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IJQRM 22,4

The use of quality management tools and techniques: a study of application in everyday situations

376 Received July 2003 Revised December 2003

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David R. Bamford and Richard W. Greatbanks Manchester School of Management, UMIST, Manchester, UK Abstract Purpose – This paper describes the use and application of a structured approach to the basic implementation of quality management tools and techniques such as the QC7 tools. Design/methodology/approach – A methodology based around the application of a structured approach to the use of basic quality management tools is adopted, and provides a simple yet powerful means by which the steps of problem solving can be sequentially linked together. Findings – Everyday process examples are used to highlight the benefits of such tools and techniques in contributing to a greater understanding of the process by the process operator or owner. For each example, the use of appropriate tools or techniques are examined and their application analysed. The paper then goes on to discuss the wider implications of quality management tool application within industry and business. Research limitations/implications – It is not suggested the examples detailed are thoroughly scientific in methodology but they do serve to illustrate that by applying the tools in a systematic manner, even the simplest of processes can be understood in greater detail. Practical implications – The following are key for the successful implementation, use and success of applying the QC and M7 tools and techniques: in-depth knowledge of the process; formal training in problem-solving techniques; appropriateness of tools selected for use; and apply simple models at all levels in the organisation to aid communication and learning. Originality/value – The paper concludes by arguing that the wider use of the tools, ideally by the process operatives themselves, tangibly lead to a fuller understanding of specific processes. This will ultimately impact upon their organisation. Keywords Quality management, Process efficiency, Quality improvement Paper type Research paper

Introduction As a population, we are generally introduced to some of the basic quality management tools very early within our education. The tally chart (five-bar gate) and histogram, for instance, are often introduced during primary or junior education as an aid to basic numeracy. Other charting techniques, such as pie and run charts are taught not long after. In many respects, we grow up with these simple methods and are aware of their application well before leaving secondary education and embarking on any form of career. Despite the fact that quality management, as a self-standing discipline, has been around for over 20 years (Bunney and Dale, 1997), it is remarkable that many of these International Journal of Quality & Reliability Management Vol. 22 No. 4, 2005 pp. 376-392 q Emerald Group Publishing Limited 0265-671X DOI 10.1108/02656710510591219

The authors would like to acknowledge the work and assistance of all the final year TQM2 students, specifically Nicola Land, Yim Yan Luk, Claire Smith, Patrick Ryan, and Roisin McNeil whose work is detailed here. Their enthusiastic cooperation, assignment data and analysis was the original idea for this paper.


simple yet powerful tools are not fully integrated within the day-to-day process improvement aspects of business and industry. In the authors’ experience of teaching and advising companies on quality management issues, very few examples have been found where even the basic quality management tools, such as QC7, have been fully exploited. This is particularly true for the average small to medium sized enterprise (SME). Yet the application of such tools is one of the least problematic aspects of any quality improvement initiative, and arguably provides the most direct and immediate improvement to a process (Dale, 2003). The fact that many of these tools have been with us from an early age might explain, to a degree, the reticence and lack of subsequent application within quality management problem solving. After all, it hardly looks impressive for the process operator, engineer or quality manager, to start marking up five-bar gates on a flip chart. Nonetheless, the simple acquisition of raw data in this manner is often the start of a much more in-depth quality improvement application. Some definitions and distinctions are required, of firstly the “process” and secondly of “tools and techniques”. What is a process? The Concise Oxford Dictionary (2002) defines it as “. . .a course of action or a procedure”; it goes on to state “. . .especially in a series of stages in manufacturing or some other operations”. Slack et al. (2001) say a process defines the relationship between the “component” products and services. Munro-Faure and Monro-Faure (1993, p. 20) relates to a process as “. . .any activity which takes an input and transforms it into an output”. The authors’ identify most closely with this definition. What are tools and techniques? According to McQuater et al. (1995), they are practical methods, skills, means or mechanisms that can be applied to particular tasks. They are used to facilitate positive change and improvements. A single tool may be described as a device that has a clear role, often narrow in focus and used on its own. Examples of specific tools are cause and effect diagrams, Pareto analysis, relationship diagrams, control charts, histograms and flowcharts. A technique has a wider application, often resulting in the need for more thought, skill and training to be used effectively. Techniques can be thought of as a collection of tools, for example statistical process control (SPC) uses charts, graphs, histograms, etc. Examples of techniques are SPC, benchmarking, quality function deployment (QFD), failure mode and effects analysis (FMEA), and design of experiments (DOE). Dale and McQuater (1998), provide a useful distinction between a quality management tool, such as Pareto, cause and effect, and charting, and a technique, such as SPC or QFD. They suggest that a “tool” is a simple stand-alone application; whereas a “technique” tends to be a more comprehensively integrated approach to problem solving that might rely on a number of supporting tools. An example of this is SPC, where the production of a control chart is an essential application of charting skills. Within the field of quality management there appears to be no shortage of literature that describes in various depth the application of QC7 tools and other techniques. For example, Ishikawa (1976) and Juran (1988a, b) provide “how to” manuals for the implementation of improvement quality tools; Barker (1989) talks through and discusses the use of “the seven new QC tools”; Dale and McQuater (1998) directly tie-in the application and use of quality management tools and techniques with managed business improvement; and Bunney and Dale (1999) dedicate an entire chapter to the explanation, description, use and possible outcomes through the application of quality

