{"id":8833,"date":"2020-09-25T00:57:32","date_gmt":"2020-09-25T00:57:32","guid":{"rendered":"https:\/\/oden.io\/?p=8833"},"modified":"2021-03-29T18:33:02","modified_gmt":"2021-03-29T18:33:02","slug":"achieving-shorter-lead-times-with-predictive-analytics-is-not-a-pipe-dream","status":"publish","type":"post","link":"https:\/\/oden.io\/blog\/achieving-shorter-lead-times-predictive-analytics\/","title":{"rendered":"Achieving Shorter Lead Times With Predictive Analytics Is Not A Pipe Dream"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"8833\" class=\"elementor elementor-8833\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-41fe503 blthead elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"41fe503\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-84ef4f6\" data-id=\"84ef4f6\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-inner-section elementor-element elementor-element-7c4cd9d bconcept2firstblock elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7c4cd9d\" data-element_type=\"section\" id=\"bloglt\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-01e3c30\" data-id=\"01e3c30\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3560d51 elementor-widget elementor-widget-text-editor\" data-id=\"3560d51\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.21.0 - 30-04-2024 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<div class=\"tb-blog-category text-uppercase tb-white-c tb-f10-lg tb-line1-3\" style=\"text-align: left;\"> <a class=\"tb-category-btn tb-style1 tb-radious-3\" href=\"https:\/\/oden.io\/blog\/category\/prescriptive-analytics\/\">Predictive Analytics<\/a><\/div>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3fd1a61 ltarticleheadtitle elementor-widget elementor-widget-heading\" data-id=\"3fd1a61\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.21.0 - 30-04-2024 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h1 class=\"elementor-heading-title elementor-size-default\">Achieving Shorter Lead Times With Predictive Analytics Is Not A Pipe Dream <\/h1>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9162319 elementor-author-box--layout-image-left elementor-author-box--align-left elementor-author-box--image-valign-middle elementor-author-box--avatar-yes elementor-author-box--name-yes elementor-author-box--biography-yes elementor-widget elementor-widget-author-box\" data-id=\"9162319\" data-element_type=\"widget\" data-widget_type=\"author-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<link rel=\"stylesheet\" href=\"https:\/\/oden.io\/wp-content\/plugins\/elementor-pro\/assets\/css\/widget-theme-elements.min.css\">\t\t<div class=\"elementor-author-box\">\n\t\t\t\t\t\t\t<a href=\"https:\/\/oden.io\/author\/oden-technologies\/\" class=\"elementor-author-box__avatar\">\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/secure.gravatar.com\/avatar\/8062d425d2467e5fed2326a2aeee5437?s=300&#038;d=mm&#038;r=g\" alt=\"Picture of Oden Technologies\" loading=\"lazy\">\n\t\t\t\t<\/a>\n\t\t\t\n\t\t\t<div class=\"elementor-author-box__text\">\n\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/oden.io\/author\/oden-technologies\/\">\n\t\t\t\t\t\t<h6 class=\"elementor-author-box__name\">\n\t\t\t\t\t\t\tOden Technologies\t\t\t\t\t\t<\/h6>\n\t\t\t\t\t<\/a>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-author-box__bio\">\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-907d197 conceptheadingright\" data-id=\"907d197\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-25b07aca elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"25b07aca\" data-element_type=\"section\" id=\"01\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-6436dc00\" data-id=\"6436dc00\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-35b024a7\" data-id=\"35b024a7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-68b0663c\" data-id=\"68b0663c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-d2185ee elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d2185ee\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-51259b0e\" data-id=\"51259b0e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-12f6a0e9\" data-id=\"12f6a0e9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5651a1d3 elementor-widget elementor-widget-eael-divider\" data-id=\"5651a1d3\" data-element_type=\"widget\" data-widget_type=\"eael-divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"eael-divider-wrap divider-direction-horizontal\">\n\t\t\t\t\t\t\t<div class=\"eael-divider horizontal solid\"><\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-15905c54 elementor-widget elementor-widget-heading\" data-id=\"15905c54\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Predictive analytics has found several use cases in manufacturing. IoT sensors, cloud analytics, and big data have all come together to make it possible to predict outages or downtime, waste, safety hazards and other aspects of manufacturing. <\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2974c483 elementor-widget elementor-widget-text-editor\" data-id=\"2974c483\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The key to <span style=\"color: #2fa5ae;\"><a style=\"color: #2fa5ae;\" href=\"https:\/\/info.oden.io\/oden-technologies-rfs-case-study\">a successful predictive analytics deployment<\/a><\/span> is its ability to predict outcomes quickly and accurately. Like all learning models, the accuracy of the result depends on the amount of effort, time, and data that has been invested in training the model.