ES382 - Quality techniques · ES382 – Quality Techniques – Student ID: 1105638 4 The...

ES382 - QUALITY TECHNIQUES

The report introduces quality and reliability techniques in the context of a

high volume manufacturing company. To improve their market position,

relevant tools are applied in design, development and production that will

create more economic processes and improve customer satisfaction.

University of

Warwick- School of

Engineering-

Student ID: 1105638

ES382 – Quality Techniques – Student ID: 1105638

1

Table of Contents Introduction ..............................................................................................................................................................2

Customer Satisfaction...............................................................................................................................................3

Design and development ..........................................................................................................................................7

FMEA, FMECA and FTA .........................................................................................................................................7

Reliability testing during design and development ..............................................................................................8

Taguchi’s parameter and tolerance design ..........................................................................................................9

Production ............................................................................................................................................................. 10

Statistical Process Control ................................................................................................................................. 10

Process capability .............................................................................................................................................. 12

Testing before shipping the product ..................................................................................................................... 13

Product in service .................................................................................................................................................. 14

Conclusions ............................................................................................................................................................ 14

References ............................................................................................................................................................. 15

Word count: 3490


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Introduction

The tools and techniques presented in this report range from understanding customer expectations to the

design, development and production of finished goods, and monitoring the reliability of goods in use.

The quality tools introduced will be able to reduce costs in three categories based on the ISO 9004-1 quality

management standard. (Campanella, 1999) In addition, the more explicit benefits are explained for each

technique, which will help the management of the high volume manufacturing company to understand the

appropriateness and usefulness of the tools established. The report provides a brief overview on how each tool

works, what their benefits are and how they are brought about, and what support mechanisms have to be put

in place for the tools to be successful.

Quality costs

The ISO 9004-1 standard defines three quality cost approaches:

Quality-Costing

Process-Cost

Quality-Loss

Quality-Costing divides quality costs into four groups that are prevention, appraisal, internal failure and

external failure costs. In this report we focus on investing in prevention, which stipulates investment in quality

tools but significantly reduces costs in the other three categories. The tools and techniques will also decrease

intangibles and tangibles losses in accordance with the quality-loss approach. Intangible losses such as lost

sales as a result of customer dissatisfaction are reduced, as well as tangible losses such as scrap and rework,

which interlink with the internal and external failure costs. Process costs are reduced through savings in raw

materials, labor and energy, which are attributable to the elimination of non-value-adding activities.

(Campanella, 1999)


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Customer Satisfaction

David & Goetsch (2014) remark that meeting customer expectations is the unequivocal minimum to gain

customers but in order to gain a competitive advantage in the market the expectations should be exceeded to

create true customer value. The Kano model (figure 1) takes this further (Kano, 1984). It emphasizes that the

minimum a company should do is to meet the basic

quality criteria. If the product meets the spoken

performance criteria that can measured through

interviews and questionnaires, the customer will be

satisfied but is not alone sufficient for a

competitive advantage. Excitement qualities are

the Unique Selling Points (USPs) of the product that

differentiate the company’s product in the market.

Bergman & Bengt (1994) and Gummesson (1988) remark that customer satisfaction and perceived quality

depend on three factors: customer’s expectations, his/her experience with the product and the company’s

image. Customer requirements and expectations can be measured as qualitative characteristics of the product:

intuitive to use, reliable, long lasting, trendy etc. (Fiorenzo, 2002)

As perceived quality is highly dependent on customer expectations, it is not possible to have a quality product

without understanding the customer’s expectations first. (Ficalora & Cohen, 2010) Quality Function

Deployment (QFD) provides a mechanism to understand these expectations, how to meet them by controlling

the customer experience and how to manage development efforts to maximize customer satisfaction. In order

to understand customer requirements, several sources of information can be used such as interviews and focus

groups. (Bergman & Bengt, 1994) There are sources that should not be used, for instance Ficalora & Cohen

(2010) note that surveys are an inflexible source of information. When gathering the Voice of the Customer

(VOC) one should ask open-ended questions and probe deeper into the expectations of the customer (Ficalora

& Cohen, 2010).

Figure 1. Adapted from Kano (1984)


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The fundamental rationale for executing the QFD is to understand how customer value can be created through

design and process choices. The QFD tool House of Quality (HOQ) is a powerful tool that translates the

customer needs to design features of the product. The HOQ illustrates the customer needs on the left (WHATs)

of the matrix and on the top the responses (HOWs/product features) of the engineering team. In figure 2, a ski

manufacturer has found out that the customer values a good edge grip on hard pistes (WHAT), and this quality

can be manipulated through design features (HOWs) such as weight and shape of the ski.

