7 Quality Tool

The quality of products and services has become a major decision factor in most businesses, today. The consumer considers quality of equal importance to cost and schedule. As a result of this, continuous quality improvement has an important role for the organization. If organizations want to achieve continuous quality improvement, they need to use proper quality tools and techniques. Tools are required systems for the organizing and the managing improvement across an organization. This paper defines that the tools have the important role of monitor, obtain and analyze data for assigning and solving the problems of process.

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Introduction

The seven quality tools have a long history. All of these tools have been in use since 1920’s. Seven quality control tools can be used for improving the performance of the all processes of production or service. So, this paper’s aim is to introduce these 7QC tools.

These seven quality tools which are basic for all other tools are:

Check sheet
Histogram
Pareto diagram
Cause-and-effect diagram.
Scatter plot
Flow chart
Control chart

Tools are included for generating and organizing ideas, evaluating ideas, analyzing processes, determining root causes, planning, and basic data-handling and statistics.

Application of Quality Tools

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 [1].These tools are also fundamental to Kaizen and Juan’s approach to quality improvement [2]. These seven basic quality control tools, which introduced.

by Dr. Ishikawa, are: 1) Check sheets; 2) Graphs (Trend Analysis); 3)Histograms; 4) Pareto charts; 5) Cause-and-effect diagrams; 6) Scatter diagrams; 7) Control charts. Figure 1 indicates the relationships among these seven tools and their utilizations for the identification and analysis of improvement of quality [3].

The current approach for using 7QC tools, according to EOQ (European Organisation for Quality) [4], is shown in Fig. 2. The process of data acquisitions includes three tools (Check sheet, Histogram and Control chart), and the process of analysis another four tools (Pareto diagram, Cause and effect diagram, Scatter plot, and Flow chart).
Figure 2: Current approach for using 7QC tools (according to EOQ)

There is a distinction between the two approaches represented in Figs. 1 and 2. The approach in Fig. 1 is much older (1990) and therefore, there are some key distinctions. Some tools which are now used only for analysis ere at that time considered as tools for identification or for both processes (identification and analysis). But even then scientists were attempting to find appropriate utilizations of each tool in different processes and methodologies of improvement [5]

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Check sheet

Check sheets are simple forms with certain formats that can aid the user to record data in an firm systematically. Data are “collected and tabulated” on the check sheet to record the frequency of specific events during a data collection period. The main advantages of check sheets are to be very easily to apply and understand, and it can make a clear picture of the situation and condition of the organization. They are efficient and powerful tools to identify frequently problems, but they dont have effective ability to analyze the quality problem into the workplace.[6]
Figure 3: Check sheet (Tally) for telephone interruptions[6]

Histogram

Histogram is very useful tool to describe a sense of the frequency distribution of observed values of a variable. It is a type of bar chart that visualizes both attribute and variable data of a product or process, also assists users to show the distribution of data and the amount of variation within a process. It displays the different measures of central tendency (mean, mode, and average)[6].

Figure 4: Histogram for variables [6]

Pareto Analysis

Wilfred Pareto, an economist, published Cours d’economie politique (1896–97), which included his famous law of income distribution. It was a complicated mathematical formulation in which he attempted to prove that the distribution of incomes and wealth in society is not random and that a consistent pattern appears throughout history, in all parts of the world and in all societies. When he discovered the principle, it established that 80% of the land in Italy was owned by 20% of the population. Later, he discovered that the Pareto principle was valid in other parts of his life, such as gardening: 80% of his garden peas were produced by 20% of the pea pods. The principle provides rough approximations and recognizes that effort and reward are not linearly related.It applies in business, as rough approximations:

Approximately 80% of process defects arise from no more than 20% of the process issues.
Approximately 20% of the sales force is likely to produce 80% of the company’s revenues.
Approximately 80% of sales are likely to come from 20% of the product/service range.
The law applies in the finance team’s work:
Approximately 80% of purchase invoices will be for small amounts (e.g., under $2,000).
Approximately 80% of the time spent by the finance team is not adding much value.
About 80% of the chart of account’s codes are not worthwhile having.
About 80% of all month-end reporting is adding little or no value.
About 80% of the total debt will reside with about 20% of the customers. [7]

Pareto principle was developed by Juran in 1950. A Pareto chart is a special type of histogram that can easily be apply to find and prioritize quality problems, conditions, or their causes of in the organization [8]
Figure 5 : Pareto Analysis : reasons for returned goods [9] The results are often presented in two ways: (1) ranked data as a bar chart and (2) cumulative percentages as a graph. Figure 5 is an analysis of the reasons for returned goods. Pareto analysis, while simple in terms of its construction, is extremely powerful in presenting data by focusing attention on the major contributor(s) to a quality problem in order to generate attention, efforts, ideas and suggestions to hopefully gain a significant overall reduction in these problems. [9]

