{"id":45803,"date":"2021-05-26T10:36:41","date_gmt":"2021-05-26T17:36:41","guid":{"rendered":"https:\/\/www.cohu.com\/?p=45803"},"modified":"2025-04-01T08:48:27","modified_gmt":"2025-04-01T15:48:27","slug":"solve-industrial-programs-with-analytics","status":"publish","type":"post","link":"https:\/\/www.cohu.com\/tignis\/solve-real-industrial-problems-with-analytics","title":{"rendered":"Solve Real Industrial Programs with Analytics"},"content":{"rendered":"\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g28v5p-746d8baae8d89148fb421ccddfa3a141\">\n.avia-section.av-m8g28v5p-746d8baae8d89148fb421ccddfa3a141 .av-parallax .av-parallax-inner{\nbackground-repeat:no-repeat;\nbackground-image:url(\/wp-content\/uploads\/2025\/03\/resources-banner-1500x333.jpg);\nbackground-position:50% 50%;\nbackground-attachment:scroll;\n}\n.avia-section.av-m8g28v5p-746d8baae8d89148fb421ccddfa3a141 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class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-ig608p-4f8d1c0453663a84f522f5ebe321d648\">\n.flex_column.av-ig608p-4f8d1c0453663a84f522f5ebe321d648{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-ig608p-4f8d1c0453663a84f522f5ebe321d648 av_two_third  avia-builder-el-1  el_before_av_one_third  avia-builder-el-first  first flex_column_div av-zero-column-padding  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a\">\n#top .av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a{\npadding-bottom:10px;\ncolor:#ffffff;\nfont-size:40px;\n}\nbody .av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n#top #wrap_all .av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a 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.av-special-heading-tag{\nfont-size:33px;\n}\n.av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-subheading{\nfont-size:15px;\n}\n}\n<\/style>\n<div  class='av-special-heading av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a av-special-heading-h1 custom-color-heading blockquote modern-quote  avia-builder-el-2  avia-builder-el-no-sibling  av-inherit-size av-linked-heading'><h1 class='av-special-heading-tag '  itemprop=\"headline\"  >PHYSICS, MACHINES AND DATA<\/h1><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-2pyrzd-6af470dabd56f3b979785905ac98eceb\">\n.flex_column.av-2pyrzd-6af470dabd56f3b979785905ac98eceb{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-2pyrzd-6af470dabd56f3b979785905ac98eceb av_one_third  avia-builder-el-3  el_after_av_two_third  avia-builder-el-last  flex_column_div av-zero-column-padding  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8sqnek7-5a54248de211167837b6e7f1c5ebaa5a\">\n.avia-image-container.av-m8sqnek7-5a54248de211167837b6e7f1c5ebaa5a img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-m8sqnek7-5a54248de211167837b6e7f1c5ebaa5a .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-m8sqnek7-5a54248de211167837b6e7f1c5ebaa5a av-styling- avia-align-center  avia-builder-el-4  avia-builder-el-no-sibling '   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><img decoding=\"async\" fetchpriority=\"high\" class='wp-image-45060 avia-img-lazy-loading-not-45060 avia_image ' src=\"\/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-300x89.png\" alt='Tignis, A Cohu Analytics Solution logo in white' title='Tignis-Logo-cohu-tagline-all white-transparent'  height=\"89\" width=\"300\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-300x89.png 300w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-1030x305.png 1030w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-768x228.png 768w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-1536x455.png 1536w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-2048x607.png 2048w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-1500x444.png 1500w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-705x209.png 705w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/div><\/div><\/div><\/div>\n<\/div><\/div><\/main><!-- close content main element --><\/div><\/div><\/div><div id='after_section_1'  class='main_color av_default_container_wrap container_wrap fullsize'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-full alpha units'><div class='post-entry post-entry-type-page post-entry-45803'><div class='entry-content-wrapper clearfix'>\n<div  class='flex_column av-2y29t5-8eac002ebd25dfff14a3d2eed1a49041 av_one_full  avia-builder-el-5  el_after_av_section  el_before_av_post_metadata  avia-builder-el-first  first flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8gaohgy-e779b05cc50cec788c64ad3beca2e266\">\n#top .av_textblock_section.av-m8gaohgy-e779b05cc50cec788c64ad3beca2e266 .avia_textblock{\nfont-size:40px;\n}\n<\/style>\n<section  class='av_textblock_section av-m8gaohgy-e779b05cc50cec788c64ad3beca2e266 