Figure 2. (Matzler, Kurt & Hinterhuber, 1998)

In addition to guiding the designing of the product, the House of Quality can be used for benchmarking and

competitive assessment by understanding the company’s current offerings and the competitors’ offerings. This

can be used to prioritise improvement needs and to identify market gaps.

House of Quality in itself is already a powerful tool, but the QFD process can be further extended to four

phases as illustrated in figure 3.


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Figure 3 (Fiorenzo, 2002; Crow, 1992)

This four phase model consists of:

1. Product planning

House of quality

2. Part deployment

Breakdown of the product into subsystems and specification of important features of each

subsystem

3. Process planning

A top-down approach: first design the main process flow, and then break down to smaller

processes.

4. Quality control, or Production planning as Ficalora & Cohen (2010) define it

Production instructions are designed to the operator level

The usefulness of the four-phase QFD comes from the translation of phase 1 HOWs to phase 2 WHATs, and

phase 2 HOWs to phase 3 WHATs and so on. Hence, the Voice of the Customer is cascaded all the way from

design to the production phase as illustrated in figure 4. (Ficalora & Cohen, 2010)


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Figure 4. QFD Cascade (Govers, 1996)

The cascaded QFD can be used in parallel with the other tools and techniques introduced in this report. Using

all the four phases ensures that the customer is central to all decision making from the product design to the

process design.

QFD has several positive impacts on the company’s business:

1. Increased revenues - For a company to increase revenues they have to charge more for a product or

sell more in units depending on the company’s market positioning objectives. In order to achieve this

customer value has to be added, which can be obtained through QFD.

2. Decreased costs – Reduces wasted time and material used on non-customer-value-adding activities.

3. Reduced product development cycle-time – QFD specifies clear priorities since the start of the

development cycle. This reduces the need for midcourse changes that delay product launching dates.

4. Improved communication in the organization – QFD provides common goals throughout the

organization in the form of facts and matrices for people to focus efforts on.

Ficalora & Cohen (2010) note that organizational support determines how successful QFD will be. They

emphasise three key components:

1. Management has to provide support and sufficient resources for the completion of QFD

2. Commitment of functional groups to relevant to QFD activities. Below are examples of functional

groups per development phase.

a. Product development: Purchasing, Manufacturing, Quality assurance, Marketing, Sales and

Service

b. Process development: Purchasing, Training, Marketing, Finance

3. Knowledge and skills of QFD in the organization. Every development team should have at least one

person familiar with the QFD principles, which can be provided through training.


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Design and development

FMEA, FMECA and FTA The failure modes and effects analysis (FMEA) is an analytical tool that identifies potential failures modes of a

product and its effects. Consequently, action can be taken to alleviate the effects of the failure modes. It

should be implemented at the very early stages of the product design, already when preliminary schematics

and layout drawings have been made as Neubeck (2004) denotes. The reason for this is illustrated by figure 5

that demonstrates how design changes and modifications are still inexpensive to make at the early stages of

the product’s life cycle.

Figure 5. (Knowles, 2011; Adapted from Yang & El Haik, 2002)

Whether FMEA analysis should be carried out depends on the costs and consequences of failure. So for

instance, in the car manufacturing industry, the consequences of a failure can be catastrophic both for the

company and people who are using the car. The car manufacturer cannot take the risk of failure such as its

breaking or steering system failing. For the car company to exist in the market, a reliable car is a basic

requirement or a basic quality as defined by Kano. For instance, Volvo, a car company whose unique selling

points are the safety and reliability of its cars, require its suppliers to use FMEA. (Volvo, 2012) However, the

manufacturer of tennis rackets can accept the fact that 1 out of 10,000 rackets face an early life failure.

FMEA is set up in table format. The table 1 shows an example of an FMEA form that Volvo stipulates its

suppliers to use.


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Table 1. (Volvo, 2012)

The table presents all of the possible failure scenarios of a product failing and its consequences at the customer

end. It also specifies recommended action to mitigate the failure effects. The steps used in the FMEA (Neubeck,

2004):

Create a functional block diagram including the description of the function of the product.

