CHAPTER 6: QUANTITATIVE TOOLS FOR QUALITY MANAGEMENT OBJECTIVES

Understand the principles of the seven old tools of quality and acceptance sampling inspection

TOOLS FOR STATISTICAL CONTROL OF QUALITY

Much of the content of this section can be described as being concerned with variation, ie the way things vary. The connection between variation and quality is easy to appreciate.Quality (of service and/ or product) is greatly concerned with matters such as reliability, consistency and predictability. Hence, there are strong connections between concepts of high quality and low variation. Variation is the enemy of quality. Variation is what companies spend much time and money trying to get rid of. Therefore, ways are required to understand what causes variation, in order to reduce it and, hence, raise quality.In this section, you will be introduced to some of the available tools used to recognize, evaluate and reduce variation. The seven old tools and acceptance sapling inspection will be considered. These tools for the statistical control of quality can be applied to overall business strategy and company- wide systems such as personnel, training, purchasing. Legal and financial matters and so on, as well as manufacturing processes. You will learn where to apply these tools in problem solving, and the strengths and weaknesses of each tool. It should be noted, however, that these tools are not perfect and imperfections exist. For example, cause and effect diagrams, although very useful, can become very complex and hard to decipher. Check sheets require substantial data input at the outset, while errors on plotting histograms could lead to inaccurate data.

SEVEN OLD TOOLS OF QUALITY

The seven tools of quality proposed by Dr. Ishikawa are:

1. Pareto charts

2. Cause and effect diagrams

3. Check sheets

4. Histograms

5. Scatter diagrams

6. Stratification

7. Graphs and control charts

These are referred to as the seven old tools of quality

For Ishikawa, unless a person was trained to use each of these simple and elementary seven tools, that person could not be expected to master more difficult methods. Ishikawa states that, from his experience, 95 percent of all problems in an organization can be solved by mean of these seven tools. PARETO CHARTS

The Pareto principle was observed by Joseph Juran in the late 1940s, when he found that 80 percent of effects resulted from only a 20 percent of causes.

A Pareto charts deal with numerical data. It is used to show the relative importance of a set of measurements. The Pareto charts shows the contribution of each item in relation to the total effect in order of importance. Rectangles are used to show the relative contribution of each item.

The Pareto chart is a simple graphical technique for ranking items or data from the largest frequency to the smallest. It is, in effect, a histogram of the data, with the greatest occurrence being put at the left hand side of the diagram, the least at the right hand side, and the others in descending order from left to right in between.

A cumulative frequency line is drawn on the histogram to indicate the cumulative contributions of the items. This enables easy identification of the causes of the top 80 percent of problems, the 80/20 rule.

CASE STUDY

Plastic extrusion problems

A plastic extrusion company, in the course of one days production, found a number of problems with one product. The problems have been listed in the following table and ranked in order of the number of defects.

ItemsDefect No. of defectsCumulative %

ACracks 25049

BSurface scares150 74

CIncomplete extrusion12585

DDeformation 10095

EOthers 50100

In the diagram opposite, if you take the 80/20 rule literally, and draw a line across from the 80 percent mark to the cumulative line and then down to the bar. By selecting all the bars which are completely or practically to the left of this line, you can see that 80 percent of the problems are items A, B and C. It is these areas that effort should be expected to gain improvements.Pareto chart (picture page 104)

In order to narrow down the problem, the most significant problems cracks could be further broken down into others areas for investigation, and a further Pareto chart could be produced.

The important thing is constructing a Pareto chart is in the selection of the group of the items to be analyzed.

The following points should be noted in the interpretation and use of Pareto diagrams:

Always start with the problem that will bring the greatest benefits if solved Use other problem solving tools, such as cause and effect diagrams

Prepare Pareto diagrams over a defined period of time, or accounting period, and continue to monitor them.

If the most frequent defects or losses suddenly decrease, this shows that either the improvement effort has succeeded or that the process or other factors have suddenly changed even though nothing was done about them. If the different types defects or loss decrease in an approximately uniform manner, this generally shows that control has improved

If the most frequent defect or loss changes every month (or whatever period was used), but the overall defect or ratio does not decrease very much, this shows lack of control. In other words, the Pareto chart is unstable.

The Pareto chart is a simple but extremely useful tool. For this reason, it should be used widely, in every possible situation.

ACTIVITY 22After completing this activity, turn to the feedback section

This activity relates to the following information concerning a survey carried out by an online catalogue company. The survey showed the following data concerning a variety of customer complaints

Customer complaints:

A. Didnt receive the goods offered

B. Couldnt place order online to problems with the company web site

C. Out of stock

D. Received the wrong goods

E. Didnt receive the goods within the time specified

F. Received faulty goods

FaultNo. of times %Cumulative

A150

B150

C75

D125

E75

F25

1. Complete the above table by calculating the percentage and cumulative percentage

2. Using the above data, plot Pareto chart. Identify the causes of the top 80 percent of the problems, according to the 80/20 rule.

CAUSE AND EFFECT DIAGRAMS

The cause and effect diagram is often called a fishbone or Ishikawa diagram. This type of diagram enables the analysis and communication of cause and effect relationships by moving from symptom to cause to solution

These diagrams are used for thinking through and displaying relationships between a given effect and its potential causes. The many potential causes are organized into between four and six categories all point into the main horizontal line linked to the effect which these create. Branches that point to these major categories are contributors to them, and can be broken down into levels, as shown in the diagram

Cause and effect diagrams can be used for virtual any issue in your organization. It is the only one of the seven old tools than can handle non numeric data. One drawback of a cause and effect diagram is that it could become very complex.

