Design of Experiments, Six Sigma and benchmarking in TQM

UNIT 7 DESIGN OF EXPERIMENTS, SIX SIGMA AND BENCHMARKING

Structure

7.1 Introduction

Objectives

7.2 Some Experimental Design for Quality Tools

7.2.1 One Factor at a Time Method

7.2.2 TheFullFactorialMethod

7.2.3 The Fractional Factorial Method

7.3 Taguchi's Experimental Design Method

7.3.1 Objective

7.3.2 Different Design Stages

7.3.3 Suggested Steps

7.4 Six Sigma

7.4.1 Six Sigma Methodology

7.4.2 Six Sigma Participants

7.5 Benchmarking

7.5.1 Approach

7.5.2 Steps in Formal Benchmarking Process

7.6 Summary

7.7 Key Words

7.8 Answers to SAQs

The design of experiments is a series of techniques which involves the identification and control of parameters which have a potential effect on performance and reliability of a product design andor the output of a process, with the objective of optimizing product design, process design and process operation, and limiting the effect of noise (uncontrollable) factors. The objective is to optimize the value of these design parameters to make the performance of the system immune to variation. The concept can be applied to the design of new' products and processes or to the redesign of existing ones, in order to :

optimize product design, process design and process operation,

achieve minimum variation of best system performance,

achieve reproducibility of best system performance in manufacture and use,

improve the productivity of design engineering activity,

evaluate the statistical significance of the effect of any controlling factor on outputs, and

reduce costs.

These techniques identify and control the parameters or variables, wliich have a potential influence on the output of a process. Each combination of factors and levels are used to conduct small number of experiments with different parameter values and analyze their effect on a defined output. Prediction of system performance can be made based on the analysis.

Quality Tools - Statistical In this unit, the concept of six sigma has also been introduced. Six sigma is a measure of quality that strives for near perfection. It is a disciplined and statistical methodology for eliminating defects (driving towards six standard deviations between the mean and the nearest specification limit) in any process, from manufacturing to transactional and from product to service. Benchmarking is another quality improvement tool to improve the performance of an organization by comparing the practices of a successful organization, i.e. it is an opportunity to learn from the experience of others. This also has been described in this unit.

Objectives

After studying this unit, you should be able to

.-• know the design of experiments,

understand the six sigma, and

describe the benchmarking.

TOOLS

7.2;l One Factor at a Time Method

In this method, setting of one factor is altered holding the other constant and then the resulting response is measured. This approach is easy to use and understand. However, it has many shortcomings. It does not uncover interactions among variables. It is also

' inefficient, resource intensive and costly. In addition, it is not &asy to hold the factors constant from experiment to experiment, and this itself creates variation.

7.2.2 The Full Factorial Method

Using this approach, all combinations of the factors are considered which are to be tested and best combination is found out. For example, three factors with two levels each (i.e. level 1 and level 2) would need 23 = 8 trials, as shown in Table 7.1.

Table 7.1 : The Full Factorial Method

This approach is feasible only when number of factors are small and when experimentation is easy because even with say seven factors at two levels, the minimum number of trials would be 2' (i.e. 128). Although the main effects and interactions can be

'

measured in a thorough and pure scientific manner, the associated time and costs for running such a large number of experiments are usually too high and unrealistic in industrial situations. Also, much of the information obtained from the trials would be from combinations of factors which are of little practical value. This problem may be overcome by the use of fractional factorial designs.

Trial Number

1

2

3

4

5

6

7

8

Control Factors

A

1

I

1

1

2

2

2

2

B

1

1

2

2

1

1

2

2

C

1

2

1

2

1

2

1

2

7.2.3 The Fractional Factorial Method Design of Experiments, Six Sigma and

The difficulty associated with full factorial designs regarding running too large number Benchmarking of experimental runs led to the evolution of fractional factorial designs where the chosen fraction of the full designs gives an even and balanced spread throughout all the factors being studied. Typically, a quarter of 128 experiments required for seven factors at two levels would involve just 32 experiments. In this method of experimentation, as suggested by Fisher (1925) and Plackett and Burman (1946), several factors are changed at the same time in a systematic way so as to ensure the reliable and independent study of the main factors and interaction effects. The orthogonal arrays are constructed with a limited number of runs as a subset of the full factorial layout. The subsets are balanced between columns to ensure that an even number of each level of each factor is tested during the running of the experiment. The technique of orthogonal arrays reduces the size of the experiment to a practicable level. However, some information is sacrificed by following this method. Hence, while adopting this method, technical knowledge of the people involved in the experinlent is very important to ensure that the loss of information is relatively insignificant. A typical Fisher array is shown in Table 7.2.

