improve gage capability? Reproducibility

operator training, or more clearly define measurement scale

available to the operator Repeatability

gage maintenance gage redesign to better fit application

Quality Metrics

“We best manage what we can measure”

Metrics

A strategy without metrics is just a wish. And metrics that are not aligned with strategic objectives are a waste of time. Emery Powell

If you don’t keep score, you’re only practicing. You get what you inspect, not what you

expect

Metric

A metric is a verifiable measure that captures performance in terms of how

something is being done relative to a standard,

allows and encourages comparison, supports business strategy.

A metric is a verifiable measure stated in either quantitative or qualitative terms. “95 percent inventory accuracy” “as evaluated by our customers, we are

providing above-average service”

Metric

In quality management, we use metrics to translate customer needs into producer performance measures.

Internal quality metrics scrap and rework process capability (Cp or Cpk) first time through quality (FTTQ)

Customer quality measures

Customers typically relate quality to:

Feature based measures; “have” or “have not” - determined by design

Performance measures - “range of values” - conformance to design or ideal value

True versus substitute performance measures Customers - use “true” performance measures.

example: a true measure of a car door may be “easy to close”.

true performance measures typically vary by each individual customer.

Unfortunately, producers cannot measure performance as each individual customer does.

Producers - use “substitute” performance measures these measures are quantifiable (measurable units). Substitute measure for a car door: door closing effort

(foot-pounds).

Other example: light bulb true performance measure -- brightens the room substitute performance measure – wattage or lumens

Educating Consumers

Sometimes, producers educate consumers on their substitute performance measures.

What are substitute performance measures for the following customer desires: Good Gas Mileage Powerful Computer

What is the effect of educating consumers on performance measures?

Identifying effective metrics

Effective metrics satisfy the following conditions: performance is clearly defined in a measurable

entity (quantifiable). a capable system exists to measure the entity

(e.g., a gage). Effective metrics allow for actionable

responses if the performance is unacceptable. There is little value in a metric which identifies

nonperformance if nothing can or will be done to remedy it.

Example: Is net sales a good metric to measure the performance of a manufacturing department?

Acceptable ranges

In practice, identifying effective metrics is often difficult. Main reason: non-performance of a metric does not

always lead to customer dissatisfaction. Consider the car door example again, if door

closing effort is the metric, will a customer be dissatisfied if the actual effort is 50 foot-pounds versus 55 foot-pounds.

Producers typically identify ranges of acceptable performance for a metric. (a) For services, ranges often referred to as break points. (b) In manufacturing, these ranges are known as targets,

tolerances, or specifications.

Break points

Break points are levels where improved performance will likely change customer behavior.

Example: waiting in line Suppose the average customer will only wait for 5

minutes Wait longer than 5 minutes -- customer is

dissatisfied. 1-5 minutes -- customer is satisfied. less than 1 minute -- customer is extremely

satisfied Should a company try to reduce average wait

time from 4 to 2 minutes.?

Targets, tolerances and specifications Target (nominal) - desired value of a

characteristic. A tolerance specifies an allowable

deviation from a target value where a characteristic is still acceptable.

TARGET

-1 +1

Lower specification limit (LSL)

Upper specification limit (USL)

The Use of Statistics in Quality

Chapter Four

Statistical Process Control (SPC)

A methodology for monitoring a process to identify special causes of variation and signal the need to take corrective action when appropriate

SPC relies on control charts

A few notes on SPC’s historical background Walter Shewhart (Bell Labs 1920s) - suggested

that every process exhibits some degree of variation and therefore is expected. identified two types of variation (chance cause) and

(assignable cause) proposed first control chart to separate these two types of

variation. SPC was successfully applied during World War

II as a means of insuring interchangeability of parts for weapons/ equipment.

Resurgence of SPC in the 1980s in response to Japanese manufacturing success.

The basics

“Don’t inspect the product, inspect the process.”

“If you can’t measure it, you can’t manage it.”

Barriers to process control

Tendency to focus on volume of output rather than quality of output.

Tendency to measure products against a set of internal conformance specifications that may or may not relate to customer expectations.

The SPC approach

The SPC approach is designed to identify underlying cause of problems which cause process variations that are outside predetermined tolerances and to implement controls to fix the problem.

The SPC steps

Basic approach: Awareness that a problem exists. Determine the specific problem to be solved. Diagnose the causes of the problem. Determine and implement remedies. Implement controls to hold the gains

achieved by solving the problem.

SPC requires the use of statistics

Quality improvement efforts have their foundation in statistics.

Statistical process control involves the collection tabulation analysis interpretation presentation

of numerical data.

Statistic types

Deductive statistics describe a complete data set

Inductive statistics deal with a limited amount of data

Statistics

POPULATION

Parameters: 2

SAMPLE

Statistics: x, s, s2

InferentialStatistics

Deductive

Inductive

Types of data

Variables data - quality characteristics that are measurable values. Measurable and normally continuous; may

take on any value. Attribute data - quality characteristics that are

observed to be either present or absent, conforming or nonconforming. Countable and normally discrete; integer

Descriptive statistics

Measures of Central Tendency Describes the center position of the data Mean Median Mode

Measures of Dispersion Describes the spread of the data Range Variance Standard deviation

Measures of central tendency: Mean

Arithmetic mean x =

S where xi is one observation and N is the number of observations

So, for example, if the data are : 0,2,5,9,12 the mean is (0+2+5+9+12)/5 = 28/5 = 5.6

N

i

ixN 1

1

Measures of central tendency: Median - mode Median = the observation in the ‘middle’ of

sorted data Mode = the most frequently occurring value

Median and mode

100 91 85 84 75 72 72 69 65

Mean = 79.22

Median

Mode

Measures of dispersion: range

The range is calculated by taking the maximum value and subtracting the minimum value.

2 4 6 8 10 12 14

Range = 14 - 2 = 12

Measures of dispersion: variance

Calculate the deviation from the mean for every observation.

Square each deviation Add them up and divide by the number of

observations

n

xn

ii

2

2

1(

Measures of dispersion: standard deviation The standard deviation is the square root of

the variance. The variance is in “square units” so the standard deviation is in the same units as x.

n

xn

ii

2

1(

Standard deviation and curve shape

If is small, there is a high probability for getting a value close to the mean.

If is large, there is a correspondingly higher probability for getting values further away from the mean.

The normal curve

A normal curve is symmetrical about The mean, mode, and median are equal The curve is uni-modal and bell-shaped Data values concentrate around the mean Area under the normal curve equals 1

The normal curve

If x follows a bell-shaped (normal) distribution, then the probability that x is within

1 standard deviation of the mean is 68% 2 standard deviations of the mean is 95 % 3 standard deviations of the mean is

99.7%

One standard deviation

68.3%

Two standard deviations

95.5%

2 2

Three standard deviations

99.73%

3 3

The standardized normal

x scale

z scale

-3 +3+2+--2

-3 +3+2+1-1-2 0

= 0

= 1