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tools and techniques. According to Spring et al. (1998), it is generally acknowledged that the use and application of quality tools and techniques within an effective problem-solving methodology are essential to understand and facilitate improvement in any process. There does, however, appear to be a lack of discussion relating to the poor levels of application of these tools, particularly in the SME manufacturing sector. Dale and McQuater (1998) report that the use of tools and techniques is not as widespread and effective as might be expected, and suggest that part of the problem is due to insufficient training in the use and application of these approaches. It is evident that many of the tools and techniques used do require a sound basis of training and education. Spring et al. (1998) have identified that the results from the application of a particular tool or technique rely heavily on the skill and experience of those implementing it. The more simplistic tools and techniques, such as the seven quality control tools (e.g. Pareto analysis, cause and effect, control charts and check sheets) described by Ishikawa (1976) are usually perceived as too simplistic and not appropriate (Lamb and Dale, 1994). Another less defined issue is the mindset of supervisors and engineers whom, when introduced to QC7 tools and other techniques, seem to regard these as an additional workload over and above their current responsibilities. The experience of one of the authors, when delivering a short industrial course to a small group of manufacturing engineers, would support this. After some 20 hours of teaching and project work over a four-week period, presentations were made to senior management. Despite being taught and coached in the use of the QC7 and other appropriate tools, none of the engineers referred to or utilised any such approaches in the presentation, seemingly preferring to “revert back to type” in the presence of senior management. Manufacturing engineers, presumably with a mandate to maintain and improve manufacturing processes should be using these improvement tools as second nature as a consequence of performing this function. Rather than training, as Dale and McQuater (1998) suggest, perhaps the problem is one of perceived relevance, or pressure of work and a lack of time. Ozeki and Asaka (1990), in their Handbook of Quality Tools, devote the first three chapters to the role of the process supervisor and engineer in utilising these tools. From their discussion it would appear that the role of manufacturing supervisor in Japan is much wider than just a preoccupation with production output. This perhaps demonstrates further evidence of cultural and individual role differences between east and west. Dale (2003) notes that no one technique is more important than any other, but that they are all different and applicable in different situations. Each technique has unique qualities and can emphasise the same data in different ways. This concept is well illustrated with the Japanese saying, “a warrior should never have a favourite weapon” (Sun Tzu, 3rd Century BC, 1988). Hence, a simple pie chart can be just as useful as using the more complicated tool of SPC. They just highlight the data in different ways and give the best analysis of information when used in conjunction with each other. By using a combination of tools and techniques it is possible to: . highlight complex data in a simple, visually powerful way; . evaluate areas that cause the most problems; . give direction for areas to be prioritised; . show relationships between variables;


establish causes for failure; show distribution of data; and determine whether the process is acting in a state of statistical control and highlight the effect of special causes of variation where present.

Management tools and techniques

The key message is that without an effective employment and mix of tools and techniques, it is difficult to solve problems. Put another way “if you only have a hammer, it is surprising how many problems look like nails” (Dale, 2003, p. 309).

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. . .

Methodology In order to consider the potential application of quality management tools and techniques, in the widest possible context, their use in everyday applications was sought. The research design was based on approaching final year undergraduate students to consider the potential use of these tools within their everyday lives (this was part of their module assessment criteria). This provided a large number of different applications from which to select the most appropriate examples for further analysis. In most examples, the researchers adopted the role of participant – observer (Easterby-Smith et al., 1991) within the research. In an attempt to control the reliability of this work, the process of research was defined and structured around a basic application model (Figure 1). The stages of the research process were sequential and followed the steps below: (1) detailed definition of the initial application; (2) consideration of process performance and independent variables within the process; (3) collection of process data; (4) analysis of data by a structured application of appropriate tools; and (5) drawing conclusions and recommendations about the process itself, and the subsequent application of the tools used. The method detailed in Figure 1 above provides a structured approach to the application of the basic tools of quality management. Similar structured approaches to problem solving have been recommended by many (Tennant, 2001; Dale and McQuater, 1998; Straker, 1995), and are common within best practice industrial applications. Within this research, the benefits of such approaches are that they provide a simple manner of linking the output of one tool to an input of another, thus providing sequentiality to the process of data collection and analysis. Within each example, students were asked to consider not only the process to be analysed, but from which perspective the performance of the process was to be investigated, i.e. reduction in the time taken, improvement in the “quality” of the process output, or reduction in the cost of the process. Students were asked to select one performance perspective in order to simplify the application of these tools. Selection of the performance perspective can be considered similar to defining the “unit of analysis” within case study research (Yin, 1994). Furthermore, once the performance perspective is established, definition of dependent and independent variables associated with the process can be undertaken.


