• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
9 basic seven tools of quality

9 basic seven tools of quality






Total Views
Views on SlideShare
Embed Views



0 Embeds 0

No embeds



Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment
  • Quote taken from http://wit.ksc.nasa.gov/spc/7_tools.cfm
  • “ Democratizing Statistics” refers to the will of Ishikawa to spread Quality control throughout the workplace. The desire to make Quality control comprehendible for all of the workers.
  • Also known as Ishikawa Diagrams and Cause and Effect Diagrams. By mapping out a company’s problem, new thoughts and ideas can arise to better the situation. Sheds light on situations. Diagrams begin with the problem to be solved in a rectangle.
  • Kaoru Ishikawa 1915 – 1989, Japanese Quality Pioneer Focused on quality statistics and total quality management throughout an organization
  • The other six tools are: Pareto Charts, Stratification, Check Sheets, Histograms, Scatter Diagrams, Shewhart’s Control Charts and Graphs.
  • Each causal level can be completed in order(i.e. secondary then tertiary), or by relationship (responsiveness then time)
  • Group answers should include the following: Worker dissatisfaction as the ‘head’ of the diagram Major causes should include environment, equipment, and management Secondary causes for environment Worker training, worker empowerment, cleanliness, wages, benefits, etc. Secondary causes for equipment: Effectiveness of equipment, age and maintenance requirements, lack of new technologies, etc. Secondary causes for Management: Leadership qualities, Involvement, Attention, Expertise, human relations, etc.
  • Histograms are used to show the different frequencies in a process. It is useful for identifying trends and relationships that can lead to quality improvements.
  • This slide is basically the beginning of my tutorial. I start off with the explanation of what exactly a Histogram is. A Histogram is a variation of a bar chart in which data values are grouped together and put into different classes. This grouping allows you see how frequently data in each class occur in the data set.
  • This slide gives further explanation about what a Histogram is. I explain the relationship between the height of the bars in the Histogram, to the amount of data values in each class. More data values mean taller bars. The opposite would be true for fewer data values, meaning lower bars for fewer data values. Next is a simple example of what a histogram might look like.
  • This is a simple example of what a Histogram looks like. This gives my audience at least some picture of what a very basic histogram would look like.
  • Here, I name some of the uses for a Histogram. I tell what makes this tool useful in making quality improvements. Since the histogram is such a useful tool, it can have many uses. Histograms can be used to display large amounts of data in a simple chart view. They can be used to find any patterns that the data might reveal. They can be used to tell relative frequency of occurrence for certain data values. They can also be used to see the distribution, or any variations in the data values. One other use would be to make predictions regarding the future based on the way the data values pan out.
  • This slide serves to give my audience some idea of where the Histogram came from. I tell a little bit about Isikawa and what he was about. Then I tied in his development of the Histogram. Kaoru Ishikawa was a pioneer in quality improvement, not only for Japan, but for the rest of the world as well. Although his practices weren’t very highly regarded in the U.S., any company that seeks to improve quality would ultimately have to use his practices. This is what makes his work so important.
  • This slide goes right along with the previous one. It explains that the Histogram was developed by Ishikawa, along with the other basic seven tools of quality. These tools were the major contribution of Ishikawa to the quality improvement community. The other six tools include Pareto Charts, Cause and Effect Diagrams, Check Sheets, Scatter Diagrams, Flowcharts, and Control Charts.
  • Histograms are a valuable tool for quality improvement, as long as you know how to use them properly. First, you have to pick a process that you would like to measure. This can be anything from number of items output per week, to the number of calls incoming per day. Basically anything that occurs over an extended period of time. You need to be able to collect a whole lot of data for a histogram, at least 100 data values. The more data values, the better. After you collect all of your data, you need to assemble a table of data values. The important thing here is that you must take into account the frequency of data values.
  • The next part in using a Histogram is to calculate some statistics so you can make a chart. You need to calculate the mean, minimum, maximum, standard deviation, class width, number of classes, skewness, and kurtosis. Mean is the average of all values. Minimum is the smallest value. Maximum is the biggest value. Standard Deviation is how widely spread the values are around the mean. Class Width is the x-axis distance between the left and right edges of each bar in the histogram . Number of Classes is the number of bars in the Histogram. Skewness is the alignment of the Histogram. Kurtosis is a measure of the pointiness of the distribution. After you calculate these statistics, you can create the actual histogram.
