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Statistical Quality Control

• Statistical Quality Control uses statistical techniques & sampling to monitor & test the quality of goods & services.
  • Acceptance sampling determines to accept or reject a product
  • Statistical process control determines if process is operating within acceptable limits.
  • Inspection is the appraisal of goods/services against standards.
  • Inspection
• Statistical Process Control Planning Process
  • Define important quality characteristics,
  • and how to measure
    • For each characteristic,
      • a. Determine a quality control point
      • b. Plan
        • how to inspect
          • Where to Inspect in the Process
            • Raw materials
            • and purchased parts
            • Before a costly operation
            • Before an irreversible process
            • Before a covering process
            • Finished products
        • how much to inspect
        • where centralized or on-site
          • Centralized vs. On-Site Inspection
      • c.
        • Plan the corrective action process.
          • The Quality Control Steps
          • Type of Variations
          • Control Charts
          • Designing Control Charts
          • Individual Unit and Moving Range Charts
          • Control Charts for Attributes
          • Using Control Charts
• Statistical Process Control (SPC) Steps
  • Take periodic samples from process
    • Compare to predetermined limits
      • If outside limits, stop process and take corrective action
      • If inside limits, continue process
  • Types of Variations
    • Random variation: Natural variations in the output of process, created by countless minor factors
    • Assignable variation: A variation whose source can be identified
  • Control Chart
    • Purpose: to monitor process output to distinguish between random and assignable variation
    • A time ordered plot of sample statistics (e.g. means) obtained from an ongoing process
    • Upper and lower control limits define the range of acceptable variation
    • Control Limits: The dividing lines between random and nonrandom deviations from the process mean
      • Upper and Lower Control Limits for Sample Mean Chart
    • Designing Control Charts
      • 1. Determine a sample size
      • 2. Obtain 20 to 25 samples
      • 3. Establish and graph preliminary control limits
      • 4. Plot sample statistic values on control chart
      • 5. Are any points outside control limits (CL)?
        • a. NO
        • assume no assignable cause
        • b. YES
        • investigate and correct
    • Control Charts for Variables
      • Sample Mean control charts
        • used to monitor the mean of a process.
        • x-bar charts
      • Sample Range control charts
        • used to monitor the process dispersion
        • R charts
• Control Charts for Attributes
  • p-Chart - Control chart used to monitor the proportion of defectives in a process
    • When observations can be placed into two categories.
      • Good or bad
      • Pass or fail
      • Operate or don’t operate
    • When the data consists of multiple samples of several observations each
  • c-Chart - Control chart used to monitor the number of defects per unit
    • only when number of occurrences per unit of measure can be counted; non-occurrences cannot be counted.
      • Scratches, chips, dents, or errors per item
      • Cracks or faults per unit of distance
      • Breaks or tears per unit of area
      • Bacteria or pollutants per unit of volume
      • Calls, complaints, failures per unit of time
• Process Capability
  • Design specifications
    • Range of acceptable values established by engineering design or customer requirements
  • Process variability
    • Natural variability in a process
  • Process capability
    • Process variability relative to specification
• six sigma quality
  • Improve proces capacity
    • Simplify
    • Standard-ize
    • Mistake-proof
    • Upgrade equipment
    • Automate
  • Design of Experiments
    • Taguchi suggested a more concise set of experiments by changing the level of more than one factor at a time
    • Identify controllable factors that could influence variation
    • Set each factor to 2 or more levels,
    • Measure the variation in the process