How AI Is Transforming Maintenance Tracking

Maintenance tracking is just what it sounds like — it’s the process of tracking and documenting repairs and maintenance performed on assets and facilities to ensure they’re functioning optimally. Generally, maintenance tracking starts with a solid asset management system in which your maintenance team knows what pieces of equipment are the most crucial to their business and, therefore, require the most maintenance work. From there, maintenance tracking involves recording maintenance work like routine services, part replacement, repairs, inspections and system upgrades to ensure asset performance remains optimal. 

Of course, there are several ways to track your maintenance operations, with the traditional spreadsheet method still being the most popular. In fact, 70 percent of manufacturers report that they still collect data manually, according to the Manufacturing Leadership Council. But that’s expected to change exponentially in the coming years, with 44 percent of manufacturers from the same study reporting that the amount of data they collect more than doubled compared to the previous two years. While 30 percent of respondents reported using manufacturing data to predict operational performance, another 60 percent reported that predictivity will be a primary objective by 2030.

And using artificial intelligence is the key way they’re going to be able to gather and analyze that maintenance data to be used for predictive maintenance measures. Read on to learn more about how exactly.

How Maintenance Tracking Systems Work 

First, let’s consider how equipment maintenance tracking works. It helps businesses streamline maintenance schedules, mitigate risk of breakdowns, increase asset lifespans and collect tangible data for key decision-making. For example, suppose you’re a maintenance technician at a manufacturing plant who received an assignment to perform a routine inspection on a conveyor system. Historical maintenance tracking indicated that every several years the bearings on the conveyor motor got worn down, causing extensive downtime at the facility. 

So, this time around, you took a preventive maintenance approach, inspected it and noticed that the bearings did, in fact, need to be replaced. In this case, asset tracking helped you catch an issue before it led to a costly breakdown. 

Now, let’s break down different ways that maintenance teams track their work to get a better understanding of their effectiveness. The three most common methods include:

Traditional (Paper-Based)

Physical, paper-based checklists or spreadsheets have long been the golden standard when it comes to tracking maintenance tasks. While this approach is considered antiquated, it’s still the most common, as maintenance teams continue to stick with what they know when it comes to recordkeeping. But that’s changing.

Computerized 

Computerized maintenance management system software (CMMS software) is becoming more mainstream when it comes to assigning maintenance tasks and recording and storing maintenance records digitally. Apart from providing a central location for information for every piece of equipment in your maintenance program, a CMMS typically has a mobile app. 

The cloud-based software solution allows maintenance technicians to receive task assignments on their mobile devices as well as retrieve procedures and processes on how to perform the required work. Once the task is completed, the technician records the steps taken digitally, which are automatically accessible to relevant leadership and compiled as part of the team’s overall maintenance analytics. 

Hybrid 

A hybrid technique uses both traditional paper-based records and maintenance tracking software for digitally storing and managing maintenance processes. This approach allows technicians to record maintenance reports by hand and upload them online to leverage the security, data analysis and maintenance tracking benefits of digital systems. 

However, it lacks the digital work requests and personalized dashboards and notifications that streamline your maintenance team’s workflow.

Key Components of Maintenance Tracking Systems

The following aspects are critical for tracking all maintenance activities effectively:

• Asset inventory management: Tracks equipment location, condition and maintenance history
• Work order management: Assigns, tracks and manages maintenance tasks
• Parts inventory management: Ensures spare parts are stocked and available

Examples of AI in Maintenance Tracking

Artificial intelligence is quickly reshaping maintenance tracking with automation, data tracking and enhanced analytics. Integrating AI into your maintenance tracking can offer the following capabilities:

• Maintenance schedule optimization: AI uses historical equipment data, including usage patterns and condition monitoring, to make predictions regarding potential failures to schedule maintenance technicians more strategically than humans can. 
• Work order automation: AI automatically generates, schedules and assigns work orders based on routine maintenance schedules, predictive analytics and real-time equipment data. This process saves time on manual scheduling while ensuring timely maintenance and reducing equipment downtime. 
• Real-time data tracking: Artificial intelligence continuously monitors machinery sensors and operational data to make informed insights around potential issues and required maintenance. It provides real-time maintenance reporting for more informed decision-making from organizational leadership. 
• Analyzing sensor data: Automatically detect abnormalities like part failures before they turn into major breakdowns and downtime. Equipment sensors track equipment in real time to identify patterns and trends that support your maintenance scheduling and extend equipment lifecycles. 
• Predicting equipment failure probabilities: AI analyzes both historical and real-time data to model the probability of part and equipment failures, enhancing your maintenance scheduling capabilities to proactively schedule repairs on minor issues before they escalate. 

How General Electric Uses AI for Maintenance Tracking

General Electric’s Predix platform collects data from sensors embedded in the company’s industrial equipment and uses machine learning models to predict when equipment might fail or require maintenance. The AI system can analyze vast amounts of real-time data from machines in the field, such as temperature, pressure, vibration and more. This allows GE to perform maintenance just in time, rather than adhering to fixed schedules, which reduces unnecessary repairs and extends the lifespan of machinery.

This AI-driven approach benefits GE through:

• Cost savings: Issues are addressed before they turn into failures that are expensive to repair and result in extensive downtime.
• Increased equipment lifespan: Parts are replaced and issues are repaired before they turn into more major issues that could result in complete breakdowns.
• Better operational efficiency: Data-backed decision-making ensures that maintenance is only performed as needed, freeing up technicians and reducing unnecessary labor costs. 

How AI Benefits Maintenance Tracking

Integrating AI into your scheduling, data tracking and work order generation can transform your business for the better. Core benefits of using AI for maintenance tracking include:

Automated Inventory Management

AI ensures you’ll always have the parts and tools you need on site by leveraging predictive analytics and usage data to forecast demand. Increase uptime by not having to wait for parts to ship. Prevent over- or under-stocking issues by always having just the right amount of inventory. 

Predictive Maintenance Solutions

AI can predict potential equipment failures by continuously analyzing historical and current sensor data to encourage timely interventions before major issues arise. This extends your equipment’s lifespan while preventing costly unplanned downtime.

Reduced Downtime

By predicting when scheduled maintenance is needed and identifying anomalies that indicate issues with machinery, artificial intelligence ensures that the right maintenance tasks are performed to prevent major breakdowns that result in unplanned downtime.

Decreased Maintenance Costs

AI-driven maintenance tracking ensures that maintenance is done proactively to mitigate downtime risk and only schedule maintenance technicians when needed, streamlining your processes while reducing costs from unnecessary labor and expensive breakdowns. 

Enhanced Safety

Leveraging AI to catch and address minor issues with equipment before they turn into a major safety risk. Timely preventive maintenance scheduling prevents critical safety checks from being overlooked. 

Data-Backed Decision-Making

Prevent human error by leveraging advanced technology to automatically track historical and real-time data across facility-wide equipment to catch issues and schedule routine maintenance. 

Make the Move to Digital Maintenance Management With Coast’s CMMS

To leverage AI’s predictive capabilities for revolutionizing maintenance tracking, your business needs a strong digital foundation as a first step. Whether you’re looking for a basic maintenance management software or a more advanced enterprise asset management (EAM) provider to enhance your maintenance planning, Coast has everything you need to digitize your work order scheduling, inventory management and maintenance reporting. Centralize all system- and maintenance-related data into a single intuitive platform to gain better visibility, improve team collaboration, mitigate the risk of costly breakdowns and make it easier to integrate advanced technologies like AI in the future. 

Ready to see how digitization can enhance all of your maintenance workflows? Book a demo today to discover how Coast can simplify your operations from the ground up.