CASE STUDY

The following illustrates how a cause and effect diagram was used in on hospital:

A major hospital was concerned about the length of time required to get a patient admitted from emergency into the hospital as an inpatient. Delays appeared to be caused by beds not being available. The problem was tackled by a team who produced a cause and effect diagram. The four categories they identified as contributing to a bed not being available were:

Environmental services

Emergency department

Medical/ surgical unit

Admission department

They identified several potential causes in each category, some of which are illustrated on the cause and effect diagram opposite.

ACTIVITY 23After completing this activity, turn to the feedback section

Using the conclusions drawn from the Pareto chart in the previous activity, use a cause and effect diagram to further break down the most significant problem.

CAUSE AND EFFECT DIAGRAM (Page 107)CHECK SHEETS

Check sheets are used to collect data manually in a reliable, organized way.

A check sheet is a form, in diagram or table format, that has been prepared in advance for recording data

If data is collected is an unorganized way, it is quite likely to end up as a jumble of numbers on some convenient scrap of paper. When you collect data in an organized way, you make fewer mistakes and it is available for easy interpretation. Good information, ie answers to questions, is always based on data, ie facts. Simply collecting data does not mean that you will have useful information. The data may not be relevant or specific enough to answer the question in hand.

The key issue is not, How do you collect data?, but rather, How do you generate useful information?

To generate information you need to:

Formulate precisely the question you are trying to answer.

Collect data and facts relating to that question.

Present the data in a way that clearly addresses the question.

Check sheets can be used for counting individual items such as defects or other characteristics. They can be used to record a number of measurements of a single item and visually portray the distribution of these measurements. Check sheets can also record the frequency and location of defects as a pictorial representation.

CASE STUDY

The following example illustrates how a check sheet was used in one company:

A company held a typing test for junior typist and measured their scores on a check sheet as follow:

Typing test check sheetType: Date:

Examiner: Test No:

Type of errorCountTotal

Missing letters IIII IIII IIII IIIII16

Extra letters IIII IIII8

Wrong letters IIII IIII IIII IIII IIII II22

Reversed lettersIIII II6

Spelling errors IIII IIII IIII IIII IIII IIII II26

Total errors26

This test enabled the examiner to identify areas for improvements.Usually a check sheet, with its simple collection and analysis format, is intended for a quick answer to a single question.

For a check sheet to be effective, a great deal of effort has to be put in at the very beginning when deciding what data needs to be collected. Effective check sheets are those in which visible presentation of the data can be interpreted immediately, without the need for additional processing. HISTOGRAMS.

A histogram graphically represents the n in a given set up data. It shows the frequency, or number of observations of a particular value or within a variation specified group

Using a histogram, the shape of the distribution can be clearly seen and deductions can be made about the parent population.Histograms are used to:

Display patterns of variation

Provide visual information about process behavior.

Help make decisions concerning where to focus improvement efforts.

In a histogram, the data is displayed as a series of rectangles of equal width and varying heights.

A sufficient number of measurements must be obtained, ie, between 50 and 100 data values should be collected to be able to give a usable shape to the distribution.

Common histogram shapes are represented diagrammatically on the opposite page and are explained as follows: Normal histogram this shows data obtained from a stable process. Double peak (bimodal) histogram double peaks appear if you mix data from different periods, or the process changed in mid stream. Isolated island histogram mixing data from another distribution procedures this. Two processes are being measured. Cliff edged histogram Eliminating all data that does not meet specification is one way to produce this type histogram. Alternatively incomplete data is being used Cog toothed (or comb) histogram this can be due to faulty measurement, or rounding errors. It could also be due to mixing data from a number of different periods. The following errors in plotting histograms are easy to make:

Low bar height with gaps here, the bar range is too narrow, and also there may be too few measurements. High bar height, few bars Here, the bar range is too wide, and also there may be too few measurements. In any histogram where a distribution differs from the expected shape, the underlying process should be examined to find the real causes of this.

Finally, any conclusion drawn should be confirmed through further study and analysis.

Histogram (Page 112)

ACTIVITY 24After completing this activity, turn to the feedback section.

1. Using the typing test case study provided in the check sheet section, plot a histogram

2. Name the shape of the histogram

3. What is the implication of the shape of the histogram? (for example, where do you think improvements could be made?)

SCATTER DIAGRAMS

A scatter diagram is used to discover and display relationships between two associated sets of data.

Whilst they do not provide rigorous statistical analysis, they often point out important relationships between variables.

The scatter diagram displays the relationships as clouds of points. Relationships between the associated sets of data are inferred from the shape of the clouds.

A positive relationship between x and y means increasing values of x are associated with increasing values of y.

A negative relationships means increasing values of x are associated with decreasing values of y.

Where a plot appears as either a horizontal or vertical pattern of points it suggests that the variables are independent.

Statistical correlation analysis is used to interpret scatter diagrams. However, where non linear correlations appear, rough estimates may be made by dividing them into linear sections and calculating a straight line relationship for each of those sections. This linear association in where the pattern of points approximates a straight line. A linear association can be negative and either weak or strong, depending on the scatter of points about the straight line. The strength of a linear association may be assessed by finding the correlation coefficient, which takes values on the scale -1, to +1, where:

-1 indicates perfect negative association.

0 indicates no linear association.

+1 indicated perfect positive association.

Values close to +1 or -1 indicate strong association, value around 0.5 indicate weak association, and values close to zero indicate no linear association. Two of these are illustrated below. Scatter diagram (Page 115)

STRATIFICATION

Stratification is simply dividing a set of data into meaningful groups. It is the method of grouping data by common points or characteristics in order to better understand similarities and relationships of data. It can be applied to all forms of presentation of data. Data collected for Pareto charts, check sheets, histogram, scatter diagrams, graphs and control charts can be stratified.