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Figure 1. Research process model framework

The examples The following were produced as a response to a coursework assignment for the final year undergraduate module in quality management delivered in the second semester of the BSc Honours degree in Management at the School of Management, UMIST, Manchester, UK. The examples illustrate that many of the basic quality management tools and techniques can be applied in a practical and rigorous way to everyday activities and applications-processes. We present these as “food for thought� and suggest that if such applications were more common in business as a whole, a great


deal more “problems” would be better understood and appropriate solutions identified and implemented. The applications illustrate that quality improvement tools can be applied to a great variety of non-industrial processes. Such examples support the authors’ belief that a wider application of quality improvement tools and techniques would lead to a better understanding of process problems and eventually increase the internal knowledge of process performance.

Management tools and techniques 381

Example 1: washing household dishes In this example, the selected process was the washing of household dishes, a typical process that most of us have undertaken at one time or another. The performance perspective adopted in this example was to understand the different factors that contributed to an increase in dishwashing time. Process definition. The process was first defined as encompassing all activities from turning the water tap on, until the dirty dishwater was rinsed away after completion. Data collection and analysis tools. The process was measured to the nearest second by a stopwatch and recorded each day for 31 days. The process was timed from the moment the tap was turned on. Measurement stopped when the last of the water had drained from the washing-up bowl. Data were collected in minutes and seconds then converted into seconds to facilitate calculations. Table I summarises the tools and techniques used to collect the initial data and to analyse this process.

Tool

Use

Comment

Tally chart

Time frequency within different time bands tallied Machinery, manpower, method and materials used as “ribs” to identify potential causes of process taking longer than 250 seconds Identified relationships between causes in a hierarchical way

Easy and efficient way to collect occurrence data Powerful visual tool to categorise possible ideas of why failure occurred

Cause and effect diagram

5 why

Brainstorming

Tried to identify causes of process taking longer than 250 seconds. Already done above!

Control charts

Time taken to wash-up was the key data point. Four sub groups of seven were used to represent a week

This gets to the real root of a problem even though it can be quite tedious! Simply, this tool asks “Why”, five times, to a problem occurring Getting all possible ideas down. This is particularly useful for categorising ideas and prioritising areas for improvement. These charts are more statistical than the above examples. They determine whether the process is in a state of statistical control and so highlight whether actions are required to bring the process under control. They also highlight special cause of variation and whether the process is occurring within limits which are acceptable to the process owner (Specification Limits)

Table I. Washing household dishes


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Recommendations. From the analysis of example 1, a number of recommendations were made to improve (in this case minimise) the process of washing up: . washing dishes after every meal was more efficient (from a time reduction perspective) than waiting until all dishes in house are dirty; and . failure to pre-soak items added significantly to the time taken.

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Further suggestions included eliminating the need to wash-up by eating out, using paper plates, or buying a dishwasher, however, it was noted that these solutions all required extra expenditure. This simple application of the selected tools and techniques enabled the implementation of a number of improvements. First, from a data collection perspective, it was noted that detailed data were required. For instance, it was not sufficient to only record just the time taken. The meal cooked and number of people eating should also be recorded to understand the implications and relationship of these independent variables on the process of washing up after the meal. This also leads to the potential use of a scatter diagram to plot and establish a correlation between the type of meal and washing time, or the number of people and washing time. Such basic relational data are often vital in establishing how the process reacts with other independent variables. Only by using all the above tools and techniques was it possible to make such precise conclusions.

Example 2: supermarket shopping Process definition. In this example, a regular supermarket shopping visit was the subject of analysis. The performance perspective of this example was to develop a greater understanding of the factors and circumstances that increased the time taken to complete the shopping activity. Data collection. Check sheets were used to record the required data on 24 occasions: time in/out, total time, queuing time, day of the week, number of friends present, total money spent, number of items. Time was measured in minutes and seconds. Data analysis tools. Flow chart, cause and effect diagram, check sheet, scatter diagram, Pareto analysis, pie chart, control charts. Table II comments on the appropriateness of these tools for the selected process. The use of the scatter diagram was helpful in confirming relationships between variables, such as positive relationship between shopping time and amount spent, and shopping time and number of items purchased (see Discussion later). Recommendations. From the analysis of this process, a number of recommendations were proposed: . shopping should take place on a Monday, ideally before late afternoon; . always have a list of items to buy; . shop alone or with just one friend. More friends Âź more deliberation; and . use a basket not a trolley. Less fits in, therefore less spent and easy to carry home. From these recommendations it is clear that a greater understanding of this process was gained by the application of these basic tools and techniques.