The following are the basic steps in constructing a Pareto diagram:

Agree the problem which is to be analysed.
Decide the time period over which data are to be collected.
• Identify the main causes or categories of the problem.
Decide how the data will be measured.
Collect the data using, for example, a checksheet.
Tabulate the frequency of each category and list in descending order of frequency (if there are too many categories it is permissible to group some into a miscellaneous category, for the purpose of analysis and presentation).
Arrange the data as a bar chart.
Construct the Pareto diagram with the columns arranged in order of descending frequency.
•Determine cumulative totals and percentages and construct the cumulative
percentage curve, superimposing it on the bar chart.
Interpret the data portrayed on the diagram. [9]

Cause and effect diagram

Cause and effect diagram was developed by Dr. Kaoru Ishikawa in 1943. It has also two other names that are Ishikawa diagram and fishbone because the shape of the diagram looks like the skeleton of a fish to identify quality problems based on their degree of importance. [10]

This diagram can provide the problem-solving efforts by “gathering and organizing the possible causes, reaching a common understanding of the problem, exposing gaps in existing knowledge, ranking the most probable causes, and studying each cause[11]

The generic categories of the cause and effect diagram are usually six elements (causes) such as environment, materials, machine, measurement, man, and method, as indicated in Figure 6. Furthermore, “potential causes” can be indicated by arrows entering the main cause arrow [10]
Figure 6. The cause and Effect Diagrams (Fishbone Diagram) [10]

Scatter Diagram

Scatter diagrams or scatter plots are used when examining the possible relationship or association between two variables, characteristics or factors; they indicate the relationship as a pattern – cause and effect. For example, one variable may be a process parameter (e.g. temperature, pressure, screw speed), and the other may be some measurable characteristic or feature of the product (e.g. length, weight, thickness). As the process parameter is changed (independent variable) it is noted, together with any measured change in the product variable (dependent variable), and this is repeated until sufficient data have been collected. [9] The shape of the scatter diagram often shows the degree and direction of relationship between two variables, and the correlation may reveale the causes of a problem. Scatter diagrams are very useful in regression modeling [12] The scatter diagram can indicate that there is which one of these following correlation between two variables: a) Positive correlation; b) Negative correlation, and c) No correlation, as demonstrated in Figure 7. [6]
Figure 7. Scatter Diagrams [6]

Flowchart

A process flow chart is simply a tool that graphically shows the inputs, actions, and outputs of a given system. These terms are defined as follows:

Inputs—the factors of production: land, materials, labor, equipment, and management.

Actions—the way in which the inputs are combined and manipulated in order to add value. Actions include procedures, handling, storage, transportation, and processing.

Outputs—the products or services created by acting on the inputs. Outputs are delivered to the customer or other user. Outputs also include unplanned and undesirable results, such as scrap, rework, pollution, etc. Flow charts should contain these outputs as well

Flow charting is such a useful activity that the symbols have been standardized by various ANSI standards. There are special symbols for special processes, such as electronics or information systems (see Figure 8). [13]
Figure 8 : Flow chart symbols

Control Chart

This chart as a problem solving tool can apply methodically to detect and analyze the areas or points of process may have had potential problems by documenting and explaining an operation, so it is very useful to find and improve quality into process, as shown in Figure 9. [6]
Figure 9. Flow chart of review process

Statistical quality control is a field that dates back to the 1920s. Dr. Walter A.Shewhart of the Bell Telephone Laboratories was one of the early pioneers of the field. In 1924 he wrote a memorandum showing a modern control chart, one of the basic tools of statistical process control.

A typical control chart is shown in Fig. 10, which is a graphical display of a quality characteristic that has been measured or computed from a sample versus the sample number or time. Often, the samples are selected at periodic intervals such as every hour. The chart contains a center line (CL) that represents the average value of the quality characteristic corresponding to the in-control state. (That is, only chance causes are present.) Two other horizontal lines, called the upper control limit (UCL) and the lower control limit (LCL), are also shown on the chart. These control limits are chosen so that if the process is in control, nearly all of the sample points will fall between them. In general, as long as the points plot within the control limits, the process is assumed to be in control, and no action is necessary. However, a point that plots outside of the control limits is interpreted as evidence that the process is out of control, and investigation and corrective action are required to find and eliminate the assignable cause or causes responsible for this behavior. The sample points on the control chart are usually connected with straight-line segments so that it is easier to visualize how the sequence of points has evolved over time. [14]
Figure 10 : A typical Control Chart [13]

Conclusion

This paper presents the definition of seven quality tools. (Check sheet, Histogram, Pareto diagram, Cause-and-effect diagram, Scatter plot, Flow chart, Control chart) Scientists have researched the optimal use of these basic tools in different processes and improvement methodologies for many years. These tools are essential tools and have been used in process improvement for many years. Basic quality control tools can be used from the beginning of product development up to management of a process and shipment. The main purpose of these tools is to identify and analyze a potential problem / problem.

