Furthermore, incidents of machine failure put a strain on the relationships between OEMs (original equipment manufacturers) and their industrial clients. When something goes wrong, manufacturers likely try to take care of the issue themselves before reaching out to the OEM. By the time the OEM is notified of the issue, a lot of time and business has been lost.
To help their clients ensure high availability, reduce unplanned downtime, and minimize costly machine breakdowns, most OEMs offer maintenance services. However, not all maintenance services are the same. And in recent years, the one that has proven itself to be the most effective is predictive maintenance.
Types of maintenance in manufacturing
There are different types of maintenance strategies today’s OEMs use to service their clients. And oftentimes, the type of maintenance an OEM offers reflects its level of digital maturity. In today’s digital-first B2B landscape, many traditional maintenance services simply don’t work for high-profile and digitally mature manufacturers.
So which maintenance strategies are the most effective for both OEMs and manufacturers? To properly answer this question, we have to first take a quick look at the different types of maintenance services offered by today’s OEMs.
- Reactive maintenance, which works on the break-and-fix model, is a traditional maintenance strategy that only addresses an issue when it happens. OEMs using this approach may or may not be prepared for an incident. Depending on the gravity of the incident, parts may be out of stock, personnel might be unavailable, and everyone is put on hold until details are aligned. This can result in extensive downtime, missed deadlines, and revenue loss. Plus, customer relationships are negatively impacted by the incurring delays. This is why many OEMs have recently shifted toward proactive and condition-based maintenance, which we will also discuss below.
- Preventive or proactive maintenance, calendar-based maintenance, or time-based maintenance (TBM) is one step ahead of the reactive approach. This strategy is based on the mean-time-between-failures (MTBF) metric, so it involves planning ahead and scheduling at different intervals to replace parts before they break down. Even so, there is still an element of surprise involved and unexpected downtime can still occur. Although planning and scheduling help maintain equipment uptime, for the most part, unforeseeable events and emergencies are not taken into account. Many OEMs have evolved their maintenance strategies to preventive or proactive ones in recent years.
- Usage-based maintenance is somewhat similar to preventive maintenance, the difference being that it accounts for variable machine usage. Using this strategy, manufacturers can prevent equipment over-maintenance and save valuable resources in the process.
- Condition-based maintenance is a more evolved strategy that builds upon usage-based maintenance. It involves more frequent monitoring that leverages data to identify signals of wear and tear, performance declines, and unavoidable equipment failures. This helps OEMs and manufacturers save resources as maintenance is only performed when necessary. By connecting sensors to machinery and constantly monitoring performance, OEMs can collect data and analyze it to determine whether or not maintenance is required. Most OEMs today have implemented condition-based maintenance within their service offerings.
- Predictive maintenance far exceeds the maintenance strategies mentioned so far. This modern strategy maximizes the potential of data-driven insights, artificial intelligence, machine learning, and IoT devices to accurately predict when equipment maintenance is necessary. Based on these predictions, manufacturers and service providers can schedule maintenance beforehand, order parts that are out of stock, and plan in advance to ensure business continuity and customer satisfaction. With predictive maintenance, labor costs are reduced, unplanned downtime is almost eliminated, and over-maintenance or under-maintenance are less likely to occur. For most manufacturers, predictive maintenance is a dream that has yet to come true.
- Prescriptive maintenance (RxM) leverages the same industrial IoT and machine learning technology as predictive maintenance to generate recommendations for maximizing the potential and extending the life of industrial equipment. However, to reach this level of maintenance, OEMs need to focus on implementing predictive maintenance first. And that is still a struggle for many.
Transitioning to predictive maintenance
Most OEMs are stuck with proactive or condition-based maintenance. Like predictive maintenance, both proactive and condition-based maintenance are used to minimize equipment downtime by performing maintenance work only when it is needed.
However, predictive maintenance evolves proactive or condition-based maintenance through machine learning and connected sensors that measure vibration, temperature, and other aspects to precisely determine when maintenance work is required.
This ability to predict equipment failure enables OEMs to reduce storage costs as they will need to keep fewer parts in stock and only order them when they know they will be needed. Additionally, predictive maintenance significantly reduces and sometimes even eliminates equipment breakdowns, which results in increased production, sales, and customer satisfaction. And this, of course, translates to greater revenue and ROI.
So, if predictive maintenance is better and more profitable than proactive and condition-based monitoring, why are so many OEMs struggling to implement it? Because, while predictive maintenance is advanced, it’s also costly. To implement this maintenance strategy, OEMs need to make significant investments in technology and labor.
It may not make sense to everyone to invest in predictive maintenance. For example, if the equipment is not expensive or if strict health and safety norms are not required, it could make more sense to invest in proactive or condition-based maintenance. Before investing in predictive maintenance, OEMs need to consider the approximate value of their machinery, equipment history, the type of technology needed, and talent acquisition to ensure the right experts are available.
For those who lack the necessary resources, predictive maintenance is a distant dream. However, for bigger OEMs, the process of implementing predictive maintenance has already begun. By 2024, the global predictive maintenance market is expected to reach around $23.5 billion, with an annual growth rate of nearly 40%.
Considering the global pandemic that has reset the rules in the B2B landscape and acknowledging the advantages created by predicting machine failures, predictive maintenance is likely to become the preferred way to address incidents as safely and reliably as possible in the near future.