Best Practices -- Where Lean Meets Six Sigma

Dec. 21, 2004
Bringing the two concepts together delivers faster results.

With all the fervor of the freshly converted, proponents of Six Sigma and lean manufacturing have frequently clashed over the alleged superiority of one ideology above the other, fighting over resources and conflicting cultural approaches to improvement. But in recent years practitioners have begun to integrate elements of the two strategies. "Most lean programs -- especially those rooted in kaizen breakthroughs -- are centered on teamwork. Too often, Six Sigma [has] had an elitist strain, with black belts left alone to crunch numbers and work on long projects in offices far from the factory floor," states Anand Sharma and Patricia E. Moody co-authors of "The Perfect Engine" (2001, The Free Press), which touts the benefits of a "LeanSigma" approach to operational improvement . Bringing the two concepts together delivers faster results by establishing baseline performance levels and focusing the use of statistical tools where they will have the most impact. Most companies using both methodologies began by applying basic lean-manufacturing techniques -- the 5Ss, standardized work and the elimination of waste. "As they reduced unneeded inventory, like lowering a water line to expose the rocks, they discovered the need for even more advanced methods of uncovering the root cause of abnormalities," note Sharma and Moody. Once lean techniques eliminate much of the noise from a process, Six Sigma offers a sequential problem-solving procedure, the MAIC cycle (design/measure, analyze, improve and control), and statistical tools so that potential causes are not overlooked, and viable solutions to chronic problems can be discovered. "If you do just Six Sigma, you're not going to maximize the potential of your organization. You have to do both," says Mike Carnell, president of Six Sigma Applications. "Lean's really an enabler for Six Sigma." Carnell is co-author with Barbara Wheat and Chuck Mills of a modern fable that chronicles the work of a consultant as she helps a manufacturer adopt a combination of lean and Six Sigma principles. The book, "Leaning into Six Sigma" (2001, Publishing Partners), begins with first impressions, basic housekeeping, work cells, preventive maintenance, and moves through gage repeatability and reproducibility, Design of Experiments (DOE) and Analysis of Variance (ANOVA), explaining the various concepts along the way. "The point of Six Sigma is not, and never was, to introduce new tools," says the primary character. "We really don't need any new tools at this point, since we rarely use the more sophisticated ones that we have. The Six Sigma Methodology focuses on being able to link the tools together into a logical flow." Management direction and project selection are key. Highly trained black belts shouldn't be spending months on projects that won't have a bottom-line impact. Conversely, the solutions to many complex and long-standing problems can't be resolved using intuitive methods in a week or less. "There are some real basic skills -- TQM, green belt and yellow belt kind of skills -- that are going to fix a huge amount of your problems," says Carnell. "If you go and make everything a Six Sigma problem, you're going to constipate your system and waste a lot of resources."

Lean Techniques And Principles
  • Workplace organization
  • 5S
  • Standardized work
  • Waste identification and elimination (seven elements of waste)
  • Value-stream mapping
  • Team-based, multi-skilled workforce
  • Kaizen events (one week)
  • Jidoka (Error proofing)
  • Just-in-time
  • Cellular manufacturing
  • One piece flow (takt time)
  • Set-up time reduction (SMED)
  • Pull system (kanbans)
  • Production smoothing
  • Balanced work flow
  • Inventory Reduction
  • Visual Management
  • Total Productive Maintenance (TPM)
    Six Sigma Techniques And Principles
  • MAIC -- Design/Measure, Analyze, Improve, Control
  • Yellow, green, black and master black belts
  • Variation reduction
  • Project focus (one to three months)
  • Statistical process control (Cp, Cpk)
  • Measurement system assessment (Gage R&R)
  • Root cause analysis and hypothesis tests
  • Design of Experiments, Taguchi Methods
  • Regression analysis
  • Analysis of Variance (ANOVA)
  • FMEA (Failure Modes and Effects Analysis)
  • Evolutionary Operation (EVOP)
  • Response Surface Methodology (RSM)
  • Process stability Send submissions for Best Practices to Editorial Research Director David Drickhamer at [email protected].
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