Page 83 - Six Sigma Advanced Tools for Black Belts and Master Black Belts

OTE/SPH
OTE/SPH
Char Count= 0
JWBK119-05
68 August 31, 2006 2:55 Fortifying Six Sigma with OR/MS Tools
5.5 CONCLUSIONS
Regardless of which industrial sector a BB is being employed in, he needs to adopt a
systems view of the operations of an enterprise. Current BB training programs are no
longer adequate for the increasingly demanding customers of the twenty-frist century.
A new breed of BBs will need to integrate OR/MS techniques into their Six Sigma
toolset so that it can remain relevant. A new roadmap is formulated and presented in
Table 5.3 to meet these emerging needs.
Not all the OR/MS tools will be used in a project, but they serve as a re-
minder/checklist. In this way, a BB can remain focused on the project while being
alert to other tools that may be useful in providing a solution. It could be argued that
a Six Sigma BB armed with OR/MS techniques would operate like a ‘Super Belt’, with
breath and depth well beyond what is found in the routine toolset of BBs coming from
a regular Six Sigma training conveyor belt.
In addition to OR/MS techniques, there is also an emerging trend of integrating
artificial intelligence and information systems technologies, such as data mining, 8
fuzzy logic and neural networks, into Six Sigma programs -- in particular, DFSS for
software development. As the scope of Six Sigma application expands with time, more
cross-functional tools will be integrated with Six Sigma to achieve even wider and
deeper business performance improvement. The current integration of OR/MS tools
is only part of the itinerary in the journey towards Six Sigma excellence.

REFERENCES

1. Goh, T.N. (2002) A strategic assessment of Six Sigma. Quality and Reliability Engineering
International, 18, 403--410. See also Chapter 2, this volume.
2. Hillier, F.S. and Lieberman, G.J. (2001) Introduction to Operations Research, 7th edition Boston:
McGraw-Hill.
3. Harry, M.J. and Schroeder, R. (2000) Six Sigma: The Breakthrough Management Strategy Revo-
lutionizing the World’s Top Corporations. New York: Dowbleday.
4. Snee, R.D. (2000) Guest editorial: Impact of Six Sigma on quality engineering. Quality En-
gineering, 12(3), ix--xiv.
5. Montgomery, D.C. (2001) Editorial: Beyond Six Sigma. Quality and Reliability Engineering
International, 17(4), iii--iv.
6. Tang, L.C. and Paoli, P.M. (2004) A spreadsheet-based multiple criteria optimization frame-
work for Quality Function Deployment. International Journal of Quality and Reliability Man-
agement, 21(3), 329--347.
7. Recker, R. and Bolstorff, P. (2003) Integration of SCOR with Lean & Six Sigma. Supply-Chain
Council, Advanced Integrated Technologies Group.
8. Goh T.N. (2002) The role of statistical design of experiments in Six Sigma: Perspectives of a
practitioner. Quality Engineering, 14(4), 659--671.
9. Martello, S. and Toth, P. (1990) Knapsack Problems: Algorithm and Computer Implementations.
New York: John Wiley & Sons, Inc.
10. Zhang, C.W. and Ong, H.L. (2004) Solving the biobjective zero-one knapsack problem by
an efficient LP-based heuristic. European Journal of Operational Research, 159(3), 545--557.
11. Tang, L.C. and Xu, K. (2002) A unified approach for dual response surface optimization.
Journal of Quality Technology, 34(4), 437--447. See also Chapter 20, this volume.
12. Lam, S.W. and Tang, L.C. (2005) A graphical approach to the dual response robust design
problem. In Proceedings of the Annual Reliability and Maintainability Symposium, pp. 200--206.
Piscataway, NJ: Institute of Electrical and Electronics Engineers.