{"id":3868,"date":"2024-07-29T00:00:00","date_gmt":"2024-07-29T00:00:00","guid":{"rendered":"https:\/\/www.educa.pro\/diseno-de-experimentos-doe"},"modified":"2024-07-29T00:00:00","modified_gmt":"2024-07-29T00:00:00","slug":"design-of-experiments-doe","status":"publish","type":"post","link":"https:\/\/educa.pro\/en\/articulos\/diseno-de-experimentos-doe\/","title":{"rendered":"Design of Experiments (DOE): optimise your results"},"content":{"rendered":"<p>The <strong>design of experiments (DoE) <\/strong>is an essential tool for optimisation and <a href=\"https:\/\/educa.pro\/en\/articles\/the-5-ss-of-kaizen\/\" rel=\"noopener noreferrer\" target=\"_blank\">improvement <\/a>of processes in various fields. By applying statistical techniques, DoE enables the analysis of multiple variables to understand their influence on the results and thus <strong>to improve the quality, accuracy and efficiency of products and processes<\/strong>. This methodology not only facilitates the rigorous evaluation of experiments, but also supports informed decision-making. <\/p><p>We will now explore the key components of the DoE, its practical applications across different industries, and the steps required to develop a<strong> effective experimental design<\/strong>. <\/p><h2><strong>What is design of experiments (DoE)?<\/strong><\/h2><p>The <strong>design of experiments (DoE) <\/strong>It is a methodology that applies statistics to analyse and optimise processes associated with the creation of objects, designs or projects from which a certain level of quality, precision and efficiency is expected. To this end, the following are used:<strong> variables of various types<\/strong> and their responses are analysed to identify, in detail, the factors that have the greatest influence on the results. <\/p><p>In this regard, the DoE makes it possible to evaluate experiments on the basis of accurate data, which helps not only to study in detail a <strong>a large number of processes<\/strong>, but also to make informed decisions regarding the design of both products and processes. The analyses are, therefore, rigorous, as they require<strong> answers<\/strong> on the subject under investigation. <\/p><p>The DoE\u2019s objectives can therefore be summarised as follows: <\/p><ul><li><strong>Set parameters\/conditions<\/strong> for a process <\/li><\/ul><ul><li><strong>Identify aspects<\/strong> to optimise resources <\/li><\/ul><ul><li><strong>Anticipating the <\/strong><a href=\"https:\/\/educa.pro\/en\/articles\/cause-and-effect-diagram\/\" rel=\"noopener noreferrer\" target=\"_blank\"><strong>effects <\/strong><\/a>regarding some changes to the process <\/li><\/ul><ul><li><strong>To reduce any form of variability<\/strong> during the process. <\/li><\/ul><p>In view of this, experimental design is applied in a wide range of fields where a <strong>high level of accuracy<\/strong>. These include, in particular, the pharmaceutical industry, industrial design, <a href=\"https:\/\/educa.pro\/en\/articles\/sustainable-packaging\/\">consumer goods industry<\/a>, amongst others. <\/p><h2><strong>Components of the experimental design<\/strong><\/h2><p>These components are: <\/p><h3><strong>Variable inputs<\/strong><\/h3><p>The input variables or parameters are the <strong>conditions under which the experiment is carried out<\/strong>. They are therefore manipulated to obtain specific results. These variables are classified into two types. On the one hand, the <strong>controllable variables<\/strong> are those that can be changed. On the other hand, the <strong>uncontrollable variables<\/strong> are those that cannot be changed. Analysing these variables provides an in-depth understanding of the extent to which they influence the final result. <\/p><h3><strong>Adjustment levels<\/strong><\/h3><p>These levels refer to the values applied to the input variables during the experiment. These levels <strong>are modified or manipulated in order to determine the effects of the modifications<\/strong>, which makes it easier to explore ranges within which the minimum or exact specifications of the experiment could be met, such as improving a product or optimising a process. <\/p><h3><strong>Answers<\/strong><\/h3><p>The responses are the results obtained after manipulating the variables, including the necessary adjustments. With these, the <strong>engineers and specialists<\/strong> Those in charge of the experiment are evaluating the results of the study; that is to say, <strong>are investigating the performance of the variables<\/strong>. This analysis is based on the objectivity of the data collected; furthermore, the data are statistically processed to ensure reliable measurements. <\/p><h2>Applications of Design of Experiments (DoE)<\/h2><p>Among the most common uses are: <\/p><h3><strong>Consider alternatives<\/strong><\/h3><p>One of the main applications of the DoE is the <strong>evaluation of multiple alternatives<\/strong> with regard to a design or process. In other words, this type of methodology is very useful for understanding how the combination of certain variables leads to a particular outcome, particularly in the development of processes or products, or in the <strong>optimisation of operating conditions<\/strong>. <\/p><h3><strong>Maximise the response<\/strong><\/h3><p>As we have seen, the response is as expected. In this respect, experimental