<\/p><p>However, the lead time for achieving a high correlation to outcomes means lost insights and opportunities. Achieving a short lead time to train the model will also mean the accuracy is higher, and the predicted outcomes\u2019 reliability is better. Hence, manufacturing companies are always pursuing to accelerate the lead time to train a predictive model successfully.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-749c239a\" data-id=\"749c239a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-204c011b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"204c011b\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-2f6520d9\" data-id=\"2f6520d9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-4976f279\" data-id=\"4976f279\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-74b5af1a elementor-widget elementor-widget-heading\" data-id=\"74b5af1a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Shorter Lead Time For Actionable Insights<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-34d60fa9\" data-id=\"34d60fa9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-757e73c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"757e73c\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-16 elementor-top-column elementor-element elementor-element-7a6e912\" data-id=\"7a6e912\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-2f4e8360\" data-id=\"2f4e8360\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-20e53c61 elementor-widget elementor-widget-eael-divider\" data-id=\"20e53c61\" data-element_type=\"widget\" data-widget_type=\"eael-divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"eael-divider-wrap divider-direction-horizontal\">\n\t\t\t\t\t\t\t<div class=\"eael-divider horizontal solid\"><\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-447679d5 elementor-widget elementor-widget-heading\" data-id=\"447679d5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The key to achieving shorter lead time depends on the quality of the training data set and selecting the most appropriate model.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-635a4e1d elementor-widget elementor-widget-text-editor\" data-id=\"635a4e1d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The availability of sufficient (relevant) training data, suitable correlation analysis, and adequate training is critical in crashing the lead time. A correlation of 85% is a safe point to start running real-time data.<\/p><p>The training method also impacts the lead time. Most processes across industries aim to achieve the same outcome \u2013 improved efficiency through lesser waste or greater asset utilization.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-16 elementor-top-column elementor-element elementor-element-60c02443\" data-id=\"60c02443\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-7c29d38 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7c29d38\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-fa6824d\" data-id=\"fa6824d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-3a07a77\" data-id=\"3a07a77\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-22613ca elementor-widget elementor-widget-heading\" data-id=\"22613ca\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Prerequisites To Accelerate Training<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-42e9abc\" data-id=\"42e9abc\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-ad17c34 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ad17c34\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-460d7ad\" data-id=\"460d7ad\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-7cdc62f\" data-id=\"7cdc62f\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e730212 elementor-widget elementor-widget-eael-divider\" data-id=\"e730212\" data-element_type=\"widget\" data-widget_type=\"eael-divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"eael-divider-wrap divider-direction-horizontal\">\n\t\t\t\t\t\t\t<div class=\"eael-divider horizontal solid\"><\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9910eac elementor-widget elementor-widget-text-editor\" data-id=\"9910eac\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\tThe checklist of actions required for a faster TAT of training a predictive model is nothing new. It is as old as predictive modeling itself. However, business dynamics and project milestone pressures often can result in ignoring the basics. Ironically, this leads to longer lead times, making overall schedule adherence more of a challenge. The following is a critical checklist that can help ensure a faster training of a predictive model using an optimal data set.