“I use DOE to help my clients optimize processes for value-added products while minimizing production costs. In the manufacturing of wood products like value-added oriented strand board panels and specialty plywood panels, there are several parameters that affect the process. DOE is the tool to deal with processes with so many variables.”
- Dr. David Barrett. Professor, Department of Wood Science, Vancouver. BC, Canada.
“Designed experiments can help untangle the nature of complex and otherwise confusing relationships faster than many of the alternatives. ‘Thinking DOE’ helps one think more systematically, regardless of the application.”
“Imagine the feeling of finding something you really want when it is on sale at a deeply discounted price. A well thought-out experiment allows you to find-out so much for relatively little time and effort; you just can't beat it for economy, efficiency, and effectiveness. And it is so beautiful, watching knowledge unfold like a flower.”
- Larry Smith. Manager, “Champion of Quality”, Ford Motor Company, Dearborn, Michigan.
“When I need to adjust one thing to improve performance, or when the single source of problem is known, often I can arrive at the solution intuitively. But when I’m dealing with more than one factor, or looking for unknown sources of problem, DOE comes to help.”
“I use DOE to identify if the process parameters for enhancing the ceramic tensile strength. It saves me a lot of time by avoiding testing all the process probabilities. DOE/Taguchi method is an effective tool for me to study my process by experimental means.”
“Like all other quality tools, DOE is an important technique. But, the benefit is in the way one uses it. You have to learn how to apply first.”
“When comes to deciding what’s best for my product and process designs, opinion and judgments slow me down. When I make decisions based on DOE results, everyone agrees.”
“I consider DOE to be the tool to give finishing touch before settling on designs. I believe we gain a lot when we use DOE to fine tune product designs before release, and optimize processes before production begins.”
“I use DOE to solve production related problems when basic disciplines (like 8D) do not offer the technical solution.”
- Unknown Practitioners
“I use DOE in HAZOP (Hazard & Operability Studies) and QRA (Quantitative Risk Analysis) of offshore structure/process platform, Oil rigs and On-land oil installation like Group gathering stations etc. I am quite well versed in Six Sigma techniques, and also of Dr Taguchi method of OA (Orthogonal Arrays) as a tool in the analysis phase of Six Sigma as well as Dr. Taguchi's concept of loss function for a robust design. “
- S.R. IYER. March 28, 2003
“Simulation models of manufacturing systems involve many design or operation parameters. The optimal settings for these must be determined by running the model many times. DOE provides an efficient and effective way to conduct experiments with the model of the system after the model has been verified and validated. It allows the KPOV (key process output variables) to be modeled in terms of the KPIV (key process output variables). In general, DOE leads to a better understanding of the system and interactions among the design variables or operational variables.”
- Dr. S. Balachandran. Professor of Industrial Engineering, UW-Platteville,
“DOE helps to reduce product/process development time and hence costs associated with product/process development process. It improves process yield, reliability and process capability. It can be used to reduce product performance sensitivity to various sources of noise (such as environmental variations, manufacturing imperfections, product-to-product variations, machine performance deterioration, etc.) “
- Dr. J Antony. Intl. Mfg, Centre, University of Warwick, Coventry, England, UK.
Your learning strategy - For comprehensive knowledge of the technique, you would want to know about (1) theory and math, (2) application methods, and (3) Philosophy and working disciplines (planning). Do not spend too much in the theory and statistical calculation. You need to focus on what they mean rather than how it is done. Try to muster the application methods and standard experiment design techniques. Understand the philosophy and follow the discipline well. This is what give s you the most benefits. The theory and application methods are routine and same for all projects, the experiment planning is what will be unique to your project. Unfortunately, it is something you will not learn well by reading. To know it well, learn from expert practitioners or learn as you go on applying.
How to acquire application skill -
* Review and download DOE Topic Overview in PDF format from link in this site (Free)
* Search for other literature in the web (Free)
* Visit your local library and borrow a book or two on the subject (Free)
* Buy books if you can afford (costs vary between $50 - 150 USD, 2004 price)
* Download DEMO software (Free for L-8 experiments)
* Design small (L-4, L-8, or L-9) experiments and hand-calculate numbers (All above are must for students and researchers)
* If you are not comfortable, consider attending our public seminar. This will sort-circuit your learning time and help build the skills for immediate applications.
* If your interest is to companywide applications, consider hosting our 4-day seminar with application workshop. This will make all attendees ready for immediate application. You should definitely consider onsite seminar when your projects involve people from many areas within your organization. The purpose is not to make everyone an expert, but have all understand the benefit and secure support for the project. Most optimistically, a few among the attendees in a session (10 - 20 people) will develop and maintain application skills.
* Should your interest be in an immediate project application, seek help with application. Often, cost of outside consultation will be minimum compared to potential benefits for the project. The area you would most benefit from experienced consultant, is in the experiment planning (brainstorming). After you have acquired
the knowledge about the technique, it is the discipline you need to follow in planning the experiments that will take longer time to develop.