Identify failure effects

List a detection mechanism for each failure

The reliability engineer’s judgment is central in the FMEA process, and hence for the tool to work relevant

industrial experience is needed, or else training should be provided.

Failure modes, effects and criticality analysis (FMECA) takes the FMEA further by providing a numerical

quantification of the failure modes. In the business-to-business environment, the customer such as Volvo will

use FMECA for risk assessment and as an input to the Fault Tree Analysis (FTA). Fault trees are constructed

from logic gates and failure mode blocks with a top-down approach. FMECA combined with FTA analysis is an

efficient tool in identifying what failure modes are critical in the failure of the whole system. (Neubeck, 2004)

Reliability testing during design and development Reliability testing is highly useful as it reveals inherent weaknesses in the product design. Some tools that can

be used are the reliability enhancement testing (RET), in which the product/component is put under stress that

is increased in consecutive steps over time. Highly Accelerated Life Test (HALT) is a similar tool that uses

stepped stress modes but it is conducted in a short period of time in accelerated conditions. HALT can be

expensive to perform, which is the reason why RET is sometimes preferred. RET and HALT are to be performed

in the design and development phase of the product. (Neubeck, 2004)

Some of the objectives and benefits of reliability testing according to Neubeck (2004) are the following:

Uncover weaknesses in the product design


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Provide a level of confidence in the product

Confirm functionality features of the product

Find the breaking point of the product

Taguchi’s parameter and tolerance design Taguchi’s methods of product and process design have been widely spread in Japan for some decades now, and

US companies such as AT&T, Ford and Eaton are among companies that started using these methodologies

already in the 1980s. (Bendell, et al., 1989)

Two steps should be completed before using Taguchi’s parameter design. Both steps require engineering

knowledge and innovation with regard to Research & Development (R&D). The two steps are often called as

System design.

1. Product design comprising the selection of materials, parts and initial product parameter values

2. Process design including the selection of production equipment and initial values for process factors.

QFD’s cascade model will help to complete the two steps. After they have been completed, the parameter

design can be implemented. It involves experimental design techniques utilising orthogonal arrays and the

signal-to-noise (S/N) ratio. (Bendell, et al., 1989) Two concepts should be understood concerning the

parameter design: controllable and noise factors. Bendell et al. (1989) use the injection moulding process as an

example where the controllable factors are the choice of material and mould temperature. Noise factors are

variables that are very difficult, impossible, or expensive to control. In the injection moulding process these

noise factors can be for instance the ambient temperature and humidity (outer noise), ageing of machinery

(inner noise) or manufacturing imperfections (between product noise).

The parameter design uses orthogonal arrays to experiment with different levels of control factors and

combinations of noise factors. The primary purpose of the Parameter design is to optimize the product’s

functional characteristics, whilst minimising the effect of noise factors.

Most engineers in the United States jump straight into the tolerance design before optimizing the product

parameters, which has a huge impact on cost and quality (Bendell, et al., 1989). These companies start

tinkering with the product parameters only when problems occur or they receive customer complaints. This

type of strategy has been named firefighting, which is costly for the company due to expensive corrective

action and unsatisfied customers.

Despite the benefits that Parameter design can bring, it has its own limitations, too:

Can be costly to do if the product is complex and prototype production is expensive

Does not necessarily find the optimum

S/N ratio does not allow differentiation between mean and variation

Uses standard arrays, which makes it ineffective if the number of control factors do not fit the

standard matrix


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After Parameter design has been conducted, Tolerance Design is used to define how much variation is allowed

in part specifications. Through the use of orthogonal arrays in the parameter design phase we can establish

which control factors have the greatest effect on variability. Taguchi’s loss function denotes how the tightening

of the most critical tolerances leads to lowest costs. It helps to decide whether the cost of tightening tolerance

limits is less than the money lost due to current variability. As a result, the loss function helps to prioritise

efforts in the optimisation of tolerance limits. (Taguchi, et al., 2005)

Taguchi, et al. (2005) note that a successful implementation of these tools require:

1. Management commitment

2. Corporate Leader/Corporate team. In other words, someone to lead the implementation.

3. Training

4. Integration with other strategies. Usefulness comes into effect with other techniques such as

QFD and FMEA.

5. Comparison of the new results to the previous ones in terms of product functionality, scrap,

customer satisfaction and other measures, which have an impact on the company’s

profitability.