Tool

Use

Comment

Flow chart

Employed to provide a diagrammatic picture of the whole process

Cause and effect diagram

Developed to determine and break down the main causes: people, process, food/product, equipment/facilities Simply used to record key data

Flow chart showed only the framework of the process – therefore could not be used to identify major causes of wasted time Could not help in eliminating causes but did assist in determining which were the most important Needs to be flexible and meaningful Some data could not be presented using the scatter diagram

Check sheet Scatter diagram Pareto analysis Pie chart Control charts

Used to identify the relationship between sets of data, in this case four different pairs. Categorised measurement using the same unit of measure (frequency) for each cause To obtain a better “picture” of the process, relevant data were grouped in terms of “day” Used to indicate the trends within the data

Management tools and techniques 383

Pareto diagram was the most useful tool Careful selection of “pie sections” is necessary to present most usable data Helped to evaluate the effectiveness of improvement measures by comparing the existing process and the new process

Example 3: analysis of swimming performance Process definition. The performance perspective in this example was to identify each of those factors which resulted in a decrease in the time taken to swim 250 meters (Table III). Data collection. A stopwatch was used to record the time taken to swim 250 meters (10 lengths of pool). The information was recorded over 31 separate sessions in minutes and seconds. The data were converted to seconds for analysis purposes. Data analysis tools. Histogram, run chart, scatter diagram, cause and effect diagram, Pareto diagram, control chart were used to analyze this process. The cause and effect diagram was initially used to identify potential causes for poor performance (defined as times greater than 3 minutes 50 seconds). Data were collected in the time of session, how busy the pool was, pool temperature and general well being of the swimmer (level of fatigue and calorie intake). Scatter diagrams of these factors (treated as independent variables) and the time taken (treated as the dependant variable) were produced. Strong positive relationships were identified between the time and number of people in the pool, and the time of day. A negative relationship was identified between time and pool temperature. Recommendations. . Optimum time (as defined by the lowest time taken) to swim is when the swimming pool is quiet, weekdays between 11 am and 1 pm; . negative correlation between pool temperature and best times recorded; and . negative correlation between amount of practice and best times recorded.

Table II. Analysis supermarket shopping


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Tool

Use

Comment

Histogram

Tabulated the data

Run chart

Simply plotted the available data sequentially Inputted the data and used a “trial” number as the independent variable Used the 4M approach (manpower, machines, materials, methods)

Offered a good visual representation of the findings The run chart was very motivational Need to choose the “right” variable to compare – very important if diagram to mean anything An “attractive” tool to use – gives immediate feedback and easy to reorganise Best tool was the Pareto diagram used in conjunction with a cause and effect diagram An inherent understanding of the QM tools and techniques is required if data analysis is to be done properly

Scatter diagram Cause and effect diagram

Table III. Analysis of swimming performance

Pareto diagram

Easy to use with the data collected

Control chart

Had to convert the data into seconds for it to “work”

Example 4: commuting to and from university Process. Student bus travels into university. The performance objectives were to identify which of the various bus companies completed the same daily journey in the least time. Data collection. Forty data points recording: bus company used, time of day, cost, number of stops bus made, elapsed time in minutes and seconds, time in seconds. Other factors such as weather were also recorded. Data analysis tools. Histogram, cause and effect diagram, control charts, and regression analysis were used. Relationships between the time of day and number of passengers were identified as important factors in the time taken of the bus journey. The analysis also identified that there were significant differences between the mean journey times for different bus companies. Interestingly, the bus company that charged the lowest fare consistently achieved the shortest journey times. After further investigation the explanation was that this bus company had fewer stops along the route. The number of stops was then scatter plotted against journey time and a strong positive correlation was found. Poor weather was also found to increase journey times across all bus companies (Table IV). Recommendations. . The bus companies should promote the use of travel cards and “bus passes”, this would speed up the boarding time at stops, as drivers would no longer have to issue tickets and change. Published journey times would then be feasible. . Increased driver training and monitoring would standardise driver performance. . Loading of buses on specific routes should take into account university semester times and periods of bad weather (bad weather ¼ more students taking a bus). Example 5: waiting time in a doctors surgery Process definition. Measuring the patient waiting time at a GP practice. The performance objectives of this study were to identify factors that could help reduce


