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Lean Thinking and Methods - Six Sigma


Six Sigma consists of a set of statistical methods for systemically analyzing processes to reduce process variation, which are sometimes used to support and guide organizational continual improvement activities. Six Sigma's toolbox of statistical process control and analytical techniques are being used by some companies to assess process quality and waste areas to which other lean methods can be applied as solutions. Six Sigma is also being used to further drive productivity and quality improvements in lean operations.

Six Sigma was developed by Motorola in the 1990s, drawing on well-established statistical quality control techniques and data analysis methods. The term sigma is a Greek alphabet letter (σ) used to describe variability. A sigma quality level serves as an indicator of how often defects are likely to occur in processes, parts, or products. A Six Sigma quality level equates to approximately 3.4 defects per million opportunities, representing high quality and minimal process variability.

It is important to note that not all companies using Six Sigma methods are implementing lean manufacturing systems or using other lean methods. Six Sigma has evolved among some companies to include methods for implementing and maintaining performance of process improvements. The statistical tools of Six Sigma system are designed to help an organization correctly diagnose the root causes of performance gaps and variability, and apply the most appropriate tools and solutions in addressing those gaps.

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Method and Implementation Approach

A sequence of steps called the Six Sigma DMAIC (Define, Measure, Analyze, Improve, and Control) is typically used to guide implementation of Six Sigma statistical tools and to identify process wastes and weaknesses. Six Sigma DMAIC phases include:

  • Define. This phase focuses on defining the project improvement activity goals and identifying the issues that need to be addressed to achieve a higher sigma level.
  • Measure. In this phase, the aim is to gather information about the targeted process. Metrics are established and used to obtain baseline data on process performance and to help identify problem areas.
  • Analyze. This phase is concerned with identifying the root cause(s) of quality problems, and confirming those causes using appropriate statistical tools.
  • Improve. Here, implementation of creative solutions - ways to do things better, cheaper, and/or faster - that address the problems identified during the analysis phase takes place. Often, other lean methods such as cellular manufacturing, 5S, mistake-proofing, and total productive maintenance are identified as potential solutions. Statistical methods are again used to assess improvement.
  • Control. This phase involves institutionalization of the improved system by modifying policies, procedures, and other management systems. Process performance results are again periodically monitored to ensure productivity improvements are sustained.

Some organizations have opted to integrate their kaizen (or rapid continual improvement) processes with Six Sigma approaches. This typically results in the use of statistical tools to aid the identification and measurement of improvement opportunities during and following kaizen event implementation.

It should be noted that some lean experts believe that Six Sigma, as implemented in some organizations, can be contradictory to lean principles. In such cases, Six Sigma experts, often known as "black belts", lead improvement efforts without actively involving workers affected by the improvement effort. Lean experts typically contend that employee involvement and empowerment is critical to fostering the continual improvement, waste elimination culture that is a foundation of lean thinking.

It should be noted that Six Sigma techniques can be relatively sophisticated, and are most frequently utilized by larger organizations and organizations willing to devote resources and talent for developing Six Sigma statistical capabilities.

Several examples of Six Sigma statistical tools are described below.

  • Capability Analysis. This tool assists in the maintenance of suitable product specifications. Using this statistical model and analyzing a frequency histogram of an observed production data sample, the long run defects per million opportunities can be determined. Such analyses can consider both "short-term" variability that determines the absolute best a process can produce, and a "long-term" variability that assesses how well a process responds to customer needs.
  • Gauge Repeatability & Reproducibility Studies. These studies quantify measurement error by assessing whether measurement processes and equipment produced consistent and accurate measurement outcomes. Without such studies, satisfactory parts might be rejected and unsatisfactory parts accepted. Such errors can lead to lost sales and unnecessary waste.
  • Control Charts. Control charts are often used to ensure that essential product characteristics remain constant over time, and to help identify when problems exist. Periodic sample measurements are plotted against the mean and range to see if any noticeable process shifts or other unusual events had occurred. When characteristics cannot be measured, charts are based on the proportion of defective items in a lot. CuSum (Cumulative Sum of Measurements) Charts can also be used to monitor the cumulative sum of deviations against a target value.
  • Accelerated Life Tests. Statistical techniques such as a Weibull Distribution and Arrehnius Plot are used to estimate the failure time distribution of products, and to test products designed to last for long periods of time. Such tests are often essential when testing must be conducted under aggressive time constraints, and must engage "stress test environments" such as high temperature, thermal cycling, or high humidity, to evaluate product life.
  • Variance Components Analysis. Isolating product variability problems is particularly critical to quality assurance. With this technique, different sources of variability are isolated to help assess where variations in product quality are occurring. Such analyses also provide insight into the sources of variability for process improvement efforts.
  • Pareto Analysis. By weighting each type of defect according to severity, cost of repair, and other factors, Pareto charts are used to determine which types of defects occur most frequently. This information facilitates prioritization of response actions. Fundamental to the Pareto principle is the notion that most quality problems are created by a "vital few" processes, and that only a small portion of quality problems result from a "trivial many" processes.

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Implications for Environmental Performance

Potential Benefits:
By removing variation from production processes, fewer defects inherently result. A reduction in defects can, in turn, help eliminate waste from processes in three fundamental ways:
  1. fewer defects decreases the number of products that must be scrapped;
  2. fewer defects also means that the raw materials, energy, and resulting waste associated with the scrap are eliminated;
  3. fewer defects decreases the amount of energy, raw material, and wastes that are used or generated to fix defective products that can be re-worked.
Six Sigma tools can help focus attention on reducing conditions that can result in accidents, spills, and equipment malfunctions. This can reduce the solid and hazardous wastes (e.g., contaminated rags and adsorbent pads) resulting from spills and leaks and their clean-up. (See Total Productive Maintenance).
Six Sigma techniques that focus on product durability and reliability can increase the lifespan of products. This can reduce the frequency with which the product will need to be replaced, reducing the overall environmental impacts associated with meeting the customer need.
Potential Shortcoming:
Lack of technical capacity to effectively utilize Six Sigma tools can potentially decrease effectiveness of the strategy, and/or result in unexpected waste if inappropriately applied.

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Useful Resources

Breyfogle, Forrest W. III. Implementing Six Sigma: Smarter Solutions Using Statistical Methods (New York: John Wiley & Sons, 1999).

Winiarz, Marek L., James Fang and Howard Fuller. Six Sigma Programs Yield Dramatic Improvement Through Application of Lean Manufacturing Methods in the Printed Circuit Board Industry. SAE Technical Paper Series (Warrendale, PA: SAE International, 2001).

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