How to Implement Predictive Maintenance in Your Business

Every business has at least one piece of equipment that would seriously impact their service, production, or everyday operations if it suddenly broke down. As technology has advanced, predictive maintenance has emerged as a solution to this issue. How can business owners implement it effectively to get the most benefits?

What Is Predictive Maintenance?

Predictive maintenance is a maintenance strategy that monitors machines’ conditions to forecast likely events. It uses data analytics to anticipate and prevent equipment failures. Businesses use it to estimate when their machinery will break down to avoid unexpected downtime and delays.

Since unplanned downtime carries an immense financial and reputational cost, many businesses consider predictive maintenance critical. According to one survey, seven in 10 organizations believe it is a strategic priority.

Unlike its counterpart, preventive maintenance, predictive maintenance doesn’t happen at specific, pre-set intervals regardless of equipment condition. Instead, it occurs as needed and before signs of failure appear. Businesses can maintain an optimal maintenance schedule this way, keeping their machines in top shape.

Countless factors can accelerate wear, causing equipment to fail sooner than expected. For instance, while projector bulbs last around 2,500 hours, poor storage, misuse, or a lack of upkeep could make them burn out faster — potentially in the middle of an important meeting. In these scenarios, preventive maintenance isn’t as accurate or effective as it should be.

Why is predictive maintenance better than preventive? While a comparison is somewhat subjective, the first strategy is typically more effective. Since 80% of equipment failures happen at random, it pays to maintain machinery on a continuous schedule instead of a pre-set one.

How Can AI Be Used in Predictive Maintenance?

Traditional predictive maintenance relies on a manual, paper-based process where someone tracks metrics like the vibration rate, oil readings, and emissions to tell whether a machine shows signs of failure. Recently, artificial intelligence has emerged as an alternative.

Predictive maintenance is one of the most well-funded use cases of AI in industrial sectors since it reduces operational costs and helps guide business decisions. It’s already become very prevalent even though advanced algorithms have only existed for a few years.

With AI-driven predictive maintenance, an algorithm takes over and automates most manual processes like data collection and analysis. Now, businesses can receive insights into their machine’s condition in real-time and around the clock instead of on a weekly or monthly basis.

Since AI can integrate directly with other technologies, its insights are based on real-time conditions instead of a subjective interpretation of metrics. While an algorithm’s training process can introduce bias, it’s still more accurate than people are — and immune to human error.

Machine learning algorithms can evolve, meaning they adjust to how condition data differs as equipment gets new components or changes over time. This feature also means businesses don’t have to worry about their predictive maintenance technology becoming outdated one day.

While AI-driven predictive maintenance performs best with real, current data, organizations can also use synthetic or simulation-generated information. This technology is highly versatile, so it can adapt to their business-specific needs.

What Is the Value of Predictive Maintenance in Business?

Businesses can experience a range of benefits from implementing predictive maintenance technology. Considering experts project its market value will reach $64.3 billion in 2030 — an increase of nearly $60 billion from 2020 — its potential is undeniable.

Fewer Delays and Less Downtime

Predictive maintenance helps businesses fix indicators of failure before they become an issue, resulting in fewer delays. Considering the cost of one hour of downtime increased by around 50% from 2020 to 2022, this benefit is significant.

Lower Operating Costs

Fewer critical equipment failures result in significant cost savings. Typically, reactive maintenance expenses are 50% higher because businesses have to schedule technicians at the last minute, order replacement parts, and reassign employees.

Heightened Customer Satisfaction

Customer satisfaction directly correlates to operational delays. After all, four in 10 businesses experiencing unexpected downtime due to equipment failure can’t deliver on time. Predictive maintenance helps them consistently meet people’s expectations.

Improved Brand Reputation

Businesses that use predictive maintenance substantially lower the risk of a defective part going to a customer, improving their brand reputation. In fact, they have 78.5% fewer product defects on average. Over time, this improvement could give them a competitive advantage.

Increased Employee Productivity

Predictive maintenance reduces the number of delays and instances of unplanned downtime businesses experience, ensuring their employees can work without interruption. As a result, they experience significant productivity gains.

Lower Carrying Costs

Businesses don’t have to keep as many replacement parts on hand when critical failures become less likely. This development reduces inventory by up to 30%, resulting in as much as a 20% reduction in carrying costs. As an added bonus, it also saves valuable floor space.

When Should Businesses Use Predictive Maintenance?

The ideal scenario for predictive maintenance implementation involves a business that repeatedly experiences critical equipment failures or spends a large percentage of its operational budget on machine upkeep. However, this strategy has broader applications.

Any business that depends on one or more pieces of equipment to deliver services to customers, produce products or keep employees productive should consider implementing this predictive strategy. This way, they can minimize delays and avoid costly downtime.

Once businesses have implemented predictive maintenance, it becomes an ongoing part of their operations — meaning their technology continuously checks for signs of equipment failure, and they respond as needed.

Consequently, having technicians on hand is essential. After all, there’s no point in businesses having a technology that can predict an imminent equipment failure when they don’t put those insights to use as soon as possible.

Tips for Implementing Predictive Maintenance Effectively

Leveraging predictive maintenance can be challenging for those without prior experience. In fact, research shows four in 10 businesses think their current program needs improvement. Effective implementation begins with strategy.

1. Define a Business Use Case

Businesses shouldn’t approach full-scale, immediate implementation unless they know exactly what they’re doing. Sometimes, incremental adoption is ideal. In this case, they should prioritize their assets to decide which needs this maintenance strategy the most.

2. Ensure Data Is Relevant

While the temptation to use every scrap of data available can be strong, businesses should prioritize relevancy and accuracy above all else. For example, it’s not necessary to include a maintenance report if it’s from years ago when a machine had different components.

3. Audit the Algorithm

The algorithm is maintaining the equipment, but who is maintaining it? Businesses should have an on-staff AI engineer or outsource upkeep to a third party to ensure their model remains accurate and effective after absorbing new information.

4. Align Skills With Needs

Many businesses make the mistake of rushing implementation to reap the benefits of predictive maintenance as soon as possible. In these scenarios, their outcomes aren’t as good as they should be because their workforce’s skills don’t align with their new technical needs.

The Bottom Line of Predictive Maintenance in Business

Businesses leveraging predictive maintenance can benefit from reduced costs, increased customer satisfaction, and higher employee productivity. Notably, their implementation strategy must be sound and calculated to get the best return on investment.