Below is an example of stratification of data collected for a histogram. The combined histogram shows the weld strength of a piece of equipment produced on the same welding machine but using two operators.

As you can see it is a typical double peak (bimodal) histogram. This immediately shows that data has been mixed. So, the obvious thing is separate out of the data collected from the two operators. This data is stratified into two separate histograms, which can then allow a decision to be made about the process and the operators.

Stratification (Page 116)

GRAPHS AND CONTROL CHARTS.

Graph show patterns or change in any sequence of measurements. A line graph is used when measuring several different items that can be shown on the same scale, to show how they change relative to each other. Line graphs which plot events over time are also called run charts. These run charts are considered as one of the seven old tools.

Where upper and lower control lines are marked on a run chart, it then forms the basis of a control chart

CONTROL CHARTS

Control charts are used to identify causes of variation in a process, and to assess and maintain the stability of a process. When processes are charted and they show little variation, they do not have any features of interest, show no trends, no sudden shifts, no patterns just random variation. As far as anyone can see, the nature of the behavior of a process does not change over the time period represented the run chart.

This lack of variation can be useful. If the behavior of a process has been unchanging for a while and if nothing occurs to change that boring behavior, it is very likely to continue the same boring behavior into the future behavior of the process.

In statistical control Processes which demonstrates such boring but useful behavior as said to be:

In statistical control

Exhibiting controlled variation

Stable

Processes which are in statistical control have the distinct advantage of being predictable, although this is never 100 percent certain. Out of statistical control Interesting processing are interesting precisely because they are unpredictable: they exhibit unexpected changes in behavior. They are:

Out of statistical control

Exhibiting uncontrolled variation

Unstable

For each process, safe margin could be drawn, one a little above the current data and one a little below.

The prediction could be made that future data will continue to be comfortably contained between these safety margins and to show no trends, shifts, patterns, and so on; ie it will behave randomly.

There are well established methods for calculating from the data where the safety margins should be drawn, rather than just drawing them in by eye. If those well established methods are used, the safety margins are then called control limits

The random variation seen in processes which are in statistical control does have causes. These causes are referred to as common causes. In summary, if there is no evidence that the variation in the results you are observing is really

different from the sort of behavior you might get when tossing coins or throwing dice (processes which are in statistical control), it is not worth wasting time trying to find reasons for particular results. On the other hand, when you see evidence of a real change (for example, a result that is quite untypical of what youre seen what youre just seen. This type of change, seen in processes out of statistical control, is usually caused by a special cause. It appears that Dr Deming first used the terms common cause and special cause around 1947 in discussions on prison riots (see Out of the crisis, page 314). He asked the questions: Did something special occur to spark off a riot?

Or

Was it due to the procedures, the environment, the morale of both the prisoners and the prison staff, the way the staff treated the prisoners, and so on, ie, the common state of affairs (the system)

However, Dr Walter Shewhart identified these causes in the early 1920s, whilts working on a quality and reliability problem for Western Electric Company of Chicago.

CASE STUDY

The Western Electric Company of Chicago was hard at work trying to improve telephone technology and equipment. The aim was reliability: to make things alike so that people could depend on them. For a while they made great progress. But, then the rate of progress slowed. They were still trying as hard, if not harder than before. They were still pouring time and money into improvement effort, but somehow it just wasnt working any more.

They found that, the harder they tried to achieve consistency and uniformity, the worse were effects. The more they tried to shrink variation, the larger it got. When Shewhart tackled the problem, he became aware of two kinds of mistakes:

1. Treating a fault. Complaint, mistake, accident as if it came from a special cause when in fact there was nothing special at all; in other words, it came from the system, from random variation due to common causes.

2. Treating any of the above as if it came from common causes when in fact it was due to a special cause.

Shewhart decided the root of Western Electrics problems lay in their failure to understand the difference between common causes and special causes, and that mixing them up was making things worse. ACTIVITY 25

After completing this activity, turn to the feedback section Dr Deming warned us that if we weigh in at problem without understanding, we only make things worse. This is easy to prove. So lets prove it, this time by means of an exercise. You will need a couple of dice.

In this exercise, the preferred result is always 30. Each result will be obtained by adding the process tweaker value to the score on the dice. The process can then be fine turned by means of a process tweaker.

1. As the dice will give us a score of between two and twelve, with an average of seven, set the process tweaker initially at 23 (The tweaker value plus the average dice score will equal the desired total 30)

If you throw your dice and you are lucky enough to get a score of seven (by throwing a two and a seven, for example), then your total will be:

Process tweaker + dice score =

23+ 730

But suppose you are not that lucky. For example, you obtain a dice score of ten. Then your total will be:

Process tweaker + dice score =

23+ 1033

2. Lets try to make the next result closer to that nominal value of 30. The process tweaker is changed from 23 down to 20. Another way of thinking of this is to adjust it to the value where, if only it had been there when we just threw the dice, we would have obtained the preferred result of 30. That is, we would have had: Process tweaker + dice score =

20+ 1030

3. Suppose your first five dice score are 10, 8, 6, 7 and 4, respectively. These have been entered into the table below. Then, with your own dice, continue the experiment until you have 50 entries in the table. (We have only provided a table with 8 entries here, as an example)Stage number 12345678

