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ts\u002Fpredictive-maintenance-solutions",{"rendered":439},"“One Predictive Maintenance To Go, Please!”",{"rendered":441,"protected":442},"\u003Ch2 class=\"wp-block-heading\" id=\"h-predictive-maintenance-solutions\">Predictive Maintenance Solutions\u003C\u002Fh2>\n\n\n\u003Cblockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n\u003Cblockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n\u003Cp>\u003C\u002Fp>\n\u003Ccite>\u003Cem>“Hello! We’ve heard you are offering Industry 4.0 Analytics, can we buy this predictive maintenance solution? And if yes, when will you deliver it?”\u003C\u002Fem> – Every. customer. ever.\u003C\u002Fcite>\u003C\u002Fblockquote>\n\n\n\n\u003Cp>\u003C\u002Fp>\n\n\n\n\u003Cp>In an exaggerated way, these first sentences of an introductory call by a new customer of our Industrial Analytics team represents the perception of predictive maintenance related projects spread all over the German market very well.\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\n\n\n\u003Cp>It’s always the same questions we are faced with when dealing with predictive maintenance: \u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>What should the solution look like?\u003C\u002Fstrong>\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>And anyway, what \u003Cem>is\u003C\u002Fem> predictive maintenance?\u003C\u002Fstrong>\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>After a few sessions, it most likely turns out that predictive maintenance, as the company trying to buy this mysterious predictive maintenance solution is calling it, should be replaced by a more general concept of \u003Cstrong>machine learning methods\u003C\u002Fstrong> brought to manufacturing businesses.\u003C\u002Fp>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-truth-is-there-is-no-one-size-fits-all-solution-for-predictive-maintenance\">Truth is: there is no ‘one size fits all’ solution for predictive maintenance\u003C\u002Fh2>\n\n\n\u003Cp>The practical truth is, that there is no one size fits all solution. Every project needs individual problem identification and solution, even if we are talking about the same use case for the 42th time.\u003C\u002Fp>\n\n\n\n\u003Cp>Firstly, the real-world problems might differ from use case to use case.\u003Cbr>For example, the underlying goals that should be addressed by a predictive maintenance solution:\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Is the goal to \u003Cstrong>reduce downtime\u003C\u002Fstrong> as it costs a lot?\u003C\u002Fli>\n\n\n\n\u003Cli>Or is the main goal to \u003Cstrong>lower spare part costs\u003C\u002Fstrong>?\u003C\u002Fli>\n\n\n\n\u003Cli>Or to \u003Cstrong>optimize personnel rosters\u003C\u002Fstrong> for service technicians?\u003C\u002Fli>\n\n\n\n\u003Cli>Or is it all three together?\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>Only a profound \u003Cstrong>analysis \u003C\u002Fstrong>of the real-world business and its underlying processes allows a well-fitting definition of the future target variables available in the data.\u003C\u002Fp>\n\n\n\n\u003Cp>And this keyword brings us to the second point. We always have to match the data available with all its potential quality and availability issues to the real-world machine learning solution we want to build. And moreover, the Data Scientists have to translate and map the advanced analytics methods available for this future solution. This whole approach is the absolute necessary foundation for successful machine learning projects.\u003C\u002Fp>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"start-small\">Start small\u003C\u002Fh2>\n\n\n\u003Cp>Predictive maintenance alone has many facets as we rarely \u003Cstrong>predict the failure of a machine\u003C\u002Fstrong> in the field in general, but, first put a lot of effort into the concretization of the very problem at hand.\u003C\u002Fp>\n\n\n\n\u003Cp>In most cases, we predict not the complete failure of a machine but start with analyzing the failure of a few critical parts in order to find a promising starting point. In other cases, there is \u003Cstrong>no historic failure information available \u003C\u002Fstrong>and we cannot use predictive modelling. This is because it would be preferable, but we have to fall back on unsupervised learning methods for anomaly detection (usually in very high dimensional space with not so many extreme \u002F unusual values we are hunting for). Nevertheless, it’s not only predictive maintenance with its challenges, manufacturing companies tackle the whole machine learning topic in most cases for the very first time. By doing that, they encounter very multilayered challenges regarding the establishment of a whole new professional service at the intersection of mathematics, computer science and process analysis.\u003C\u002Fp>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"are-there-best-practices\">Are there best practices?\u003C\u002Fh2>\n\n\n\u003Cp>To sum up, \u003Cstrong>there obviously are common challenges all manufacturing companies are facing during their first contacts with machine learning.\u003C\u002Fstrong> Although the buzzword “predictive maintenance” is on everyone’s lips, there still are no real best practices in the market. And besides predictive maintenance, there are plenty of (in most cases simpler!) machine learning use cases for manufacturing companies like predictive quality or production planning optimization.\u003C\u002Fp>\n\n\n\n\u003Cp>In almost all cases, the professional relationships to our manufacturing customers started with a first request for a predictive maintenance solution and in all cases we started to interlace advanced analytics methods with the culture and DNA of the company in order to face the challenges during implementation of new data driven solutions.\u003C\u002Fp>\n",false,{"rendered":444,"protected":442},"\u003Cp>Predictive Maintenance Solutions It’s always the same questions we are faced with when dealing with predictive maintenance:  After a few sessions, it most likely turns out that predictive maintenance, as the company trying to buy this mysterious predictive maintenance solution is calling it, should be replaced by a more general concept of machine learning methods brought to […]\u003C\u002Fp>\n",2365,"open","standard",{"_acf_changed":442,"content-type":8,"inline_featured_image":442,"footnotes":8},[450],46,[],[453],137,[455],136,[457,436,458,459,460,461,462,463,464,465],"post-2364","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-insights","cbtw_industries-manufacturing","cbtw_services-smart-industrial-solutions",{"authors":8,"article_authors":442,"direct_external_link":8,"post_region":8,"layout_builder":467},[468],{"acf_fc_layout":469,"title":195,"ui_v2":470,"short_description":471,"cbtw_related_group_filters":472,"items":442,"link":477,"hubspot_form_id":8,"mainPostId":430,"region":481},"block_insights",true,"Access related expert insights",{"cbtw_related_category_filter":473,"cbtw_related_tag_filter":474,"cbtw_related_industry_filter":475,"cbtw_related_service_filter":476},[221,197],[],[],[],{"title":478,"url":479,"target":480},"View all insights","https:\u002F\u002Fcbtw.tech\u002Finsights","_self","global","\u003C!-- This site is optimized with the Yoast SEO Premium plugin v25.4 (Yoast SEO v25.4) - https:\u002F\u002Fyoast.com\u002Fwordpress\u002Fplugins\u002Fseo\u002F -->\n\u003Ctitle>How Predictive Maintenance Solutions Transform Industries\u003C\u002Ftitle>\n\u003Cmeta name=\"description\" content=\"Explore how predictive maintenance solutions are revolutionizing industry practices, reducing downtime, and saving costs.\" \u002F>\n\u003Cmeta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \u002F>\n\u003Clink rel=\"canonical\" href=\"https:\u002F\u002Fcbtw.tech\u002Finsights\u002Fpredictive-maintenance-solutions\" \u002F>\n\u003Cmeta property=\"og:locale\" content=\"en_US\" \u002F>\n\u003Cmeta property=\"og:type\" content=\"article\" \u002F>\n\u003Cmeta property=\"og:title\" content=\"“One Predictive Maintenance To Go, Please!”\" \u002F>\n\u003Cmeta 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