Why Predictive Maintenance?
If you, like many other businesses, follow a traditional maintenance routine, you must be faced with the challenge of lost production time due to machines being offline. Ultimately, unpredicted downtime causes reduced production capacity that leads to poor operational efficiency. You therefore need a solid maintenance strategy to optimise your plant’s operations and derive business value.
Traditional Vs Predictive Maintenance
Traditionally, businesses adopted regular, planned and proactive maintenance practices, often replacing the perfectly fine machine parts in anticipation of a breakdown, to avoid any downtime of a critical asset. It was not only difficult to predict machine’s failure but also very expensive to implement any digital maintenance strategy. This encouraged organisations to continue to use the traditional maintenance strategy.
Predictive maintenance is now a relatively inexpensive and technologically advance concept to address all these challenges. You start with connecting a variety of sensors that measure health of your machinery via parameters such as temperature, vibrations, rotations, and liquid flow and collect vast amount of data. The advance technological capabilities such as machine learning and AI then use this data to predict potential machine failure that translates to a better and optimised maintenance schedule.
How to Build a Predictive Maintenance Strategy?
A good starting point is to first identify the desired business outcome. You need to ask yourself a few questions such as – ‘How will my business benefit from predictive maintenance? How will asset reliability and availability play a crucial role in my business roadmap?’ Once you identify the value add, then you need to assess your maintenance strategy. Ask yourself – ‘How do I currently determine when it’s time to repair, service or replace an asset? What are the critical assets I would like to prioritise in my maintenance strategy? Do I have any historical data or tools to leverage on?’
