Process Improvement Methodologies
References (sources of graphics):
(1) Fiore, Clifford, Accelerated Product Development: Combining Lean and Six Sigma for
Peak Performance, Productivity Press, NY, NY, 2005.
(2) Hamilton, Bruce, “Toast Kaizen, An Introduction to Continuous Improvement & Lean
Principles,” Greater Boston Manufacturing Partnership, University of Massachusetts,
Boston, MA, 2005 (DVD).
(3) Insights On Implementation-Improved Flow: Collected Practices and Cases, Ralph
Bernstein, Editor, Productivity Press, 2006.
(4) Jacobs, Robert F. and Chase, Richard B., Operations and Supply Management: The
Core, 2nd Edition, McGraw-Hill/Irwin, NY, NY, 2008.
(5) Nahmias, Steven, Production & Operations Analysis, 5th Edition, McGraw-Hill/Irwin,
NY, NY, 2005.
(6) Nave, Dave, “How to Compare Six Sigma, Lean, and the Theory of Constraints,”
Quality Progress, March 2002, pgs 73 – 78.
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
Six Sigma Approach

Define, measure, analyze, improve, control (DMAIC) cycle
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
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 σ there would be 2 defective parts per billion
(0.000000002 fraction defective)
Motorola’s and GE’s value of 3.4 defects per million is due to the fact
that a shift of 1.5 σ in the process mean is assumed
An example process
– spec = 1.250 ± 0.005, μ = 1.250, σ = 0.002, UCL & LCL = 3 σ (μ and σ
estimated from sample parameters)
– Six Sigma process: μ = 1.250 in, σ = 0.000833 in (0.005 in/6)
– Six Sigma process with a 1.5 σ shift to the mean: μ = 1.25125 in, σ =
0.000833 in
Process Capability Index

Process capability index (Cpk)
   LSL USL   
C pk  min 
or
3 
 3
 ,   estimated from sample parameters

For (Motorola’s and GE’s) Six Sigma process
4.5 
 7.5
C pk  min 
or
 min 2.5 or 1.5  1.5

3 
 3
Example Problem
Lean Case Study

Quality Parts Company