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p style=\"text-align: center;\"><strong>It takes a Triad of Skill Sets to Solve Real Industrial Problems with Analytics<\/strong><\/p>\n<\/div><\/section><\/div><div  class='av-post-metadata-container av-av_post_metadata-f2d93833baf55c47e6bd6c86e40f308e av-metadata-container-align-center  avia-builder-el-7  el_after_av_one_full  el_before_av_hr  av-metadata-container-1'><div class='av-post-metadata-container-inner'><span class=\"av-post-metadata-content av-post-metadata-meta-content\"><span class=\"av-post-metadata-content av-post-metadata-published\"><span class=\"av-post-metadata-published-date\" >26. May 2021<\/span><\/span><span class=\"av-post-metadata-content av-post-metadata-separator\">|<\/span><span class=\"av-post-metadata-content av-post-metadata-category\"><span class=\"av-metadata-before av-metadata-before-categories\">in<\/span><span class=\"av-post-metadata-category-link\" ><a href=\"https:\/\/www.cohu.com\/category\/thought-leadership\/\" >Thought Leadership<\/a><\/span><\/span><\/span><\/div><\/div><\/p>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8t25391-39260fa6e7d6022a5f36742c2f35864e\">\n#top .hr.hr-invisible.av-m8t25391-39260fa6e7d6022a5f36742c2f35864e{\nheight:50px;\n}\n<\/style>\n<div  class='hr av-m8t25391-39260fa6e7d6022a5f36742c2f35864e hr-invisible  avia-builder-el-8  el_after_av_post_metadata  el_before_av_one_full '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<div  class='flex_column av-1ltc3hq-d678e11ab7342dc0fff705952edff81f av_one_full  avia-builder-el-9  el_after_av_hr  el_before_av_hr  first flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-14x5t1q-e294f4ddae26e829f179d795019a7ad0\">\n.avia-image-container.av-14x5t1q-e294f4ddae26e829f179d795019a7ad0 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-14x5t1q-e294f4ddae26e829f179d795019a7ad0 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-14x5t1q-e294f4ddae26e829f179d795019a7ad0 av-styling- avia-align-center  avia-builder-el-10  avia-builder-el-no-sibling '   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><img decoding=\"async\" fetchpriority=\"high\" class='wp-image-45804 avia-img-lazy-loading-not-45804 avia_image ' src=\"\/wp-content\/uploads\/2025\/03\/Triad-of-Skills-banner-image-1-1030x361.jpeg\" alt='' title='Triad-of-Skills-banner-image-1'  height=\"361\" width=\"1030\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2025\/03\/Triad-of-Skills-banner-image-1-1030x361.jpeg 1030w, \/wp-content\/uploads\/2025\/03\/Triad-of-Skills-banner-image-1-300x105.jpeg 300w, \/wp-content\/uploads\/2025\/03\/Triad-of-Skills-banner-image-1-768x269.jpeg 768w, \/wp-content\/uploads\/2025\/03\/Triad-of-Skills-banner-image-1-705x247.jpeg 705w, \/wp-content\/uploads\/2025\/03\/Triad-of-Skills-banner-image-1.jpeg 1200w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/div><\/div><\/div><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g8t5wg-76ecfb6e051b02404cc71f111581bb48\">\n#top .hr.hr-invisible.av-m8g8t5wg-76ecfb6e051b02404cc71f111581bb48{\nheight:50px;\n}\n<\/style>\n<div  class='hr av-m8g8t5wg-76ecfb6e051b02404cc71f111581bb48 hr-invisible  avia-builder-el-11  el_after_av_one_full  el_before_av_one_full '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<div  class='flex_column av-tc5sy6-c610ce4ce37ddc8755f7c103673cd0c6 av_one_full  avia-builder-el-12  el_after_av_hr  avia-builder-el-last  first flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g8tpp9-ff1fa86bc0dd446fd91b41108625f035\">\n#top .av_textblock_section.av-m8g8tpp9-ff1fa86bc0dd446fd91b41108625f035 .avia_textblock{\nfont-size:18px;\n}\n<\/style>\n<section  class='av_textblock_section av-m8g8tpp9-ff1fa86bc0dd446fd91b41108625f035 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p>I regularly advise industrial and manufacturing companies about how they can leverage modern analytics and machine learning to improve their operations, their revenue, and their profitability. I\u2019m seeing a common pattern \u2013 where the successful deployment of analytics encounters resistance because one of three critical skill sets is either missing in the conversation or not incented to participate.<\/p>\n<p>To successfully deploy intelligent analytics and achieve the desired business results, three core skill sets must be successfully melded together. It is a requirement that poses a fundamental challenge to many pursuing the technology\u2019s potential, because the triad of essential skills is usually distributed across individuals or functional organizations that do not interact on a daily basis. Specifically:<\/p>\n<ul>\n<li>Automation engineers understand the data and know what data is available (and could potentially be available), and they are intrigued by the possibilities of analytics and machine learning (ML). However, they don\u2019t necessarily deeply understand the manufacturing process or equipment, such as how to recognize that a chiller is failing, so they don\u2019t understand all the kinds of problems that would be valuable to solve with data. These engineers would be motivated to fill data gaps or software tool gaps if only they knew which data was important for solving a problem.