Six Sigma: The new tool in your maintenance toolbox

David Berger says maintenance departments can benefit from Six Sigma programs.

By David Berger, P.Eng., contributing editor

A popular methodology that companies of all sizes and industries are using is Six Sigma. The technique is best known for its ability to reduce product and service quality problems. Although Six Sigma shares objectives with Lean (improved processes, waste reduction, increased productivity and greater customer satisfaction), the methodology is more data-driven, quantitative and statistically based than Lean. Be assured that maintenance departments can benefit from Six Sigma programs.

Statistics reflect reality

Six Sigma provides systematic problem-solving using a variety of statistical tools and analysis. The term “six sigma” comes from the statistics measure of deviation from the mean. For normal distributions, 68% of the population should fall within one standard deviation — one sigma — from the mean. Similarly, 95% and 99.7% falls within two and three sigma, respectively.

Assume a specification calls for a part 1.00 inch in length, with 3 sigma being equal to 0.01 inch. With 3-sigma quality, you’d expect parts to be within spec 99.73% of the time (a defect rate of 2.7 per 1,000 parts). This was the accepted quality benchmark in manufacturing before the emergence of Six Sigma.

However, some companies felt that a 3-sigma standard wasn’t good enough. Motorola, for example, observed that a process could drift by about 1.5 sigma over time. In the example above, this would cause the process mean to range from 0.995 to 1.005, which might represent a significant shift for some customers.

Thus, keeping the data points within an acceptable range required a counterbalancing tightening of tolerance. For a 1.5-sigma drift in the mean (half of 3 sigma), the sigma level tolerance would need to be half, or plus/minus 6 sigma.

The acceptable Six Sigma tolerance level is 3.4 defects per million opportunities (DPMO), that is to say at least 99.9996599% of data points should fall within plus or minus 6 sigma from the mean. Although this really represents 4.5 sigma in a normal distribution in which the mean doesn’t drift, it’s considered Six Sigma because of the expected 1.5-sigma process shift.

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Some experts argue that the 1.5-sigma process shift is more empirical than theoretical. In fact, any process mean that changes as much as 1.5 sigma should be considered statistically out of control, unpredictable and, therefore, at risk of producing defects, regardless of the customer’s specification limits. The good news is that with modern technology such as condition-based monitoring and control software, trends can be tracked for detection and correction of any significant process drift.

Even with countless variations on the theme, there are really two fundamental Six Sigma methodologies: DMAIC and DMADV. Both strive to achieve predictable, defect-free performance, and are similar to Deming’s “Plan-Do-Check-Act” approach. Although the two frameworks have similarities, there are significant differences.

DMAIC

This version — define, measure, analyze, improve and control - is applied to existing substandard business processes. Define establishes goals for improvement in line with customer demands and overall business strategy. This might be hierarchical, such as improved return on capital employed (ROCE) at the overall business strategy level, increased asset performance at the Operations and Maintenance departmental level, and reduced defects at the improvement project level.

Measure refers to tracking data related to the process, using reliable metrics relevant to the goals established in the first step. Analyze involves using statistical tools and root cause analyses to identify ways to minimize the gap between current metrics and the desired goal. Improve means optimizing processes based on analysis, using project management and change-management techniques to ensure effectiveness. Control refers to process monitoring and control to correct variances before defects appear. It requires adjusting policies and procedures, budgets, compensation and incentives, information systems, organizational structure and so on to ensure results sustainability.

DMADV

This version — define, measure, analyze, design and verify — applies to designing products or processes, or to an existing process that requires more than an incremental improvement. Define determines the design goals in light of customer demands and deliverables. Measure helps determine product, service and process characteristics, as defined by the internal or external customer. It uses actionable and quantifiable business specifications such as design failure mode effects analysis (DFMEA) as part of a reliability-centered maintenance program. It’s a risk assessment.

Analyze is how you determine the design options that best meet customer needs while minimizing risk. Design brings out the optimal design alternative and provides design details. In some cases, simulation software provides an invaluable tool for fine-tuning designs. Verify is how you validate that the design delivers on quantifiable customer needs. Start with a pilot implementation. Use project-management and change-management techniques for increased stakeholder buy-in.

Six Sigma and RCM

Some assets don’t experience enough failures to warrant statistical analysis. Reliability-centered maintenance (RCM) provides an excellent substitute for traditional Six Sigma tools during the measure and analyze steps. RCM identifies what a system is supposed to do, how it might fail, and the effect of a failure.

Optimizing asset performance and reliability comes from understanding how to better manage the failure modes through more effective maintenance policies. These policies include some combination of failure-based, condition-based and use-based maintenance. Other key variables to consider are frequency and nature of inspections, and whether to perform a major or minor maintenance procedure at various degradation stages in the asset’s life.

Six Sigma and CBM

Condition-based maintenance (CBM) is an excellent tool for monitoring during the final step of both DMAIC and DMADV methodologies. CBM can trend data to ensure that a process remains in control, either through manual inspections or via online, real-time automation. For example, if vibration readings trend outside specification tolerances, taking action can avert a potentially catastrophic failure. RCM and root cause failure analysis (RCFA) can determine the appropriate frequency and nature of data collected.

Role of the CMMS

CBM, RCM and RCFA are sophisticated measurement and analysis tools available on the most advanced CMMS packages. However, even basic CMMS packages can be useful for collecting and analyzing Six Sigma data.

For example, if the Six Sigma team wants to reduce scrap and rework, as well as improve process and machine capacity, the CMMS can track relevant metrics such as asset performance and reliability. In turn, problem, cause and action codes can be analyzed to determine maintenance-related factors affecting scrap levels, rework and process/machine capacity. The CMMS can then identify root causes for minimizing negative effects on asset performance and reliability, thereby meeting the goals of the Six Sigma project. Finally, the CMMS can be used to monitor the process during the control or verify steps of DMAIC or DMADV.

(Editor’s note: The Plant Services CMMS/EAM Software Review, posted at www.PlantServices.com/cmms_review, provides a side-by-side comparison of more than a dozen popular software packages.)

E-mail Contributing Editor David Berger, P.Eng., partner, Western Management Consultants, at david@wmc.on.ca.