Table 7.2 : Fisher Array

In this array, the columns represent the independent variables or factors to be studied and tested at one of two levels and the rows represent the tests or experiments to be performed. In an experiment which has eight experimental runs (i.e. LB), the first option or level of factor A is tested four times, and the second option or level of factor A is also tested four times. In addition to this, during the experimental run, the array tests all the combinations of options or levels of any two factors. Thus A , is tested both against BI and B2, as well as A2 similarly testing Bl and B2. The number of interactions that can be studied is dependent on the size of the array. The analysis of the orthogonal array is done by averaging the responses applicable to the level of each factor. Factor A at level 1 is given by averaging the results obtained from running experiments numbers 1 to 4 and factor A at level 2 by averaging the results obtained from running experiments numbers 5 to 8. The difference between level 1 and level 2 of each factor is an indication of the significance of that factor in influencing the response measured. Generally, the larger is the difference, the greater is the significance. The analysis array enables the strength of each level of each factor to be measured, and their relative significance in influencing the designated output to be assessed. Analysis of variance is used to estimate the significance that any factor has in influencing the measured response in relation to error (e.g. measurement and inconsistency in the setting of factor levels) in the experimental system.

Runs

1

2

3

4

5

6

7

8

Following experiment concerning the processes used in the pharmaceutical industry in the manufacture of medicines in tablet form, outlines the use of fractional factorial experiment method with the use of orthogonal arrays. The aim is to produce uniform tablets in terms of size and content and so the initial process of mixing the drug solution and the carrying medium is of paramount importance. The particle size, the even distribution 6f the drug (content uniformity) and the moisture content are controlled with

Control Factors

A

1

I

I

1

2

2

2

2

B

1

I

2

2

I

1

2

2

C

I

1

2

2

2

2

1

I

D

1

2

1

2

1

2

I

2

E

1

2

I

2

2

I

2

1

F

I

2

2

1

1

2

2

1

G

I

2

2

1

2

1

1

2

Quality Tools - Statistical small variation around the target value prior to feeding into the tablet-making part of the operation. The three measured responses are, therefore, particle size, content uniformity, and moisture content. Table 7.3 shows the layout of the experiment and the results of each experimental run from the particular combination of the factors in the run are given in Table 7.4. The experiment will indicate the combination which gives the best result, but there may be a better combination. This is done by analyzing the effect of each factor. The response of the relevant experiment where the information occurs is simply added up and averaged so that comparisons may be made between level 1 and level 2 of each factor, as shown in Tables 7.5 and 7.6. The average of the experimental run is calculated to be 3.96.