* If you are interested more about project applications, but uncertain about when and where the needs will develop, consider retaining our application assistance on demand.
Subject Overview (The Taguchi Approach):
Design Of Experiments (DOE) is a powerful statistical technique introduced by R. A. Fisher in England in the 1920's to study the effect of multiple variables simultaneously. In his early applications, Fisher wanted to find out how much rain, water, fertilizer, sunshine, etc. are needed to produce the best crop. Since that time, much development of the technique has taken place in the academic environment, but did help generate many applications in the production floor.
As a researcher in Electronic Control Laboratory in Japan, Dr. Genechi Taguchi carried out significant research with DOE techniques in the late 1940's. He spent considerable effort to make this experimental technique more user-friendly (easy to apply) and applied it to improve the quality of manufactured products. Dr. Taguchi's standardized version of DOE, popularly known as the Taguchi method or Taguchi approach, was introduced in the USA in the early 1980's. Today it is one of the most effective quality building tools used by engineers in all types of manufacturing activities.
The DOE using Taguchi approach can economically satisfy the needs of problem solving and product/process design optimization projects. By learning and applying this technique, engineers, scientists, and researchers can significantly reduce the time required for experimental investigations. DOE can be highly effective when yow wish to:
- Optimize product and process designs, study the effects of multiple factors (i.e.- variables, parameters, ingredients, etc.) on the performance, and solve production problems by objectively laying out the investigative experiments. ( Overall application goals)
- Study Influence of individual factors on the performance and determine which factor has more influence, which ones have less. You can also find out which factor should have tighter tolerance and which tolerance should be relaxed. The information from the experiment will tell you how to allocate quality assurance resources based on the objective data. It will indicate whether a supplier's part causes problems or not (ANOVA data), and how to combine different factors in their proper settings to get the best results (Specific Objectives).
Further, the experimental data will allow you determine.
- How to substitute a less expensive part to get the same performance
- How much money you can save the design improvement you propose
- How you can determine which factor is causing most variations in the result
- How you can set up your process such that it is insensitive to the uncontrollable factors
- Which factors have more influence on the mean performance
- What you need to do to reduce performance variation around the target
- How you can adjust factors for a system whose response varies proportional to signal factor (Dynamic response)
- How to combine multiple criteria of evaluation into a single index
- How you can adjust factor for overall satisfaction of criteria of evaluations
- How the uncontrollable factors affect the performance
Advantage of DOE Using Taguchi Approach - The application of DOE requires careful planning, prudent layout of the experiment, and expert analysis of results. Based on years of research and applications Dr. Genechi Taguchi has standardized the methods for each of these DOE application steps. Thus, DOE using Taguchi approach has become a much more attractive tool to practicing engineers and scientists.
Experiment planning and problem formulation - Experiment planning guidelines are consistent with modern work disciplines of working as teams. Consensus decisions about experimental objectives and factors make the projects more successful.
Experiment layout - High emphasis is put on cost and size of experiments. Size of the experiment for a given number of factors and levels is standardized. Approach and priority for column assignments are established. Clear guidelines are available to deal with factors and interactions (interaction tables). Uncontrollable factors are formally treated to reduce variation. Discrete prescriptions for setting up test conditions under uncontrollable factors are described. Guidelines for carrying out the experiments and number of samples to be tested are defined
Data analysis - Steps for analysis are standardized (main effect, NOVA and Optimum). Standard practice for determination of the optimum is recommended. Guidelines for test of significance and pooling are defined.
Interpretation of results - Clear guidelines about meaning of error term. Discrete indicator about confirmation of results (Confidence interval). Ability to quantify improvements in terms of dollars (Loss function)
Overall advantage - DOE using Taguchi approach attempts to improve quality which is defined as the consistency of performance. Consistency is achieved when variation is reduced. This can be done by moving the mean performance to the target as well as by reducing variations around the target. The prime motivation behind the Taguchi experiment design technique is to achieve reduced variation (also known as ROBUST DESIGN). This technique, therefore, is focused to attain the desired quality objectives in all steps. The classical DOE does not specifically address quality.
"The primary problem addressed in classical statistical experiment design is to model the response of a product or process as a function of many factors called model factors. Factors, called nuisance factors, which are not included in the model, can also influence the response. The primary problem addressed in Robust Design is how to reduce the variance of a product's function in the customer's environment."
-Madhav Phadke, Quality Engineering using Robust Design
- Building quality in the product design.
- Measuring quality by deviation from target (not by rejection).
2. NEW DISCIPLINE
- Complete planning of experiments and evaluation criteria before conducting experiments.
- Determining a factor's influence by running the complete experiment.
3. SIMPLER AND STANDARDIZED EXPERIMENT DESIGN FORMAT
- Orthogonal arrays for experimental design.