Tweaker value 232022242326

Dice score108674

Results 3328283127

Comparison with nominal+3-2-2+1-3

Adjust tweaker by -3 +2+2-1+3

Thus, tweaker becomes2022242326

4. Now summaries all 50 results in a histogram

5. Now lets see what would have happened if you had not kept tweaking the process. Set the tweaker at 23 and leave it there, irrespective of the results. Copy down your dice scores for stages 6 to 50 from the previous table, and this time simply get the results by adding 23 each time. Its easier and lazier than what you did before! Then, on the next page, again summarize these results in the histogram that has been started. 6. Compare the two histograms. As youll already have realized. The second histogram is more tightly clustered around the desired value of 30 than the first one was! The second process performs better than the first one. Yet, the second process was the lazy one. It was much less complicated, both to operate and in terms of the amount of arithmetic involved. This time we didnt tweak the process at all. We put in less effort - and got better results! In the first process, we worked harder but got worse results. 7. What connections can you make between the account of Western Electric and the exercise you have just carried out?CONTROL LIMITSAs we have already mentioned, you can place line on yours graph which represent the upper and lower limits of acceptable variation. But, how do we decide where to draw control limits? The control limits must satisfy two criteria:

1. They must be far enough apart to comfortably contain virtually all the data produced by the process when it is statistical control.2. They must be sufficiently close together for some of the data points to lie outside them when the process is out of statistical control.

The distance between the control limits and the Central Line needs to reflect the common cause, ie natural variation in the process. The standard deviation is the statisticians traditional method for measuring variation. The standard deviation is in effect, an indication of the typical or representative distance between the individual items of data and their average. The control limits are placed symmetrically either side of the Central Line (the average value) at a distance of t standard deviations from the Central Line, ie the upper control limit (UCL) and the lower control limit (LCL). From experience, Shewhart found that t = 3 represented acceptable control limits. These control limits had to take into account the fact that if you try to avoid making either of the following mistakes:1. Treating a fault as a common cause

2. Treating a fault as a special case.

You will always end up making the other. For example, to always avoid Mistake 1 we could set the control limits very wide. The trouble is that we would then make Mistake 2 at every opportunity instead. Hence, t = 3 represents an acceptance comprise. If the process stay in control, from time to time there will be points that are very close to the control limits and every occasionally, a point stray just beyond the limits. There are the occasional occurrences of Mistake 1, arising from necessary comprise between the mistakes of the both kinds.

It is precisely because the control limits, set at t = 3, comfortably contain virtually all the data from the process while it stray in statistical control, that we can regard a point outside the limits as representing a signal of a likely special cause. It is a signal that it will be profitable for us to take the trouble to look for it and the sooner, the better. INTERPRETATION OF CHARTS

How do we decide if a process is in a statistical control?

In control indicatorsA process is described as being in statistical control when the only variation present is that due to common causes, the variations that are inherently part of the system and are there at all time unless a change is made to the process. A control chart such as X, where the quantity being plotted may be assumed to be at least approximately normally distributed, should therefore satisfy the following conditions when the process is in control: 1 No points fall outside the control limits.2 The number of points above and below the centre line is approximately equal.3 The points appear to fall randomly above and below the centre line with no shifts, systematic trends or cyclical patterns.4 Most of the points (approximately two-thirds) are close (meaning less than one-third of the distance out to the control limits) to the centre line with the remainder (approximately one-third) further out toward the control limits.Out-of-confrol indicatorsAny one point outside a control limit would be taken to indicate the presence of a special cause, usually requiring immediate investigation so that it can be eliminated. A series of points outside or near the same control limit would indicate that the special cause has not been removed and the process is continuing to run out of control with a shifted process average. Out-of-control signals include:1 Any two out of three consecutive points in zone A (beyond the t=2 limit) on the same side of the centre line. Note that chance suggests that we should expect about one point in 20 to be in zone A if the process is in control. Extreme deviations from this expectation either way will indicate the presence of a special cause.2 Any four out of five consecutive points in zone B or beyond (beyond the t=1 limit) on the same side of the centre line. Here, we expect about one point in three to be in zone B or beyond and deviations from this will indicate a special cause.3 At least eight (the limit here may alternatively be set at seven) consecutive points fall on the same side of the centre line.4 At least six consecutive points show a continuing increase or decrease in value (a systematic trend).5 At least 14 points oscillate successively up and down. Remember the charts are based on the plotted values coming at random from the distribution of the chosen statistic and, thus, any cyclical pattern present in a chart is indicative of a special cause.

6. At least eight consecutive points avoid zone C (close to the central line). Remember that approximately two out of three points are expected to fall in zone C so this condition, referred to as 'hugging the control limits', is indicative of a special cause.7. At least fifteen consecutive points are in zone C only. This is the opposite of the above and is referred to as 'hugging the centre line'. At first sight this may appear to be good, but this is not necessarily the case. If it is the result of a known process improvement, then the control limits may be recalculated in line with the new estimate of variation. However, hugging the centre line may be due to a special cause such as a problem with the sampling or measurement.

Control chart and graphs diagram

1 2 3456 7 8 9 10 11 12 13 14 15

In summary, the control chart essentially serves two roles:1. Prediction - When the process is in statistical control, the control limits predict the likely range of variation in the future - the near future, at least. This allows you to think about questions like: Is this process good enough for our current purposes? How important is it to try to improve its performance?Since it is impossible to improve everything, the control chart will help you to establish priorities.2 Diagnosis - The control chart helps diagnose when such prediction is feasible and when it is not.If the process is in statistical control, such prediction is feasible. When the process is out of statistical control, such prediction is not feasible.If the control chart judges the process to be in statistical control, improvement effort should be directed at the process as a whole, using information over a relatively long period of time. Do not be distracted by shorter-term data or, even worse, individual data-points.If the process is out of statistical control, initial improvement effort needs to be directed at trying to identify and remove or negate the effects of the special causes of the instability; in that case, it is justifiable, indeed necessary, to investigate shorter-term effects in the data, particularly as guided by points which are beyond the control limits.ACCEPTANCE SAMPLING INSPECTION

