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The Magnificent Seven Tools for Process Quality Improvement

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Title: The Magnificent Seven Tools for Process Quality Improvement


1
The Magnificent Seven Tools for Process Quality
Improvement
  • BUAD 820
  • Data Analysis Quality Management

2
The Magnificent Seven
  • The seven major tools are
  • 1) Flowchart or process mapping
  • 2) Check Sheet
  • 3) Histogram or Stem and Leaf plot
  • 4) Pareto Chart
  • 5) Cause and Effect Diagram
  • 6) Scatter Diagram
  • 7) Control Chart

3
Flowcharts
  • Identifies the sequence of activities or the flow
    of materials and information in a process
  • Can be useful in pin-pointing problems,
    error-proofing the process, streamlining it by
    eliminating non-value added steps, and in
    reducing variation

4
Example of a process flow chart
5
Check Sheets
  • Check sheets customized data collection forms
    designed to collect relevant operating data about
    the process under investigation
  • A check sheet can range from being very simple,
    to very complex involving different dimensions
    (time of the day, day of the week, source of
    production, raw material used)
  • You can read more about check sheets here
    http//www.hci.com.au/hcisite3/toolkit/data.htm

6
Check sheet for the process of handling of
returned goods at a company
7
Data organizing tools
  • Once collected, raw data is typically summarized
    (reduced, or compacted) this can be done in
    several ways
  • Stem-and-leaf plot
  • Frequency distributions
  • Histograms

8
Raw data on the bursting strength of 100 randomly
selected soda bottles
9
Stem and leaf plot for bursting strength data
10
The Frequency Distribution and Histogram
  • Frequency Distribution
  • Arrangement of data by magnitude
  • More compact than a stem-and-leaf display
  • Graphs of observed frequencies are called
    histograms.

11
Frequency distribution for soda bottle bursting
strength data
12
Histogram for soda bottle bursting strength data
13
Graphical Displays
  • What is the overall shape of the data?
  • Are there any unusual observations?
  • Where is the center or average of the data
    set located?
  • What is the spread of the data? Is the data
    spread out or close to the center?

14
Pareto Chart
  • The Pareto chart is a frequency distribution (or
    histogram) of attribute data arranged by
    category.
  • Plot the frequency of occurrence of each defect
    type against the various defect types.

15
Reasons for nonconforming soda bottles
16
Pareto chart for reasons of nonconformance
17
Cause and Effect Diagram
  • Once a defect, error, or problem has been
    identified and isolated for further study,
    potential causes of this undesirable effect must
    be analyzed
  • Cause and effect diagram is a useful way of
    categorizing potential causes leading to a
    quality problem
  • Read more about cause and effect diagram here
    http//www.hci.com.au/hcisite2/toolkit/causeand.ht
    m

18
How to Construct a Cause-and-Effect Diagram
  • Define the problem or effect to be analyzed.
  • Form the team to perform the analysis. Often the
    team will uncover potential causes through
    brainstorming.
  • Draw the effect box and the center line.
  • Specify the major potential cause categories and
    join them as boxes connected to the center line
  • Identify the possible causes and classify them
    into the categories in step 4. Create new
    categories, if necessary.
  • Rank order the causes to identify those that seem
    most likely to impact the problem.
  • Take corrective action.

19
Cause and effect diagram for incorrect deliveries
made by a package delivery company
20
Scatter Diagram
  • The scatter diagram is a plot of two variables
    that can be used to identify any potential
    relationship between the variables
  • The shape of the scatter diagram often indicates
    what type of relationship may exist

21
Scatter plot for relationship between apartment
size and its rent (n25)
Scatter plot suggests that there is a positive,
linear relationship between Rent and Size
22
Control Charts
  • Types the control chart
  • Variables Control Charts
  • These charts are applied to data that follow a
    continuous distribution (measurement data)
  • Attributes Control Charts
  • These charts are applied to data that follow a
    discrete distribution

23
Control chart examples
  • Attribute chart for percent of non-ready rooms at
    a major hotel
  • Variable chart for average delivery time for
    baggage handlers at the same hotel

24
Popularity of Control Charts
  • 1) Control charts are a proven technique for
    improving productivity.
  • 2) Control charts are effective in defect
    prevention.
  • 3) Control charts prevent unnecessary process
    adjustment.
  • 4) Control charts provide diagnostic information.
  • 5) Control charts provide information about
    process capability.
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