Table 7.3 : Layout of the Experiment

Table 7.4 : Results of the Experiment - -

A

B

C

D

E

F

G

Table 7.5 : Analysis of the Effect of Factors and Levels

Control Factors

Mixing speed

Drying temperature

Chopping speed

Drying mechanism

Drying time

Mixing time

Solution addition rate

1

2

3

4

5

6

7

8

Table 7.6 : Comparison between Level 1 and Level 2

Level 1

High

High

Long

Type A

Long

Long

Fast

Level 2

Low

Low

Short

Type B

Short

Short

Slow

G

Fast

Slow

Slow

Fast

Slow

Fast

Fast

Slow

A

High

High

High

High

Low

Low

Low

Low

Particle Size

3.8

4.5

5.3

4.9

4.4

2.9

2.3

3.6

C

Long

Long

Short

Short

Short

Short

Long

Long

B

High

High

Low

Low

High

High

Low

Low

C

3.550

4.375

0.825

Level 1

Level 2

Difference

D

3.950

3.975

0.025

D

Type A

TypeB

TypeA

TypeB

TypeA

Type B

TypeA

TypeB

F

4.175

3.750

0.425

E

3.900

4.025

0.125

A

4.625

3.300

1.325

1

G

3.475

4.450

0.975

B

3.9

4.025

0.125

E

Long

Short

Long

Short

Short

Long

Short

Long

F

Long

Short

Short

Long

Long

Short

Short

Long

Table 7.7 : Final Result

A2 Mixing speed = 3.96 - 3.300 = 0.660

GI Solution add. rate = 3.96 - 3.475 = 0.485

Cl Chopping speed = 3.96 - 3.550 = 0.410

F2 Mixing time = 3.96 - 3.750 = 0.210

Total below average = 3.96 - 1.765 = 2.195

The construction of the orthogonal array shows the significance of each of the factors in relative value to each other in terms of their effect on influencing the value of the output or response, in this case 'particle size'. Thus mixing speed, solution addition rate, chopping speed and mixing time have the greatest effect in that order, and drying time and drying temperature are, in this example, of no relative significance at all. The other useful property the balance of the array gives is the additive effect of each of the main control factors in the value of the response beyond the experimental average. In this example, particle size is required to be as small as possible. The effect below average is shown in Table 7.7. The total below average is used as a prediction of the result if the process is set up using a combination of factor level settings that reflect their best effect on the output, in this case A2, CI, FI , GI. The other factors B, D and E can be set at the level where least cost is incurred. This may be B2 (lowest temperature), E2 (shortest drying time) and perhaps either Dl or Dz according to the lower capital cost, or the lower operating cost. The predicted results are compared with the results obtained by a confirmation run. The closer the confirmation run is to the prediction, the team thinking in the construction of the experiment can be considered to be better.

7.3 TAGUCHI'S EXPERIMENTAL DESIGN METHOD

Professor Genichi Taguchi devised a quality improvement technique that uses experimental design methods for efficient characterization of a product or process, combined with a statistical analysis of its variability. This approach allows quality considerations to be included at an early stage of any new venture: in the design and prototype phase for a product; during routine maintenance; or during installation and commissioning of a manufacturing process. In other words, the need for mass inspection is eliminated by building quality into the product and process at the design stage.

Taguchi's definition of quality is quite different from that of many people in the field of quality. He defines quality in a negative way as 'the loss imparted to society from the time the product is shipped'. This loss includes the cost of customer dissatisfaction which may lead to loss of reputation and goodwill for the company. Indeed, apart from the direct loss to the company arising from warranty and service costs, there is an indirect loss due to market-share loss and increased marketing efforts needed to overcome lack .of competitiveness.

Taguchi uses his loss function approach to establish a value base for the development of quality products. This function recognizes the need for average performance to match customer requirements, and the fact that variability in this performance should be as small as possible. According to Taguchi, a product does not cause a loss only when it is outside specification but whenever it deviates from its target value. Larger is the deviation from the target, larger will be society's (producer's and consumer's) loss. This loss can be approximately evaluated by Taguchi's loss function, which united the financial loss with the function specification through a quadratic relationship. This loss, as proposed by Taguchi, is proportional to the square of the deviation from the target. Figure 7.1 provides the basic formula for the loss function L (Y) and a graphical representation of the loss to society when the performance (Y) of a product deviates from the desired target t. M is Producer's loss (in monetary terms) when the customer's tolerance D is exceeded.

Design of Experiments, Six Sigma and Benchmarking

Quality Tools - Statistical

Figure 7.1 : Taguchi's Loss Function

7.3.1 Objective The objective of Taguchi's design is improvement in the process and productdesign through the identification of easily controllable factors and their settings, which minimize the variation in product response while keeping the mean response on target. This is achieved during Taguchi's parameter-design stage by removing the bad effect of the cause rather than the cause of the bad effect. Furthermore, since the method is applied in a systematic way at a pre-production stage (off-line), it can greatly reduce the number of time-consuming tests needed to determine cost effective process conditions, thus saving in costs and wasted products.

According to Taguchi, the behaviour of a product or a process is characterized in terms of two factors - controllable (or design) factors and uncontrollable (or noise) factors. Controllable factors are those whose values may be easily adjusted or set by the designer or process engineer whereas uncontrollable (or noise) factors are sources of variation associated with the production or operational environment and they are difficult or virtually impossible to control. So, ideally, the overall performance should be insensitive to their variation. Controllable factors can further be classified as

Target Control factors (TCF) or signal factors, which affect the average levels of the response of interest.