  • The five shapes that a histogram could take include: normal distribution, positively skewed, negatively skewed, bi-modal distribution, and multi-modal distribution.
  • After completing the Histogram, its’ use as a tool for quality improvement can be seen. You can look at the completed histogram and analyze its’ shape. Along with the statistics that you calculated, you can get a good idea of where any problems might be, or where to make any changes to the process.
  • These numbers represent the customers order at the order window at the pizza store. For example, the first customer didn’t order any pizza, the second ordered 2 slices, the third ordered 1 and so on. It should be noticed that the highest order was 7 slices and the lowest was 0. This is used to find the range which is used to find the column width for the histogram.
  • With this information computed, all that is left to do is chart the histogram.
  • Helpful in showing orders frequencies and variation.
  • Most common types of orders placed is valuable information. Knowing that the average customer will order 2 slices of pizza can be implemented into Acme’s strategic plan. By taking at least 2 slices up to the window at peak hours, this can improve Acme’s customer service and speed. It makes the line move much faster making the perceived quality higher for the customer.
  • Vilfredo Pareto (1848-1923) originated the 80/20 Rule, which states that 80% of the problems comes from only 20% of the causes. Pareto Analysis is very similar to Histograms but it incorporates this theory into it. Pareto Analysis adds weight to the most frequently occurring things.
  • The % column represents the slices percentage of total frequency. This dictates the order of the Pareto diagram which is always scaled according to size.
  • This sheds light on the most frequently ordered quantities. It is also common to plot percentages on the same graph.
  • Answer #1: The Pareto Analysis Shows percentages. Is ordered to reflect frequency of occurrences. Answer #2: Helps identify trends. Useful for quality improvements and planning processes.
  • The rectangle, diamond and line are the standard symbols for flowcharts. There can be extra/different symbols depending on the process/business. The important thing is that it is consistent and maps out the process efficiently. Once flowcharts are effectively drawn they can shed light on possible problems or improvements.
  • Acme’s flowchart
  • Answer: Since we know that 2 slices is the most common order we could possibly add a step between Time to close and take customer order. If we brought two slices up to the window during peak hours this would quicken service. There are multiple improvements that can be made on the process. The class can brainstorm on ways of improving this flowchart. Note that a decision must be made at each triangle before the next step can begin.
  • Scatter plots take place on an X and Y graph. Whichever variable is on the bottom should be the dependent variable. This means that the Y variable changes according to changes in X. In the upcoming example, Minutes cooking the pizza’s will directly affect the number of defective pies that are produced. Scatter plots are useful for finding direct or indirect relationships which can then be used to analyze/improve quality.
  • This is meant to show the data. It isn’t too difficult for students to see that there is a direct relationship between Minutes cooking and defects. But the Scatter Plot will make this easier to see.
  • There is a direct relationship between time spent cooking by employees and defects. As Time cooking increases, so does the amount of defects.
  • Answer: There is obviously some kind of process problem with the number of defective pies being produced. Maybe the cooks are getting sloppy from working too fast. Or maybe morale is low and there is just apathetic work being done. Whatever the case, if this was actually happening, quality improvements would have to be studied and implemented.
  • Run Charts are used to plot data based on time. It’s very useful for identifying trends and cycles. The X-axis is usually the time element and the y axis is the process to be tracked. The following slide shows another Acme example that should make this easy to understand.
  • Ask the class what trends they can identify. Week 2’s Thursday was a rainy day. Business Peaks between 1 and 3 each night so this is very valuable information to the management. Also with the exception of the rainy day, business seems to increase with warmer weather. Have the class come up with any other trends they can see or ideas to help improve quality based on this information. Such as higher staffing between 1 and 3 or higher inventory levels/preparation etc.
  • Control charts are a means of regulating a process. It tracks the output of a process and its conformance to the company’s standards. As long as the process stays within the upper and lower limits then the process is “safe” and normal. Any observations made outside of the limits are irregular and problematic. They need to be immediately researched to improve quality. A process that consistently stays “safe” is a good quality process.
  • X= mean The majority of observations have fallen close the average. The one that’s under the lower limit is irregular, it needs to be examined and fixed.
