Second in a series by Paul Casto, VP Value Implementation, Meridium
The Six Sigma work process is well known and used extensively in almost all areas of business to identify improvement opportunities, formulate solutions, capture value and implement long-term control. It is a versatile performance methodology that can be used to improve almost any process. Six Sigma distinguishing features include:
- Use of cross functional teams
- Statistical data analysis
Additionally, Six Sigma is designed to deliver significant financial benefits quickly. Business leaders are focused on delivering value that shows up on the income statement. When the financial discipline of Six Sigma is coupled with the high potential return of reliability projects, it provides a work process capable of capturing and translating significant and verifiable financial value to an organization's bottom line.
What is Six Sigma?
The definition of Six Sigma has evolved over time and can be defined in the following ways (1):
- As a metric
- As a methodology
- As a management system
Six Sigma as a metric is used to statistically measure quality performance. In many process control applications, the operations team strives to remain within 3 sigma control. As can be seen in Figure 1, 4 sigma control yields 6,210 defects per million opportunities (dpmo), while 6 sigma control yields only 3.4 defects per million. Six Sigma is essentially zero defects.
Over time, the emphasis on Six Sigma as a metric for 3.4 dpmo has lessened and it has become a methodology to improve business performance. This methodology involves understanding the customer's needs, aligning key business processes to support these needs and then using strong statistical data analysis tools to understand and control the variations that lead to defects. Six Sigma provides a defined roadmap, called the DMAIC process, which leads to rapid and sustainable improvement.
Six Sigma can also be used as a management system. This is done by linking the key goals of the organization to the implementation and use of the Six Sigma process. This linking helps ensure that the results are sustainable over time.
Six Sigma breakthrough strategy
Six Sigma provides a disciplined and structured method for problem solving. Unlike continuous improvement methodologies, a Six Sigma project is focused on a specific problem. Six Sigma teams are cross-functional and are brought together with the sole purpose of solving a specific problem. After completion of the project, the team is disbanded and the systems they have put in place are used to ensure sustainability.
Six Sigma problem solving is statistically driven and based on the premise that the answer to many problems is hidden in the data. Consequently, it sometimes requires extensive data gathering and analysis to clearly identify the defects and provide insight on how to eliminate or control these problems. The statistical analysis of data focuses the solution space to those alternatives with the highest impact on the defect. Some major factors that have impact on the value of projects are:
- Cost savings (reduction in cost of goods sold)
- Customer relationships
- Improvement in revenue (sales)
- Quality and yield
When using Six Sigma in reliability projects, the cross functional resources making up the team typically include craftspeople, operators, supervisors and engineers. This cross functional approach produces ideas that the reliability engineer may not have thought of on his/her own, making the final solution more robust.
Most importantly, Six Sigma is focused on the delivery of rapid and significant financial improvements. Six Sigma organizations have financial guidelines that force the process owners to drive out cost and realize the gains identified in the project. Financial benefit is seen in gross profit and this can come from either a reduction in cost of goods sold or increased revenue (sales). Delivering gross profit is a convincing sales tool that can be used to increase the visibility and impact of reliability to upper management.
One of the key concepts of Six Sigma is that a targeted variable can be controlled by understanding and controlling its inputs. The Six Sigma analysis begins with the formula y = ƒ(x1, x2,..., xk) where y is the output to be controlled. As thesecritical variables (y's) are identified, the factors that influence y (which are the x's) must be determined. By controlling these inputs (x's) the output variable y can be controlled. Thus we say that y is a function of x1, x2,..., xk. It should be noted that not all factors (the x's) have a measureable impact on the output and those factors should not be included in the analysis as this creates no value.
A powerful aspect of Six Sigma is the disciplined, data driven methodology used to solve problems. Adhering to this process will lead the team to measure the problem, verify the root cause, break old accepted processes and procedures, measure results and sustain the change. This is referred to as the DMAIC process and consists of five interconnected steps:
Six Sigma and Reliability
Reliability is defined as the probability that a device, system or process will perform its prescribed duty without failure for a given time when operated correctly in a specified environment. Reliability is measured based on failures. The six sigma focus on defect elimination through discipline and statistical analysis can be used to successfully drive reliability improvements.
In the case of reliability, the Six Sigma focus on defect elimination is a focus on the elimination of failures. These failures represent defects in the reliability process. The elimination of these failures (defects), which is the elimination of the root cause of the failure, will improve reliability performance. As previously discussed, a major precept of Six Sigma is that the answers to many problems are hidden in the data and statistical methods are often needed to analyze this data to develop solutions. Similarly, reliability improvement methods are data driven and require a disciplined approach. Six Sigma methods work well with the reliability process and many of the same tools used to solve reliability problems such as RCA, RCM/FMEA, and Pareto analysis, are applicable when solving problems using the six sigma process.
Solving reliability problems involves data analysis, developing new maintenance strategies, improving faulty designs and fixing broken work processes. In many reliability applications, the user is searching for a breakthrough in performance, which is the focus of the Six Sigma methodology. While Six Sigma isn't the solution for all reliability problems, it can work well when applied to the right reliability projects. The alignment of reliability and Six Sigma work processes offers the opportunity to develop improved results from a reliability program. It is likely that most companies are already doing some sort of basic reliability program which may include preventive and predictive maintenance, RCA, RCM, etc. In addition, many companies have implemented a classic Six Sigma program. However, few companies have linked the strengths of the two programs together to achieve higher level results. The synergies linking Six Sigma and reliability excellence processes together and their associated benefits include:
- The Six Sigma data driven, disciplined methodology can be helpful in solving tough reliability problems.
- The Six Sigma cross functional, hands-on team approach can provide additional insight, data and a broader range of potential solutions than the reliability engineer alone would develop.
- The Six Sigma emphasis on the use of strong statistical data analysis tools like failure modes and effect analysis (FMEA) links failure modes and causes (defects) to maintenance, reliability and operations actions which is an important component in solving reliability problems.
- The use of Six Sigma links reliability projects to a company's gross profit line of the income statement which will be supported by management.
In the October issue of the APM Advisor we discussed the application of Lean to maintenance and reliability. Next month, we'll conclude our overview of improvement methodologies used to improve manufacturing performance when we discuss the Theory of Constraints (TOC).
(1) Motorola University, http://www.motorola.com/content.jsp?globalObjectId=3088
About the author
Paul Casto, CRE, CQE, CSSBB, CMRP, VP Value Implementation, Meridium, is a leading practitioner in reliability and maintenance improvement methodologies. He has hands-on experience in reliability, maintenance, operations and engineering in the chemical, steel, aluminum, automotive, aerospace, consumer goods and construction industries. Paul holds a Bachelors degree in Electrical Engineering from West Virginia University, a Masters degree in Engineering Management from Marshall University Graduate College, an MBA from Clemson University, and a Masters in Maintenance Management and Reliability Engineering from the UT/Monash University program. Paul is an ASQ certified Six Sigma Black Belt, holds ASQ certification in Reliability Engineering and Quality Engineering and is a SMRP Certified Maintenance and Reliability Professional. He is currently studying R&M improvement methodologies in the UT graduate engineering program.
Click here to read Improving Asset Performance with Six Sigma.
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Click here to read Why Corporate Improvement Initiatives like Six Sigma Can Fail.