Tool

Use

Comment

Histogram

Used to visualise the total time duration of each bus trip and the total number of stops made by the bus Performed to ensure correlation equation was satisfactory Several different data groups were analysed using control charts

Histograms used in conjunction with scatter diagrams worked best in providing rich visual information Allowed interesting data to present itself for discussion Control charts proved less useful – the process monitored was dependent upon the random element of the “first bus that came along” A cause and effect diagram was very useful in highlighting specific problem areas

Regression analysis Control charts

Cause and effect diagram

Machinery, method, maintenance, personnel, mother nature used as categories for discussion

patient waiting time. Since two GPs operated from this surgery, factors that resulted in a difference between each GPs patient waiting times were also identified. Data collection. Data were collected over a two-day period during two afternoon surgeries when both doctors were present. Time was recorded using a stopwatch in the reception area. The following data were recorded: duration of total visit; duration of consultation; time spent waiting for appointment; punctuality of patient arriving for scheduled appointment; and punctuality of doctor seeing the patient relative to the appointment time. Data analysis tools. Pie chart; Pareto analysis; cause and effect diagram; scatter graph; histogram; control and run charts were used in this study. Within this study, one important factor was the punctuality of the doctor in calling a patient, i.e. the difference between the appointed time and the actual time called. One of the GPs was significantly worse at maintaining the schedule of daily appointments. When the actual arrival time of the patient was considered, patients for this doctor were less punctual in arriving for their appointment. Another factor was the time of the appointment. Scatter diagrams were used to identify another important relationship between patient delay and the time of day. The later in the day the appointment was, the greater the difference in appointment time to the actual time (Table V). Recommendations. . The doctors should aim to start the surgery punctually to alleviate the problem of backlog from the beginning. . Place a notice in the waiting room explaining to patient that appointments are ideally scheduled to last between 5 and 10 minutes. This may help them to cut down on time spent in the waiting room. . Place a notice in the waiting room explaining to patient that one appointment is ideally meant to deal with one problem. If they have more than one problem ask them to make two consecutive appointments in future. . Have an “open surgery”. This could be a “no appointment” surgery in the morning where patients arrive and are seen on a first come, first served basis. The afternoon surgery could continue to operate on an appointment basis. This

Management tools and techniques 385

Table IV. Commuting to university


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Tool

Use

Comment

Pie chart

Important to establish the main causes for the process not running smoothly. Once this was done in puting the numbers is straightforward

Pareto analysis

Actually constructed by first putting the data into a bar chart, then moving the data around

Gives a powerful visual aid to show the split of causes for lateness of the two doctors Enables complex data to be highlighted in a simple way Does not show any relationship between the data points Does not conclusively show areas to prioritise A starting block in the problem analysis Evaluates the area which causes the most problems Gives direction as to which area to prioritise, i.e. the doctor not being punctual to the scheduled appointment time Does not show relationships between variables This is useful after Pareto analysis to establish problem sources for the areas highlighted Powerful visual tool to categorise possible ideas of why failure occurs Gives no relationship between the different causes Does not prioritise causes Use to determine whether there is a relationship between two types of variable data

386

Cause and effect diagram Simple visual way of sorting ideas into categories and recording them in a logical manner (appointment system, extended consultation, patients, receptionists)

Scatter graph

Histogram

Measure of two factors to determine a possible relationship, for example – the later in the day an appointment is, the less punctual the doctor will be Simple (static) visual Showed the distribution of data representation of “time” Does not show possible causes groupings for distribution

Control charts / run chart Dynamically visual representation of punctuality. Individual patient plotted against punctuality of the appointment

Table V. Analysis of waiting time in a doctors surgery

Useful to determine whether the process is in a state of statistical control and so determines whether actions are required to bring the process under control Highlighted causes of special variation Shows whether process is occurring within specification limits Gives a statistical value as to the capability of the process Does not indict what the special causes of variation are Does not show any relationships between causes


could be piloted on a one-month trial and the process re-evaluated. Potentially, this could eradicate the problem of punctuality to appointment time, as no appointments would exist. Table VI summarises some of the other assignments themes and the typical tools and techniques used within the analysis. Whilst we would not suggest the above examples are thoroughly scientific in methodology (nor were they ever meant to be), they do serve to illustrate that by applying these tools in a systematic manner, even the simplest of processes can be understood in greater detail. It should also be stressed that when a process is selected for improvement, the nature of the improvement must be clearly specified, i.e. to reduce the time taken, or the cost, or to improve performance, etc. Without this focus, the effectiveness of data collection and any consequent analysis is reduced.