The final tool in this section looks at an area of quality management that has lost favor over recent years.Inspection may be defined as:The process of measuring, examining, testing, gauging or otherwise comparing an item with applicable requirements

Apart from ad hoc inspection for investigatory purposes, there are conventionally four types of inspection: Source inspection - All inputs are checked at the beginning of a process and/or all outputs are checked during the process 100 per cent inspection - All outputs are checked after processing has taken place Sampling inspection - A sample of output is checked after processing has taken place. It is a procedure whereby a sample is taken from a batch or lot and subsequently inspected. Dependent upon the inspection findings, the lot is either rejected or accepted. Sampling is used as an alternative to 100 per cent inspection. Results from inspecting the sample provide a snapshot, or approximation, of the condition of the whole batch or series of batches. Sampling is, therefore, more economical than 100 per cent inspection but less precise. Inspection associated with control charting - A small number of items are checked regularly during processing.Since quality cannot be inspected into a product, service or process, then inspection after processing should only be used where: Ability to control the process itself is not possible The process is inherently not capable of meeting customer requirements An unforeseen problem has been discovered after the process has been competedSampling should not be used instead of control charts as the primary means of controlling quality. Used in conjunction with control charts in a TQM environment, it can help contain poor quality whilst information for process improvement is being gathered. Poka-yoke and source inspection should also be considered for the development of a new process/ product before sampling is considered.When using any sampling inspection technique, the following must be specified: Batch (or lot) size - This is literally the number of items that have been presented for inspection, for example, the number of items manufactured or the number of items delivered D Acceptable Quality Level (AQL) - The AQL is defined as the maximum per cent defective units or maximum number of defects per hundred units that, for the purposes of sampling inspection, can be considered satisfactory, or tolerable, as a process average. The AQL should be agreed between the supplier and the customer (by contract) or implicitly (accepted practice) between supplier and customer D Sample size - The selection of sample size represents a trade-off between the cost and effort involved in inspection and the probability of obtaining a reasonable estimate of batch quality. Sample sizes are proportionately larger for small batches than they are for large batches.ACTIVITY 26After completing this activity, turn to the feedback section.

The following table lists the techniques discussed in this section and the questions they can help answer with reference to quality problem solving. Summarize their strengths and weaknesses.

Techniques Questions asked Weaknesses Strengths

Pareto charts Which are the significant problems?Very simplisticCan be used widely

Cause and effect diagrams What cause the problems?

Stratification How is the data made up?

Check sheetsHow often is it done/ how many involved?

HistogramsWhat does the data look like?

Scatter diagrams What are the relationships between factors?

Control chart What variations exist/ how to control them?

CHAPTER 7: QUALITATIVE TOOLS FOR QUALITY MANAGEMENTOBJECTIVES:

When you have completed this section, you should be able to:Understand the principles of qualitative management tools for developing quality systems.NON STATISTICAL APPROACHES

In the previous section, you looked at some quantitative management tools. However, statistics are not the only means of developing quality systems. This section deals with qualitative management tools. This includes the seven 'new' management tools, as well as: Five whys Five whys CATWOE Moments of truth Value management Seven wastes analysis Brainstorming Quality function deployment Flowcharts.These techniques may be combined with statistical analysis or stand alone. Cause and effect diagrams have already been discussed under the seven 'old' management tools in the previous section. As with quantitative methods of quality analysis, qualitative methods are also imperfect. For example, the seven wastes analysis must be conducted from the point of view of the customer and can often be complex.

FIVE WHYS This is a simple technique which requires concentration and focus, and for this reason it is often best carried out in pairs. When presented with a problem, the questioner asks why this is so. When given an answer, the questioner asks 'why' the answer is so. This repeats until the root cause of the problem is found. For example:The blocks were late arriving 'Why were the blocks late arriving? => 'Because the driver got lost 'Why did the driver get lost? => 'Because he did not get the map I sent 'Why didn't he get the map you sent? => 'Because he did not see the manager after the post arrived 'Why didn't....With skilled questioning, five whys are often enough to find the root cause of the problem, which is what gives the technique its name.ACTIVITY 27

Use the five whys' technique for a problem that occurred in your organization/department, or one with which you are familiar. Discuss this with your tutor or other students.CATWOE

In some organizations, it is not easy to define what systems are trying to achieve. For example, does a library system exist to lend the maximum number of books to the maximum number of people, or to raise cultural awareness in the neighborhood, or...?This is where communication and openness are particularly important, because, without them, quality cannot be defined.Peter Checkland (1981) suggested that soft systems should be built around a description of what the system should do. This description - sometimes referred to as a root definition and sometimes as a 'mission' - is arrived at through group discussion.The description should include sentences on each of the following: Customers Actors - The people involved Transformations sought - What needs to change through the application of this system World view - The philosophy of the system Owners of the system Environment-The operating environment of the system.The elements of this description are known by the mnemonic, CATWOE.When you have obtained this description, you will be able to begin the work of gap analysing the ideal against the reality.MOMENTS of TRUTH (MoT)

Jan Carlzon, the former head of the airline SAS, and Albrecht and Zemke, in Service America! (1995), have raised the profile of 'Moments of Truth' which usually occur out of sight of senior managers. They are the points in time when customers come into contact with people inside the organization (receptionists, telephonists, sales staff, service personnel and so on) and form an opinion about the quality of the organization. Monitoring the effect of each and every one of these transactions may not be possible, but by combining the principle of 'quality is the responsibility of all' (Deming, Moller) with Pareto analysis to focus on the most important points of contact, systems can be devised to manage the key issues.VALUE MANAGEMENT

This applies to both services and products and combines both quantitative analysis and creativity. First, the functions of the product or service are listed out in detail and then they are ranked according to importance. This is usually done through the pairing technique where two functions are compared and one is ranked higher or lower than the other. This process continues until all functions have been compared with all other functions. The most important functions, as defined by this process, are then selected for further analysis.A brainstorming session is then held to suggest new ways of achieving the functions being analyzed. No evaluation is permitted at this stage.When the brainstorming is over, each of the ideas is evaluated quantitatively or qualitatively as appropriate and the results fed back into the production or service process.