Zeynep Akça Gürel, Electrical Engineer – Quality Assurance

REFERENCES

[1] Tague,N.R.The quality toolbox, ASQ Quality press, Wisconsin 2005.
[2] Osanna, P.H., Durakbasa, M.N., AfjehiSadat, A. Quality in Industry, Vienna University of Technology, TU AuM, Wien, 2004.
[3] Kerzner, H. (2009). Project Management: A Systems Approach to Planning, Scheduling, and Controlling (10th ed.). Hoboken, New Jersey: John Wiley & Sons, Inc.
[4] N.N. Quality Management Systems, translation of DGQ educational material, 10. issue 2001, SZK, Ljubljana, Slovenia, 2002
[5] Soković, M., Jovanović, J., Krivokapić, Z., & Vujović, A. (2009). Basic quality tools in continuous improvement process. Journal of Mechanical Engineering, 55(5), 1-9.
[6] Neyestani, B. (2017). Seven basic tools of quality control: The appropriate techniques for solving quality problems in the organizations. Available at SSRN 2955721.
[7] Parmenter, D. (2007). Pareto’s 80/20 Rule for Corporate Accountants. John Wiley & Sons. Parmenter, D. (2007). Pareto’s 80/20 Rule for Corporate Accountants. John Wiley & Sons.
[8] Juran, M., and Godfrey, A. (1998). Juran’s quality handbook (5th ed.).Washington, DC: McGraw-Hill Companies, Inc
[9] Dale, B. G., Van Der Wiele, T., & Van Iwaarden, J. (2007). Managing quality. John Wiley & Sons.
[10] Neyestani B. (2017, February). “Principles and Contributions of Total Quality Mangement (TQM) Gurus on Business Quality Improvement.”
[11] Omachonu, V. K. & Ross, J. E. (2004). Principles of total quality (3rd ed.). Boca
Raton, Florida: Taylor & Francis.
[12] Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.).
Danvers, MA: John Wiley & Sons, Inc.
[13] Pyzdek, T., & Keller, P. A. (Eds.). (2003). Quality engineering handbook. CRC Press.
[14] Montgomery, D. C., & Runger, G. C. (2010). Applied statistics and probability for engineers. John Wiley & Sons.

[1] Tague,N.R.The quality toolbox, ASQ Quality press, Wisconsin 2005. [2] Osanna, P.H., Durakbasa, M.N., AfjehiSadat, A. Quality in Industry, Vienna University of Technology, TU AuM, Wien, 2004. [3] Kerzner, H. (2009). Project Management: A Systems Approach to Planning, Scheduling, and Controlling (10th ed.). Hoboken, New Jersey: John Wiley & Sons, Inc. [4] N.N. Quality Management Systems, translation of DGQ educational material, 10. issue 2001, SZK, Ljubljana, Slovenia, 2002 [5] Soković, M., Jovanović, J., Krivokapić, Z., & Vujović, A. (2009). Basic quality tools in continuous improvement process. Journal of Mechanical Engineering, 55(5), 1-9. [6] Neyestani, B. (2017). Seven basic tools of quality control: The appropriate techniques for solving quality problems in the organizations. Available at SSRN 2955721. [7] Parmenter, D. (2007). Pareto’s 80/20 Rule for Corporate Accountants. John Wiley & Sons. Parmenter, D. (2007). Pareto’s 80/20 Rule for Corporate Accountants. John Wiley & Sons. [8] Juran, M., and Godfrey, A. (1998). Juran’s quality handbook (5th ed.).Washington, DC: McGraw-Hill Companies, Inc [9] Dale, B. G., Van Der Wiele, T., & Van Iwaarden, J. (2007). Managing quality. John Wiley & Sons. [10] Neyestani B. (2017, February). “Principles and Contributions of Total Quality Mangement (TQM) Gurus on Business Quality Improvement.” [11] Omachonu, V. K. & Ross, J. E. (2004). Principles of total quality (3rd ed.). BocaRaton, Florida: Taylor & Francis. [12] Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.).Danvers, MA: John Wiley & Sons, Inc. [13] Pyzdek, T., & Keller, P. A. (Eds.). (2003). Quality engineering handbook. CRC Press. [14] Montgomery, D. C., & Runger, G. C. (2010). Applied statistics and probability for engineers. John Wiley & Sons.