The day-to-day operations of firms are changing as a result of technology. Major corporations are putting more effort into enhancing product quality, automating procedures at lower costs, and streamlining equipment maintenance. Due to technology advancements, manufacturers can now use predictive maintenance (PdM) instead of the reactive (run-to-failure) strategy.

Unexpectedly, this change also leads to notable increases in overall equipment effectiveness (OEE). Read to see how.

Predictive Maintenance’s Effect on OEE

Sensors are used in predictive maintenance, a data-driven maintenance plan, to continuously monitor the performance and health of assets. It uses that data to perform a series of built-in prediction algorithms to anticipate when a certain asset may fail. This makes it possible for maintenance teams to plan preventative work right before problems take place, cutting down on both the length and frequency of unplanned and planned downtimes.

Organizations can no longer experience unneeded production halts or breakdowns, which are frequent with reactive maintenance plans, thanks to PdM. It guarantees that all the production resources will be accessible for the duration of the production cycles with the least amount of disruption to the production schedules. The Overall Equipment Effectiveness (OEE) for all production processes is greatly increased when production assets are more readily available.

By ensuring that all assets inside a production facility continue to perform at their peak levels, a PdM program’s implementation lowers the performance loss component of the OEE equation.

It also reduces the likelihood of unheard-of equipment failures and the use of worn-out, misaligned, flawed, or outdated parts. As a result, the final product’s quality improves. In other words, since quality losses caused by machine problems are greatly decreased, the number of defects is minimized.

Following These Steps Will Help You Transition From Reactive to Predictive Maintenance

PdM programs should provide businesses with a number of additional advantages in addition to raising production facility OEE scores. Improved operational safety, increased profitability, longer useful lives for vital assets, and higher customer satisfaction are some significant effects of PdM.

An overview of how to set up the switch from reactive to predictive maintenance is provided below.

1. Develop a plan for implementation

A proper implementation strategy must be created before switching from reactive to predictive software. To identify and rank the equipment that should be a part of the PdM program, a thorough audit of all production assets will be conducted during the first phase. Priority is given to costly-to-replace and difficult-to-access assets, vital assets (those that operate continuously), and assets with a history of frequent failure.

It is crucial that past maintenance data is carefully examined after identifying the assets that need to be included in the pilot program in order to build a preliminary prediction model and carry out a thorough Failure Mode and Effect Analysis (FMEA) on the assets.

Following a thorough FMEA, a list of high-risk, high-priority equipment is produced. The list is then used to develop a pilot program by establishing milestones, coming up with evaluation procedures, setting OEE improvement goals, and specifying data collection strategies.

2. Install new infrastructure

To enable predictive algorithms tremendous produce correct forecasts, a ton of data must be gathered and input into them. Several condition monitoring sensors must be installed on the pilot equipment in order to accomplish this. These sensors gather data in real-time and send it to a central database for analysis via specialized IoT networks. They are capable of measuring a wide range of signals, including electrical currents, vibrations, noise, and corrosion levels.

A suitable prediction algorithm is created using these maintenance data as well as past maintenance data. At this point, advanced systems use AI and machine learning technology. The algorithms compare the sensor data that is delivered to the predetermined conditions, issuing warnings whenever a divergence is found.

A user-friendly dashboard is necessary to facilitate communication between machines and maintenance staff. It is critical that the data gathered be safeguarded from abuse or outside threats that can disrupt industrial processes.

3. Practice, test, and get feedback

Adopting a PdM program implies that major adjustments will be made to a variety of maintenance tasks. For instance, in order to adapt to new procedures, some manual readings are deleted, and new maintenance technologies, equipment, and procedures are added. If the firm doesn’t currently have a CMMS that offers predictive maintenance or similar software, it will also look to implement one.

It is crucial that all maintenance personnel receive sufficient training on newly introduced technology while the pilot program is being tested. They are instructed on how to use the new systems and told of the changes to their jobs throughout this time.

The pilot equipment will be put through a variety of production schedules by maintenance crews, who will track how they react to the changes. The accuracy of predictive algorithms will increase as more data is pushed into them.

4. Expand and improve the PdM program

A PdM program’s contribution to an organization’s production schedule and output must be thoroughly tested and verified. In an effort to gather a ton of useful data, the pilot software is put through a variety of operational scenarios. The organization can expand or improve its PdM program with the help of the essential insights gained during the pilot phase.

Predictive algorithms or infrastructure improvements are used to close any gaps found during the testing process. The business can move forward with the program’s progressive, full-scale implementation after all issues have been handled. The program should be scaled up gradually and without placing an undue demand on the available resources.

For PdM programs to remain relevant and competitive, they need lifetime upgrades that are ongoing. The updates are essential for enhancing data security, speeding up data collection and processing, and optimizing dialog between machine and human interfaces.

When you consider the long-term savings PdM programs can provide for OEE, they are very cost-effective. PdM is an appealing method, but it should be implemented carefully to prevent frequent mistakes.

Conclusion

To avoid production losses during the changeover, appropriate planning and strategic execution are required. Some processes may have high upfront costs because they need the assistance of experts (third-party service providers or in-house technicians). Choosing the right PdM model has the ability to transform manufacturing businesses into more intelligent and profitable systems, despite the difficulties.