Variability control factors (VCF) which affect the variability in the fesponse, and

Cost factors which affect neither the mean response nor the variability, and so can be adjusted to fit economic requirements.

The approach of variability distinguishes the Taguchi approach from traditional tolerance methods or inspection-based quality control. The idea is to reduce variability by changing the variability control factors, while maintaining the required average performance through adjustments to the target control factors.

7.3.2 Different Design Stages

There are three distinct stages of designing in quality, as suggested by Taguchi :

' System Design

In system design, technical knowledge and scientific skills are used for the development of the basic configuration of the system, which involves the selection of parts and materials, the use of feasibility studies and prototyping.

Parameter Design Design of Experiments, Six Sigma and

In parameter design, while keeping the response of interest on target, settings of Benchmarking the controllable factors expected to reduce the performance variation (caused by the noise factors) are identified. Attempt is made to reduce or remove the effect of the noise factors rather than the noise factors themselves. By systematically varying the noise factors at each of the various settings of the controllable factors, the effect (variation) is simulated during the experiment. The controllable factor settings are represented by the rows of an experimental design (inner array), usually a fractional orthogonal array, where every level (setting) of a factor occurs with every level of all other factors the same number of times. At every level- combination of the controllable factors, some observations are obtained while changing the settings of the noise factors assuming that the noise factors can be controlled and changed (at least for the purposes of the experiment). A fractional orthogonal array can then be utilized to determine the level-combinations of the noise factors (outer array). In such a case, the experimenter can simulate the variability (effect) of the noise factors on each controllable-factor setting and determine the setting which minimizes this variability. Two types of performance measures, noise performance measure (NPM) and target performance measure (TPM) are calculated from the observation of each setting of the controllable factors. The noise performance measure (NPM) reflects the variation in the response at each setting and its analysis will determine the controllable factors which can affect (and thus control) this variation. The target performance measure (TPM) reflects the process average performance at each setting and its analysis reveals those controllable factors, which are not variability control factors, but have a large effect on the mean response (the target control factors). These can be manipulated to bring the mean response to the required target. As a TPM, Sample mean X of the observations in each trial may be used. Many measures for the NPM have been suggested. According to Taguchi, when there is a target value to be achieved for the response signal to noise ratio (SNR) is used which estimates the inverse of the coefficient of variation, i.e. the ratio of process mean (p) and process standard deviation (0). For each experimental trial, SNR is computed

according to the equation ( SNR = 1 Olog,,, - where and s are respectively [:: 1 the sample mean and sample standard deviation of n observations in each trial. Taguchi also recommends the consideration of interaction effect between the factors A and B (i.e. A x B), when the effect of one factor A (on the response) depends on the settings of another factor B. Depending on the number of factors, orthogonal arrays can be constructed and the interaction effect can be studied.

Allowance or Tolerance Design

Taguchi recommends the use of allowance design to remove the effects of the (outer or inner) noise factors if parameter design fails to do so. In this, some additional factors are considered which were excluded earlier due to cost related factors and tolerance redesign is advocated if that also fails by retaining the optimum nominal levels for the factors (as identified by parameter design), but reducing the tolerances of certain crucial factors (components) in an optimal and costeffective way so that the overall variability in the response is reduced to acceptable levels. A trade off can be involved by relaxing the tolerances of certain non-crucial components. In other words, this is the stage when the decision is taken on how best to remove the noise factors, having failed to remove their effect. Therefore the requirement for tolerance design is that it should take place as the last resort, only after the parameter-design stage.

7.3.3 Suggested Steps

There are certain steps that Taguchi suggests to be taken in carrying out experimental studies.

Quality Tools - . Stati~iicaI Define the Problem

A clear statement of the problem to be solved is provided.

Determine the Objective

The output characteristics (responses) to be studied are identified and eventually optimized. The method of measurement is determined.

Conduct a Brainstorming Session

This is a very important stage in performing an experimental study. Managers and operators closely related to the production process or the product under consideration should get together in order to determine the controllable and uncontrollable factors, and to define the experimental range and the appropriate factor levels. Taguchi believes that it is generally preferable to consider as many factors (rather than many interactions) as is economically feasible for the initial screening.

