TABLE OF CONTENTS

1.0 Introduction ………………………………………….………………………… 2 2.0 The Development of 7 Basic Tools…………………………………………….. 3 3.0 Data Figures ………………………………………..…………………… 3 4.0 Histogram ..………………...…………………………………………………… 5 5.0 Process Control Chart ..………………………….…………………………… 5 6.0 Patero Analysis …………………………………………………………….. 8 7.0 Cause and Effect Diagram ……..………………..……………….……….……. 9 8.0 Trend Analysis ……………………………………………………………………11 9.0 Scatter Diagram…...……………………………………………..………………. 13 10.0 Conclusion…….…………………………………………………………..……... 15

THE SEVEN BASIC QUALITY TOOLS
Project Risk, Procurement and Integration Management

1. INTRODUCTION
The last two decades have been a period of tremendous turmoil and change in the business environment. Competition in many industries has become worldwide in scope, and the pace of innovation in products and services has accelerated. These changes in business environment have resulted in organisations attempting to transform themselves to become more competitive.
Since the early 1980s, many companies have gone through several waves of improvement programs, starting with Just-In-Time (JIT), then moving on to Total Quality Management (TQM), Lean Production, Six Sigma and many other various management programs. Implementing these programmes would require tools for data collection, measure and monitor.
There are seven quality control tools which are: * Data figures * Pareto analysis * Cause-and-effect * Trend analysis * Histograms * Scatter diagrams * Process control chart
This essay discusses the characteristics, applications and implementation of these tools.

2. THE DEVELOPMENT OF THE 7 BASIC TOOLS FOR IMPROVEMENT
The seven quality tools were first emphasized by Ishikawa (in the 1960s), who is one of the quality management gurus. His original seven tools include stratification, which some authors later called a flow chart or a run chart. They are also called the seven "basic" or "old" tools. After that other new tools have been developed for various purposes but the basis for every work is related to the 7QC tools. These tools are also fundamental to Kaizen and Juan’s approach to quality improvement.
These simple but effective "tools of improvement" are widely used as ‘graphical’ problem-solving methods and as general management tools in every process between design and delivery. No single tool is superior than the other. The current approach for using 7C tools by the European Organisation for Quality is to use three tools (Check sheet, Histogram and Control chart) for data acquisition, and the process of analysis another four tools (Pareto diagram, Cause and effect diagram, Scatter diagram and Control charts). Even then, scientists were still attempting to find the optimum utilisations of each tool in different processes and methodologies of improvement.

3. DATA FIGURES
Data figures, also known as check sheet, is a simple form of collecting data to compile in such a way that it can easily be used, understood and analysed automatically. It is sometimes a handy tool for both qualitative and quantitative data gathering and analysis. Data figures gather data so that decisions can be based on facts rather than unreliable evidence and has been useful in Lean Six Sigma and various improvement management projects.

Using data figures is appropriate when the data can be observed and collected repeatedly by either the same person or the same location. It is also an effective tool when collecting data on frequency and identifying patterns of events, problems, defects, and defect location, and for identifying defect causes. The Data Figures, once completed, actually becomes a graphical representation of the data collected been collected, thus there is no need any computer software, or spreadsheet to record the data. It can be simply done with pencil and paper.
Data Figures have the following main functions: (i) Production process distribution check is to study where the distribution lies; (ii) Defective item check helps to investigate total number of different types of defects occurring; (iii) Defect location check determine where the common defects; (iv) Defect cause check helps to find the type and possible causes of defects; and (v) Confirmation check on the finished product or work.
Data figures are implemented using these simple steps: (i) Clearly identify what is being observed; (ii) The data collection must be made easy as possible; (iii) Group the data to make it valuable and reliable; and (iv) Be creative to get most information with least amount of effort.
Some of the advantages of data figures are as follows: (i) Simple and effective way to display data. (ii) Convert raw data into useful information. (iii) Very useful to distinguish opinions from facts.

4. HISTOGRAM
A histogram is a vertical bar chart that shows the distribution of a set of data. It consists of equal intervals marked on the horizontal axis. The width of the bars for each interval are equal and without any space between them.
The purpose of a Histogram is to take the data that is collected from a process and then display it graphically to view how the distribution of the data, centres itself around the mean, or main specification. From the data, the histogram will graphically show: (i) The center of the data. (ii) The spread of the data. (iii) Any data skewness (slant, bias or run at an angle). (iv) The presence of outliers (product outside the specification range). (v) The presence of multiple modes (or peaks) within the data.

