August 31, 2023
by Dan Roberge / August 31, 2023
While today’s world feels very online with the rise of remote work and an overwhelming amount of technological advancements, there’s no denying that our physical spaces are still precious.
Given the massive disruption to daily life during a global pandemic, the importance of in-person facilities remains paramount.
From hospitals to airports, parks, and residential buildings, thousands of facilities must be kept safe, sanitary, and in good working order. A lot of work is conducted regularly to keep public physical spaces well-maintained, not just in terms of fixes and upkeep but also meticulously scheduling routine maintenance activities to prevent future disastrous and costly potential breakdowns.
While the thought of maintenance teams at work may conjure images of workers using tools and equipment to keep machines and spaces running smoothly, it’s not the case anymore.
The maintenance industry has increasingly embraced technology to assist in fulfilling work orders and using advancements like predictive maintenance software to keep facilities operating at peak efficiency.
Maintenance and facility management has experienced a major evolution over the last decade, thanks to automation and digital tools like computerized maintenance management software (CMMS).
What was once an industry operated through paper work-order requests and spreadsheets has become more streamlined and cost-effective.
There are several different maintenance strategies facilities managers and their teams may implement depending on the task type - reactive, preventive, and predictive.
Predictive maintenance is one of the most significant areas where technological advancements like artificial intelligence (AI) come into play.
Predictive maintenance is a proactive maintenance strategy that utilizes advanced technologies and data analytics to predict and prevent failures before they occur. It involves the continuous monitoring of equipment and systems to collect real-time data, which is then analyzed to identify patterns, anomalies, and potential issues.
By predicting maintenance needs before they arise, companies can optimize maintenance schedules, reduce downtime, and avoid costly unplanned breakdowns.
This approach is crucial for several reasons.
Predictive maintenance helps extend an asset’s lifespan by addressing issues at their earliest stages, thus minimizing wear and tear. It improves operational efficiency and reduces overall maintenance costs as resources are utilized more effectively. It enhances workplace safety by mitigating the risks associated with equipment failures.
In recent years, the integration of AI has transformed various industries, many of which require maintenance of their facilities in some fashion.
AI-driven predictive maintenance is redefining how industries manage their assets, ensuring smooth operations by analyzing historical data and real-time information to anticipate equipment issues, enabling proactive maintenance, and reducing the need for emergency repairs.
Technology already plays a significant role in implementing predictive maintenance in facilities and will continue to become more significant. IoT devices and sensors are being deployed to monitor equipment performance and collect real-time data.
Machine learning algorithms analyze this data and identify patterns indicative of potential failures. Additionally, cloud-based platforms and data analytics tools facilitate the storage, processing, and visualization of collected data, enabling maintenance teams to make better-informed decisions.
A wide range of industries benefit from planning maintenance activities ahead of time, using technology to assign associated tasks to technicians, and tracking their completion. The manufacturing space is one where early fault detection and diagnosis are vital.
The manufacturing of consumer goods is considered an asset-intensive sector, and a 2023 McKinsey study shows that 99% of senior leaders in the sector have transformed their maintenance strategies within the last five years.
Traditional maintenance practices often relied on periodic inspections or predetermined maintenance schedules.
Conversely, AI can detect subtle changes in equipment behavior and performance, even in complex systems with numerous variables. By spotting these early signs of faults, maintenance teams can address issues proactively, preventing catastrophic breakdowns and significantly extending equipment lifespan.
Transportation is another space where time is of the essence. Downtime can result in precious cargo and deliveries not making it to their destinations on time, and delays can have a domino effect, leading to dissatisfaction at the very least.
With preventive and predictive maintenance, organizations can schedule upkeep on their fleet or other vehicles during planned downtime or when it is most convenient for operations, minimizing disruptions to production.
As a result, the overall efficiency of facilities increases, leading to higher productivity and better resource utilization.
Implementing AI in predictive maintenance may require an initial investment, but the long-term benefits outweigh the costs. This is especially important in industries that face ongoing budget cuts and minimized resources.