Design the Experiment

The appropriate experimental designs are selected by taking into account the controllable and noise factors. Conduct the experiment: Perform the experimental trials and collect the experimental data.

Analyze the Data

The performance measures (TPM and NPM) for each trial run of the inner array are evaluated and analyzed using the appropriate statistical analysis techniques.

Interpret the Results

Optimal levels for the variability control factors (VCF) and target control factors (TCF) are identified. For the VCFs, the optimal levels are those which maximize the NPM (minimize variability in the response), and for the TCFs, they are those which bring the mean response nearest to the target value. The process performance under the optimal conditions are then predicted.

Run a Confirmatory Experiment

It is necessary to confirm, by some follow up experimental trials, that the new parameter settings improve the performance measures over their value at the initial settings. A successful confirmation experiment alleviates concerns about the possibilities of a wrong choice for factor levels and experimental design, wrong assumptions of no interactions or improper assumptions underlying the response model. If the predicted results are not confirmed, or the results are not unsatisfactory, additional experiments have to be carried out.

SAQ 1 -

(a) What is Design of Experiments?

(b) What are the different factorial experimentation methods? Explain in brief.

7.4 SIX SIGMA

Six Sigma was born in Motorola about 15 years back. It is a high performance, data driven approach for analyzing the root causes of business processes1 problems.and solving them. It links Customers, Process improvements and financhl results. Sigma is a

Greek alphabet and is used in statistics as a measure to denote the standard variation in a Design of ~xperiments,

process. More specifically sigma measures the capability of the process to perform Six Sigma and Benchmarking

defect free work. A defect is anything that results in customer dissatisfaction like defective component, wrong shipment, delayed deliveries, high cycle time, missed calls. The sigma value indicates how often defects are likely to occur. As sigma increases, cost of poor quality goes down (as shown in Figure 7.2) while profitability, productivity and customer satisfaction go up.

Cost of Poor Quality 200h

Sigma ~ e v e i

7.2 : Variation of Cost of Poor Quality with Sigma Level

Many manufacturing company processes operate at 3 sigma level which translates into approx. 67,000 defects per million, while service company processes are often at 1 to 2 sigma level, i.e. 690,000 to 308,000 defects per million. Six Sigma's target is to achieve less than 3.4 defects or errors per million opportunities where an opportunity is defined as a chance for nonconformance, or not meeting the required specifications. To achieve Six Sigma from a business results perspective, waste that is generally called Cost of Poor Quality (COPQ), must be reduced in order to improve net profit margins twenty to forty percent.

Six Sigma is not about establishing a separate quality ivory tower within a company or organization and it is not about cost avoidance. It is an enterprise-wide strategy that effectively develops employees within a company to have the knowledge and capability to solve problems, to improve decision-making and subsequently improve the overall performance of the enterprise from a financial and customer perspective.

When Six Sigma is properly implemented as a roadmap and a management framework, it -- - consistently delivers breakthrough results throughout the business. As a system, it combines the best problem solving tools and methods with capable employees under the umbrella of a comprehensive leadership framework, to rapidly achieve reduced costs, higher quality, lower cycle times, improved overall customer satisfaction and a lower investment in equipment and inventory; all leading to increased market share, revenue, profits, and ultimately shareholder value. The real challenge with six sigma is not the statistics. It is getting to the point where one can meaningfully measure a business's current performance against dynamic customer requirements while developing the internal abilities to respond to changing market place conditions.

7.4.1 Six Sigma Methodology a The fundamental objective of the Six Sigma methodology is the implementation of a

measurement-based strategy that focuses on process improvement and variation reduction through the application of Six Sigma tools. As a way of running a business, Six Sigma is a highly disciplined process, which helps companies, and individuals develop and deliver near perfect products and services. It is an enterprise-wide strategy

Quality Tools - StptioticaJ that effectively develops a capability and a desire within individuals to improve decision making, solve business problems and improve the overall performance of the enterprise.

The Six Sigma philosophy holds that every process can and should be repeatedly evaluated and significantly improved in terms of time required, resources used, quality performance, cost and other aspects relevant to the process. It prepares employees with the best available problem-solving tools and methods. At its core, Six Sigma utilises a systematic five-phase problem solving methodology called DMAIC : Define, Measure, Analyse, Improve and Control. This is illustrated in Figure 7.3.