design helps to improve this response. But what is the point of this? These<strong> testing and evaluation procedures <\/strong>They are used to identify the factors that lead to the best response, that is, the best performance. <\/p><h3><strong>Minimise the impact on quality<\/strong><\/h3><p>A key aspect of DoE is minimising the impact on quality. How is this achieved? Once analysed, the data obtained from the experiments provide insight into the <strong>areas where the factors need to be modified or adjusted<\/strong>. Quality is understood to mean the absence of waste and the improvement of performance. <\/p><h3><strong>Optimising processes<\/strong><\/h3><p>But one cannot fail to mention the <strong>process optimisation as part of the DoE<\/strong>. Thanks to this process, it is possible to increase production efficiency, reduce costs and, consequently, <strong>to increase productivity<\/strong>. The variables used make it easier to identify factors that have a negative impact on overall performance. This identification enables corrective measures to be taken. <\/p><h3><strong>Quality control<\/strong><\/h3><p>Design of experiments (DoE) is also essential for quality control. This ensures that processes remain strictly in line with the <strong>specified parameters or conditions <\/strong>to ensure that the specifications are met. The industrial engineers and other professionals involved monitor any variations closely so that changes can be made where necessary. <\/p><h2><strong>Steps for developing the experimental design <\/strong><\/h2><p>These steps are: <\/p><h3><strong>Step 1. Description<\/strong><\/h3><p>It begins with a description of the experimental problem and the objectives, both general and specific. The input variables are then estimated, as well as the<strong> hypothesis guiding the experiment<\/strong>. This first step is essential for proper planning of the procedure. <\/p><h3><strong>Step 2. Specification <\/strong><\/h3><p>Next, it is essential to define the input variables and their adjustment levels. It is necessary to define the <strong>the conditions under which the experiment will be carried out<\/strong>. <\/p><h3><strong>Step 3. Design<\/strong><\/h3><p>The design involves specifying the <strong>working methodology<\/strong>. Building on this, practical implementation requires consideration of materials, resources, activities and analytical work. <\/p><h3><strong>Step 4. Collection <\/strong><\/h3><p>At this stage, the experiment is carried out as planned. Data on the process are also collected, so that there is a <strong>thorough record-keeping<\/strong> of the tests carried out according to the input variables. <\/p><h3><strong>Step 5. Adjustment <\/strong><\/h3><p>The adjustment is made every time a search is carried out <strong>clarify any ambiguities<\/strong> or inconsistencies during the experiment. Furthermore, when adjustments are made, it is common to repeat the experimental tests in order to obtain new results as expected. <\/p><h3><strong>Step 6. Prediction<\/strong><\/h3><p>This is the final stage of design of experiments (DoE). With the <strong>appropriate statistical analysis<\/strong>, it is possible to determine which of the variables, or combination of variables, optimises the process or improves the response. This helps to improve future designs, processes or techniques.<\/p><h2><strong>How do you choose the most suitable DoE?<\/strong><\/h2><p>That said, <strong>How do you choose the most suitable design of experiments (DoE)? <\/strong>The following points should be taken into account: <\/p><ul><li>Please bear in mind the <strong>aim of the experiment<\/strong>. <\/li><\/ul><ul><li>Consider the most functional design. <\/li><\/ul><ul><li>Set the <strong>nature of the design<\/strong> (single or multifactorial). <\/li><\/ul><ul><li>Specify the type of design, whether it is a control or experimental design. <\/li><\/ul><ul><li><strong>Answer the questions<\/strong> to which answers are being sought. <\/li><\/ul><ul><li>Correctly define the <strong>working groups <\/strong><\/li><\/ul>","protected":false},"excerpt":{"rendered":"<p>El dise\u00f1o de experimentos (DoE) es una herramienta fundamental en la optimizaci\u00f3n y mejora de procesos en diversos campos. Mediante [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":3869,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[1],"tags":[],"class_list":["post-3868","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/educa.pro\/en\/wp-json\/wp\/v2\/posts\/3868","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/educa.pro\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/educa.pro\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/educa.pro\/en\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/educa.pro\/en\/wp-json\/wp\/v2\/comments?post=3868"}],"version-history":[{"count":0,"href":"https:\/\/educa.pro\/en\/wp-json\/wp\/v2\/posts\/3868\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/educa.pro\/en\/wp-json\/wp\/v2\/media\/3869"}],"wp:attachment":[{"href":"https:\/\/educa.pro\/en\/wp-json\/wp\/v2\/media?parent=3868"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/educa.pro\/en\/wp-json\/wp\/v2\/categories?post=3868"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/educa.pro\/en\/wp-json\/wp\/v2\/tags?post=3868"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}