1. Quality planning and control
Chapter 17
Quality planning and control
Source: Archie Miles

2. Quality planning and control
Operations strategy
The market requires … consistent quality of products and services
Operations management
Design
Improvement
The operation supplies …
the consistent delivery of products and services at specification or above
Planning and control

3. 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’.

4. High quality puts costs down and revenue up
Quality up
Image up
Processing time down
Inspection and test costs down
Rework and scrap costs down
Inventory down
Service costs down
Complaint and warranty costs down
Capital costs down
Price competition down
Sales volume up
Productivity up
Revenue up
Scale economies up
Operation costs down
The net effect of all these consequences is that revenues increase and costs reduce. In other words, the overall effect of raising quality levels is to increase profitability.
Profits up

5. Perceived quality is governed by the gap between customers’ expectations and their perceptions of the product or service
Gap
Gap
Customers’ expectations for the product or service
Customers’ perceptions of the product or service
Customers’ expectations for the product or service
Customers’ perceptions of the product or service
Customers’ perceptions of the product or service
Customers’ expectations for the product or service
Expectations > perceptions
Expectations = perceptions
Expectations < perceptions
So the continuum between quality being perceived as poor through to it being perceived as good is primarily a function of the nature and extent of any gaps between customers’ expectations and their perceptions. The implication of this is that in order to manage customers’ perceived quality levels, both their expectations and their perceptions must be managed.
Perceived quality is poor
Perceived quality is acceptable
Perceived quality is good

6. The operation’s domain
A ‘gap’ model of quality
Management’s concept of the product or service
The customer’s domain
Previous
experience
Word-of-mouth communications
Image of product or service
Gap 4
Customer’s expectations concerning a product or service
Customer’s perceptions concerning the product or service
Gap ?
Customer’s own specification of quality
The actual product or service
Gap 1
Finally, the organization may be influencing the customers’ image of the product or service, for example through its advertising or other promotional activity, in such a way as to conflict with its actual reality. This is called gap 4.
The operation’s domain
Organization’s specification of quality
Gap 3
Gap 2

7. The perception–expectation gap
Action required to ensure high
Main organizational
perceived quality
responsibility
Ensure consistency between internal quality specification and the expectations of customers
Marketing, operations, product/service development
Gap 1
Marketing, operations, product/service development
Ensure internal specification meets its intended concept of design
Gap 2
Ensure actual product or service conforms to internally specified quality level
Gap 3
Operations
Ensure that promises made to customers concerning the product or service can really be delivered
Gap 4
Marketing

8. 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

9. Attribute and variable measures of quality
Attributes
Variables
Measured on a continuous scale
Defective or not defective?
Light bulb works or does not work
Diameter of bulb
Number of defects in a turbine blade
Length of bar

10. Quality of conformance
fitness for purpose
Reliability
ability to continue
working at accepted
quality level
Quality of design
degree to which
design achieves purpose
Quality of conformance
faithfulness with which the
operation agrees with design
Variables
things you can measure
Attributes
things you can assess and accept or reject

11. Process control charting
Some aspect of the performance of a process is often measured over time
Question:
“Why do we do this?”
Time
Some measure of operations performance

12. Process control charting
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
Some measure of operations performance

13. Process control charting
The 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.
Time
Elapsed time of call

14. Process control charting
0.8
2.2
3.6
After the second sample
0.8
2.2
3.6
After the first sample
0.8
2.2
3.6
Fitting a normal distribution to the histogram of sampled call times
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

15. Process control charting
–3 standard
deviations
+3 standard
99.7% of points
–2 standard
deviations
+2 standard
95.4% of points
–1 standard
deviation
+1 standard
A standard
 = sigma
Frequency
68% of points
Elapsed time of call (seconds)
The chances of measurement points deviating from the average are predictable in a normal distribution

16. Process control charting
If we understand the normal distribution, which describes random variation when the process is operating normally, then we can use the distribution to 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.
Elapsed time of call
Time

17. X X X Process variability A P A P A P A P On/off target – accuracy: A
Scatter – precision: P X
A P
A P

18. Process control charting
In addition to points falling outside the control limits, other unlikely sequences of points should be investigated.
UCL
C/L
LCL
Alternating and erratic behaviour – investigate!

19. Process control charting
In addition to points falling outside the control limits, other unlikely sequences of points should be investigated.
UCL
C/L
LCL
Suspiciously average behaviour – investigate!

20. Process control charting
In addition to points falling outside the control limits, other unlikely sequences of points should be investigated.
UCL
C/L
LCL
Two points near control limit – investigate!

21. Process control charting
In addition to points falling outside the control limits, other unlikely sequences of points should be investigated.
UCL
C/L
LCL
Five points on one side of centre line – investigate!

22. Process control charting
In addition to points falling outside the control limits, other unlikely sequences of points should be investigated.
UCL
C/L
LCL
Apparent trend in one direction – investigate!

23. Process control charting
In addition to points falling outside the control limits, other unlikely sequences of points should be investigated.
UCL
C/L
LCL
Sudden change in level – investigate!

24. Low process variation allows changes in process performance to be readily detected
Time
Process distribution A
Process distribution B A B
Process distribution B B
Process distribution A A
Time

25. Process variation and its effect on process defects per million opportunities (DPMO)
LSL
USL
LSL
USL
LSL
USL
LSL
USL
3 sigma process variation
= defects per million opportunities
4 sigma process variation
= 6200 defects per million opportunities
5 sigma process variation
= 230 defects per million opportunities
6 sigma process variation
= 3.4 defects per million opportunities

26. Ideal and real operating characteristics
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 contains less than 0.04% defective items is 1
Producer’s risk (0.05)
1.0
0.9
0.8
0.7
In this real operating characteristic (where n = 250 and c = 1), both type 1 and type 2 errors will occur
0.6
Probability of accepting the batch
0.5
0.4
Type 1 error
Type 2 error
0.3
0.2
0.1
AQL
LTPD
Consumer’s risk (1.0)
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
Percentage actual defective in the batch

27. Key Terms Test
Quality
Consistent conformance to customers’ expectations.
Quality characteristics
The various elements within the concept of quality, such as functionality, appearance, reliability, durability, recovery, etc.
Quality sampling
The practice of inspecting only a sample of products or services produced rather than every single one.

28. Key Terms Test Statistical 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 sampling
A 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 charts
The charts used within statistical process control to record process performance.

29. Key Terms Test Process capability
An arithmetic measure of the acceptability of the variation of a process.
Control limits
The 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.

30. Key Terms Test Six Sigma
An 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 defect
The idea that quality management should strive for perfection as its ultimate objective, even though in practice this will never be reached.