Maintenance is critical for asset-intensive companies to be viable in the market. Equipment needs to be maintained for production to continue.
The 21st century has presented the maintenance world with the opportunity to advance their maintenance strategy with new technology. Predictive maintenance utilizes sensor data and connected software systems to predict when a failure is likely to occur, thereby reducing unplanned downtime and breakdowns, increasing uptime and maximizing utilization.
Asset sensors combined with data historians, EAM systems and advanced maintenance software, have enabled maintenance organizations to optimize their maintenance processes by implementing a predictive maintenance program and move away from reactive or preventive maintenance and their associated challenges. To review the challenges with reactive and preventive maintenance, read our blog Reactive vs Preventive vs Predictive Maintenance.
The aim of predictive maintenance is first and foremost to predict when an asset failure might occur, and in turn, prevent the failure.
Monitoring for future failure allows maintenance to be conducted at the exact time it is needed, rather than too early, while the part still has life, or too late, when the part has already failed.
This means the frequency of maintenance can be as low as possible, while still preventing unplanned downtime and breakdowns (Reactive Maintenance) and without incurring the costs associated with time interval maintenance (Preventive Maintenance).
Predictive maintenance does not come without its challenges, however. It requires an intricate system of asset sensors and software systems, as well as advanced changes to maintenance and data management processes.
Maintenance organizations cannot simply just become a predictive organization. There are several factors they need to review before they can dream of having a successful predictive maintenance program.
Predictive maintenance is the way of the future. Every day, more technology is being released, pushing maintenance organizations towards this strategy. The benefits of a successful predictive maintenance program mean improved efficiency, and decreased maintenance costs. But how does an organization overcome the challenges and successfully implement a predictive maintenance process?
Predictive maintenance may seem like an unattainable nirvana for maintenance teams. A concept that is nice to dream about, but impossible to actually achieve.
Machines telling you when a problem is occurring, so you can fix it before it breaks down? How is this actually possible? A successful predictive maintenance program is only possible when an organization prepares for it properly.
It is important for organizations to consider several operational areas when they decide to switch to a predictive maintenance program.
Before your organization can implement a predictive maintenance strategy, you must first understand your asset criticality.
Knowing the answers to these questions help your organization to focus their maintenance processes on your most critical assets and assist in making critical decisions related to your predictive maintenance strategy. Additionally, rules can be aligned with operational readiness to meet different business goals.
Predictive maintenance is dependent on new age technology. Asset sensors will need to be installed on ‘old-clunky machines’ in order for asset information to be collected. Additional software may be required for data to be collected and effectively managed. These implementations need to take place before a predictive maintenance program can begin.
Predictive maintenance inherently saves maintenance teams’ time and makes them more efficient. Your organization will need to determine how you want to utilize these time savings. You may choose to complete more work orders in a day. Alternatively, you may decide that preventive maintenance is still appropriate in certain scenarios and use these tasks to fill the additional time. Whatever the scenario, you will need a plan for the additional time savings.
When implementing any new software, system or process, user adoption should always be a number one concern. Simply put, without user buy-in, the new system will fail. Predictive maintenance disrupts all your current maintenance processes. It is important to ensure that plans are put in place for process implementation and user training.
When an organization begins tracking their assets, the volume of data can quickly become overwhelming. Systems, processes, and people need to be put in place to ensure the right data is being tracked to enable the organization to accurately predict failures and malfunctions.
Predictive maintenance strategies can be complicated, especially if your organization was not previously capturing asset data. A pilot program is a more simplistic way to transition your team from a solely reactive or preventive maintenance program to a predictive program.
A pilot program allows organizations to test that sensors, systems, and team members are all on the same page and that your strategy is working as imagined. If problems arise, processes can be adjusted to promote success on larger scale implementations.
Pilot programs also provide organizations with a cost-effective way to test the ROI of their proposed predictive maintenance strategy. Organizations can set up the predictive process on one or two critical assets as a base point to prove the value of the process change. Part of becoming a predictive maintenance organization involves convincing management of its benefit. For this to be possible, you need a sound ROI. Contact us to talk to an expert today.
When predictive maintenance is working effectively as a maintenance strategy, maintenance is only performed on machines when completely necessary, meaning maintenance is only performed on machines when a failure is likely to occur. This strategy gives maintenance organizations with several cost savings areas including:
These cost saving areas cannot be realized without proper planning prior to transitioning your maintenance strategy. Maintenance organizations need to be sure they are approaching their predictive maintenance strategy not from a 50,000-foot level, but from the ground, with all the details in clear view.