AI can use its predictive powers to fix mechanical problems before they arise, saving money and eliminating downtime
Most businesses understand the power of preventative maintenance. A good maintenance plan can fix problems before they happen. But what would happen if you had a technology that aided predictive maintenance?
Enter artificial intelligence (AI). More nimble and intelligent than a plan based solely on statistical norms, AI and machine learning (ML) can provide real-time data on the actual condition of equipment. It can save industries, such as manufacturing, time and money by signaling when it’s time for maintenance or repairs.
Predictive maintenance with AI is just one of the many ways the exploding technology can help organizations evolve their operations, enhance decision-making, empower staff, and do much more.
Understanding AI Predictive Maintenance
IBM defines AI predictive maintenance as follows: “By collecting data from sensors and applying advanced analytical tools and processes such as machine learning (ML), predictive maintenance can identify, detect, and address issues as they occur, as well as predict the potential future state of equipment, and so reduce risk.”
Statistics have shown that predictive maintenance can significantly decrease the number of machine stoppages and significantly increase the lifespan of equipment.
Predictive AI integrates with technologies like the Internet of Things (IoT). By using predictive analytics and artificial intelligence, data collected from sensors on equipment and machinery can be analyzed. AI-driven predictive maintenance can alert you to malfunctions as they occur and use extensive data to predict the future condition of your equipment.
Not only can you be confident that your machinery is working at optimum efficiency, but you can make repairs before problems cause costly work stoppages and order the replacement parts needed ahead of time. Businesses can save time and money with AI preventative maintenance and boost efficiency.
Reducing Downtime
In today’s connected industrial processes, one failing machine in a network might stop all production, leading to time and money losses. AI can help avoid unexpected downtimes and assist in timing maintenance and repairs as needed. AI can also schedule complex maintenance at off-high-capacity times.
By anticipating and stopping malfunctions, you can repair problems before they threaten workers’ safety. Timely maintenance also extends equipment life.
Cost Savings
Deloitte points out that “on average, predictive maintenance increases productivity by 25%, reduces breakdowns by 70%, and lowers maintenance costs by 25%.” The technology uses a huge trove of data and analytical methods to predict malfunctions before they happen, allowing companies to do repairs during less costly off-peak hours.
Predictive maintenance with AI can save money and time in several ways, including:
- 5 to 10% cost savings on operations, and maintenance, repairs, and operations (MRO)
- 10 to 20% with increased equipment uptime
- 20 to 50% on reduced maintenance planning time
Implementing AI Solutions
The first step in integrating AI into your existing processes is to thoroughly assess your business needs and your readiness to adopt transformative technological solutions.
While AI is powerful, it’s not a one-size-fits-all solution. It’s essential to identify where AI can address specific challenges or optimize operations in clearly defined areas.
Start by targeting manual, repetitive tasks that consume significant time and could benefit from AI-driven efficiency. Next, conduct a detailed evaluation of your current technological infrastructure to determine how seamlessly AI can be integrated.
Be aware that some legacy systems might present integration challenges, especially if your data is decentralized or siloed across departments. AI relies on large volumes of high-quality data for effective training, so incomplete or inaccessible data can hinder the success of an AI implementation.
Measuring Effectiveness
Since implementing an AI solution can represent a big investment of time, money, and effort for an organization, it’s important to have the right metrics and key performance indicators(KPIs) in place to know whether or not your investment is paying off.
Are you hitting your targets—like increasing sales, boosting customer satisfaction, or improving operational efficiency? Without understanding AI’s value, making informed decisions, like whether to scale up, becomes challenging.
AI metrics can include ones covering:
- Financial impact
- Performance
- Accuracy of AI outputs
- Efficiency
KPIs can give insights into:
- Customer satisfaction
- Operational efficiency
- Customer satisfaction
- Revenue growth
Using these indicators, you can calculate ROI (return on investment), showing how direct and indirect cost savings balance your investment in the technology.
Challenges and Solutions
AI can be a transformative solution for your business, offering significant benefits in various scenarios. For instance, if your operations involve low-cost equipment with readily available parts, you might not need an advanced AI system, but the right AI tools can still optimize processes and enhance efficiency.
Integrating AI with your existing technological infrastructure, especially if it includes complex legacy systems, can present challenges. However, with the right approach and consultation from a seasoned digital transformation team, these challenges can be effectively managed, paving the way for seamless AI adoption.
Another crucial aspect is equipping your staff to use the new AI tools and processes effectively. Start by conducting an AI skills gap analysis to pinpoint the specific training your team needs. From there, implement or develop training programs that cover essential AI concepts, foster technical skills, and provide practical, hands-on experience. As AI technology evolves rapidly, it’s also important to instill a culture of continuous learning among your employees.
When defining an AI project, it’s vital to determine the type of data needed to train your model. Sourcing this data can be approached in several ways, each with its own strengths and challenges, but with the right strategy, you can ensure your AI model is built on data gathered from the following:
- In-house data preparation
- Publicly available datasets
- Web scraping
- Data marketplaces
- AI data services providers.
If you are using in-house data but are stymied by its location in different parts of a legacy setup, you might need to consult with a digital transformation expert on how best to access and work with it.
Change the trajectory of your business
AI-driven predictive maintenance can significantly boost a business’s efficiency by cutting back on downtime and reducing repair costs. Manufacturers who have implemented the technology have been able to anticipate equipment failures, improve their maintenance practices, and realize substantial cost savings.
Core BTS has worked with many clients to realize AI solutions’ direct and indirect benefits. “We believe deeply that AI isn’t just about driving cost savings or improving efficiencies,” says Kevin Thimjon, CEO of Core BTS. “It’s about improving and impacting the lives and businesses of clients and their end customers while helping to change the trajectory of entire industries.”
Contact us to see how Core BTS can help change the trajectory of your business with predictive maintenance and other AI solutions.