Production

Statistical Process Control The Statistical Process Control (SPC) charts describe whether a certain process is in control or not. If the

process is in statistical control the company can predict what will happen next and the production will run

smoothly. Even if the process is delivering unacceptable results e.g. 10% rejects and 20% late orders, the

management will know that the production will continue like this. However, if the process is out of statistical

control we cannot predict what the production will be like in the future, and improvent efforts will have

minimal impact as the special cause variation will nevertheless affect the outcome of the production.

(Stapenhurst, 2005)

Traditionally manufactured parts have been divided into two categories of “good” and “bad” parts. However,

this type of concept requires costly activities such as inspection, rework and scrap.The aim of SPC is to

eradicate the need for these activities by eliminating special causes, which are unpredictable unwanted events.

This will in turn lead to a stable process that produces well performing goods without interruptions. Wheeler’s

Effect of Entropy describes how SPC should be a continous process because of the natural tendency of the

processes drift toward entropy and chaos as demonstrated by figure 7.


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Figure 6. (Wheeler & Chambers, 1992)

There are numerous benefits arising from the use of SPC (Oakland, 2008):

Direct benefits

Improves communication between shopfloor and management. Thanks to SPC, management

cannot blame shop floor employees on the bad process. As a result, job satisfaction is increased.

Better understanding of the process.

Indirect benefits due to reduced variation that SPC contributes to

Improved reputation for consistent quality products

When integrated with the use of Process Capability analysis further benefits are attained

In order for the SPC to work a few key support mechanisms have to be put in place:

Support and commitment from the senior management

Training that establishes a uniform approach to SPC throughout the organisation

Oakland (2008) makes an important notion that organisations will have a number of processes or problems

that need addressing. Consequently, one process should be tackled at a time, for which Pareto analysis can be

applied to prioritise the areas that need most improvement. Furthermore, tackling one problem at a time will

help to engage the interest and commitment of people in the organisation. As a result it has the potential to

start a ripple effect toward quality thinking in the organisation as the positive implications of SPC are realised.


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Process capability SPC helps us to get rid of special causes, which leads to a stable process. After the process stability is

established, we can focus on process capability. A capable process produces parts that fit inside the tolerance

limits and conforms to product specification requirements. The tolerance limits are defined in the tolerance

design phase using Taguchi’s loss function to define economic tolerance limits. However, fundamentally the

tolerance limits originate from the customer who is satisfied with a certain level of product performance, which

is partly due to right tolerance limits.

Calculating process capability

For a product to be within the tolerance limits, the total variation cannot be more than the difference between

upper and lower tolerance limits. The potential of the process to fit inside the tolerance limits is calculated

using the Cp value. (Oakland, 2008)

However, the Cp value does not denote the centre of the process spread. For instance, B and C would have

same Cp values in figure 5.

Figure 7. (Knowles, 2011)

Cpk value, however, takes into account both the process variation and centring. The aim is to have Cp=Cpk,

which means that the process has minimum variation leading to minimum costs in accordance with Taguchi’s

loss function indicates. Cpk can be calculated for both upper and lower limits as follows:

,

If the process is not capable, variation can be reduced by taking fundamental action on the process or by

recalculating the control limits until process capability is achieved. When acceptable capability values are

achieved (Cpk=2 value gives a high level of confidence), control charts for future process control and data

recording should be set up. (Oakland, 2008)


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The reduced variation owing to process capability will lead to

Reduced external failure costs due to a decrease in the number warranty claims, customer

complaints etc.

Reduced internal failure costs owing to reduction in scrap, rework and wasted time

Reduced need for inspection, which can be both costly and ineffective.

Highly conforming products that are necessary for the use of lean manufacturing methods such as

the just-in-time (JIT) system

(Oakland, 2008)

Testing before shipping the product

The purpose of Environmental Stress Screening (ESS) is to reduce the number of early failures (also known as

infant mortality or burn-in) as demonstrated by the bathtub curve below. (O'Connor, 2012)

Figure 8. (O'Connor, 2012)

The method of ESS depends on the product that is being tested. For instance, a simple power-on test may be

used for computers and TVs, whereas mechanical products such as cars may require vibration and thermal

stress tests.

As part of the manufacturing process, ESS should be used especially in products that contain many

components. For instance, a company that I worked for used ESS successfully for their electric motors that

contain relatively many components. Consequently, ESS reduced the number of faulty products going to the

customer, which increased customer satisfaction and reduced the cost of non-conformance. However, if the


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product is for instance a Mars chocolate bar, then ESS is unnecessary.