\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-1944fbf\" data-id=\"1944fbf\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-0c645be elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0c645be\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-875dc91\" data-id=\"875dc91\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-70255c7\" data-id=\"70255c7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7c05dac elementor-widget elementor-widget-heading\" data-id=\"7c05dac\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Defining The Objective<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c8e43e6 elementor-widget elementor-widget-text-editor\" data-id=\"c8e43e6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\tDefining a clear objective is a critical step in the process of building a predictive analysis model. A well-defined problem statement can define the data required for testing, which can help save time in the successive stages. A vague or constantly fluctuating problem statement, on the other hand, can blow things out of proportion by leading to gathering irrelevant data. For instance, if the problem to solve is wastage due to in-line geometric deviations, factors not influencing geometric deviations will add complexity and training duration to the model. This way, the predictive analytics will be focused on the problem it is intended to address and can be trained with the right data. The nature of the problem will influence other aspects like the training model, accuracy or tolerance, data structure, and need for normalization.\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-b68a4d7\" data-id=\"b68a4d7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-2a255d9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2a255d9\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-16 elementor-top-column elementor-element elementor-element-b770857\" data-id=\"b770857\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-d7b0350\" data-id=\"d7b0350\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e0dda65 elementor-widget elementor-widget-heading\" data-id=\"e0dda65\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Picking The Right Data Points<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-92664d2 elementor-widget elementor-widget-text-editor\" data-id=\"92664d2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\tOnce the problem statement has been defined, the subsequent step involves picking out appropriate data points. The benefit of a well-defined problem statement can be harvested in this stage. As discussed earlier, the data fields that must be included and those that should not clog the model will define the TAT for training the model. \t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-16 elementor-top-column elementor-element elementor-element-ba140dd\" data-id=\"ba140dd\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-c453d05 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c453d05\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-1f0d7c9\" data-id=\"1f0d7c9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-0672b77\" data-id=\"0672b77\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-99a5f5e elementor-widget elementor-widget-heading\" data-id=\"99a5f5e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Using The Right Quality And Quantity Of Data<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6ebbe83 elementor-widget elementor-widget-text-editor\" data-id=\"6ebbe83\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Finding the right quality and quantity of data is a crucial step in <span style=\"color: #2fa5ae;\"><a style=\"color: #2fa5ae;\" href=\"https:\/\/info.oden.io\/achieving-cutting-edge-production\">training an ML model.<\/a><\/span> Aspects of the model like fidelity, tolerance, and reliability largely depend on this stage. Too little data can result in a high level of approximation. It will also make data selection difficult as the rejection of anomalies and normalization, if required, will become difficult. On the other hand, large volumes of data can help build an accurate and reliable model. However, managing volumes of data will need sophisticated big data and deep learning skills. Moreover, with unstructured data like image or unconstrained text, developing labels or processing information becomes complex, resulting in longer training cycles.<\/p><p>Finding a right balance between the quantity and quality of data can help achieve a healthy correlation with a shorter lead time.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-c809c54\" data-id=\"c809c54\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-f49e6b4 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f49e6b4\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-0a28ee7\" data-id=\"0a28ee7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-7aa2e3b\" data-id=\"7aa2e3b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6c60e3a elementor-widget elementor-widget-heading\" data-id=\"6c60e3a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Identifying The Right Data Source<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-280d904 elementor-widget elementor-widget-text-editor\" data-id=\"280d904\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\tThe source of the data is as important as the data itself. It plays an essential role in influencing the fidelity and frequency of the data. If the objective is to develop a predictive model for a particular objective in diverse environments, then the data source should also be diverse. This ensures a deliberately random data set. However, if the scope is very particular and is relevant only to a constrained environment, it is sufficient to choose a source that gives similar data. Respect for privacy when the data set contains information that makes any individual identifiable is critical. The use of such data is governed by different regulatory guidelines and laws in different countries.