  • The average Diameter can be calculated by taking the average of a sample number of pizzas. As long as the sample’s average is close enough to 16 inches to satisfy management (ex. Within +/- .01 inches) then the average can be said to be 16 inches. Then from that management can decide what is the biggest/smallest allowable pies that are acceptable.
  • Monitoring the pizza process, this example shows how almost every pie is within specifications. The process should be analyzed to discover why the one small pie was produced and corrected to improve quality.
  • Once the process is fixed the Control Chart continues to flow, any further abnormalities also need to be studied and fixed.
  • All of these tools together can provide great process tracking and analysis that can be very helpful for quality improvements. These tools make quality improvements easier to see, implement and track.

9 basic seven tools of quality 9 basic seven tools of quality Presentation Transcript

  • The Basic Seven (B7) Tools of Quality"As much as 95% of quality related problems in the factory can be solved with seven fundamental quantitative tools." - Kaoru Ishikawa By Zaipul Anwar Business & Advanced Technology Centre, Universiti Teknologi Malaysia
  • What are the Basic Seven Tools of Quality? Fishbone Diagrams Histograms Pareto Analysis Flowcharts Scatter Plots Run Charts Control Charts
  • Where did the Basic Seven come from? Kaoru Ishikawa Known for “Democratizing Statistics” The Basic Seven Tools made statistical analysis less complicated for the average person Good Visual Aids make statistical and quality control more comprehendible.
  • The Basic Seven (B7) Tools of Quality Fishbone Diagrams No statistics involved Maps out a process/problem Makes improvement easier Looks like a “Fish Skeleton”
  • Fishbone (Cause and Effect or Ishikawa) Diagrams (1 of 4) Named after Kaoru Ishikawa  Japanese Quality pioneer Resembles skeleton of a fish Focus on causes rather than symptoms of a problem Emphasizes group communication and brainstorming Stimulates discussion
  • Fishbone (Cause and Effect or Ishikawa) Diagrams (2 of 4) One of Seven basic tools of Japanese Quality Leads to increased understanding of complex problems Visual and presentational tool
  • Use in Organizations (1 of 2) Can be used to improve any product, process, or service  Any area of the company that is experiencing a problem  Isolates all relevant causes
  • Creating Fishbone Diagrams (1 of 4)• As a group: 1. Establish problem (effect) -state in clear terms -agreed upon by entire group 2. Problem becomes the “head” of the fish -draw line to head (“backbone”)
  • Creating a Fishbone Diagram (2 of 4)3. Decide major causes of the problem - by brainstorming - if the effect or problem is part of a process the major steps in the process can be used4. Connect major causes to backbone of the fish with slanting arrows
  • Creating a Fishbone Diagram (3 of 4)5. Brainstorm secondary causes for each of themajor causes6. Connect these secondary causes to theirrespective major causes7. Repeat steps 5 & 6 for sub-causes dividingwith increased specificity - usually four or five levels
  • Creating a Fishbone Diagram (4 of 4)8. Analyze and evaluate causes and sub-causes-may require the use of statistical, analytical, and graphical tools9. Decide and take action
  • Example (1 of 4) Step 1 & 2: Poor Service (“backbone”) (“head”)
  • Example (2 of 4) Step 3 & 4: Responsiveness Appearance Poor Service Attention Reliability
  • Example (3 of 4) Step 5, 6, & 7: Appearance Responsiveness equipment time personnel facility accuracy Poor Service One on one courtesy service dependability Attention Reliability
  • Example (4 of 4) Step 8 & 9:  Use tools to analyze and evaluate causes  Pareto diagrams, charts, and graphs  Statistical analysis for causes in processes  Decide and take action  Use fishbone diagram, analysis and evaluations to find causes that can be fixed  Take action to eliminate and fix problem causes
  • Exercise Create a Fishbone (cause and effect, Ishikawa) Diagram for the following: Management at Ham Industries has noticed that the productivity of its workers is well below the standard. After interviewing its employees, it was noticed that a vast majority felt dissatisfied and unhappy with their work. Your boss has asked you and a group of your peers to find the causes of worker dissatisfaction . Include all possible causes to at least the secondary level.
  • The Basic Seven (B7) Tools of Quality Histograms Bar chart Used to graphically represent groups of data
  • What is a Histogram? A Histogram is a variation of a bar chart in which data values are grouped together and put into different classes. This grouping allows you see how frequently data in each class occur in the data set.
