Increasing the reliability of assets and reducing the overall costs of an organisation go hand-in-hand. If steps are taken to ensure that assets are performing well consistently, there will be no need to incur additional expenses later. Sometimes manufacturers falsely believe that purchasing the best assets in the market is enough to guarantee their durability and reliability. Such logic is flawed since machines require constant checkups to ensure their optimal performance.
Chiaroni, MIP project leader of Schimdt MacArthur Fellowship, explains predictive maintenance as a form of technology which focuses on a series of “practical, continuous measures to monitor the state of the asset”. In other words, it does not carry out its analytical capabilities based on assumptions and instead relies on the data processed. It moreover depends on Artificial Intelligence (AI) and Machine Learning (ML)to sufficiently predict the evolution of the asset.
Previously, manufacturers depended on manual checkups. Alternatively, data scientists had to spend their energy and time analysing and assessing possible risks where data was gathered digitally. However, with predictive maintenance, everything is done by software, thereby only requiring human intervention when deploying maintenance teams. According to the Statista Research Department, the market for global predictive maintenance is expected to reach around $23.5 billion by 2024. This article will, therefore, explain how predictive maintenance increases asset reliability and decreases costs.
How Does Predictive Maintenance Increase Asset Reliability
A typical predictive maintenance system has several modules incorporated into it. It first acquires and stores data in a cloud-based platform or on an edge computer and subsequently transforms the raw data into machine learning language. Predictive maintenance helps increase asset reliability by way of:
Condition Monitoring
Imagine that there are various sensors attached to multiple assets in a factory. Each of these sensors monitors the health of the asset, and their primary role is to warn the manager if issues are currently present. At this stage, however, the issue has fully formed in the machine and is an indication to the manager to repair the asset before it gets worse.
Asset Health Evaluation
This is a module that simply lets managers monitor the health status of an asset. Managers can, for instance, monitor how features in the asset have changed over time and determine the main contributor to the asset’s decline. Information on the life cycle of the asset can additionally be found here.
Prognosis
An integral module in predictive maintenance, it is here that predictions on potential failures are made. This is done with the help of machine learning models, which can accurately forecast breakdowns. The system warns the manager of a potential issue that may arise. This knowledge lets asset managers gain complete control of the asset’s health and avoid being taken by surprise. They can schedule maintenance before the issue even surfaces in the machine and fix it completely. It also automatically updates the remaining life cycle remaining in the asset, generally lengthening its original lifespan.
Decision Support System
Unlike any other analytical tool, predictive maintenance holds a recommendation module which tells the manager the best course of action. It is not a general recommendation but rather one that is most suitable for the asset manager.
How Does Predictive Maintenance Decrease Costs
With every module under the predictive maintenance software that increases asset reliability, the more opportunities it provides the manager to reduce its overall costs. According to a report by ARC Advisory Group, only 18 per cent of assets have an age-related failure pattern, whereas the remaining 82 per cent of asset failures occur randomly. Imagine how much of the costs you eliminate with technology such as predictive maintenance that can detect such failures in advance. To understand how much finances a manager is saving, consider a situation whereby an asset without predictive maintenance software breakdown. Firstly, the manager will have to halt operations in that asset completely. This will mean that production will not occur until the asset is repaired. As a result, the asset manager loses the sales it could have generated if all assets were functioning properly.
Secondly, the cost to repair an asset that already breaks is more expensive than the cost incurred to repair an item that is yet to be broken. Predictive maintenance can warn managers before the issue surfaces, thereby depicting the asset to be in better health conditions than an asset that is not at all able to function. Moreover, the root cause of the issue is identified when the predictive maintenance tool detects the issue. In contrast, tests will be required to be carried out first to find the reason for the breakdown without a predictive maintenance system. All this will add up to a higher financial expense than investing in preventive measures.
One major advantage of predictive maintenance is that it allows managers to budget their finances. As risks are highlighted in advance, factories can strategise to ensure enough finances are available for maintenance. The technology, moreover, takes into account the financial stance of the organisation when recommending solutions to rectify any issues. In other words, predictive maintenance provides a unique solution according to the size of the company.
In essence, with all the expenses that the manufacturing, engineering or construction industries have to incur for other areas in the production process, implementing predictive maintenance increases asset reliability and significantly decreases costs. As an additional bonus, it also reduces waste and energy typically produced.
Cerexio To Help You Make Smart Decisions
The Cerexio Predictive Maintenance Tool is integrated into all its multifaceted solutions, which are powered by industry 4.0 technology. One of the few software solution providers that incorporate customised tools advantageous to data-heavy corporates, the number of valuable insights you receive is unlimited. Centralising big data from all assets in the factory to one place, Cerexio uses AI and ML tools to self-learn all possible forms of risks that could occur. With this understanding of historical data in mind, the predictive maintenance tool monitors your assets in real-time. It can easily point to the correct date your assets will require repairs or maintenance. With a warning message dispatched as early as possible, you can schedule repairs on a date of your choice before any breakages appear or affect your production. What is unique about this solution is that it also recommends the best form of action, that is, energy, resource and cost saving.
Single-handedly increase the life cycle of your assets by giving them due care and attention when required. The system will further remind you again close to your scheduling date to ensure maintenance is carried out with no delay. By adopting such a solution, you can avoid incurring expensive costs on repairs and eliminate any downtime. Connect with us to find out how you can avail this one-of-a-kind solution.
Empower Yourself With A Forward-Thinking Solution
If you are a data-centric company, incorporating predictive maintenance into your operations will be a game changer. There is nothing worse than having all the data you need and yet not being able to extract the most critical data applicable to you. You no longer have to hire an expert or data scientist to spend long hours on a computer, trying to predict what may occur or provide other data-driven insights. You can instead receive information on any impending risks to your assets with one glance. Moreover, with a click of a button, receive detailed insights that elaborate comprehensively why your factory lacks performance or is struggling to hold a proper profit margin. Take advantage of such information to strategise your future operations. After all, you have the key to your organisation’s success by empowering yourself with predictive maintenance.