Management tools and techniques 387

Discussion The examples illustrated here clearly show that quality tools may be applied to a wide range of non-industrial process. Weller (2000) agrees with this statement. In a paper investigating school attendance problems in the USA, he acknowledges that brainstorming, cause and effect diagrams, Pareto charts, graphs, surveys and checklists were all successfully applied. As demonstrated by the specific examples given within this paper, a brief introduction to the QC7 and M7 tools and techniques, coupled with a focus upon a process the student was familiar with, provided insightful and specific corrective actions. In a paper looking at the use of quality management tools and techniques within Malaysian small and medium industries (SMIs), Ahmed and Hassan (2003) concluded that the tools were basic but could be applied for both long and short-term goals in many small industries and all medium ones. This ensured more benefits for them both in the long and short run and the advantages of application of the basic tools could then be realised. Ahmed and Hassan went on to Problem

Tools and techniques used

Monitoring the time taken to complete 100 sit-ups

Affinity diagram/cause and effect/scatter diagram/run chart/Pareto/control charts/arrow diagram Scatter diagram/histograms/tally charts/Pareto/cause and effect Flow chart/histogram/run chart/Pareto/cause and effect/check sheet/control charts Flow chart/cause and effect diag/run chart/control chart/Pareto analysis Line graph/Pareto/cause and effect/control charts/histogram Brainstorm/cause and effect/Pareto/control charts/radar graphs/process mapping Pareto/flow chart/control charts/Ishikawa diagram/histogram Flow chart/control charts/cause and effect diagram/relations diagram/tree diagram/histogram

Train journey to and from UMIST using “Wales and West� Services Bus travel to UMIST Making a cup of tea The process of cueing in a new track while DJ-ing, with the aim of reducing the time taken Investigation into call duration (at an IKEA store) Analysing the time taken to serve a customer with a pint of beer Analysis of the process of preparing an evening meal

Table VI. Summary of other tools and technique applications


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state that this led to a better understanding of the process by those who were either directly or indirectly involved in it, with the tenet that users must understand the applicability of a particular tool before it is applied. Interestingly, the choice of tools and techniques made by the students was weighted in favour of the QC7 tools (the seven original tools) as defined by Ishikawa (1982). These were ranked: (1) control charts; (2) cause and effect diagrams; (3) histograms; (4) Pareto charts; and (5) scatter diagrams. This despite an equal amount of time being spent on teaching and emphasising the M7 management tools. Table VII shows the choice of tools used in relation to the problems to be tackled. Scheuermann et al. (1997) report that the least often used tools are affinity diagrams and selection grids. These two tools are used for the purpose of problem identification and solution planning. The reason that these two tools are not used as much may be the fact that several other tools are more popular and are designed for the same purpose. However, some of the tools and techniques are frequently used; Ahmed and Hassan (2003), for example, reveal from their study that check sheets, process flow charts, histograms, cause and effect diagrams and Pareto charts were the most commonly used amongst the QC7. From their study FMEA, tree, matrix and arrow diagrams were the most commonly applied M7 techniques. An interesting area for further investigation would be to identify which factors specifically influenced the choices made – familiarity, ease of use, the way it was taught, bias of lecturer? The concept of applying the quality management tools and techniques outside the traditional arena of process manufacturing has many potential problems. Yasin et al. (2002) agree this in an interesting paper looking at the application of contemporary managerial philosophies in a hospital. They report that tools and techniques which have proven to be effective in the manufacturing sector were not being systematically implemented in healthcare organisations. They attribute this to historic internal barriers to change and a lack of strategy to integrate the implementation of such philosophies. Yasin et al. go on to say that even when these tools are utilised, a piecemeal approach to implementation often results in sub-optimal performance or indeed complete failure. Ahmed and Hassan (2003) agree stating that a lack of methodical analysis is a major weakness of SMIs in the application of tools and techniques. Training and thoroughness in use would appear to be key issues here. There are other benefits from this approach, principally the motivation and enthusing of staff. Tennant et al. (2002; p. 291) concur “staff should be trained in the problem solving process, and the related quality tools and techniques as a means of motivating them to focus on continuous improvement�. Ahmed and Hassan (2003) sum up the major limitations faced by most appliers of the quality management tools and techniques: (1) lack of knowledge; (2) lack of resources or facilities for training; (3) difficulty in affording absence of employees for training.


