C hapter 17

Slack, Chambers and Johnston, Operations Management 5th Edition © Nigel Slack, Stuart Chambers, and Robert Johnston 2007

Chapter 17

Quality planning and control

Source: Archie Miles



Operations strategy

Design Improvement

Planning and control

Operations management


The operation supplies …the consistent delivery of products and services at specification or above

The market requires … consistent quality of products

and services


The various definitions of quality

The transcendent approach views quality as synonymous with innate excellence.

The manufacturing-based approach assumes quality is all about making or providing error-free products or services.

The user-based approach assumes quality is all about providing products or services that are fit for their purpose.

The product-based approach views quality as a precise and measurable set of characteristics.

The value-based approach defines quality in terms of ‘value’.


Quality upQuality up

Profits upProfits up

Processing time down

Processing time down

Inventory down

Inventory down

Capital costs down

Capital costs downComplaint and

warranty costs down

Complaint and warranty costs

down

Rework and scrap costs

down

Rework and scrap costs

down

Inspection and test costs

down

Inspection and test costs

down

Productivity up

Productivity up

Service costs downService

costs down

Image upImage up

Scale economies up

Scale economies up

Price competition

down

Price competition

down

Sales volume up

Sales volume up

Revenue up

Revenue up

High quality puts costs down and revenue up

Operation costs down

Operation costs down


Customers’ expectations

for the product or

service

Customers’ perceptions

of the product or

service

Gap

Perceived quality is poor

Perceived quality is good

Expectations > perceptions

Expectations = perceptions

Expectations < perceptions

Perceived quality is governed by the gap between customers’ expectations and their perceptions of the product or service

Gap

Perceived quality is acceptable


for the product or

service


of the product or

service


for the product or

service


of the product or

service


The operation’s domain

Management’s concept of the

product or service

The customer’s domain

Previousexperience

Word-of-mouth communications

Image of product or service

Customer’s own specification of

quality

Organization’s specification of

quality

The actual product or service

Customer’s expectations concerning a

product or service

Customer’s perceptions

concerning the product or service

Gap 1

Gap 2Gap 3

Gap 4

A ‘gap’ model of quality

Gap ?


The perception–expectation gap

Action required to ensure high perceived quality

Main organizational responsibility

Gap 1

Gap 2

Gap 3 Operations

Gap 4 Marketing

Ensure consistency betweeninternal quality specification andthe expectations of customers

Ensure internal specification meets its intended concept of design

Ensure actual product or service conforms to internally specified quality level

Ensure that promises made to customers concerning the product or service can really be delivered

Marketing, operations, product/service development

Marketing, operations, product/service development


Quality characteristics of goods and services

Functionality – how well the product or service does the job for which it was intended

Appearance – the aesthetic appeal, look, feel, sound and smell of the product or service

Reliability – the consistency of performance of the product or service over time

Durability – the total useful life of the product or service

Recovery – the ease with which problems with the product or service can be rectified or resolved

Contact – the nature of the person-to-person contacts that take place


Attribute and variable measures of quality

Attributes Variables

Defective or not defective?Measured on a continuous scale

Light bulb works or does not work Diameter of bulb

Number of defects in a turbine blade Length of bar


Variablesthings you can measure

Attributesthings you can assess and accept or reject

Qualityfitness for purpose

Reliabilityability to continue

working at acceptedquality level

Quality

Quality of designdegree to which

design achieves purpose

Quality of conformancefaithfulness with which the

operation agrees with design


Process control charting

Time

Som

e m

easu

re o

f op

erat

ions

per

form

ance

Some aspect of the performance of a process is often measured over time

Question:

“Why do we do this?”



Time

Som

e m

easu

re o

f op

erat

ions

per

form

ance

Some aspect of the performance of a process is often measured over time

Question:“How do we know if the variation in process performance is ‘natural’ in terms of being a result of random causes, or is indicative of some ‘assignable’ causes in the process?”



Time

Ela

psed

tim

e of

cal

lThe last point plotted on this chart seems to be unusually low.How do we know if this is just random variation or the result of some change in the process which we should investigate?

Some kind of ‘guidelines’ or ‘control limits’ would be useful.