SEVEN WASTES ANALYSIS

This technique, developed by Taiichi Ohno of Toyota, is independent of any one statistical technique but provides a framework for analyzing productivity failures in both product and service industries. In so doing, it shows where quality systems need to be improved. The seven wastes are:1 Waiting - For example, at an airline check-in.

2 Transporting - For example, should Goods be part-assembled in various locations or fully assembled in one?3 Inappropriate processing - Over-engineering; for example, the hotel manager insists on greeting all guests, when an official 'greeter' may be sufficient.4Unnecessary inventory - This is linked to just-in-time production.5Unnecessary motions - Ergonomic analysis is required.6Defects or errors - See Crosby in section 2 of this module.7Overproduction.Such analysis needs to be seen from the point of view of the customer and is often complex.BRAINSTORMING

Brainstorming is a creative technique used to generate ideas. As such, it can be used in conjunction with many of the tools mentioned previously, for example: Cause and effect analysis Process Decision Program Charts (PDPC) Arrow diagrams Matrix diagrams Relations diagrams Tree diagrams.In a reverse role, affinity diagrams can be used to organize ideas from a brainstorming activity. Brainstorming can be defined as:A means of getting a large number of ideas from a group of people in a short time.

This enables spontaneous, open-ended discussions to take place to produce as many ideas as possible.There are four guidelines in brainstorming:1 Suspending judgement - Means that everybody, including the facilitator, must avoid evaluation. This means that evaluative comments, such as 'that will not work' or 'that sounds silly' must be avoided. Wild ideas must be laughed with and encouraged, not laughed at.2 Free-wheeling - Means encouraging, drifting or dreaming, letting go of barriers and inhibitions and seeking to bring the subconscious into play. The wilder the idea, the better.3 Quantity - Means the more ideas, the merrier. Quality has to be put on one side to obtain the maximum amount of different ideas.4 Cross-fertilising - Means picking up somebody's idea, developing it and suggesting other ideas from it. You must never get upset if someone else takes your idea and develops it into other ideas. The objective of brainstorming is to produce as many ideas as possible.

ACTIVITY 28

After completing this activity, turn to the feedback section. Read the following example and complete the activity below.

One particular hospital has a very efficient checking in system for seeing the orthodontist and young patients are usually seen within five minutes of their appointment time. But, if X-rays are required, the patient has to walk several hundred metres to the opposite end of the hospital and then down three flights of stairs to have the X-ray taken by qualified specialists. They then have to wait anything up to 20 minutes before making the return journey and joining the queue for the orthodontist once more. The orthodontist himself cannot justify the use of a full time radiologist and all the equipment.

1 Use the seven wastes analysis to identify the specific quality problems which the orthodontist is incurring.

2 Use the brainstorming method to generate possible options and solutions to these problems.

QUALITY FUCNTION DEVELOPMENT

The Quality Function Deployment (QFD) technique is used in both service and product industries to increase understanding between different processes in a quality system. It begins with determining primary and detailed customer requirements and ranking them in order of importance as the customer perceives them. This can be a long process, one where 'noise' in the shape of internal perceptions and values can easily intervene. Customer focus groups may be used to establish rankings which are placed to the left of the central matrix (see the diagram below). On the right of the central matrix are placed customer perceptions of how the product/service ranks compared to competitor products/services. This gives the basis of a SWOT analysis as customer requirements on the left are linked to customer evaluations of performance against competitors on the right.Above the central matrix, technical characteristics of the service or product are listed in terms that would be understood by customers and below the central matrix are listed technical characteristics which can be compared with those of competitor products/services, for example, grams per portion.The roof of the house allows technical characteristics to be examined and evaluated for the effects of interaction. For example, large wheels on a trolley may make it more maneuverable, but it may also make it too large for easy storage.Once the characteristics have all been listed, the matrix is completed with symbols devised by the team to show where customer requirements are met in whole or in part. Discussion will then show how the QFD can lead to systems improvement and greater customer satisfaction.A simple example of how QFD could be applied to a retail bakery service. (Page 137)

FLOWCHARTING

Previous quality tools discuss a process, but do not define it. This is where flowcharting comes in. Flowcharts document the process and answer the question, 'What is done?'Having documented what is done, you can then use the various tools, as appropriate, to look at ways in which the process can be improved.A. flowchart is a diagram that shows the sequential steps within a process. It uses sets of standard symbols (usually rectangles) to document the process and presents them in a pictorial format that is easy to understand. It can be used to describe an existing process, or it can be used to design a new process.A flowchart can be used to: Analyze relationships between sequential activities in a process Detail actions and decisions within a process Identify potential problem areas in a process Help identify where measurements should be carried out when investigating Show where control points should be placed in a process Train people in understanding the process.Benefits of flowcharts include: Improved knowledge of the process Flowcharting is a powerful team-forming exercise Process flowcharts are valuable tools in training programs for new employees.SEVEN NEW MANAGEMENT TOOLS