Original quality tools

New quality tools

Train to university X Bus to university Making a cup of tea DJ-ing Call duration (call centre) Bus to university Walking to university Train to university Swimming – lap time Serve a pint of beer GP patient waiting time Preparing evening meal

X

X

X

X

X

X

X

X X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X X

X X

X

X X

X

X

X

X

X

X

X

X

X

X

X

X

X X

X

X

X

X

X X

X

X

X

X

X

X

X

X

X X

X

X

X X

X

X

X X

X

X

Matrix Cause data and Scatter Control Relations Affinity Systems Matrix Check sheet Histogram Graphs Pareto effect diagram charts diagram diagram diagram diagram analysis

Increasing fitness level Washing up Supermarket shopping X Swimming (250 meters) Bus to university 100 sit-ups

Process Process decision program chart

X

X

X

X

X

X

X

X

X

X X

X

X

X

X

Arrow Flow Brain5 diagrams charts storming Why

Others

Management tools and techniques 389

Table VII. Choice of tools used/process analysed


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While not insurmountable, these issues require attention before systematic, strategically driven, and successful application of the QC and M7 tools can proceed. From our own study into the application of quality management tools and techniques it would appear that where the tools are applied successfully, appropriateness and simplicity in the approach adopted are winning criteria. Tennant et al. (2002) agree in their research into the continuous improvement process adopted at Severn Trent Water. They conclude that although there are many different problem-solving methods available, the best are simple models that can be applied at all levels from senior management to junior staff. The model(s) used should enhance employee involvement, as employees are a decisive element in making it successful. So the suitability of QC7 tools to aid and support problem solving is generally accepted. What is less clear is the use of such tools to confirm ideas or opinions regarding the relationship between independent and dependent variables of a process. As an instance, example 2 used scatter diagrams to confirm a positive relationship between the time taken for shopping and both the number of items purchased and the amount spent. Whilst these relationships might seem obvious at first, it is the collection and analysis of data to confirm such opinions that is the important issue. Understanding what you want from a particular tool or technique, its pre-requisites, benefits and obstacles in implementing is critical to success and use (Spring et al., 1998). In many industrial process applications, the participants have much “opinionated” experience, but very rarely are these opinions tested through data collection and subsequent analysis. The phrase “Speak with data, not opinions” is clearly the basis of this approach. Only by such an approach can the process be therefore fully understood by the process operator and management.

Conclusions The practical examples given within this paper clearly support the view of the authors that the basic “quality” tools and techniques can be applied to everyday activities and tasks. The major benefit of such application is a greater understanding of the process to which such tools have been applied. The example of the GPs surgery demonstrates how through data collection and analysis, a significant difference in the way each of the GPs operated was revealed (see example 5). Clearly, the greater use of all data collection and analysis tools and techniques should be encouraged, not just in relation to quality issues, but in all aspects of our day-to-day lives. By encouraging the greater use of such techniques within the working environment, greater process knowledge and understanding will lead to fewer issues of poor quality or dissatisfied customers. This would give those who adopt these techniques a potentially powerful competitive advantage and is readily acknowledged and demonstrated within a number of the so-called “excellent” companies, notable Motorola who have been acclaimed as the initiators of the six-sigma approach to quality (Breyfogle, 1999; Tennant, 2001). From our research here, we suggest that the following are key for the successful implementation, use and success of applying the QC and M7 tools and techniques: (1) in-depth knowledge of the process; (2) formal training in problem solving techniques; (3) appropriateness of tools selected for use; and


(4) application simple models at all levels in the organisation to aid communication and learning. As previously stated, an interesting area for further investigation would be to identify which factors specifically influenced the choices made – familiarity, ease of use, the way it was taught, bias of lecturer? Also, fuller research into which areas outside manufacturing are actively applying the QC and M7 tools and techniques – which industries are using them? The methodology applied for collection of the research data wholly appropriate and consistent with the perceived outcomes required. It generated ample data which facilitated discussion and the drawing of specific conclusions. To conclude, whilst quality management has undoubtedly progressed over the last 20 years, perhaps we should ask ourselves, as quality practitioners, whether we have ever fully developed (and therefore benefited from) the fundamentals of quality management, i.e. the collection and analysis of primary data? Certainly, the anecdotal evidence collected by the authors from external sources suggests there is a growing interest in the targeted application of these tools and techniques within service related sectors, particularly health and retail.