\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-78716af\" data-id=\"78716af\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-448208e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"448208e\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-05f87aa\" data-id=\"05f87aa\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-3d13d9e\" data-id=\"3d13d9e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e72bb87 elementor-widget elementor-widget-heading\" data-id=\"e72bb87\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Preparing The Data<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b6ad5cf elementor-widget elementor-widget-text-editor\" data-id=\"b6ad5cf\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\tNormally, three types of data are available for modeling: demographic, behavioral, and psychographic. While these diverse types of data have to be standardized, structured, and normalized, care must be taken to ensure that the process does not lessen its intelligence. Although this can be one of the most time-consuming stages in the process, a combination of technology with relevant data can help quickly arrive at the optimum level of preparedness.\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-88701cd\" data-id=\"88701cd\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-c7afeeb elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c7afeeb\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-448244c\" data-id=\"448244c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-9a0dde9\" data-id=\"9a0dde9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b70fbc1 elementor-widget elementor-widget-heading\" data-id=\"b70fbc1\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Picking The Right Model Type<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-487a9d3 elementor-widget elementor-widget-text-editor\" data-id=\"487a9d3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\tIdentifying the right ML algorithm can go a long way in achieving a shorter lead time. Selecting the right algorithm will largely depend on the problem the model aims to solve. For instance, if the model has to predict a binary outcome &#8211; either a &#8220;yes&#8221; or a &#8220;no,&#8221; then a binary classification model would suffice. However, for predictions to questions like &#8220;how many units of a particular product will be rejected as waste?&#8221;, the regression model can be more appropriate. These are examples of supervised learning models. Similarly, unsupervised models based on cluster analysis and association study can be used to train models that group or categorize outcomes.\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-58c421b\" data-id=\"58c421b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-4545ce8b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4545ce8b\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-5b3b714a\" data-id=\"5b3b714a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-16fb6d72\" data-id=\"16fb6d72\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-632b98a6 elementor-widget elementor-widget-heading\" data-id=\"632b98a6\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Why Is Shortening Lead Time Necessary?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-3093acfc\" data-id=\"3093acfc\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-19162337 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"19162337\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-1123fdbe\" data-id=\"1123fdbe\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-5114989c\" data-id=\"5114989c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-51e006d5 elementor-widget elementor-widget-eael-divider\" data-id=\"51e006d5\" data-element_type=\"widget\" data-widget_type=\"eael-divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"eael-divider-wrap divider-direction-horizontal\">\n\t\t\t\t\t\t\t<div class=\"eael-divider horizontal solid\"><\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e526c27 elementor-widget elementor-widget-text-editor\" data-id=\"e526c27\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The purview of predictive analytics extends far beyond just maintenance activities. Shorter lead times to achieving a predictive model means more than just a quick transition from a predictive model to a preventive model. It results in the following:<\/p><ul><li>Minimizes the amount of data needed to implement<\/li><li>Accelerates turnaround time while minimizing waste<\/li><li>Enables manufacturers to align activities with demand forecasts<\/li><li>Improves and supports decision making<\/li><li>Enhances personnel and equipment safety across the factory floor<\/li><\/ul><p>These benefits, combined with the ability to minimize or avoid unplanned downtime, help reduce the overall manufacturing cost.