From the example of a histogram above, it can be shown that there is one main peak and also two secondary peaks on either side of the main peak.
Histogram is useful to: (i) Summarise large data sets graphically to make it easier to understand. (ii) Create a powerful tool for analysing data by communicating information graphically. (iii) Compare process with specification limits to determine quickly the quality of the product. (iv) Assist in decision-making through interpreting the shapes, size and spread of data.
There are many ways to create histograms. Some of the important parts in the histogram can be outlined below: (i) A Title that briefly describes the information in the Histogram; (ii) Horizontal X-axis shows the scale of values which the large data sets of measurements, which are grouped into certain ranges, are summarised; (iii) Vertical Y-axis shows the number of times the values within the range occurs. The number of times is also called ‘frequency’. (iv) Bars represents the number of times the values occurs and also shows the length of the intervals. (v) Legends that provide additional information.
Histogram should be viewed as a snapshot which does not reflect process information over time. It works well when: * The data has a really big range * There is one set of data * The data is collected using a frequency table.

5. PROCESS CONTROL CHARTS
A Process Control Chart is a graph or chart with limit lines, called control lines. There are basically three kinds of control lines: * the upper control limit (UCL), * the central line (actual nominal size of product), * the lower control limit (LCL).
Control charts are used to illustrate the stability of a process. It indicates upper and lower control limits, and often includes a middle (average) line, to help detect trend of plotted values. If all data points are within the control limits, variations in the values may be due to a common cause and process is said to be 'in control'. If data points fall outside the control limits, variations may be due to a special cause and the process is said to be out of control. The control chart helps us identify data elements that is deviating by exceeding the control limit.

Control charts are generally used in a production or manufacturing environment and are used to control, monitor and improve a process. The control chart is a statistical tool used in quality control to: (i) analyse and understand process variables, (ii) determine process capabilities, and to (iii) monitor effects of the variables on the difference between target and actual performance. Table: Examples of Process Control Chart

The most typical examples of a control charts include the following: (i) X-Bar & R Charts: The most widely utilized charts in project management, however, are only successful if 5 or less subgroups are analysed. (ii) X-Bar & S Charts: Using this type of a variable control chart is effective for 5 or more subgroups and the S or Standard Deviations are considered in both upper and lower control limits. (iii) p Control Charts: Effective when elements are not equal. A p Control Chart might be used to determine how many accidents occur each day at a chosen intersection. (iv) c Control Charts: Explores elements that are nonconforming such as exploring mass-production of one similar product where the elements per unit do not conform to the norm.
Knowing which control chart to use in a given situation will assure accurate monitoring of process stability. It will eliminate erroneous results and wasted effort, focusing attention on the true opportunities for meaningful improvement.

6. PARETO ANALYSIS
The Pareto diagram is a graphical overview of the process problems, in ranking order of the most frequent, down to the least frequent, in descending order from left to right. Thus, the Pareto diagram illustrates the frequency of fault types. Using a Pareto, you can decide which fault is the most serious or most frequent offender. Table: Pareto Chart Example
The basic underlying rule behind Pareto's law is that in almost every case, 80% of the total problems incurred are caused by 20% of the problem cause types; such as people, machines, parts, processes, and other factors related to the production of the product. Therefore, by concentrating on the major problems first, you can eliminate the majority of your problems. The few items that have the largest amount of occurrence is your more frequent problem, than are the many items that only happen once in a while. This is called the "vital few over the trivial many" rule. Quite often, once you cure several of the "big hitters" you also eliminate some of the smaller problems at the same time.

The Pareto diagram is normally used to: (i) Separate the few major from problems from many possible problems which improves the improvement efforts; (ii) Arrange data according to its importance and priority; and (iii) Identify problems using data rather than perception.
There are eight steps in performing the Pareto Analysis: (i) Define the activity or system of interest which risk-related information is needed. (ii) Define the specific risk-related factors of merit that best characterize the problems of interest. (iii) Subdivide the activity or system for analysis into its major elements, such as operations or subsystems. (iv) The analysis will begin at this level. (v) Determine which elements of the activity or system lead to the problems of interest. (vi) Collect and organize relevant risk data for elements of theactivity or system. (vii) Use data to estimate the contributions of activity or system elements that were not screened from consideration in the previous step. (viii) Present the data graphically on barline charts, showing the contributions of each activity or system element to the problems of interest.