  • What is a Histogram (cont.) Higher bars represent more data values in a class. Lower bars represent fewer data values in a class. On the next slide is an example of what a Histogram looks like.
  • Example of a Histogram
  • Uses for a HistogramA Histogram can be used: to display large amounts of data values in a relatively simple chart form. to tell relative frequency of occurrence. to easily see the distribution of the data. to see if there is variation in the data. to make future predictions based on the data.
  • Where did the Histogram Come From? The Histogram was first implemented by Kaoru Isikawa, one of Japans’ most renowned experts on quality improvement. Isikawa spent his life trying to improve quality in Japan.
  • Where did the Histogram Come From? (cont.) His major contributions to quality improvement are known as the basic seven tools of quality. Included in his basic seven tools of quality is the Histogram.
  • How do Histograms Work? First, you need need to pick a process to analyze. Next, you need a large amount of data, at least 100 data values so that patterns can become visible. Then, you need to assemble a table of the data values that you collected with regards to frequency of data values.
  • How do Histograms Work? (cont) Next, you need to calculate some statistics for the Histogram, including: mean, minimum, maximum, standard deviation, class width, number of classes, skewness.... Then, you actually create the Histogram using these statistics.
  • How do Histograms Work? (cont) After you have created a Histogram, it will take one of five shapes: Normal Distribution:
  • How do Histograms Work? (cont) Positively Skewed: Negatively Skewed:
  • How do Histograms Work? (cont) Bi-Modal Distribution: Multi-Modal Distribution:
  • How do Histograms Work? (cont) Once your Histogram is complete, you can analyze its shape, as well as the statistics that you came up with. This analysis will help you to make better decisions toward quality improvements.
  • Constructing a HistogramFrom a set of data compute sum mean (x) Max Min Range (max-min)
  • Constructing a Histogram Use range to estimate beginning and end Calculate the width of each column by dividing the range by the number of columns Range = Width # of Columns
  • Acme Pizza Example Let’s say the owner wants a distribution of Acme’s Thursday Night SalesData Set from last Thursday(slices) 02122413121224341432232122122142212122121212121 21222121211222314223222123224224412223221224212 421721223121121222122121222424
  • Acme Pizza ExampleMean = 2.032258Max = 7Min = 0Range = 7QuestionFor 7 columns what would the width be? Range/Columns=7/7=1 slice
  • Acme Pizza Example Histogram 70 65 60# times ordered 50 40 33 30 20 12 8 10 0 0 1 0 1 2 3 4 5 6 7 Slices of Pizza
  • Constructing a HistogramHow is this helpful to Acme? 2 slices of pizza most common order placed Distribution of sales useful for forecasting next Thursday’s late night demand If you were an Acme manager how could you apply this information?
  • The Basic Seven (B7) Tools of Quality Pareto Analysis Very similar to Histograms Use of the 80/20 rule Use of percentages to show importance
  • Pareto Analysis, how to use it 1. Gather facts about the problem, using Check Sheets or Brainstorming, depending on the availability of information. 2. Rank the contributions to the problem in order of frequency. 3. Draw the value (errors, facts, etc) as a bar chart. 4. It can also be helpful to add a line showing the cumulative percentage of errors as each category is added. This helps to identify the categories contributing to 80% of the problem. 5. Review the chart – if an 80/20 combination is not obvious, you may need to redefine your classifications and go back to Stage 1 or 2.
  • Acme Pizza (Example 1)Slices Frequency % 0 1 .3 1 33 13.09 2 65 25.79 3 8 3.17 4 12 4.76 5 0 0 6 0 0 7 1 .3
  • Acme Pizza (Example 1) The completed Pareto Analysis results in the following graph: 70 # times ordered 60 50 40 30 20 10 0 2 1 1 2 4 3 3 4 7 5 5 6 6 7 Slices of Pizza
  • Acme Pizza (part 2) Critical Thinking How does the Pareto Analysis differ from the Histogram? How can this be a useful tool to the Acme boss?
