{"id":17868,"date":"2025-11-28T08:29:50","date_gmt":"2025-11-28T08:29:50","guid":{"rendered":"https:\/\/www.siveco.com\/?p=17868"},"modified":"2025-12-01T09:02:03","modified_gmt":"2025-12-01T09:02:03","slug":"siveco-group-acquires-a-stake-in-elipsis-to-strengthen-its-iot-and-data-offering-3","status":"publish","type":"post","link":"https:\/\/www.siveco.com\/en\/blog-gmao\/siveco-group-acquires-a-stake-in-elipsis-to-strengthen-its-iot-and-data-offering-3\/","title":{"rendered":"Predictive maintenance: moving from promises to action"},"content":{"rendered":"\t\t
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Often presented as the key to a trouble-free industry, predictive maintenance raises many hopes, and a few illusions. Its real value lies less in its technological promise than in its ability to reason, prioritise and choose. Between data, IoT and common sense, a pragmatic approach is needed: making predictive maintenance a lever for overall performance, rather than a digital gadget. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Maintenance\t\t\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Presented as a technological Holy Grail capable of eliminating breakdowns, predictive maintenance has generated high expectations. But in practice, the reality is more nuanced. Anticipating does not mean predicting everything, and digitisation does not eliminate the need to make choices. To make it a real performance lever, you still need to know where predictive maintenance is useful, how to feed it, and how to apply it wisely. <\/p>\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\t\t<\/div>\n\t\t

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Corrective maintenance will not disappear\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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In many discussions about digital transformation in industry, corrective maintenance is presented as an anomaly that must be eliminated. As appealing as this vision may be, it ignores a fundamental reality: not all breakdowns are equal, and not all of them need to be prevented. A light bulb at the end of its life, for example, does not require the same strategy as a critical pump on a production line. Eliminating corrective maintenance entirely would therefore mean investing heavily in preventing minor breakdowns, at the risk of wasting time, money, energy and valuable human resources. <\/p>

The right balance does not lie in eliminating risk, but in the ability to arbitrate, prioritise and choose which breakdowns need to be anticipated and which can be repaired. Corrective maintenance still has its place, provided it is targeted and not imposed. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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No technology without common sense<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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In a predictive maintenance strategy, everything starts with data, which is the very basis of models and analyses. However, deploying IoT sensors only makes sense if guided by clear objectives. Too often, IoT projects focus on measurements that are easy to collect (temperature, vibrations, etc.) and neglect essential data, such as physical impacts or environmental data, which have a significant effect on actual risks. <\/p>

The goal is not to deploy sensors for the sake of collecting data. First, you need to ask the right questions: How accurately does the data need to be collected? What specific elements need to be measured to address business challenges? By considering these questions, IoT becomes a valuable decision-making tool rather than a superfluous layer of technology. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Thinking smart maintenance\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Once priorities have been set, implementing predictive maintenance relies on using the right tools and, above all, choosing the right partners. Simply deploying sensors is not enough; they must be intelligently integrated into an overall process. <\/p>

This is where POC approaches and team involvement come into their own. By testing use cases on a small scale, companies can quickly validate the most effective solutions, involve teams in the process, and adjust tools before deploying them on a large scale. <\/p>

At the same time, choosing the right partners is essential to the success of the project. Implementing a predictive maintenance policy involves many aspects: sensor management, data security, information transmission and analysis, definition of business rules, etc. All of these areas require specialised experts to ensure smooth integration and successful implementation. <\/p>

If these aspects are poorly managed, there is a risk that projects will stagnate or fail because stakeholders are not speaking the same language and issues are multiplying. It is therefore strategic to choose partners who are capable of managing the entire project, from deployment to analysis of results. <\/p>

When viewed in this way, predictive maintenance becomes a global performance lever: reducing risks, improving energy efficiency, optimising working conditions and, ultimately, creating a better environment for teams. These benefits, which are often invisible at first glance, must be fully integrated into industrial strategies. <\/p>

Predictive maintenance should no longer be a technological promise, but a concrete lever, integrated into an industrial strategy adapted to the reality on the ground. It is in this operational clarity that its true power lies. <\/p>

By Laurent Cr\u00e9tot, Sales and Marketing Director at Siveco Group.<\/strong><\/p>\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\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Often presented as the key to a trouble-free industry, predictive maintenance raises many hopes, and a few illusions. Its real value lies less in its technological promise than in its ability to reason, prioritise and choose. Between data, IoT and common sense, a pragmatic approach is needed: making predictive maintenance a lever for overall performance, […]<\/p>\n","protected":false},"author":3,"featured_media":17879,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[110],"tags":[],"class_list":["post-17868","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-expert-opinion"],"yoast_head":"\nPredictive maintenance: moving from promises to action - Siveco<\/title>\n<meta name=\"description\" content=\"According to Siveco Group, predictive maintenance should be used as a lever for overall performance, not as a digital gadget.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.siveco.com\/en\/blog-gmao\/siveco-group-acquires-a-stake-in-elipsis-to-strengthen-its-iot-and-data-offering-3\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Predictive maintenance: moving from promises to action - 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