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Quick reference
Experiments and Design
Experiments are an inquiry process that is often used to support the design process. Experiments determine the relationship between independent factors or variables associated with the design or use of the item under investigation and dependent factors which indicate the performance of the item under investigation.
When to use
Experiments should be used when there is uncertainty about how the independent factors will impact the dependent factors. The data created by the experiments are used to model the relationship. If the relationship is already known, experiments are not needed. Experiments can assist in the design of new products/services/processes, upgrade of existing products/services/processes or when conducting problem solving analyses with products/services/processes.
Instructions
The goal for experiments is to establish the relationship between the independent factors and the dependent factors. Therefore, the independent factors must be controllable and the dependent factors must be measurable. The experimental study is designed to create enough data to enable:
- Creation of a model between the independent and dependent factors,
- Expansion of knowledge about a product, service, or process,
- Establish a cause and effect between an independent factor and specific dependent factor condition or level,
- Optimization of the design of a product, service, or result.
Experiments are particularly helpful when changing a technology or design elements of a product, service, or process. In that case the experimental data provides valuable insight into likely performance of the changed system. Experiments are not needed when the design is not changing; either because it is being restored to a known configuration or the attribute under investigation has no performance impact.
Experiments have always been used by designers, but the experimental method has changed. The most common method is trial and error. The method taught in most technical schools as the scientific method is One-Factor-At-A-Time (OFAAT). Two statistically based methods are the full factorial Design of Experiments which determines the performance at the edges and corners of operation. The fractional factorial Design of Experiments statistically combines a much smaller number of experiments than OFAAT or full factorial DOE to determine the characteristics of the design space.
Hints & tips
- Most of the time, experiments will prove beneficial in understanding design performance.
- The selection of independent factors should be those that vary but are controllable by user/operators.
- The selection of dependent factors should be easily measurable and directly relate to the product, service, or process performance.
- Experiments can become very expensive, so plan your experiment approach to conduct the minimum needed to gain understanding about the issue under investigation.
- 00:04 Hello, I'm Ray Sheen.
- 00:05 Let's start our discussion of Design of Experiments by setting a baseline for
- 00:10 the use of experiments in the design process.
- 00:14 First let's take a minute to agree on what we mean by experiments.
- 00:18 Experiments are investigative research.
- 00:21 By that we mean that we are using the experimental process to uncover new
- 00:25 understanding or confirm a conjectured hypothesis.
- 00:28 At the end of the experiment,
- 00:30 there should be a conclusion based upon the experimental results.
- 00:34 As part of this investigation,
- 00:36 we control a number of factors known as independent factors or variables.
- 00:40 And then we have one or more dependent factors or
- 00:43 variables that we measure during the experiment.
- 00:46 This dependent factor is often something that we want to understand or control.
- 00:51 By conducting the experiment, we determine how the independent factors impact
- 00:56 the observed result we see in the dependent factor.
- 00:59 We can begin to understand to what extent the dependent factor depends upon
- 01:03 the independent factors.
- 01:05 This can be useful in several ways.
- 01:07 If we gain a thorough understanding of the relationship, we can create an accurate
- 01:12 mathematical model that can be used for predicting performance.
- 01:16 The experimental results may be used to expand knowledge of a subject, or
- 01:20 may be used to demonstrate cause and effect between the independent factors and
- 01:24 the dependent factor.
- 01:25 With this understanding, we can tune product and process designs for
- 01:30 optimal performance.
- 01:31 This leads us to the question of when we should use experimental design.
- 01:37 And there are some obvious situations where it would add value.
- 01:40 One is the creation of a new generation or a category of products.
- 01:43 Experimental design helps to understand the performance characteristics of
- 01:47 the new product.
- 01:48 Another time is when doing a technology change or upgrade to a product or process.
- 01:53 Experiments will provide the information needed to understand the new capabilities,
- 01:57 model the performance, and then optimize the product or
- 02:00 process with this new technology.
- 02:02 A third occasion is when changing an existing product or
- 02:05 process to resolve a problem.
- 02:07 This is often the reason why Lean Six Sigma projects use Design of Experiments.
- 02:11 The changes to some element of the product or process is an independent factor
- 02:15 that will affect the overall product or process performance.
- 02:19 The experiments are used to optimize the product or
- 02:22 process with that new technology.
- 02:24 This is normally needed when the problem being solved is a common cause problem.
- 02:28 That means that the problem is inherent in the existing design and
- 02:32 a new design is required to eliminate or control the problem.
- 02:35 The experiments support the creation, verification, and
- 02:39 documentation of this new design.
- 02:41 While experiments can be very helpful in the design process,
- 02:45 they don't fit every situation, so we need to know when not to use them.
- 02:49 Experiments can be costly and time-consuming.
- 02:52 I'll make the assumption that your boss wants an answer quickly and
- 02:55 doesn't wanna spend a lot of money to get there.
- 02:57 So if I can answer the question without experiments, it's often best to do so.
- 03:01 Let's look at a few instances where experiments are not necessary.
- 03:06 If the project you're working on is tasked with solving some problem and
- 03:10 you've determined that the problem is due to a special cause,
- 03:13 you don't need to experiment.
- 03:14 You need to get rid of the special cause.
- 03:17 So for instance, if you determined that the reason the machine was making bad
- 03:21 parts was that the tooling used in the machine is broken, fix the tools.
- 03:25 You don't need to do experiments to understand how badly the tools were
- 03:29 broken.
- 03:30 Just fix it and then the process returns to the normal condition.
- 03:34 Another case could be a design modification that doesn't really modify
- 03:38 the design.
- 03:39 It could be design change to do a line extension.
- 03:42 Adding one more color option to the product selection or
- 03:45 translating the product documentation into another language.
- 03:48 This may expand the product offering, but
- 03:50 it does not change the product performance.
- 03:53 So let's wrap up this discussion by introducing the idea that there are many
- 03:57 ways to conduct a set of experiments.
- 03:59 Scientists, designers, and
- 04:01 inventors have been using experiments since the dawn of man.
- 04:04 When faced with a technical problem or a challenge, someone has tried or
- 04:08 experimented with several options until they find one that worked.
- 04:12 Sometimes, experiments have quickly provided knowledge or
- 04:15 insight into what works.
- 04:17 But many times, it is not, and numerous experiments were needed.
- 04:21 Thomas Edison and his staff conducted thousands of experiments when working on
- 04:25 inventing the light bulb.
- 04:27 Many of these created light, but not to the level of brightness,
- 04:30 length of operation, and fragility that would be needed for a commercial product.
- 04:35 However, each experiment added to the base of knowledge.
- 04:38 When we consider the approach to the experimental process and the methods used
- 04:42 to plan the experiments, they fall into one of four general categories.
- 04:46 The trial and error or lucky guess method, which is the most common.
- 04:51 The one-factor-at-a-time or OFAAT method,
- 04:53 which is often referred to as the scientific method of experimentation.
- 04:57 Then there is the full factorial design of experiments, which determines the envelope
- 05:02 of performance for the product or process by looking at the combination of min and
- 05:07 max settings of all the independent factors.
- 05:09 Finally, there is the fractional factorial design of experiments.
- 05:13 This technique uses a statistical analysis of a subset of
- 05:16 the full factorial DOE to estimate and optimize the design.
- 05:20 Each of these are viable, and in fact,
- 05:23 may be the preferred approach under certain project conditions.
- 05:26 We'll consider each one in the next few lessons of this course.
- 05:31 Experiments have historically been a part of the design process.
- 05:34 But as we saw, there are several different ways to organize your experiments.
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