Predictive maintenance: a low hanging AI fruit for manufacturing businesses

The universal push towards using digital technologies to enable business processes is being felt by manufacturing companies worldwide. In this post, we will discuss how manufacturers can use Google Cloud IoT Core solutions and ML models to enable predictive maintenance.

2 years ago   •   2 min read

By Angelique Tzanakakis

The Internet of Things (IoT), put simply, is a network of physical objects that collect and transmit data via the internet. What this means for you is that you can continuously monitor assembly line equipment and machines using IoT sensors, and then use Google Cloud’s platform services to analyse the data to detect deviations, predict failures and perform maintenance at the optimal time.

The scalability of the cloud makes it possible for manufacturing plants with large, complex assembly line installations to capture, analyse and act on massive data volumes generated by their machinery, all in real-time.

AI/ML algorithms can be trained to predict failures so that machines can be fixed before they break. If staff need to be deployed for maintenance or repairs, they will have insight into the work that needs to be done before they even look at the equipment. The algorithms could even be trained to make smart decisions against given criteria, for example, switching off a machine that is overheating before it fails.

This translates into tangible cost savings and higher profitability, given that unplanned downtime is thought to cost industrial manufacturers around $50 billion a year. Equipment failure is the cause of 42% of unplanned downtime. While other research shows that poor maintenance strategies can reduce a plant’s overall productive capacity by 5-20%.

Furthermore, manufacturers can pursue preventative and predictive maintenance based on data insights, rather than following a philosophy of scheduled maintenance. This can help reduce maintenance costs, improve uptime and allow for more efficient use of assembly-line machinery. See, for example, AB InBev, who reduced manufacturing costs and improved the quality of their beer using Google Cloud platform services.

With the economic pressure caused by the pandemic, prolonging the lifespan of equipment, improving plant efficiency and reducing costs have become priorities for most manufacturers. By some estimates, predictive maintenance increases production line availability by 5-15% and reducing maintenance costs by 18-25%.

Predictive maintenance is just one of many ways that the IoT and AI/ML are helping manufacturing enterprises to drive business outcomes. Contact us to learn more about how we can work with you in your mission to become a data-driven manufacturer.

Spread the word

Keep reading