Process Improvement Methodologies

1
Process Improvement Methodologies

• References (sources of graphics)
• Fiore, Clifford, Accelerated Product Development
  Combining Lean and Six Sigma for Peak
  Performance, Productivity Press, NY, NY, 2005.
• Hamilton, Bruce, Toast Kaizen, An Introduction
  to Continuous Improvement Lean Principles,
  Greater Boston Manufacturing Partnership,
  University of Massachusetts, Boston, MA, 2005
  (DVD).
• Insights On Implementation-Improved Flow
  Collected Practices and Cases, Ralph Bernstein,
  Editor, Productivity Press, 2006.
• Jacobs, Robert F. and Chase, Richard B.,
  Operations and Supply Management The Core, 2nd
  Edition, McGraw-Hill/Irwin, NY, NY, 2008.
• Nahmias, Steven, Production Operations
  Analysis, 5th Edition, McGraw-Hill/Irwin, NY, NY,
  2005.
• Nave, Dave, How to Compare Six Sigma, Lean, and
  the Theory of Constraints, Quality Progress,
  March 2002, pgs 73 78.

2
Comparison of Three Commonly Adopted Improvement
Methodologies

• See reference, How To Compare Six Sigma, Lean and
  the Theory of Constraints
• Comparing the main points of the three
  improvement methodologies Six Sigma, Lean
  Thinking, and Theory of Constraints

3
Six Sigma Approach

• Define, measure, analyze, improve, control
  (DMAIC) cycle

4
Six Sigma Tools

• Tools common to other quality programs are used
  in Six Sigma
• Failure mode and effects analysis (FMEA)
• Structured approach to identify, estimate,
  prioritize, and evaluate risk of possible failure
  at each stage of a process
• Risk priority number (RPN) is calculated and is
  based on
• Extent of damage resulting from failure
  (severity)
• Probability failure takes place (occurrence)
• Probability of detecting the failure (detection)
• High RPN items are designated for improvement
  first
• Example
• Design of experiments (DOE)
• Statistical approach used for determining the
  cause-and-effect relationship between process
  variables and an output variable
• Approach allows for experimentation with many
  variables simultaneously

5
Six Sigma Quality

• To achieve a Six Sigma quality (according to the
  assumptions used by Motorola and GE) a process
  must produce no more than 3.4 defects per million
  opportunities
• Assuming a process follows a normal distribution
  and given design limits of 6 s there would be 2
  defective parts per billion (0.000000002 fraction
  defective)
• Motorolas and GEs value of 3.4 defects per
  million is due to the fact that a shift of 1.5 s
  in the process mean is assumed
• An example process
• spec 1.250 0.005, µ 1.250, s 0.002, UCL
  LCL 3 s (µ and s estimated from sample
  parameters)
• Six Sigma process µ 1.250 in, s 0.000833 in
  (0.005 in/6)
• Six Sigma process with a 1.5 s shift to the mean
  µ 1.25125 in, s 0.000833 in

6
Process Capability Index

• Process capability index (Cpk)
• For (Motorolas and GEs) Six Sigma process

7
Example Problem
8
Lean Case Study

• Quality Parts Company