As businesses scale and build increasingly complex equipment ecosystems, unanticipated failures drive up the cost of both routine maintenance and unexpected downtime. Without a proactive approach to maintenance, they often struggle to improve productivity and keep costs in check.
For many organizations, predictive maintenance offers a much-needed solution for optimizing asset performance while avoiding expensive repairs and operational interruptions. When implemented correctly, this approach is the key to transitioning from reactive costs to proactive savings.
Let's explore how predictive maintenance reduces costs, minimizes downtime, and improves efficiency.
The problem with reactive maintenance
Reactive maintenance addresses equipment failures and malfunctions after they happen. At first glance, that might not seem so problematic.
But once you experience the unexpected costs and downtime that result, it's easy to see how disruptive reactive maintenance can be. For example:
- Your facility's HVAC system breaks down on the hottest day of the summer, damaging temperature-sensitive inventory or equipment.
- A critical manufacturing system's motor fails at the start of the workweek, preventing your team from starting on time-sensitive tasks.
- Multiple vehicles in your fleet run into tire wear issues, one after another, causing delayed deliveries.
While preventive maintenance might help avoid some of these failures, there's a better solution. Predictive maintenance alerts you to specific conditions in real time so you can take immediate steps to address the problem before any downtime may occur.
What is predictive maintenance?
Predictive maintenance is the practice of using condition monitoring and machine learning (ML) technology to anticipate potential equipment and system failures, issuing alerts about problems before they escalate.
It includes these core components:
- Internet of Things (IoT) sensors to collect data
- Cloud infrastructure to transfer, process, and store data
- Big data analytics and ML algorithms to evaluate data and spot patterns
- A human-machine interface to view data visualizations and monitor alerts
A predictive maintenance program monitors operations in real time. It predicts likely failures and suggests appropriate courses of action based on a combination of performance history and custom parameters.
Most importantly, it lets you deploy resources and address time-sensitive issues before complete breakdowns occur. As a result, predictive analytics are key to avoiding mechanical or system downtime that would lead to headaches or compromise your organization's workflow.
Yet avoiding unexpected failures and preventing unanticipated costs are just two benefits of predictive maintenance. Because this process helps keep equipment and systems running smoothly, it also minimizes deterioration, optimizes performance, and increases the mean time to failure of these critical assets.
Reactive vs. predictive maintenance: A comparison
Could rethinking your approach to maintenance work improve your organization's workflows? Here's a side-by-side look comparing reactive vs preventive vs predictive maintenance.
Real-time insights
Of the three approaches, only predictive maintenance offers visibility into how your equipment, systems, and fleets are performing in the moment. This proactive approach uses condition monitoring to gather data, measure against historical asset performance and preset parameters, and issue relevant alerts in real time.
With a preventive approach, you use a maintenance schedule to preemptively address probable issues, based on the equipment's age and usage. But because it follows a routine schedule, preventive maintenance doesn't provide real-time alerts about potential problems as they unfold.
Reactive maintenance takes the opposite tack. Since this approach responds to failures after they happen, it focuses more on post-event feedback rather than real-time insights.
Opportunity costs
Organizations that rely on reactive maintenance often end up with malfunctioning systems that prevent them from maintaining full capacity. These productivity lapses add up over time, and the opportunity cost increases with each unexpected failure.
Because a preventive approach relies on scheduled maintenance, it tends to reduce unanticipated expenses. But since it doesn't have a way to detect breakage beforehand, this approach can increase both maintenance expenses and opportunity costs.
Instead of reacting to failures or relying on corrective maintenance, predictive maintenance work tends to reduce both unexpected costs and unanticipated downtime.
Amplified issues
Occasional unplanned maintenance or lapses in productivity may not create critical issues for your business. But repeat problems can lead to major delays or drive up costs substantially, compromising long-term progress.
Suppose you use a preventive approach for routine work but you still end up with unplanned maintenance costs. Or you use a reactive approach to address a series of breakdowns, far exceeding your annual maintenance budget. Down the line, those costs may mean you make less profit or have less to invest into the business.
Predictive maintenance helps you avoid amplifying problems down the line. It allows you to anticipate maintenance expenses, and it can save you even more by making ongoing service contracts obsolete.
Simplifying predictive maintenance implementation
Between the infrastructure, the analytics, and the dashboards, developing a predictive maintenance solution can certainly be complex. But with Viam, the process is much more straightforward than you might think.
Monitor virtually any metric
Whether you need to monitor temperature fluctuations, measure energy levels, or conduct a vibration analysis, you can do so by investing in a data monitoring tool and building a predictive maintenance solution.
Once you apply sensors to machinery or systems, you can use viam-server to measure and analyze the data. Set ranges for normal performance or breakpoints for potential issues. Then, use Viam's platform to see when metrics reach a certain point.
With Viam's visualizations in place, you can monitor equipment and take steps to fix it while it's still in operation. This solution gives you more flexibility by letting you pinpoint potential problems in advance.
Use an agnostic platform
Pieces of equipment from different manufacturers aren't designed to work together. This may limit the machinery you purchase, resulting in an ecosystem that's more expensive and less functional than would be ideal.
