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[S955]Statistics Design Of Experiments

by Tony Jacowski, Ton

Six Sigma stresses making alterations in existing business processes for improving overall efficiency. To make effective alterations, it is important to first understand the various aspects of business processes so that the cause and effect relationship between various processes can be determined. However, this is easier said than done because most business processes comprise of multiple sub processes, which themselves are quite complicated.

Making alterations in a simple business process having just two to three sub-processes is easy, but the task becomes quite difficult when alterations are to be made in a business process having more than ten sub-processes. The level of difficulty goes on increasing with an increase in the number of sub-processes. This is where DOE is beneficial as it can easily crawl through vast amounts of data that is generated as part of the six-sigma implementation process. This data is meant for understanding the cause and effect relationship between various processes and sub-processes, but since the amount of data generated is quite huge, advanced tools such as DOE are used.

Why Is DOE Necessary?

DOE analyses the data and generates quantifiable results, which are then used for defining the type of experiments or alterations that are to be conducted for achieving Six Sigma quality levels. Conducting pre-tests or experiments is necessary in Six Sigma because the organizational efficiency and productivity are at stake while the implementations are going on. If Six Sigma concepts and methodologies are not implemented properly, it can seriously affect the company's bottom-line and lead to redundancies.

Another thing is that organizations cannot hope to achieve desired results just by conducting wayward and misguided experiments or alterations. DOE is effective because it helps Six Sigma professionals in selecting the most suitable experiment designs that in turn would help in achieving the desired results. In the absence of DOE, it would be quite difficult to ascertain the type of experiments that are to be conducted.

Other Uses And Benefits

DOE can also be used for understanding the root cause of variations in a business process. Business processes are designed for delivering exactly the same quality and quantity, irrespective of what they are being used for, be it for manufacturing a product or rendering a service. However, maintaining this level of consistency is not always possible because the efficiency of business processes can be affected by various variable factors that are quite difficult to decipher.

This is where DOE can help because it can analyze vast amounts of data related to the variations and generate results that can be used for pointing out the root cause of variations. Once the variations have been eliminated, it would become quite easy for Six Sigma professionals to successfully carry out the implementations. DOE is thus an inseparable part of Six Sigma implementations in any type of industry.

The Six Sigma methodology has been implemented in nearly every imaginable industry. It has generated stunning results for companies in the service, hospitality, and banking industries as well as the manufacturing industry. By isolating existing business processes, companies can use the strategy to identify areas in which inefficiencies exist. The results include lower costs to bring a product to the end customer, higher quality, and improved customer satisfaction. However, the methodology has inherent limitations.

If a business process includes only a few inputs, it is relatively simple for a Six Sigma team to analyze for improvement. On the other hand, if a stream contains 100, 20, or even 10 inputs, it is far more complex to review using a traditional approach. The Design of Experiments (DoE) system was developed to help address this issue.

In this article, I'll explain the traditional Six Sigma approach as it is currently deployed throughout organizations in every industry. Then, I'll describe Design For Six Sigma (DFSS) and explain how it relates to the original methodology. Lastly, we'll explore the factors and responses that DoE was created to address.

The Traditional Approach

As we've covered in past articles, the original system follows a DMAIC (Design, Measure, Analyze, Improve, and Control) system of steps. Each step has been categorized according to its goals. The Design step is dedicated to identifying an organization's objectives and deliverables as they relate to the end customer (who might be within or outside the company).

The Measure step is devoted to understanding the current performance levels of the business process under review. It involves determining the key metrics and gathering data regarding the outputs of the process. It is during this step that performance benchmarks are established. The Improve step involves the use of statistical analysis to further understand the influence of key inputs on the performance benchmarks.

The next two steps (Improve and Control) are focused on isolating problem areas that are generating defects or inefficiencies, creating solutions, and implementing those solutions. Once they have been implemented, the Green Belts and Black Belts monitor their impact, and design a plan for handing off the monitoring responsibilities to others.

What Is DFSS?

DFSS stands for Design For Six Sigma. Though the methodology is related to the original DMAIC approach, there are important differences. While Six Sigma and DMAIC involve the analysis and improvement of existing processes, DFSS emphasizes the creation of an entirely new process. One of the specialized tools that DFSS relies upon is DoE. In a way, DFSS is an amalgamation of DMAIC and DoE.

While the last two steps of DMAIC are devoted to improving and controlling process outputs, DFSS uses two alternate steps: Design and Verify. Both focus on optimizing and measuring the experimental runs that will eventually become business processes.

Factors And Responses Of DoE

We have arrived at factors and responses (in the context of DoE) through the back door. You already know that DMAIC identifies key inputs that impact quality. You also know that the methodology monitors outputs in order to measure the success of the implemented solutions. Factors and responses are simply inputs and outputs. However, they involve a bit more complexity than the inputs and outputs used in a traditional Six Sigma deployment.

The factors are analyzed through factorial experiments. While DMAIC is designed to isolate and study one variable at a time, factorial experiments are conducted to measure the results of multiple inputs simultaneously. In the process, DoE can monitor multiple responses that result from the inputs. This is necessary in order to create an entirely new business process. Otherwise, doing so would take far too much time.

Six Sigma and Design of Experiments (and in some cases, other DFSS tools) should be integrated when multiple variables need to be analyzed at the same time. It is a relatively new combination of methodologies. But, it will likely grow in popularity as business processes become more complex.

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About Author

Both Tony Jacowski & Franklin Lucer are contributors for EditorialToday. The above articles have been edited for relevancy and timeliness. All write-ups, reviews, tips and guides published by EditorialToday.com and its partners or affiliates are for informational purposes only. They should not be used for any legal or any other type of advice. We do not endorse any author, contributor, writer or article posted by our team.

Tony Jacowski has sinced written about articles on various topics from University, Six Sigma and Information Technology. Tony Jacowski is a quality analyst for The MBA Journal. Aveta Solution's Six Sigma Online offers online and certification classes for lean six sigm. Tony Jacowski's top article generates over 90500 views. to your Favourites.

Franklin Lucer has sinced written about articles on various topics from Home Management, Surveys and Writing. This information on six sigma and lean processes is provided by BMGI, a leading education and consulting firm in the field. Franklin Lucer's top article generates over 8100 views. to your Favourites.