  • A series of Pareto charts drill down to more detail (Example 2) : Fault by Main Cause 1st level 70 100 Analysis gives 2nd level 60 80 “Design” Analysis gives 50 as main cause breakdown of Percent 60 Count 40 of failure “Design” 30 40 20 20 10 0 0 Design Faults t n en erDefect De s ig Co mp on Bu ild Oth 100 Count 57 13 4 2 50Percent 75.0 17.1 5.3 2.6 80Cum % 75.0 92.1 97.4 100.0 40 Percent 60 Count 30 40 20 10 20 0 0 le rs ule n du oto rt od atio o Sta rM libr n Defect nn ec tM r qu eM Co ld du c e IC Ca IOP Imo Co To ns AS Tra Count 21 10 8 8 5 3 2 Percent 36.8 17.5 14.0 14.0 8.8 5.3 3.5 Cum % 36.8 54.4 68.4 82.5 91.2 96.5 100.0
  • The Basic Seven (B7) Tools of Quality Flowcharts A graphical picture of a PROCESS Process Decision The process flow
  • FlowchartsDon’t Forget to: Define symbols before beginning Stay consistent Check that process is accurate
  • Acme Pizza Example (Flowchart)Window Take Customer Money? (start) Order yes Get Pizza noLockup Put More in Oven 2 Pies noAvailable? yes Time no to close? yes Take to Customer
  • How can we use the flowchart to analyze improvement ideas from the Histogram?Window Take Customer Money? (start) Order yes Get Pizza noLockup Put More in Oven 2 Pies noAvailable? yes Time to close? no Take to Customer yes
  • Want some practice?Make a flowchart for: Taking a shower Cooking dinner Driving a car Having a party Creating a FlowchartAny other processes you can think of?
  • The Basic Seven (B7) Tools of Quality Scatter Plots 2 Dimensional X/Y plots Used to show relationship between independent(x) and dependent(y) variables
  • Acme Pizza (Scatter Diagram)Minutes Cooking Defective Pies 10 1 45 8 30 5 75 20 60 14 20 4 25 6 In this simple example, you can find the existing relationship without much difficulty but…
  • Scatter Diagrams 25•Easier to see directrelationship 20 Defective Pizzas 15 10 5 0 0 20 40 60 80 Time Cooking (minutes)
  • Scatter Diagrams As a quality tool What does this tell Acme management about their processes? Improvements? 25 20 Defective Pizzas 15 10 5 0 0 20 40 60 80 Time Cooking (minutes)
  • The Basic Seven (B7) Tools of Quality Run charts Time-based (x-axis) Cyclical Look for patterns
  • Run Charts 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Slices/hour 8 9 10 11 12 1 2 3 4 8 9 10 11 12 1 2 3 4 8 9 10 11 12 1 2 3 4 Time PM- AM PM- AM PM- AM Thursday Thursday Thursday
  • The Basic Seven (B7) Tools of Quality Control Charts Deviation from Mean Upper and Lower Spec’s Range
  • Control ChartsUpper Limit XLower Limit Unacceptable deviation
  • Control ChartsAcme Pizza Management wants to getin on the control chart action•Average Diameter = 16 inches•Upper Limit = 17 inches•Lower Limit = 15 inches
  • Acme example Control ChartsUpper Limit17 inches X 16 inches=Lower Limit15 Inches Small Pie
  • Acme example #50 Control Charts•Pies within specifications wereacceptable•One abnormally small pie is“uncommon”•Should be examined for quality control
  • Logical Order for B7 Tools Big Data Data ProblemPicture Collection Analysis Identification Prioritization Cause CauseFlow Flow Check Check Pareto Pareto Histograms Histograms &&Chart Chart Sheet Sheet Analysis Analysis Effect Effect Scatter Scatter Diagrams Diagrams Control Control Charts Charts
  • Summary Basic Seven Tools of Quality Measuring data Quality Analysis “Democratized statistics”
  • Bibliography Foster, Thomas. Managing Quality. An IntegrativeApproach. Upper Saddle River : Prentice Hall, 2001. Stevenson, William. “Supercharging Your Pareto Analysis.” Quality Progress October 2000: 51-55. “Dr Kaoru Ishikawa.” Internet “http://www.dti.gov.uk/mbp/bpgt/m9ja00001/m9ja0000110.html.” 16 February 2001. “Chemical and Process Engineering.” Internet. “http://lorien.ncl.ac.uk/ming/spc/spc8.htm.” 17 February 2001.