Intro
Predictive maintenance has proven to be an effective methodology for enhancing the lifespan of valuable assets in the industrial sector, as it allows companies to predict when equipment is likely to fail or stop. This way, proactive measures can be taken to prevent downtime and reduce maintenance costs.
Besides that, a November 2021 report from IoT Analytics estimated that the $6.9 billion predictive maintenance market would reach $28.2 billion by 2026. This showcases the immense potential this technology has, allowing companies to optimize their outcomes while reducing downtime. However, if this technology isn’t adequately implemented, it may turn into a costly and ineffective practice.
In this context, let’s explore how Predictive Maintenance should be deployed.
Defining Predictive Maintenance
Predictive maintenance consists of a repairing strategy that utilizes digital data to predict equipment’s conditions. This method provides the necessary insights for a better understanding of when equipment is going to fail or stop. In this sense, it leverages historical and real-time machinery data in order to schedule intervention activities at optimal times.
The basic idea behind predictive maintenance is to replace reactive, time-based maintenance with proactive maintenance that is performed only when needed. It relies on historical maintenance records, and prediction algorithms to identify potential problems before they occur. In this context, operators can identify minimum anomalies in hard-to-reach areas and discuss intervention strategies with traceable and auditable insights.
This approach involves continuously monitoring the asset to gather and contextualize data. Then, the use of predictive analytics models and machine learning enables the identification and processing of equipment and machinery data. After integrating these applications, the system provides data-driven insights to detect and prevent damages more efficiently and accurately with budget predictability.
Indeed, this strategy presents numerous benefits, from improving the asset’s health and its impacts on the environment to safeguarding operators from possible risks. Beyond that, by visualizing the aging process of machinery and equipment, industries can optimize their asset’s life cycle. This means that enhanced asset performance comes as a result of detailed and constant monitoring of the asset’s conditions.
An adequate predictive maintenance strategy has as its goals reduced downtime, prolonged equipment life, and increased efficiency. By performing maintenance only when necessary, rather than on a fixed schedule, companies can enhance their operations and reduce maintenance costs.
Exploring predictive maintenance bottlenecks
As mentioned before, the employment of digital technologies in support of predictive maintenance has enabled the real-time monitoring of necessary data. However, to properly forecast errors and schedule maintenance plans, all equipment data must be contextualized to an only channel. If systems are not integrated, it can be challenging to get a complete picture of the equipment’s health, which can result in incorrect predictions.
As a result, companies may end up performing a lot of predictive maintenance without fully understanding the optimal time for intervention, leading to unreliable and inefficient practices. Nevertheless, if historical and real-time data are properly contextualized, operators will have the resources to aim the maintenance strategy in optimal directions. This takes us to another predictive maintenance requirement, human expertise.
In this sense, the need for human oversight still remains a fundamental component of predictive maintenance deployment. This involves data collection and the asset’s health monitoring, which is only possible with the professional’s clear understanding of the operation’s priorities, limitations, and improvement points. For this reason, companies that seek to implement this maintenance methodology need to combine it with professional know-how.
This can result in significant resources being spent without achieving the desired outcome, leading to decreased efficiency, increased costs, and lower equipment reliability. To ensure the success of predictive maintenance, it is important to have access to accurate, contextualized, and trustworthy data, as well as a human perspective to leverage those data.
How integrating systems can enable a new perspective for predictive maintenance
Businesses must look forward to implementing digital systems that can connect each part of the operation and maintenance stages. This allows them to streamline interventions and gain a comprehensive view of their equipment’s health. When systems are integrated, data can flow easily across the organization, speeding up processes and improving communication.
According to Infraspeak, 47% of global manufacturers use predictive maintenance technologies to reduce operational costs. In this context, implementing a digital system that is able to connect field information to ERP and CMMS is essential for companies seeking to improve their predictive maintenance practices and achieve projected outcomes.
By having a single, integrated system, companies can access accurate and trustworthy data quickly and easily. This will help them make informed decisions about when and how to perform maintenance without any unnecessary overlapping. In this sense, integrating applications can help businesses avoid the pitfalls of unreliable and inefficient maintenance practices.
In the case of heavy-asset industries, operations generate extensive amounts of data that require interconnectedness. For this purpose, system integration provides improved data management and analysis, allowing companies to make more informed decisions and gain a competitive advantage through data utilization. By building a maintenance strategy in these terms, companies can leverage the multiple benefits of a proactive approach.
Conclusion
Predictive maintenance is a crucial aspect of modern maintenance practices, allowing organizations to proactively identify and address potential issues before they escalate into bigger problems. However, for predictive maintenance to be effective, it is essential to have reliable and connected data systems that can provide relevant information to the predictive models.
Without trusty data and a connected infrastructure, predictive maintenance can easily fall short, resulting in ineffective decision-making and missed opportunities to improve maintenance processes. Thus, organizations must invest in data and technology infrastructure to support them and combine it with their professional expertise. Only then, industries will be able to achieve the benefits of predictive maintenance and drive improved maintenance outcomes.