<\/li>\n<\/ul>\n<ul>\n<li>Process experts are deeply knowledgeable about the equipment and\/or production processes and they know what process problems need to be solved. But they may not understand what is possible with ML and data analytics or how to use the analysis to solve problems. These subject matter experts, such as mechanical equipment specialists, equipment reliability engineers, oilfield reservoir engineers, and pharmaceutical process engineers, don\u2019t have analytics on their priority list because their time is constrained by reacting to challenges in the production line, whether output problems, equipment concerns, or system downtime.<\/li>\n<\/ul>\n<ul>\n<li>ML analytics\/data science experts understand the analytics and what is possible using analytics, but they don\u2019t necessarily understand what data is available from the equipment, or what problems need to be solved. The data science skill set is more commonly found in larger organizations such as pharmaceutical manufacturing.<\/li>\n<\/ul>\n<p><strong>Keys to success<\/strong><\/p>\n<p>If the project is handed off to just one team in the triad, it is much less likely to be successful. To optimally deploy advanced industrial analytics, it is necessary to create a business environment that connects the islands of expertise by encouraging the groups to interact with each other. Fostering the intersection of automation, process, and ML analytics insights improves the ability to identify and prioritize opportunities to solve real problems. Providing organizational support for cross-functional projects will ensure the efforts are sustained.<\/p>\n<p>Impactful insights arise from the information exchange. Automation engineers will readily seek out data that the process team says is needed. Process engineers will leap at the chance to resolve existing problems with data when they learn the automation team can make data and tools available that are easy to use. Data science teams will eagerly deploy any analytics project identified as necessary to the operation.<\/p>\n<p>One great technique to accelerate collaboration within the triad of skills is to hire individuals whose experience crosses two or more of the skill sets. These individuals can serve as translators, helping to build trust between functions and accelerate the speed of iteration. Such individuals are likely to be in high demand and hard to recruit, so another creative approach is to do internal cross training \u2013 take your process engineers and have them work on the automation or data science team for a while.<\/p>\n<p>If a combination of skill sets is not available internally, partnering with an external vendor can fill the void. For example, to further accelerate analytics development, consider engaging with a vendor whose expertise extends beyond data science and into process, equipment, and\/or reliability engineering.<\/p>\n<p>Consider the upstream oil and gas industry, for example. At Tignis we have expert data scientists who not only have PhDs in petroleum engineering but were formerly oilfield reservoir engineers. They are experts not only with the latest in machine learning, but also understand the analytics, the process, and how to use data to solve upstream business use cases.<\/p>\n<p><strong>The bottom line<\/strong><\/p>\n<p>When engaging in digital transformation projects around analytics, start by creating a cross-functionally funded team that includes all three pillars of expertise. It is not uncommon for industrial organizations to be deficient in one or more of the three pillars, most commonly data science expertise.\u00a0 When looking for external resources and suppliers to supplement your team it is best to look for organizations that have skill sets that overlap your team\u2019s.\u00a0 This will ultimately improve the overall communication of the team and accelerate the time to value of your digital transformation project.<\/p>\n<\/div><\/section><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":35,"featured_media":46051,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[413],"tags":[],"class_list":["post-45803","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-thought-leadership"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Solve Real Industrial Programs with Analytics - 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