Manufacturing is a competitive industry where standing still will mean failing behind. The industry is constantly introducing new methods of moving faster than competitors, and introducing new technologies is the avenue they use to do so.
Downtime in manufacturing is costly, and ineffective and can put processes behind schedule. On a larger scale, this can cause serious delays and problems for the manufacturing facility, all while costing them time and money.
This has led to smarter manufacturers targeting more efficiency through predictive maintenance approaches, making use of big data, artificial intelligence, and machine learning to detect potential issues before they are able to occur.
This post will take a closer look at how predictive maintenance works, how it is used, and where it will go in the future.
What Is Predictive Maintenance?
To get a full picture of predictive maintenance, it’s important to go back to where it started.
Pre-technology, most manufacturers relied on what is called reactive maintenance, which means an issue would happen and they would respond to it only then. This strategy had many downfalls, such as leading to long downtimes and higher costs of repair.
This is where predictive maintenance became key.
As a response to these challenges, predictive maintenance offered ways to meet these disruptions as they happened before they could cause further critical issues. But how does it work?
Predictive maintenance makes use of various facets in order to prevent maintenance and manufacturing errors. This refers to the use of things like performance metrics, machine learning algorithms, and other types of predictive analytics during the manufacturing process. These facets of predictive maintenance can be used to identify the ideal time for machine maintenance, which prevents breakdowns later on. In contrast to reactive maintenance, this approach detects problems before they arise versus after they have already happened.
The Benefits of Predictive Maintenance in Manufacturing
How manufacturers approach predictive maintenance is entirely reliant on ongoing data collection from multiple machine sensors and monitoring tools. Using this data gives the manufacturer a more detailed view of how the equipment is operating, enabling more real-time insights into the manufacturing process.
This more advanced perspective towards analytics and machine learning algorithms is the key to predictive maintenance in the manufacturing industry of today. Using these technologies, large amounts of data can be processed leading to the detection of patterns and potential faults in the system. As these models are based on machine learning, they can continuously improve and monitor which will lead to even more accuracy over time.
A large part of this is the use of automation tools built into predictive maintenance systems. These tools can ensure that necessary maintenance tasks are performed on time, reducing the need for human intervention and increasing the level of efficiency for the manufacturer. Furthermore, these tools support decision-making by creating maintenance schedules that take multiple factors like equipment conditions, schedules, and resources into account.
Implementing Predictive Maintenance in Manufacturing
Now that the role of predictive maintenance is better understood, it’s time to turn the attention to implementation.
Since machine learning forms such a key part of the predictive maintenance process, implementing this technology will need to make use of a team comprised of experts such as data scientists, maintenance engineers, and IT specialists. This team will collaborate with each other to develop and deploy these systems in accordance with the manufacturer’s needs.
For a predictive maintenance strategy to be accurate, it will need to be established with a data-first approach. Training and education for employees will be an integral part of ensuring that the employees can use the insights gained into actionable maintenance systems.
If the goal is to create a robust predictive maintenance methodology, it’s up to the manufacturers to invest in more advanced sensors, communication networks, and data analytics platforms for their organizations. While there is an upfront cost to doing this, it is an investment that will prevent reactive costs from coming up in the future.
Predictive Maintenance Use Cases In Manufacturing
Predictive maintenance has already been integrated into the manufacturing industry, with sectors from the automotive sector to food processing already making use of its ability to improve efficiency and the running of operations.
Below is a closer look at these two industries and how they are using predictive maintenance and machine learning to improve their service delivery.
Automotive Manufacturing
When it comes to automotive manufacturing, predictive maintenance has drastically improved unplanned downtimes by identifying potential issues in places like hydraulic systems before they could break down. Through identifying these maintenance problems early on, there has been a significant reduction in maintenance costs and an increase in product quality.
This is essential to automotive manufacturing because these hydraulic systems are essential to manufacturing aspects like assembly lines. By using predictive maintenance, the health of these systems can be monitored and checked for future leaks or pressure irregularities before they turn into major slowdowns in the building process. Predictive maintenance not only saves on the costs of maintenance in this situation but also improves the lifespan of the hydraulic systems themselves.
Food and Beverage Industry
Food safety is a key issue for manufacturers around the globe. In this industry, predictive maintenance has been implemented to monitor the condition of things like refrigeration units and conveyor belt systems to ensure that everything is performing as it should. Early detection here can help prevent breakdowns that will affect product quality and ensure that the manufacturer stays compliant with food safety regulations.
Nowhere is predictive maintenance more important than in the monitoring of optimal temperatures. In the food industry, it’s essential to prevent spoilage and maintain freshness all the way from the manufacturing process until it reaches the hands of the consumer. These predictive maintenance tools can not only keep an eye on refrigeration but can also alert the manufacturer to fluctuations that could cause food safety issues.
Machine learning algorithms in predictive maintenance also look towards the future by forecasting when these cold units might experience issues, which allows maintenance teams to step in and safeguard against potential losses.
Unveiling a Future of Unprecedented Efficiency
Predictive maintenance is more than just a strategy – it’s the way of the future for the manufacturing sector.
By making use of technologies such as machine learning and the Internet of Things (IoT), manufacturers are able to use data-driven decision-making to ensure they are following the best protocol for optimum output. This approach drastically reduces resource waste and can even contribute to sustainability efforts by helping these organizations to go green.
But how are they able to go green?
Keeping manufacturing machines in peak condition not only reduces energy consumption but also minimizes waste, which can go a long way toward helping with environmental regulation compliance.
Predictive maintenance and machine learning in manufacturing are not just about staying ahead; it’s about creating a sustainable model in which manufacturers have a data-driven method of staying ahead of the curve. This approach lends itself to improved operational efficiency which ultimately reduces costs and leads to bettered production quality.
Predictive maintenance means that by using proactive, data-driven measures, manufacturing is set to see not just an evolution but a revolution in efficiency.