You previously looked at Ishikawa's seven old tools of quality. Now you will study the seven new management tools. These tools, as listed by Shigeru Mizuno (1979) are: Affinity diagrams Relations diagrams Tree diagrams (systematic diagrams) Matrix diagrams Matrix data analysis Process Decision Program Charts (PDPC) Arrow diagramsThe point to notice is that they are different from the seven old tools that were primarily basic quantitative problem-solving tools. The principal difference is that the seven 'new' tools are management tools. They are planning tools for use by all levels of management for planning, goal setting and strategic problem solving. They are particularly useful in structuring unstructured ideas, and organizing and controlling complex projects. They are much more complicated than the seven old quality tools, and, in some cases, require computers to solve the issues involved.It is also very important to note that, although these tools are referred to as the seven new tools, this is not to imply that they are better than the seven 'old' tools. They are planning tools and serve a different function from the old tools.These seven new tools are not really new at all, since they had their origins in operations research developments in the United States of America just after the Second World War. However, these tools developed separately in the West. The Japanese refined them in the 1970s and combined them into the seven 'new' management tools which were re-exported to the West and popularized in the United States in the 1980s, to improve planning and quality efforts.The definitive work by Shigeru Mizuno on the seven 'new' tools was published in Japan in 1979, so you can see the term 'new' is rather out of date. Some modern authors refer to the first seven tools and the second seven tools when dealing with the subject. The seven 'new' tools were compiled to assist this pro-active management by ensuring: The ability to complete tasks The ability to eliminate failure The ability to assist in the exchange of information The dissemination of information to all concerned The ability to use unfiltered expression.They were complied to ensure a complete history of actions to promote improvement is available as an incentive to ensure continued motivation to improve. Some good examples of all these techniques can be found in Ozeki and Asaka's (1990), Handbook of Quality Tools.AFFINTY DIAGRAMSThe affinity diagram was a main part of the KJ Method, developed in the 1960s by Kawakita Jiro, a Japanese anthropologist, for gathering together facts, opinions, and ideas about unknown and unexplored areas. The affinity diagram gathered and organized large numbers of ideas and facts. It allowed teams to work through large quantities of information efficiently, and to identify natural patterns or groupings in the information.By using an affinity diagram, managers can focus more easily on the key issues rather than on an unorganized collection of information. The affinity diagram builds up a framework for verbal data from many sources, based on similarities, common elements and relationships. Producing an affinity diagram is a creative process which can break through preconceived ideas about the situation.A key difference between the affinity diagram and other new tools is that it starts from the bottom and builds up the hierarchy. It begins with the basic data and constructs the various groupings and hierarchical structure as an outcome of the exercise.An affinity diagram is created by choosing a theme or topic. Data is collected concerning this topic. Data can consist of facts, inferences, predictions, ideas or opinions. This data is then recorded on cards. Cards that seem to be related to each other are grouped together. Each group of cards is then discussed to identify some common characteristic or attribute of the group. An example of an affinity diagram is given on the next page.You can use an affinity diagram to determine how effective a certain theme might be if adopted, or to discover how effective a theme you are now working on is likely to be.The process of producing an affinity diagram: Is a creative rather than a logical process Attempts to find the major themes from a large number of ideas, opinions or issues.You should use an affinity diagram when: Chaos exists A team is drowning in a large volume of ideas Broad issues or themes need to be identified Breakthrough thinking is required.Affinity diagram (Page 141) and Relations diagramRELATIONS DIAGRAMSA relations diagram identifies and explores causal relationships among related concepts or ideas. The relations diagram is also known as an 'interrelationship diagraph' and you will find this term in many textbooks. It helps to clarify intertwined relationships in complex problems or situations in order to find appropriate solutions. It shows that an idea can be logically linked with more than one other idea at the same time. It allows for lateral thinking rather than constrained linear thinking.A relations diagram is used to clarify and help in the understanding of complex relationships. You can use the relations diagram to highlight those factors that are requi'retf to achieve a certain objective, just as you can use the diagram to find causes of problems and discover ways to solve them.A special characteristic of the relations diagram is that it has an unrestricted format. It is difficult, therefore, to give a 'typical' example. Each relations diagram is unique. A relations diagram is used: When analyzing complex situations where there are multiple interrelated issues To map out cause and effect relationships (but it can also map any other form of relationship). After an affinity diagram has clarified issues and problems to show the relationship between the various affinity cards (headers) Because the diagram itself is not restricted to any specific framework. This means the conception and development of ideas is facilitated. Because it enables consensus to be reached amongst the participants.In a relations diagram, short phrases or sentences are enclosed in boxes or ovals and cause and effect relationships are indicated with arrows. A cause and effect relations diagram contains one or more effects and multiple causes, with arrows pointing from cause to effect. A simple relations diagram is illustrated on the previous page. They are often much more complicated than the example shown here.The significance of arrow direction in a cause and effect diagram is as follows:1 Arrows flowing only away from a cause - This indicates a root cause, the significance of which is indicated by the number of arrows.2 Arrows flowing only into a cause - This indicates the root effect.3 Multiple arrows flowing out and into a cause - This indicates a bottle-neck. This can be difficult to eliminate due to the multiple contributory causes.A relations diagram is produced by choosing a theme, stating the problems, stating the causes believed to be affecting the problems and writing these all on between 15 and 50 cards. Chose one problem card and then determine which cards have the closest relationship to that problem. Put these cards around the problem card. These are first order cards. Choose cards that seem to be second order cards and place them a little further away from the problem card. These are second order cards, and so on. Then draw arrows going from causes to effects.Advantages of the relations diagram1 The relationships involving several different departments can be clarified.2 Comments, ideas and such can be put down exactly as stated, without any restrictions.3 When further information is added, it is easy to draw up connections between various items.4 The relations diagram simplifies the explanation of complex situations, problems or ideas, to others.Disadvantages of the relations diagram1 Since the format is unrestricted, the resulting relations diagram can vary considerably from team to team, even if they are tackling the same problem.2 The diagram can become too complicated and difficult to complete.3 When situations change, it is necessary to redraw the diagram, and this can be a time consuming process.ACTIVITY 29

List the similarities and differences between an affinity diagram and a relations diagram?

