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Statistical Process Contol

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Statistical Process Contol Powered By Docstoc
					Handout MK. Pengawasan Mutu 2011/2012




     STATISTICAL PROCESS
          CONTROL
SPC
 Statistical studies can be classified into two
  types: enumerative and analytic.
 An analytic study considers the population in
  a dynamic sense, and its objective is to predict
  or improve a process or product in the future.
 In the quality management field, statistical
  methods can be used for analyzing numerical
  data focusing on results.
 “One picture is worth a thousand words”.
        The primary goals of SPC
 Minimize production costs.
 Attain a consistency of products and services that
  will meet production specifications and customer
  expectations.
 Create opportunities for all members of the
  organization to contribute to quality improvement.
 Help both management and employees make
  economically sound decision about actions affecting
  the process.
Statistical tools used in QC applications
   Seven basic tools that have been used
    successfully in food industries for decades:
       Flow chart
       Cause and effect diagram
       Control chart
       Histogram
       Check sheet
       Pareto chart
       Scatter diagram
                        Flow Chart
                                       In a flowchart, the description
   A series of blocks with each        of each process is written
    block representing one
    major process, that                 inside the blocks. Any other
    describes an operation that         significant information is
    is studied or is used to plan       usually written outside
    stages of a project.                blocks. Each block is
                                        connected with an arrow to
   Flowcharts provide an               show where that process
    excellent form of
    documentation for a                 leads.
    process operation, and
    often are useful when
    examining how various
    steps in an operation work
    together.
                    Flow Chart
 A flowchart is important project development and
  documentation tool.
 It visually records the steps, decisions, and actions of
  any manufacturing or service operation and defines
  the system, its key points, activities and role
  performances.
                   Histogram
 A histogram is used to graphically summarize and
  display the distribution of a process dataset.
 It can be constructed by segmenting the range of the
  data into equal-sized bins (segments, groups, or
  classes).
 The vertical axis of the histogram is the frequency
  (the number of counts for each bin), and the
  horizontal axis is labeled with the range of the
  response variable.
 The number of data points in each bin is determined
  and the histogram constructed. The user defines the
  bin size.
Histogram
       Cause and Effect Diagram
 A problem is                  A similar diagram can
  systematically tracked         be used to
  back to possible causes.       systematically search
 The diagram organizes          for solutions to a
  the search for the root        problem.
  cause of a problem.
         Cause and Effect Diagram
 This diagram is created by Kaoru Ishikawa, one of the
  founding fathers of modern management  Ishikawa
  diagram/ fishbone diagram.
 Causes are arranged according to their level of importance or
  detail, resulting in a depiction of relationships and hierarchy
  of events.
 Cause-and-effect diagrams are typically constructed through
  brainstorming techniques.
 The diagram are frequently arranged into the four most
  common major categories:
   – Manpower, methods, materials, and machinery (for
      manufacturing)
   – Equipment, policies, procedures, and people (for
      administration and planning)
Cause and Effect Diagram
Example Cause-Effect Diagram
                   Scatter Diagram
   It is similar to a line graph
    except that the data point
    are plotted without a
    connecting line drawn
    between them.
   Scatter charts are suitable
    for showing how data
    points compare to each
    other.
   At least 2 measured objects
    are needed for the query
    (one for x-axis and one for
    y-axis)
                   Scatter Diagram
   Scatter diagrams are used to study possible relationships
    between 2 variables. Although these diagrams can’t prove
    that one variable causes the other, they do indicate the
    existence of a relationship. More than one measure object
    can be used for the y-axis as long as the objects are of the
    same type and scale.

   The purpose of scatter diagram is to display what happens to
    one variable when the other variable is changed. The diagram
    is to test the theory that the two variables are related.

   The slope of the diagram indicates the type of relationship
    that exist.
                     Pareto Charts
   The Pareto Principle is used      Pareto charts are used to
    by business and industry to        decide what steps need to
    work to continually improve        be taken for quality
    quality.                           improvement.