Define

Characterization Measure

I Improve k-, 'q' Optimization I

Figure 7.3 : Six Sigma Methodology

Define

At the preliminary stage we identify poorly performing areas of the company, define and launch projects with well articulated problem and objective statements that have a financially beneficial impact to the company.

Measure

Here we identify the true process and determine the most likely contributors including the statistical determination of the accuracy and repeatability of the data characterizing the process. We ask, what is the capability of the process? Using process mapping, flow charts and FMEA (Failure Mode Effects Analysis), original data is collected that will act as a baseline for monitoring improvements.

Analyse

When, where and why do defects occur? This phase applies appropriate statistical analysis such as scatter plots, InputIOutput matrices and hypothesis testing to accurately understand exactly what is happening within a given process.

Improve

In this phase, vital factors in the process are identified and experiments are systematically designed to focus on those that can be modified or adjusted to achieve the desired level of improvement.

Control

The Control phase incorporates the basic tools of Process Control to manage processes on a continual basis. Once the DMAIC process has begun, it must be managed continually to assure that benefits are sustained.

7.4.2 Six Sigma Participants

In the Six Sigma environment, participants from senior management to factory floor workers assume specific roles in the performance improvement process. The Champion, Master Black Belt, Black Belt, Green Belt and Yellow Belt (Figure 7.4) each have unique perspective on a businesses' strategic priorities, key processes and the organization's culture.

Design of Experiments, Six Sigma and Benchmarking

/V* Master Black Belts * Black Belt

Green Belts

Yellow ~ e i s

Figure 7.4: Six Sigma Particlpanta

Champions are responsible for coordinating a business roadmap to successfully achieve Six Sigma within their organization. They are responsible for the logistical and business aspects of a Six Sigma project. Champions select and scope projects that are aligned with the corporate strategy, choose and mentor the right people for the project, and remove barriers to ensure the highest levels of success.

The Master Black Belt (MBB) sits atop a skill and knowledge hierarchy that includes Black and Green Belts, with gradually increasing levels of sophisticated tool sets at their disposal. The primary activity for the MBB is being a leader and teacher. As a leader, the MBB will have responsibility for overseeing projects with multiple Black Belts and Green Belts participation that will significantly change the way the organisation does business. As a teacher, the MBB is responsible for the on-going development of existing Black Belts, Green Belts and Yellow Belts and the training for new participants.

The Black Belt is a key change agent for the Six Sigma process. Typically from among the best performers these individuals lead teams working on chronic issues that are negatively impacting the company's performance. The B P k Belt is usually assigned to a two-year dedicated position responsible for executing the Six Sigma process on selected projects.

Green Belts serve as specially trained team members within a function-specific area of the organization. This focus allows the Green Belt to work on small carefully defined Six Sigma projects, requiring less than a Black Belt's full-time commitment to Six Sigma throughout the business

Yellow Belts represent a large percentage of the workforce and is trained with skills necessary to identify, monitor and control profit-eating practices in their own processes. They are also prepared to feed that information to Black Belts and Green Belts working on larger system projects. The training of Yellow Belts builds and sustains the Six Sigma culture.

Quality Tools - Statistical 7.5 BENCHMARKING

The concept of benchmarking was popularized by the work of Camp (1989) based on the experiences of Rank Xerox company when the company started to evaluate its copying machines against the ~a ianese competition and found that the Japanese companies were selling their machines for what it cost Rank Xerox to make them. It was assumed that the Japanese produced machines were of poor quality, but this proved not to be the case. This exposure of the corporation's vulnerability highlighted the need for change.

Benchmarking is a quality improvement tool to improve the performance of an organization by comparing the practices of another organization, i.e. it is an opportunity to learn from the experience of others. A simple self-explanatory model for benchmarking is shown in Figure 7.5. It helps to develop an improvement mindset amongst staff that facilitates a better understanding of practices and processes often challenging the existing ones to achieve the goals. It has been defined in a number of ways (Adam and Vande Walter, 1995) including:

as a process for identifying and learning from the best practices in the- world,

as a search for and application of significantly better practices that lead to superior competitive performance, and

as a process of comparing the business of one organization against another to gain information about "best practices" that when creatively adapted, can lead to superior performance.

