Big Data, is one of the latest business tech trends that every business wants to be using for better operations. The maintenance industry is no exception to this trend as it could be highly valuable when it comes to maintaining your business assets and tools for optimum performance.
Due to the business tech tools and devices available and take advantage of IoT (Internet of things) technology to generate a big amount of useful data that can be analyzed. The analyzed data later can be used as the basis for strategic maintenance decision making.
Below are some solid reasons why you need big data in the maintenance industry to stay on top of maintenance operations and schedules.
Predictive Maintenance
Predictive maintenance is a natural extension of big data in the maintenance industry. Experts can use machine data along with other information to understand the health and performance of assets. The whole process involves the installation of sensors and utilizing the generated data to perfect the performance of a particular machine or device. Once a model is successfully established for an asset, the maintenance can use real-time data and details to predict when the asset will see a downtime or breakdown.
In this way, the maintenance managers are better able to schedule a predictive maintenance job for the asset to keep it in good working condition and prevent expensive breakdowns. Almost all the computerized maintenance management solutions (CMMS) come with features to plan and track predictive maintenance jobs and events for automated work orders.
PM (Predictive Maintenance) Optimization
When the important data is gathered and analyzed to understand the health of business assets, the maintenance team is better able to understand what activities are required to keep the assets running smoothly without experiencing breakdowns and failures. As a result, maintenance experts can effectively optimize PM activities and schedules.
Without having an effective predictive maintenance schedule or strategy, technicians can be overwhelmed with too many work orders at the same time. But with help of the big data and effective strategies, managers can optimize their preventive maintenance activities and work orders to keep the workload as a minimum possible on the team to get more done in less time. In this way, the maintenance staff can focus on core activities and work orders to keep the business operations running smoothly.
Spare Parts Inventory
As spar parts are usually managed by its use date, businesses are less likely to stock spare parts that have not used in a long span of time. This thinking approach can lead a business towards some serious consequences in terms of delayed repairing due to the unavailability of the needed spare part or spending more on the parts than normal rates.
It is the place where big data can improve your spare parts inventory management process. Your inventory management department can use big data and machine details to consider when they need particular spare parts in the maintenance and repair jobs. It helps you stock all the important parts to prevent any maintenance delays to make your business grow by keeping all the assets in tip-top condition.
Longer Life of Assets
On-time replacement of an asset before breakdown is not the only benefit of big data, but it can also be used to find ideas on how to extend the life of your assets and keep them running for a long run. As data provides details on common failure modes, causes of breakdowns, and poor performance, the information can be valuable for the maintenance technicians on how they can prevent asset failures and breakdowns to reduce the maintenance and repairing costs.
Final Say
In this digital world, more and more businesses are using big data and IoT technologies not only to improve the performance of their maintenance departments but to keep their assets running smoothly for better business operations. That is the reason, you should also be proficient at generating and managing the growing volume of data within the organization and ensuring it is fit for analysis to improve maintenance activities and operations.