Product in service

When the product is in the market, it is important to understand how it is performing in terms of reliability.

This can be measured using the failure rate, which can be expressed mathematically as

Where is the failure rate, K the number of failures and T the total operating time.

Other measures such as Mean Time Between Failures (MTBF) can be used. It is calculated by dividing

the total operating time by the total number of failures. (Neubeck, 2004)

These measures can be used for future improvement of the product. In addition, after enough data is

collected, the company is able to predict the future costs of failure and non-conformance, which are

important measures for the finance department with regard to cash flow.

Basic training is enough in the use of these measures.

Conclusions

The tools presented in this report are most useful when used in combination but they can be used individually,

too. However, the manufacturing process should always be in statistical control before process capability can

be improved.

In addition to the support mechanisms recommended in each section, there are a few elements that help to

build a strong foundation for the use of quality techniques. Such are represented by Dale (1994):

Commitment of leadership

A long-term strategy for quality management

Pursuit of standardisation of instructions, procedures and systems

Education and training that provide a common language for everyone in the organisation

Ensuring that participation and contribution extends all the way from the shop floor to top

management

Measurement of key indicators both internally and externally and the use of benchmarking

Cultural change toward Kaizen philosophy of continuous improvement


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References

Bendell, A., Disney, J. & Pridmore, W. A., 1989. Taguchi methods: applications in world industry. London: IFS.

Bergman, B. & Bengt, K., 1994. Quality from Customer Needs to Customer Satisfaction. London: McGraw-Hill.

Campanella, J., 1999. Principles of quality costs: Principles, Implementation and Use. 3rd ed. Milwaukee,

Wisconsin: ASQ Quality Press.

Crow, K., 1992. Seminar on Concurrent Engineering. Rome, DRM Associates.

Dale, B. G., 1994. Managing Quality. 2nd ed. New York; London: Prentice Hall.

David, S. B. & Goetsch, D. L., 2014. Quality management for organizational excellence. s.l.:s.n.

Ficalora, J. P. & Cohen, L., 2010. Quality function deployment and Six Sigma: a QFD handbook. 2nd ed. Upper

Saddle River, New Jersey: Prentice Hall.

Fiorenzo, F., 2002. Advanced quality function deployment. Boca Raton, Fla, St. Lucie Press.

Govers, C., 1996. What and how about quality function deployment (QFD). International Journal of Production

Economics, Volume 46-47, pp. 575-585.

Gummesson, E., 1988. Quality - The Ericsson Approach. Sweden: Ericsson, HF/DHQC.

Kano, N., 1984. Attractive quality and must-be quality. The Journal of Japanese Society for Quality Control,

14(2), pp. 39-48.

Knowles, G., 2011. Six Sigma. s.l.:bookboon.

Matzler, Kurt & Hinterhuber, H. H., 1998. How to make product development projects more successful by

integrating Kano's model of customer satisfaction into quality function deployment. Technovation, 18(1), pp.

25-38.

Neubeck, K., 2004. Practical reliability analysis. Upper Saddle River, New Jersey: Prentice Hall.

Oakland, J. S., 2008. Statistical Process Control. Sixth Edition.. London; Boston: Elsevier Butterworth-

Heinemann.

O'Connor, P. D. T., 2012. Practical reliability engineering. 5th edition ed. Chichester, West Sussex: Wiley.

Stapenhurst, T., 2005. Mastering Statistical Process Control: A Handbook for Perfomance Improvement Using

Cases. Oxford; Boston: Elsevier/Butterworth-Heinemann.

Taguchi, G., Chowdhury, S., Wu, Y. & associate editors, S. T. a. H. Y., 2005. Taguchi's quality engineering

handbook. Hoboken, New Jersey: John Wiley & Sons, Inc..


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Volvo, 2012. Suppliers. [Online]

Available at: http://www.volvogroup.com/suppliers/global/en-

gb/supplierapplication/standardsaccess/pages/fmea.aspx

[Accessed 1 January 2014].

Wheeler, D. J. & Chambers, D. S., 1992. Understanding Statistical Process Control. Second Edition.. Knoxville,

Tennessee: SPC Press.

Yang, K. & El Haik, B., 2002. Design for Six Sigma: A Roadmap for Product Development. New York: McGraw-Hill.

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