{"id":4255,"date":"2026-01-12T16:09:42","date_gmt":"2026-01-12T22:09:42","guid":{"rendered":"https:\/\/ykim.synology.me\/wordpress\/?p=4255"},"modified":"2026-01-14T15:14:34","modified_gmt":"2026-01-14T21:14:34","slug":"what-is-doe","status":"publish","type":"post","link":"https:\/\/ykim.synology.me\/wordpress\/what-is-doe-4255\/","title":{"rendered":"What is DOE?"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

Design of Experiments (DOE)<\/strong> is a systematic, mathematical approach used to understand and optimize the relationship between input factors and output responses [1]. It is a branch of applied statistics that allows researchers to change multiple variables at once in a controlled way to determine which factors are truly significant and how they interact with one another [3].<\/p>\n\n\n\n

Core Components of DOE<\/h3>\n\n\n\n

To build a DOE model, you must define the following four elements:<\/p>\n\n\n\n

    \n
  • Factors:<\/strong> The independent variables you intentionally change (e.g., Temperature, Pressure) [1].<\/li>\n\n\n\n
  • Levels:<\/strong> The specific settings or values assigned to each factor (e.g., 180\u00b0C and 200\u00b0C) [3].<\/li>\n\n\n\n
  • Response:<\/strong> The output or result you are measuring (e.g., Product Yield, Car Speed) [2].<\/li>\n\n\n\n
  • Noise:<\/strong> Uncontrollable variables that might cause variation in the results (e.g., ambient humidity) [2].<\/li>\n<\/ul>\n\n\n\n

    The 5 Phases of DOE<\/h3>\n\n\n\n

    A standard DOE framework follows a specific lifecycle to move from initial curiosity to a fully optimized process [2, 5]:<\/p>\n\n\n\n

      \n
    1. Planning:<\/strong> Defining the objective, identifying potential factors, and ensuring the measurement system is accurate [5].<\/li>\n\n\n\n
    2. Screening:<\/strong> If there are many potential factors (usually >5), screening experiments like Plackett-Burman<\/strong> or Fractional Factorial<\/strong> are used to narrow them down to the “vital few” [1].<\/li>\n\n\n\n
    3. Modeling:<\/strong> Once significant factors are known, a mathematical relationship (regression) is built to map how inputs affect the output [3].<\/li>\n\n\n\n
    4. Optimizing:<\/strong> Using methods like Response Surface Methodology (RSM)<\/strong> to find the exact “sweet spot” or peak performance [5].<\/li>\n\n\n\n
    5. Verifying:<\/strong> A final “confirmation run” is performed at the optimized settings to ensure the results match the model’s predictions [1].<\/li>\n<\/ol>\n\n\n\n

      Why DOE is Better than Traditional Testing<\/h3>\n\n\n\n

      Traditional testing often uses the One-Factor-at-a-Time (OFAT)<\/strong> method. DOE is statistically superior for several reasons:<\/p>\n\n\n\n

      Feature<\/strong><\/td>Traditional (OFAT)<\/strong><\/td>DOE Framework<\/strong><\/td><\/tr><\/thead>
      Interactions<\/strong><\/td>Cannot see if two factors depend on each other [3].<\/td>Specifically designed to find and map interactions [1].<\/td><\/tr>
      Efficiency<\/strong><\/td>Requires many more runs to cover the same ground [3].<\/td>Finds the best answer with the minimum number of tests [2].<\/td><\/tr>
      Predictive Power<\/strong><\/td>Only tells you about the specific points you tested [3].<\/td>Creates a mathematical “surface” to predict results at any setting [5].<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

      [Image comparing one factor at a time vs factorial design]<\/p>\n\n\n\n

      References<\/h3>\n\n\n\n
        \n
      • [1] American Society for Quality (ASQ): Design of Experiments (DOE) Overview.<\/li>\n\n\n\n
      • [2] JMP Statistics Knowledge Portal: The DOE Workflow and Analysis.<\/li>\n\n\n\n
      • [3] Czitrom, V. (1999). One-Factor-at-a-Time Versus Designed Experiments. The American Statistician<\/em>.<\/li>\n\n\n\n
      • [4] NIST\/SEMATECH e-Handbook of Statistical Methods: Process Improvement via DOE.<\/li>\n\n\n\n
      • [5] Montgomery, D. C. (2019). Design and Analysis of Experiments<\/em>, 10th Edition. Wiley.<\/li>\n<\/ul>\n\n\n\n
        \n\n\n\n
        \"\"<\/figure>\n
        <\/div>
        <\/div>","protected":false},"excerpt":{"rendered":"

        Design of Experiments (DOE) is a systematic, mathematical approach used to understand and optimize the relationship between input factors and output responses [1]. It is a branch of applied statistics that allows researchers to change multiple variables at once in a controlled way to determine which factors are truly significant and how they interact with…<\/p>\n","protected":false},"author":4,"featured_media":4254,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"_kadence_starter_templates_imported_post":false,"_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","yasr_overall_rating":0,"yasr_post_is_review":"","yasr_auto_insert_disabled":"","yasr_review_type":"","fifu_image_url":"","fifu_image_alt":"","iawp_total_views":0,"footnotes":""},"categories":[4,321],"tags":[],"class_list":["post-4255","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-semiconductor-slug","category-applied-statistics-slug"],"yasr_visitor_votes":{"stars_attributes":{"read_only":false,"span_bottom":false},"number_of_votes":0,"sum_votes":0},"jetpack_featured_media_url":"https:\/\/ykim.synology.me\/wordpress\/wp-content\/uploads\/2026\/01\/20260112-DOE-word-cloud.png","_links":{"self":[{"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/posts\/4255","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/comments?post=4255"}],"version-history":[{"count":2,"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/posts\/4255\/revisions"}],"predecessor-version":[{"id":4272,"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/posts\/4255\/revisions\/4272"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/media\/4254"}],"wp:attachment":[{"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/media?parent=4255"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/categories?post=4255"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ykim.synology.me\/wordpress\/wp-json\/wp\/v2\/tags?post=4255"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}