With Viam, these restrictions don't apply. As an agnostic platform, Viam can enable predictive maintenance on any system, using different query languages or data structures.
Viam is designed to fit with your data workflow and remain as unobtrusive as possible. This means you can build the ecosystem that works best for your business, investing in the best hardware for your use case.
Go beyond measurement
As helpful as monitoring and measuring asset performance can be, it doesn't have to be the final step in your IoT predictive maintenance workflow. With Viam, you can easily take the process a step further.
Use Viam's ML model to understand typical behavior for your systems over time. Then, prompt the platform to generate meaningful alerts when the actual behavior is outside of normal parameters.
You can also use Viam's AI to trigger the machine to address the problem itself, returning it to normal behavior. Prompt specific actions based on what the data tells you and the condition you want to restore.
For example, AI can prompt equipment to pause until the temperature sensor indicates it's safe to resume operation. Or it can switch off the primary machine and temporarily turn on a backup in its place.
Cost-saving predictive maintenance examples across industries
With Viam, companies across industries can leverage predictive analytics and condition-based maintenance. Here are two examples of organizations that rely on Viam to improve maintenance processes and achieve cost savings.
National storage chain achieves timely HVAC failure detection
Before implementing predictive maintenance, a national storage chain relied on local technicians to manage HVAC systems across multiple properties. These systems often broke down during extreme weather, leading to costly emergency maintenance.
With Viam, the storage chain implemented a system of self-powered sensors designed to monitor each HVAC unit's usage, sharing data across locations with LoRaWAN technology. The company uses a viam-server to upload this data to the cloud, seamlessly integrating the hardware.
Because Viam offers native ML technology, the storage chain is able to achieve diagnostic precision without the need for third-party integrations. Plus, the more data they ingest, the more they improve, predicting with even more accuracy and keeping facilities at a specific temperature.
Sportfishing industry leader preemptively addresses fleet maintenance
As a provider of chartered voyages and sportfishing seminars and coaching, Canyon Runner serves more than 450 boat owners and 700 captains. The company already relies on Viam's platform to collect data on the fleet's performance, GPS location, and local weather conditions, helping boaters avoid risks.
Next, Canyon Runner intends to tap into Viam's predictive maintenance solution, using fleet performance history and preset failure parameters to identify potential problems before they happen. This way, boat owners will be able to repair equipment before it breaks down, helping them save substantial time and money.
Restaurant tech company remotely monitors kitchen equipment
For most quick-service restaurants (QSRs), timely, cost-effective food preparation is the key to maintaining profit margins. Sudden kitchen equipment failures can bring food prep to a halt, leading to canceled orders and reduced profits.
With Viam, an innovative QSR tech company remotely monitors cooking appliances in customer kitchens. Sensors alert the company when a machine approaches predefined failure parameters.
This allows the company to warn local restaurant staff to take corrective action like switching off a machine or sending a technician. The local team can then adjust workflows and address the issue before it escalates.
6 Steps to get started with predictive maintenance
Curious how to map out a proactive approach to maintenance? Follow these steps to build out your predictive maintenance strategy.
1. Identify critical assets
First, make a shortlist of the equipment and systems that are essential to your organization's operations. Focus on the assets whose failure would lead to significant downtime, production delays, or repair costs.
2. Analyze potential failure modes
Use historical data to understand how your critical assets have performed over time. Review maintenance records, check manufacturer data, and coordinate with your maintenance team to get clear on the warning signs and the types of breakdowns to monitor.
3. Decide on your approach to condition monitoring
Next, choose relevant techniques and sensors for condition monitoring. Depending on what you need to measure, you might opt to collect data via IoT sensors that offer vibration analysis, power level monitoring, or thermal imaging.
4. Build data collection infrastructure
In addition to installing sensors on equipment, you'll need a reliable data collection infrastructure. With Viam Data, you can sync, store, and analyze sensor data, no matter what hardware you use. It's designed to easily integrate with diverse machines and data tooling so you can focus on what matters most for your business.
5. Establish predictive models
Combine historical and real-time data to predict how equipment and systems are likely to function in specific future conditions. With Viam ML, you can transform the sensor data you collect into intelligence that improves your predictive maintenance technique, optimizes equipment performance, and reduces costs.
6. Design data visualization dashboards
View real-time data, receive alerts, and respond on your terms. Use Viam’s in-app visualizations to monitor both real-time and historical sensor data on a customizable dashboard.
Because Viam has a modular architecture, it doesn't impose a data format. It also integrates with third-party data visualization tools, allowing you to build your own custom approach to predictive maintenance.
Gain visibility into asset condition and reduce unnecessary costs
Unplanned downtime and unexpected expenses don't have to be the norm for your business. Shift away from reactive costs and toward proactive savings by implementing predictive maintenance.
Viam makes it easy to build a system that meets your business's needs and works with your existing hardware and software. With Viam's data storage, analysis, and ML solutions, you can monitor equipment condition, identify performance patterns, and get alerts to address problems while assets are still operational.
Book a demo to see how Viam can integrate with your systems to improve diagnostics, increase mean time to failure, and reduce maintenance costs.