- -

- Comparison of own practices with company's best internal practices

- Evaluate those practices - Implement best practices

Figure 7.5 : Benchmarung Model

Within Industry Outside Industry - Comparison of - Comparison of own practices

own practices with Industry's with best best practices practices - Evaluate those

- Evaluate those pract~ces

7.5.1 Approach

practices - Implement best

- practices

Benchmarking can be either informal benchmarking or formal benchmarking. The informal benchmarking is a traditional form of benchmarking which most of the organizations carry out for years in mainly two forms :

- Implement best practices

Visits to other companies to obtain ideas on how to facilitate improvement Design of Experiments,

in one's own organization. Six Sigma and Benchmarking

The collection, in a variety of ways, of data about competitors.

This approach is not very effective because of the lack of structure and clear objectives. This approach is often branded 'industrial tourism'. To use benchmarking as a learning experience as part of a continuous process rather than a one-off exercise, a more formal approach is required. There are three main types of formal benchmarking:

Internal Benchmarking

This is the easiest and simplest form of benchmarking which involves benchmarking between business or functions within the same group of companies. Most companies commence benchmarking with this form of internal comparison. In this way, the best internal practice and initiatives are shared across the corporate business.

Competitive Benchmarking

This is a comparison with direct competitors, whether of products, services or processes within a company's market. It is often difficult, if not impossible in some industries, to obtain the data for this form of benchmarking as by the very nature of being a competitor the company is seen as a threat.

FunctionaVGeneric Benchmarking

'Funotional' relates to the functional similarities of organizations, while 'generic' looks at the broader similarities of businesses. With functional benchmarking, the partners will usually share common characteristics in the industry, whereas generic benchmarking is not restricted to an industry. It is usually not difficult to obtain access to other organizations to perform this type of benchmarking. Organizations are often keen to swap and share information in a network or partnership arrangement, particularly when no direct threat is presented to a company's business or market share.

7.%2 Steps in Formal Benchmarking Process

There are a number of steps in a formal benchmarking process. They are now briefly described i

The subject to be benchmarked is identified. A team is formed and the proper support to them as well as their roles and responsibilities of all the team members are decided to reach agreement on the benchmark measures to be used. A draft project plan is created and communicated with the required internal parties. The process for benchmarking is chosen in such a way that it should have a significant impact on customer satisfaction andlor internal efficiency.

The companies which will be benchmarked will be identified from a set of selection criteria defined from the critical success factors of the project.

A data-collection plan is developed by collecting the most appropriate means of collecting the data, the type of data to be collected, and a plan of action to obtain the data.

The data collected is tabulated and analyzed to determine the reasons for the current gap (positive or negative) in performance between the company and the best amongst the companies involved in the benchmarking exercise. The gap is usually expressed in the form of a percentage. The change in performance of the company and the benchmark company over an agreed time-frame is estimated in order to assess if the gap is going to grow or decrease, based on the plans and goals of the parties concerned.

Quality Tools - Statistical

SAQ 2

To establish the goals to close or increase the gap in performance, effective communication of the benchmarking exercise findings and gaining acceptance of the data, is ensured

An action plan is developed to achieve the goals. This step involves gaining acceptance of the plans by all employees likely to be affected by the changes.

By effective pmject-planning and management, the actions, plans and strategies are implemented and the results of the action plans are assessed.

Reassessing or recalibration of the benchmark is done if the actual performance/improvement is meeting that which has been projected. This should be conducted on a regular basis and involves maintaining good links with the benchmarking Partners.

Define quality in terms of quality loss function as suggested by Taguchi.

What is six sigma?

What is benchmarking? Briefly describe the steps to be followed in benchmarking process.

7.6 SUMMARY

This unit gives the concept of experimental design techniques for understanding the effect of controllable factors, be it a product or a process design, in minimizing variation while centering the output on a target value. These are major techniques in investigating quality problems. Besides highlighting the one factor at a time method, h l l factorial and fractional factorial method, Taguchi's off line technique for quality control has been presented which allows quality considerations to be included at an early stage of any new venture. Some new tools of quality like affinity diagrams, relations diagrams and process decision program chart have been discussed in brief. Benchmarking, a quality improvement tool to improve the performance of an organization by comparing the practices of another organization, has also been discussed.

7.7 KEY WORDS

Controllable Factors : These are factors that may be controlled during production (e.g. temperature, speed, pressure, tension, material type, etc.).