Construct a simple relations or cause and effect (see previous section) diagram to identify the relationships, comments and ideas of a process within your organization/department, or one with which you are familiar. Discuss this with your tutor or other students.TREE DIAGRAM

The tree diagram is also called the systematic diagram or the dendrogram'. It is used to show the relationships between a topic and its component elements. The key characteristic of the tree diagram is that it produces a hierarchy: it provides a simple method of breaking down a topic, one layer at a time, into its component parts.A tree diagram brings the issues and problems revealed by the affinity diagram and the relations diagram down to the practical planning stage.The tree diagram, on the next page, was produced when answering the question, 'What are the requirements for a telephone answering machine?' It has been somewhat simplified in order to clarify the logic of the tree diagram.You will see in the tree diagram that each box, or position, on the tree has only one predecessor (or parent) but has one or more successors (or children). The tree diagram starts with one parent (the root) and ends with multiple children (or leaves), which then have no further children.The method used to build up the tree diagram is both logical and systematic. The same process of analysis and breakdown is carried out for each parent. At each level, the child becomes the parent for the child/children below it.The procedure for producing a tree diagram consists of choosing a topic and breaking it down using a series of what, why or how questions. For example: What are the component parts of the parent?, Why does the parent happen?, How can the parent be achieved?.Break down the topic into the major categories. Write these onto index cards and place them beside the topic card. Draw lines to connect the cards, as necessary. Repeat the process for sub-elements, sub-sub-elements and so on, until no further breakdown is required. Having too many levels can result in an awkward and unmanageable tree diagram.Tree diagram (Page 146) and Matrix diagram Topic Major CategoriesComponent ElementsSub elements

Symbol

Relationships

Value

(O

Strong

Medium

Weak

9

3

1

Item A

Item B

Item C

Item D

Item 3

2

Item 4

(10

Item 1

(O

13

Item 2

(O

12

10

9

14

4

MATRIX DIAGRAM AND ANALYSISThe matrix diagram shows the relationship between ideas, activities or other dimensions, when each of these consists of two or more elements or factors. Matrix diagrams are spreadsheets that graphically display these relationships in such a way as to provide logical connecting points between each element or factor.A matrix diagram is one of the most versatile tools used in planning. It can show the strength of the relationship that exists between either single pairs or elements or factors, ideas, characteristics or tasks, or a single element or factor and another complete list.It can also show whether there is no relationship whatsoever among collected parts of elements or factors.Matrix data analysis takes data and graphically displays it on a chart, to show quantitative relationships amongst variables and enable them to be more easily understood and analyzed. The method makes use of statistical multi-variate analysis techniques. This requires the use of a computer program.There are various patterns of matrix diagrams, depending on the comparisons being made. The different matrices are described by letters of the alphabet which give an indication of their shape. These can be in three dimensions. However, the L-type matrix, where data is expressed in two dimensions, is the simplest matrix pattern (see diagram on previous page).The horizontal rows and the vertical columns sum up the values of the relationship symbols, and the totals are put at the end of each row or at the foot of the column, as appropriate. These totals give an overall value for the strength of the relationship between each item and the entire other list.In the example below, item 1, whose total for the row is 13, has a strong relationship with List 2, whereas Item 3 has a weak relationship with List 2 because the total for the row is only two. You can also see that there is a strong relationship between Item 1 and Item A, item 2 and Item B, and Item 4 and Item C.PROCESS DECISION POGRAM CHARTSA Process Decision Program Chart (PDPC) is a method for mapping out every conceivable event and contingency that can occur when evaluating the progress of activities and outcomes.The format of a PDPC is like that of a tree diagram. A PDPC takes each branch of a tree diagram, anticipates possible problems or risks, and provides possible counter-measures.In any form of planning, which is a statement of what is expected to happen, things seldom happen as expected. In dealing with the unexpected you can cope with problems as they arise (firefighting) or you can use risk management to anticipate any potential problems.When risks are identified, there are three possible routes for dealing with them:1 Risk avoidance Abandon the original planned action Choose alternative risk-free action.2 Risk reduction Change the actions so as to reduce risk Add new actions that will reduce risk.3 Contingency planning Plan to cope with the risk should it occur.CASE STUDY

The diagram below is an example of how one company aimed to make its answering machines easy for the customer to use, but with the maximum number of desirable options. The most significant risk is anything that results in the customer having difficulty using the product. Viable countermeasures are anything that will reduce the above risk, within an agreed budget.

PlanRiskCountermeasure

ACTIVITY 30 Ease of use for customers but with maximum number of desired settings

Maximum number of options might add complications

Controls not easy to use

Design and test

Instructions not clear

Test on customers

Provide quick references card. Parts inspections.

Faulty parts

Quality manufacturing procedures

Test machines in front of customers

Lack of privacy

Earphone jacks

Separate voicemail for individuals using the same machine

Time and date stamp

Does not count hang up

Indicates no. of messages

Variable- length message

Incoming messages

Quick reference card

Clear instruction

Earphone jacks

Secret access

Instructions

Incoming messages

Telephone answering machine

Erase selected messages

Easy to erase

Erasing

Operates from remote phone

Easy to use

Control easily marked Controls

Controls

6