The education sector comes to mind here. School districts and universities can save significantly on maintenance expenses by optimizing maintenance tasks. Extending the life of equipment and keeping tabs on inventory means lower replacement costs, yielding a high return on investment.
AI-powered predictive maintenance enhances workplace safety by reducing the likelihood of accidents resulting from equipment failure. Regular inspections and proactive maintenance practices help mitigate potential risks and ensure compliance with safety regulations critical in healthcare, senior care, and government sectors.
By maintaining equipment, companies can create a safer work environment for their employees, residents, and visitors alike.
AI systems optimize maintenance workflows, improve resource utilization, and enhance equipment uptime by leveraging real-time data analysis, predictive maintenance, and continuous learning,
Real-time data collection and analysis is where maintenance software is already utilizing AI and automation. This information gathering helps facilities managers and leadership make informed decisions regarding budget allocation, team member task assignments, performance metrics, and more.
AI-enabled predictive maintenance relies heavily on the continuous collection of real-time data, which can be gathered from physical sensors and IoT devices. Sensors monitor crucial parameters within the spaces of a facility, such as temperature, vibration, pressure, and humidity.
The data is then sent to AI algorithms capable of quickly processing massive amounts of information. By analyzing this data, AI can identify anomalies, patterns, and potential signs of equipment degradation or failure long before they become critical issues.
AI is slowly being incorporated into the maintenance space in ways that extend beyond how other industries leverage it.
Generating content or offering customer service via chatbots aren’t priorities typically associated with the upkeep and repair of buildings and their equipment.
Looking into the future of AI within the maintenance sphere, there are several key solutions where machine learning and even robotics could impact the future of public spaces.
AI will impact maintenance in various ways, but specifically looking at how it will be used within the context of a CMMS is vital. The global CMMS software market is predicted to grow at a CAGR of 8.81%, or up to $1.7 million, by 2028.
AI’s impact will focus on five foreseeable opportunities within that time frame.
Looking at trends for best performance based on previous task management will allow AI to process historical data and provide suggestions for better handling similar tasks.
If, for example, maintenance technician "A" is historically faster at changing light bulbs than their coworker "B," a CMMS can auto-assign tasks based on those previous successes.
On a broader scale, AI also allows the CMMS to utilize aggregate data across all customers and provide insight for a user on the best next steps. When maintenance teams wait two or more days to deal with plumbing issues, the potential cost of damage goes up 25%.
In contrast, maintenance teams that tackle plumbing issues within two hours typically have significantly less damage-incurred costs.
AI can help trend the use of certain inventory items.
For example, a facility might use 15 filters every September and only one filter every quarter after that. AI can help auto-order items when past data provides the opportunity to predict future usage.
The biggest obstacle for AI to help in any maintenance process is that it has very little real-time data on a physical environment.
As mentioned, wireless sensors provide real-time data and inform the maintenance team of the best ways to deal with upcoming issues. With the help of temperature sensors and local weather forecasts, AI can predict if piping in cold spaces will freeze, for example.
Predictive sensors on equipment monitor viscosity, energy consumption, vibration, and temperature, allowing AI to determine when a piece of equipment is about to fail. If a vibration isn't the same as a baseline vibration, a belt could be about to tear.
Sensors will help connect a CMMS to the physical environment and allow AI to give the user a greater understanding of where they can have the most impact.
Scanning through your daily tasks, AI can be used to suggest the best areas where your maintenance team can begin troubleshooting certain items. It can suggest ways to help resolve issues based on web content scanned and past historical aggregate data from other customers.
AI acting like a targeted Google search on how to fix a problem will help maintenance teams improve response time, reduce equipment downtime, and lower costs.
AI can help evaluate if an item should be replaced or can still be fixed based on the types of repairs done on the unit. Even though the cost of repairs may have been high in the past, they will bring increased longevity to the item and may not need replacement just yet.
This technology can be used like a personal shopper to help look at the average costs of replacements and allow maintenance teams to negotiate with their suppliers and vendors for the best rate on a new item.