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-8b5588b\" data-id=\"8b5588b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-5df07bd2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5df07bd2\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-6488cda1\" data-id=\"6488cda1\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-4a810cd5\" data-id=\"4a810cd5\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3858245 elementor-widget elementor-widget-heading\" data-id=\"3858245\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Getting Help To Deploy A Customized And Optimized Predictive Model<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-25b1e466\" data-id=\"25b1e466\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-44ac011a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"44ac011a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-77c04f\" data-id=\"77c04f\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-15a4ea6b\" data-id=\"15a4ea6b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5176c0ef elementor-widget elementor-widget-eael-divider\" data-id=\"5176c0ef\" data-element_type=\"widget\" data-widget_type=\"eael-divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"eael-divider-wrap divider-direction-horizontal\">\n\t\t\t\t\t\t\t<div class=\"eael-divider horizontal solid\"><\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4c8ca79a elementor-widget elementor-widget-text-editor\" data-id=\"4c8ca79a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>There is no shortage of choices when picking the right platform for implementing a predictive analytics strategy. Identifying and choosing the right platform is critical. Numerous platforms can train massive data sets before arriving at an optimal model with a healthy regression rate. But spending time on training and equipping a model can be expensive and often is a task with little return.<\/p><p><a style=\"background-color: #ffffff;\" href=\"https:\/\/info.oden.io\/the-oden-platform-data-sheet\" target=\"_blank\" rel=\"noopener\"><span style=\"color: #2fa5ae;\"><span style=\"text-decoration: underline;\">An ideal platform<\/span><\/span><\/a>\u00a0is one that manufacturers can quickly use to drive results rather than training and tuning. The actual benefit of predictive analytics is not infrastructure or tool setup, but rather the analytics-driven insights to achieve optimal efficiency, reduce unplanned downtime, and minimize wastage. Such models\u2019 development and deployment can be left to capable technology partners who can accelerate the transition towards predictive analytics with shorter lead time.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-86f2af2 elementor-widget elementor-widget-image\" data-id=\"86f2af2\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.21.0 - 30-04-2024 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/info.oden.io\/request-a-demo\">\n\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"216\" src=\"https:\/\/oden.io\/wp-content\/uploads\/2020\/09\/045-CTA-Images_V3_Talk-to-an-Industry-4.0-Expert-V1a-1024x216.png\" class=\"attachment-large size-large wp-image-9133\" alt=\"\" srcset=\"https:\/\/oden.io\/wp-content\/uploads\/2020\/09\/045-CTA-Images_V3_Talk-to-an-Industry-4.0-Expert-V1a-1024x216.png 1024w, https:\/\/oden.io\/wp-content\/uploads\/2020\/09\/045-CTA-Images_V3_Talk-to-an-Industry-4.0-Expert-V1a-300x63.png 300w, https:\/\/oden.io\/wp-content\/uploads\/2020\/09\/045-CTA-Images_V3_Talk-to-an-Industry-4.0-Expert-V1a-768x162.png 768w, https:\/\/oden.io\/wp-content\/uploads\/2020\/09\/045-CTA-Images_V3_Talk-to-an-Industry-4.0-Expert-V1a-1536x324.png 1536w, https:\/\/oden.io\/wp-content\/uploads\/2020\/09\/045-CTA-Images_V3_Talk-to-an-Industry-4.0-Expert-V1a-2048x432.png 2048w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-59ce040c\" data-id=\"59ce040c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Predictive Analytics Achieving Shorter Lead Times With Predictive Analytics Is Not A Pipe Dream Predictive analytics has found several use cases in manufacturing. IoT sensors, cloud analytics, and big data have all come together to make it possible to predict outages or downtime, waste, safety hazards and other aspects of manufacturing. The key to a [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":8864,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[165],"tags":[129],"_links":{"self":[{"href":"https:\/\/oden.io\/wp-json\/wp\/v2\/posts\/8833"}],"collection":[{"href":"https:\/\/oden.io\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/oden.io\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/oden.io\/wp-json\/wp\/v2\/users\/12"}],"replies":[{"embeddable":true,"href":"https:\/\/oden.io\/wp-json\/wp\/v2\/comments?post=8833"}],"version-history":[{"count":0,"href":"https:\/\/oden.io\/wp-json\/wp\/v2\/posts\/8833\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oden.io\/wp-json\/wp\/v2\/media\/8864"}],"wp:attachment":[{"href":"https:\/\/oden.io\/wp-json\/wp\/v2\/media?parent=8833"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/oden.io\/wp-json\/wp\/v2\/categories?post=8833"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/oden.io\/wp-json\/wp\/v2\/tags?post=8833"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}