0.8 2.2 3.6

After the first sample

0.8 2.2 3.6

After the second sample

0.8 2.2 3.6

By the end of the first day

0.8 2.2 3.6

By the end of the second day

0.8 2.2 3.6Fitting a normal

distribution to the histogram of sampled

call times




The chances of measurement points deviating from the averageare predictable in a normal distribution

40 100 160Elapsed time of call (seconds)

Fre

quen

cy

68% of points

–2 standarddeviations

+2 standarddeviations

95.4% of points

–3 standarddeviations

+3 standarddeviations

99.7% of points

–1 standarddeviation

+1 standarddeviation

A standarddeviation

= sigma


Time

Ela

psed

tim

e of

cal

lProcess control charting

If we understand the normal distribution, which describes random variationwhen the process is operating normally, then we can use the distributionto draw the control limits.

In this case the final point is very likely to be caused by an ‘assignable’ cause,i.e. the process is likely to be out of control.


A P A P

A PA P

X

XX

X

Process variability

Scatter – precision: P

On/off target – accuracy: A



Alternating and erratic behaviour – investigate!

UCL

C/L

LCL

In addition to points falling outside the control limits, other unlikely sequences of points should be investigated.



Suspiciously average behaviour – investigate!

UCL

C/L

LCL




Two points near control limit – investigate!


UCL

C/L

LCL



Five points on one side of centre line – investigate!


UCL

C/L

LCL



Apparent trend in one direction – investigate!

UCL

C/L

LCL




Sudden change in level – investigate!

UCL

C/L

LCL



Low process variation allows changes in process performance to be readily detected

Time

Process distribution A

A

Time

Process distribution A

Process distribution B

A

B

Process distribution B

B


USLLSL

Process variation

Process variation

Process variation

Process variation

3 sigma process variation

= 66800 defects per million opportunities






= 3.4 defects per million opportunities

Process variation and its effect on process defects per million opportunities (DPMO)

USLLSL USLLSL USLLSL


Percentage actual defective in the batch

Pro

babi

lity

of a

ccep

ting

the

batc

h

0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

1.0

Producer’s risk (0.05)

Consumer’s risk (1.0)AQL LTPD

0 0.060.050.040.030.020.01 0.07 0.08

In this ideal operating characteristic,the probability of accepting the batch

if it contains more than 0.04% defective items is zero, and the probability of

accepting the batch if it containsless than 0.04% defective items is 1

In this real operating characteristic (where n = 250 and c = 1), both

type 1 and type 2 errors will occur

Type 1 error

Type 2 error

Ideal and real operating characteristics


Key Terms TestQualityConsistent conformance to customers’ expectations.

Quality characteristicsThe various elements within the concept of quality, such as

functionality, appearance, reliability, durability, recovery, etc.

Quality samplingThe practice of inspecting only a sample of products or

services produced rather than every single one.


Key Terms TestStatistical process control (SPC)A technique that monitors processes as they produce products or

services and attempts to distinguish between normal or natural variation in process performance and unusual or ‘assignable’ causes of variation.

Acceptance samplingA technique of quality sampling that is used to decide whether to

accept a whole batch of products (and occasionally services) on the basis of a sample; it is based on the operation’s willingness to risk rejecting a ‘good’ batch and accepting a ‘bad’ batch.

Control chartsThe charts used within statistical process control to record process

performance.


Key Terms Test

Process capabilityAn arithmetic measure of the acceptability of the variation of a

process.

Control limitsThe lines on a control chart used in statistical process control to

indicate the extent of natural or common-cause variations; any points lying outside these control limits are deemed to indicate that the process is likely to be out of control.

Quality loss function (QLF)A mathematical function devised by Genichi Taguchi that

includes all the costs of deviating from a target performance.


Key Terms TestSix SigmaAn approach to improvement and quality management that

originated in the Motorola Company but was widely popularized by its adoption in the GE Company in America. Although based on traditional statistical process control, it is now a far broader ‘philosophy of improvement’ that recommends a particular approach to measuring, improving and managing quality and operations performance generally.

Zero defectThe idea that quality management should strive for

perfection as its ultimate objective, even though in practice this will never be reached.

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