The life expectancy of new parts or equipment can be considered in relation to the purchase price. For example, if a certain refrigerator typically lasts 10 years longer than another brand, the 20% higher cost results in significant savings when factoring in those 10 extra years.
AI-driven solutions are revolutionizing facility management across various sectors, including educational institutions, healthcare centers, commercial establishments, and beyond.
As these solutions grow more intelligent and evolve, they can be used to create safer, more reliable, and more cost-effective facilities. Leveraging advanced technologies and data analytics can positively impact each of these sectors.
In the education space, AI-powered surveillance systems can monitor campuses in real time, detecting potential security threats and unauthorized access. Facial recognition technology can enhance campus security by identifying individuals on watchlists or alerting staff to unusual activities.
AI can monitor the condition of school infrastructure, such as HVAC systems and electrical equipment, predicting maintenance needs before major failures occur. This proactive approach ensures students and staff a safe and comfortable learning environment.
By analyzing data from sensors and historical patterns, AI can regulate lighting, heating, and cooling to reduce energy consumption and lower operational costs.
In healthcare centers, AI-driven solutions can enhance patient safety by identifying potential equipment issues or failures and alerting teams when it’s time to repair or replace these critical assets. This technology can also monitor medical equipment performance and detect anomalies in real time.
By predicting equipment failures, healthcare centers can schedule maintenance proactively, minimizing downtime and ensuring the availability of essential medical devices.
AI can optimize commercial space utilization by monitoring occupancy levels. This data helps facility managers optimize workspace layouts, improve energy efficiency, ensure compliance with safety regulations, and maintain cost controls over lighting, temperature, and ventilation based on occupancy patterns.
In the hospitality sector, AI can personalize guest experiences, automate booking processes, and improve service. AI-driven visitor management systems can streamline check-in, enhance security, and manage visitor access to different areas within commercial establishments.
AI-driven solutions extend to various other sectors like manufacturing, transportation, and hospitality, among others.
AI can optimize production processes, predict machine failures, and ensure product quality in manufacturing. In transportation, AI can be utilized for smart traffic management, autonomous vehicles, and logistics optimization.
The cost savings and safety improvements achieved through AI-driven predictive maintenance are game-changers for various industries, paving the way for a more sustainable and profitable future.
The future of AI predictive maintenance for facilities holds tremendous potential as advancements in technology and data analytics continue to shape the landscape. Some key trends and possibilities that can be expected include advanced machine learning algorithms.
In the future, we’ll witness the development of more sophisticated algorithms capable of handling increasingly vast amounts of complex data. These algorithms will be better at recognizing patterns and trends, leading to improved predictive capabilities. AI systems will evolve to be more adaptable, continuously learning from various sources to enhance predictive accuracy.
AI systems will not only predict potential equipment failures but will also provide automated maintenance recommendations.
These recommendations will include precise instructions for maintenance tasks, required spare parts, and optimal scheduling. Maintenance teams will benefit from more efficient workflows and better planning.
As robotics and AI evolve, autonomous maintenance robots may become a reality. These robots could perform routine inspection and maintenance tasks, reducing the need for human intervention in hazardous or hard-to-reach areas.
This will improve safety, save time, and free up human technicians for more complex tasks.
In terms of maintenance technologies that any organization with physical facilities can implement today, a CMMS serves as a central hub for maintenance data and processes, enabling organizations to adopt and implement AI-driven maintenance strategies effectively.
By leveraging real-time data, trend analysis, automatic alerts, and task generation, the CMMS empowers maintenance teams to proactively address equipment issues, improve reliability, and optimize maintenance operations for greater efficiency and cost-effectiveness.
Learn more about the difference between predictive maintenance and condition-based maintenance, its benefits, and use cases.
Meet Dan Roberge, a dynamic and results-driven President and CEO with a proven track record of success in the tech industry. As a co-founder of Gnxcor Inc., he played a pivotal role in building a cutting-edge facility management software company, Maintenance Care from the ground up.
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