   Quality improvement
    involves tackling one issue
    at a time. By addressing the
    ones causing the most
    difficulty, improvements
    can be made & monitored
    for continuous progress.
                     Pareto Charts
   The number of occurrences or the costs of occurrences of
    specific problems are charted on a bar graph. The largest bars
    indicate the major problems and are used to determine the
    priorities for problem solving.
   A Pareto chart graphically summarizes and displays the
    relative importance of the differences between groups of
    data.
   A Pareto chart can be constructed by segmenting the range of
    the data into groups.
   The number of data points in each group is determined and
    the Pareto chart constructed; however, unlike the bar chart,
    the Pareto chart is ordered in descending frequency
    magnitude.
                   Control Charts
   A broken line graph          proportion of defective
    illustrates how a process    pieces changes over time.
    behaves over time.
   Samples are periodically
    taken, checked, or
    measured, and the results
    are plotted on the chart.
   The charts can show how
    the specific measurement
    changes, how the variation
    in measurement changes,
    or how the
                  Control Charts
   A control chart is a graphic display of the actual
    quality performance judged against a reference
    frame showing a central line representing the
    average quality value and upper and lower lines
    called the upper control limit (UCL) and lower
    control limit (LCL).
                  Control Charts
   Control charts are used to:
     • find sources of special-cause variation (variation
       that is caused by specific, fixable occurrences)
     • measure the extent of common-cause variation
       (variation that inherent in the process)
     • maintain control of a process that is operating
       effectively.
                 Control Charts
   Types of control charts:
    Control variable charts: X-bar and R charts
    Attribute charts: p, np, c, and u charts
               X-Bar and R-Charts
   The most commonly used of        They can be used for
    the control charts and the        controlling every step of
    most valuable.                    production process, for the
   They are ideal tools to           acceptance/ rejection of
    improve product quality           lots, and for early detection
    and process control and           of equipment or process
    help to drastically reduce        failure.
    scrap and rework while
    assuring the production of
    only satisfactory products.
                X-Bar and R Charts
   The X-bar and the R charts are used for control variables that
    are expresses in the discrete numbers such as inches, pounds,
    pH units, angstrom, percent solids, or degree of temperature
    and so on.
   The R chart is developed from the ranges of each subgroup
    data, which are calculated by subtracting the maximum and
    the minimum value in each subgroup.
   Since the R chart indicates that the process variability is in
    control, the X-bar chart can then be constructed. The center
    line is mean of the sample means.
Example of X-Bar and R Charts
   The chart of a X-bar and R should not be used
    with samples ≥10 or when the sample size is
    not constant.
               Attribute Charts
 They are also used for control of defect analysis.
 They are particularly useful for controlling raw
  material and finished product quality and for
  analyzing quality comments in consumer letters.
 Attributes control charts are used when
  measurements are too difficult to take, when
  measurements do not apply to the situation (such as
  visual checks for flaws), or when they are too costly
  to take because of time lost.
                   Attribute Charts
   p chart
     • It is the most commonly used attributes chart.
     • The value p is the fraction, or percentage, of the number
       items checked that are defective (unacceptable).
     • Large samples of 50 or more are needed.
   np chart
     • The np chart is sometimes used instead of the p chart
       because it is easier; np is simply the number rather than
       the fraction, of defective items in the sample.
     • The p and np charts differ by that constant divisor, so they
       do the same job with respect to control, process analysis,
       etc.
                 Attribute Charts
   c chart
    The c chart tracks the number of defects in constant size
    units. There may be a single type of defect or different
    types, but c chart tracks the total number of defects in
    each unit.
   u chart
•   When samples of different size are taken, u is the
    average number of defects per unit.
•   The u chart is quite similar to the c hart in function.
Example p chart:
Example np chart
Example c chart
                        References
   Hubbard, M.R. 2003. Statistical Quality Control for the Food
    Industry, 3rd Ed. Kluwer Academic/ Plenum Publisher. New York.
   Vosconcellos, A. 2004. Quality Assurance For The Food Industry.
    CRC Press. Boca Raton.

				
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posted:5/28/2012
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