Predictive maintenance helps in predicting impending machine or equipment breakdowns and reduces downtime. Thus, it helps control adversities and enhances production in manufacturing facilities.
Industrial equipment can be termed as the foundation layer of the entire operational process of a manufacturing unit. Failure in industrial systems and assets can result in a cascading effect on the operations of the unit or the factory which in turn will adversely affect the productivity of the asset(s) and production capacity of the unit. This is something organizations cannot afford especially, under current circumstances which require quick turnaround and on-demand products and services.
Why Predictive Maintenance? Before delving into the details of why we need predictive maintenance, let us first take a look at what it is. Predictive maintenance is the technology and technique behind understanding and analyzing an asset or equipment in order to determine its working condition, performance, and need for maintenance.
Scheduled maintenance increase operational cost but do not help in avoiding breakdowns. On the other hand, condition-based monitoring reduces the cost but it’s a challenge to schedule and plan the downtime. Predictive maintenance solves both the problems and helps to plan maintenance, reduce cost with scheduled maintenance and reduce downtime by avoiding failures.
A Deloitte report states that unplanned machine breakdowns are costing industrial manufacturers about $50 billion every year. Predictive maintenance with its ability to analyze asset condition, ‘predict’ breakdowns, and wear and tear saves maintenance cost for manufacturing companies.
Let us understand a few ways predictive maintenance can be used to ensure greater efficiency of industrial equipment –
Predicting machine defaults: Undoubtedly, the most dreaded thing to happen on a factory floor is the breakdown of a machine or equipment. Predictive maintenance monitors machine functions continuously and helps keep a tab on its working condition. Its algorithms check and re-check for potential damage to the machine and alert on the same. Hence, machines and equipment can be given the required attention well in advance to avoid any further damages.
Timely machine repairs: Predictive maintenance plays a key role in identifying various problems associated with machines and thus helps in the servicing and maintenance of these machines in time. This avoids delay in maintenance and reduces downtime resulting in higher efficiency levels.
Timely replacement of spares: Untimely replacement of spare parts will result in breakdowns and may result in halting of processes and production. The history of the operations of machines and equipment helps to understand usage pattern of spares and components used frequently in maintenance. Predictive maintenance can help identify the time to replace spares which will help organizations circumvent the issue of halts in production.
Saving on costs: By virtue of ensuring timely repairs and spare replacements predictive maintenance helps organizations save a considerable amount of costs. Another way predictive maintenance helps save on costs is by ensuring the right inventory of spares, equipment, and machinery. This helps avoid excess and unwanted inventory, thereby creating optimum inventory mechanism at the floor level.
Worker safety: Arguably, the most important thing to take care of on a factory floor is safety of workers. As predictive maintenance helps to continuously monitor the condition of machines, equipment and power supply, certain hazardous outages and breakdowns can be prevented or shut down well in advance, thus ensuring worker safety.
Fasten rectification measures: Certain kind of breakdowns and outages can be time-consuming and might require increased downtime. Under such circumstances, predictive maintenance helps organizations take stock of potential outages or breakdowns, put a plan in place and have the right kind of resources to fasten the rectification measures, which otherwise can be very time-consuming and cost elevating.
As per a Markets and Markets report, the predictive maintenance market is slated to touch $ 4.1 billion by 2021 from $ 1.4 billion in 2016 at CAGR of 28.4%. This will be mainly driven by the organizations’ objective of reducing costs and enhancing operations at the manufacturing unit level.
As the manufacturing and industrial world moves towards Industry 4.0, organizations are able to leverage on several emerging technologies to ensure better efficiency levels, lower downtime and increase productivity. Predictive maintenance will continue to play a significant role on the factory floor to track and analyze machines and utilize them to the fullest.
Sasken is a specialist in Product Engineering and Digital Transformation providing concept-to-market, chip-to-cognition R&D services to global leaders in Semiconductor, Automotive, Industrials, Smart Devices & Wearables, Enterprise Grade Devices, Satcom and Transportation industries.
Sasken Technologies Ltd (formerly Sasken Communication Technologies Ltd) 139/25, Ring Road, Domlur, Bengaluru 560071, India CIN# L72100KA1989PLC014226