9 basic seven tools of quality

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9 basic seven tools of quality

  1. 1. 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
  2. 2. What are the Basic Seven Tools of Quality? Fishbone Diagrams Histograms Pareto Analysis Flowcharts Scatter Plots Run Charts Control Charts
  3. 3. 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.
  4. 4. 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”
  5. 5. 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
  6. 6. 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
  7. 7. 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
  8. 8. 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”)
  9. 9. 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
  10. 10. 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
  11. 11. 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
  12. 12. Example (1 of 4) Step 1 & 2: Poor Service (“backbone”) (“head”)
  13. 13. Example (2 of 4) Step 3 & 4: Responsiveness Appearance Poor Service Attention Reliability
  14. 14. 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
  15. 15. 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
  16. 16. 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.
  17. 17. The Basic Seven (B7) Tools of Quality Histograms Bar chart Used to graphically represent groups of data
  18. 18. 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.
  19. 19. 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.
  20. 20. Example of a Histogram
  21. 21. 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.
  22. 22. 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.
  23. 23. 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.
  24. 24. 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.
  25. 25. 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.
  26. 26. How do Histograms Work? (cont) After you have created a Histogram, it will take one of five shapes: Normal Distribution:
  27. 27. How do Histograms Work? (cont) Positively Skewed: Negatively Skewed:
  28. 28. How do Histograms Work? (cont) Bi-Modal Distribution: Multi-Modal Distribution:
  29. 29. 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.
  30. 30. Constructing a HistogramFrom a set of data compute sum mean (x) Max Min Range (max-min)
  31. 31. 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
  32. 32. 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
  33. 33. Acme Pizza ExampleMean = 2.032258Max = 7Min = 0Range = 7QuestionFor 7 columns what would the width be? Range/Columns=7/7=1 slice
  34. 34. 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
  35. 35. 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?
  36. 36. 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
  37. 37. 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.
  38. 38. 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
  39. 39. 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
  40. 40. 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?
  41. 41. 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
  42. 42. The Basic Seven (B7) Tools of Quality Flowcharts A graphical picture of a PROCESS Process Decision The process flow
  43. 43. FlowchartsDon’t Forget to: Define symbols before beginning Stay consistent Check that process is accurate
  44. 44. 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
  45. 45. 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
  46. 46. 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?
  47. 47. 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
  48. 48. 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…
  49. 49. Scatter Diagrams 25•Easier to see directrelationship 20 Defective Pizzas 15 10 5 0 0 20 40 60 80 Time Cooking (minutes)
  50. 50. 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)
  51. 51. The Basic Seven (B7) Tools of Quality Run charts Time-based (x-axis) Cyclical Look for patterns
  52. 52. 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
  53. 53. The Basic Seven (B7) Tools of Quality Control Charts Deviation from Mean Upper and Lower Spec’s Range
  54. 54. Control ChartsUpper Limit XLower Limit Unacceptable deviation
  55. 55. Control ChartsAcme Pizza Management wants to getin on the control chart action•Average Diameter = 16 inches•Upper Limit = 17 inches•Lower Limit = 15 inches
  56. 56. Acme example Control ChartsUpper Limit17 inches X 16 inches=Lower Limit15 Inches Small Pie
  57. 57. Acme example #50 Control Charts•Pies within specifications wereacceptable•One abnormally small pie is“uncommon”•Should be examined for quality control
  58. 58. 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
  59. 59. Summary Basic Seven Tools of Quality Measuring data Quality Analysis “Democratized statistics”
  60. 60. 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.

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