7. CAUSE-AND-EFFECT DIAGRAM
A Cause-And-Effect diagram is useful in sorting out the causes of dispersion and organising mutual relationships. It is an excellent team problem solving tool, where a team gather together to brainstorm the potential causes and resolutions to solve the variation problem. It is also a tool that helps identify, sort and display possible causes of a specific problem or quality characteristic. It can also be used to graphically illustrate the relationship between a given outcome and all the factors that influence the Table: The Ishikawa Diagram outcome. This type of diagram is sometimes called the "Ishikawa diagram" because it was invented by Kaoru Ishikawa, or a "fishbone diagram" because of the way it looks.
Apart from being used as a quality control tool, the cause-and-effects diagrams are also used for risk analysis.
Cause and effect diagrams can be drawn like a skeleton of a fish (fishbone) or something that resembles a tree diagram. The tree diagram is preferable because the fishbone-style diagram can get complicated as the fishbone becomes more and more complex.
The ‘causes’ in a cause and effect diagram are frequently arranged into four major categories. While there are no limits on what categories you can see, there are often arranged as follows: (i) For manufacturing: manpower, methods, materials, and machinery (ii) For administration and service: equipment, policies, procedures, and people.
It is encouraged that cause and effect diagrams be generated in brain storming sessions, when there can be exchange of ideas about making improvements.
Cause and effect diagrams are useful to use the diagrams on quality control documents such as concession sheets where a cause and effect diagram must be completed every time there is any defective material. These diagrams would then be analysed on a regular basis, to bring about improvements in product build.

8. TREND ANALYSIS
The term "trend analysis" refers to the concept of collecting information and attempting to spot a pattern, or trend, in the information. Trend analysis is a mathematical technique that uses historical results to predict future outcome. This is achieved by tracking variances in cost and schedule performance. In this context, it is a project management quality control tool.
In project management, it is necessary to identify trends where historical data is utilised, within a set of mathematical parameters, and then processed in order to determine any variance from the established baseline such as budget, schedule and scope.
Table: Example of Trend Analysis Charts

In project management, it is necessary to identify trends where historical data is utilised, within a set of mathematical parameters, and then processed in order to determine any variance from the established baseline such as budget, schedule and scope.
A study of time trends may focus, therefore, on one or more of the following: (i) The overall pattern of change in an indicator over time:
The most general goal of trend analysis whether the level of indicator has increased or decreased over time, and if it has, how quickly or slowly the increase or decrease has occurred. (ii) Comparing one time period to another time period:
This form of trend analysis is carried out in order to assess the level of an indicator before and after an event. Sometimes called interrupted time series analysis. (iii) Comparing one geographic area to another:
Comparing the level of an indicator across geographic areas, only looking at one point in time can be misleading. (iv) Comparing one population to another:
Comparing the levels of indicators across population, both absolute and relative differences. Analysing the trend over time can provide information about the changing rates and the changing disparity in the rates. (v) Making future projections - A means of monitoring progress toward a national or local objective or simply providing an estimate of the rate of future occurrence.
In project management, it is necessary to identify trends where historical data is utilised, within a set of mathematical parameters, and then processed in order to determine any variance from the established baseline such as budget, schedule and scope.

9. SCATTER DIAGRAM
A scatter diagram is a graphic representation of the relationship between two variables. Scatter diagrams help teams identify and understand cause-effect relationships between “paired data” and can provide more useful information about a production process. Scatter diagram is simply the plotting of each point of data on a chart with one parameter as the x-axis and the other as the y-axis.

The diagram has four parameters: * Correlation: Measures how well the data line up. A straight linear line represents a higher correlation to each other * Slope: Measures the steepness of the data. The deeper the slope, the greater the importance in the relationship when the correlation is good * Direction: The ‘X’ variable can have a positive or negative impact on the “Y” variable. * Y intercept: Where the line drawn through the data crosses the “Y” axis.
Scatter diagrams are normally used to: (i) To examine theories about cause-and-effect relationships; (ii) To search for root causes of an identified problems; (iii) To design a control system in order to maintain benefits from quality improvement.
After collecting data and drawing the diagram, the data will be interpreted. Scatter diagrams will generally show one of six possible correlations between the variables: Correlations | Description | Strong Positive Correlation | The value of Y clearly increases as the value of X increases. | Strong Negative Correlation | The value of Y clearly decreases as the value of X increases. | Weak Positive Correlation | The value of Y increases slightly as the value of X increases. | Weak Negative Correlation | The value of Y decreases slightly as the value of X increases. | Complex Correlation | The value of Y seems to be related to the value of X, but the relationship is not easily determined. | No Correlation | No demonstrated connection between the two variables. |

10. CONCLUSION
The principle of continuous improvement using the seven basic quality tools guarantee organisations to move from static to dynamic improvement status was presented in this paper by defining the significance of the tools in today’s fast-changing business environment..
Total Quality Management (TQM) and Total Quality Control (TQC) literature make frequent mention of the seven basic tools. The tools must meet the main purpose or reason for their application. No single tool is more important in isolation, but could be most significant for a specific application.
It is evident that a continuous improvement process cannot be realised without quality tools, techniques and methods. These tools also help the quality engineer to use accessible data in decision processes. Kaoru Ishikawa, a prominent quality guru, believed that 95% of a company's problems can be solved using the seven tools as long as the passive status of using these tools is transformed into a pro-active continuous improvement approach.

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