Design Parameters : Those parameters or factors that affect the performance of a product or process.

Informal Benchmarking : The informal benchmarking is a traditional form of benchmarking which most of the organizations cany out mainly in two forms, first is by visiting other companies to obtain ideas on how to facilitate improvement in one's own organization and secondly by collection, in a variety of ways, of data about competitors.

Loss Function

Noise Factors

Six Sigma

: Loss function is an approach to establish a value Design of Experiments, base for the development of quality products, as Six Sigma and

Benchmarking suggested by Taguchi. By this function, loss to the society can be approximately evaluated by a function with respect to the performance of the product deviating from a desired target.

: Factors that either can't be controlled or for economic reasons are not to be controlled.

: Six Sigma is a high performance, data driven approach for analyzing the root causes of business processes/problems and solving them. It links customers, process improvements and financial results.

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7.8 ANSWERS TO S A Q ~

SAQ 1

(a) The design of experiments is a series of techniques which involves the identification and control of parameters which have a potential effect on performance and reliability of a product design andlor the output of a process, with the objective of optimizing product design, process design and process operation, and limiting the effect of noise (uncontrollable) factors. The objective is to optimize the value of these design parameters to make the performance of the system immune to variation. The concept can be applied to the design of new products and processes or to the redesign of existing ones, in order to :

optimize product design, process design and process operation,

achieve minimum variation of best system performance,

ac3ieve reproducibility of best system performance in manufacture and use,

improve the productivity of design engineering activity,

evaluate the statistical significance of the effect of any controlling factor on outputs, and

reduce costs.

(b) The different factorial methods can broadly be classified into one factor at a time method, Fractional factorial method and Full factorial method.

One factor at a time method : In this method, setting of one factor is altered holding the others constant and then the resulting response is measured.

The full factorial method : Using this approach, aIl combinations of the factors are considered which are to be tested and best combination is found out. For example, three factors with two levels each (i.e. level 1 and level 2) would need 23 = 8 trials.

The fractional factorial method : The difficulty associated with full factorial designs regarding running too large number of experimental runs led to the evolution of fractional factorial designs where the chosen fiaction of the full designs gives an even and balanced spread throughout all the factors being studied. Several factors are changed at the same time in a systematic way so as to ensure the reliable and independent study of the main factors and interaction effects.

Quality Tools - Stntistical SAQ 2

(a) Taguchi's definition of quality is in a negative way as 'the loss imparted to society from the time the product is shipped'. This loss includes the cost of customer dissatisfaction which may lead to loss of reputation and goodwill for the company. Taguchi uses his loss function approach to establish a value base for the development of quality products. The loss can be approximately evaluated by Taguchi's loss function, which is proportional to the square of the deviation from the target.

Six Sigma is a high performance, data driven approach for analyzing the root causes of business processes/problems and solving them. It links Customers, Process improvements and financial results. Six Sigma's target is to achieve less than 3.4 defects or errors per million opportunities where an opportunity is defined as a chance for nonconformance, or not meeting the required specifications. Most of the manufacturing company processes operate at 3 sigma level which translates into approx. 67,000 defects per million, while service company processes are often at 1 to 2 sigma level, i.e. 4

690,000 to 308,000 defects per million.

Answer can be found in the text. 4

9

FURTHER READING Design of Experiments,

Six Sigma and

Logothetis, N. (1 997), Managing For Total Quality, Prentice Hall of India.

Barrie, G. Dale (2004), Managing Quality, Blackwell Publishing, USA.

Stamatis, D. H. (1 997), TQM Engineering Handbook, Marcel Dekker Inc, USA.

Fisher, R. A. (1925), Statistical Methods for Research Workers, Oliver and Boyd : Edinburgh.

Plackett, R. L. and Burman, J. D. (1946), The Design of Optimum Multifactorial Experiments, Biometrika, 33(3), pp 305-325.

Taguchi, G. (1986), Introduction to Quality Engineering :Designing Quality into Products and Process, Asian Productivity Organization, Tokyo.

Adam, P. and Vande, R. Walter, (1995), Benchmarking on the Bottom Line :Translating Business Reengineering into Bottom-line Results, Industrial Engineering, Feb., pp 24-26.