References Ahmed, S. and Hassan, M. (2003), “Survey and case investigations on application of quality management tools and techniques in SMIs”, International Journal of Quality & Reliability Management, Vol. 20 No. 7, pp. 795-826. Barker, R.L. (1989), “The seven new QC tools”, Proceedings of the First Conference on TQM Tools and Techniques, IFS Publications, pp. 95-120. Breyfogle, F.W. (1999), Implementing Six Sigma, Wiley, New York, NY. Bunney, H.S. and Dale, B.G. (1997), “The implementation of quality management tools and techniques: a study”, The TQM Magazine, Vol. 9 No. 3, pp. 183-9. Bunney, H.S. and Dale, B.G. (1999), Total Quality Management Blueprint, Chapter 6, Blackwell Publishers, Oxford. Dale, B. (2003), Managing Quality, 4th ed., Blackwell Publishers, Oxford. Dale, B.G. and McQuater, R.E. (1998), Managing Business Improvement and Quality: Implementing Key Tools and Techniques, Blackwell Publishers, Oxford. Easterby-Smith, M., Thorpe, R. and Lowe, A. (1991), Management Research: An Introduction, Sage, London. Ishikawa, K. (1976), Guide to Quality Control, Asian Productivity Organisation, Tokyo. Ishikawa, K. (1982), What is Quality Control?, Prentice-Hall, Englewood Cliffs, NJ. Juran, J.M. (1988a), Quality Control Handbook, (Chapters 2 and 16), McGraw-Hill, New York, NY. Juran, J.M. (1988b), The Quality Control Handbook, 4th ed., McGraw-Hill, New York, NY. McQuater, R.E., Scurr, C.H., Dale, B.G. and Hillman, P.G. (1995), “Using quality tools and techniques successfully”, The TQM Magazine, Vol. 7 No. 6, pp. 37-42. Munro-Faure, L. and Monro-Faure, M. (1993), Implementing Total Quality Management, Biddles Ltd, Guildford and King’s Lynn. Oxford English Dictionary (2002), Oxford English Dictionary, Oxford University Press, Oxford. Ozeki, K. and Asaka, T. (1990), Handbook of Quality Tools, Productivity Press, Cambridge, MA.

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Scheuermann, L., Jhu, Z. and Scheuermann, S.B. (1997), “TQM success efforts: use more quantitative or qualitative tools?”, Industrial Management & Data Systems, Vol. 97 No. 7, pp. 264-70. Slack, N., Chambers, S. and Johnston, R. (2001), “Operations Management”, Financial Times, Prentice-Hall, London. Spring, M., McQuater, R., Swift, K., Dale, B. and Booker, J. (1998), “The use of quality tools and techniques in product introduction: an assessment methodology”, The TQM Magazine, Vol. 10 No. 1, pp. 45-50. Straker, D. (1995), A Toolbook for Quality Improvement and Problem Solving, Prentice-Hall, London. Sun Tzu (1988) (3rd Century BC), The Art of War, 1988 translation by Cleary, T., Shambhala Publications, Boston, MA. Tennant, G. (2001), Six Sigma: SPC and TQM in Manufacturing and Services, Gower, Aldershot. Tennant, C., Warwood, S.J. and Chiang, M.M.P. (2002), “A continuous improvement process at Seven Trent Water”, The TQM Magazine, Vol. 14 No. 5, pp. 248-92. Weller, L.D. (2000), “School attendance problems: using the TQM tools to identify root causes”, Journal of Educational Administration, Vol. 38 No. 1, pp. 64-82. Yasin, M.M., Zimmerer, L.W., Miller, P. and Zimmerer, T.W. (2002), “An empirical investigation of the effectiveness of contempory managerial philosophies in a hospital operational setting”, International Journal of Health Care Quality Assurance, Vol. 15 No. 6, pp. 268-76. Yin, R.K. (1994), Case Study Research: Design and Methods, 2nd ed., Applied Social Science Methods Series, Vol. 5, Sage Publications, London. Further reading Anjard, R.P. (1995), “Management and planning tools”, Training for Quality, Vol. 3 No. 2, pp. 34-7. Bamford, D.R. and Greatbanks, R.W. (2002), Q7 and M7 Tool Lectures, UMIST, Manchester, 21 January, 11 February. Dale, B. (1999), Managing Quality, 3rd ed., Blackwell Publishers, Oxford. Deming, E. (1986), Out of the Crisis, MIT, Productivity Press, MIT, Cambridge, MA. Hellsten, U. and Klefsjo (2000), “TQM as a management system consisting of values, techniques and tools”, The TQM Magazine, Vol. 12 No. 4, pp. 238-44. Kane, V.E. (1989), Defect Prevention, Chapters 5, 8, 9, 10, 11, 12, and 13, Marcel Dekker, New York, NY. Lascelles, D.M. and Dale, B.G. (1990), “The use of quality management techniques”, Quality Forum, Vol. 16 No. 4, pp. 188-92. Mears, P. (1994), Quality Improvement Tools and Techniques, McGraw-Hill, New York, NY. Mizuno, S. (1988), Managing for Quality Improvement – The 7 New QC Tools, Productivity Press, Cambridge, MA. Swanson, R. (1995), The Quality Improvement Handbook: Team Guide to Tools and Techniques, Kogan Page, London. Tague, N.R. (1995), The Quality Toolbox, ASQ, Milwaukee, WI.

Lectura 1 the use of qm tools  

Artículo de aplicaciones de TQM

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