Downtime has long been a top source of trouble in otherwise optimized supply chains. When this occurs, operations slow down considerably or even come to a complete halt — leading to huge losses in productivity and profits. Not only does this dramatically increase labor and operational expenses, it can cause issues for customers and clients, preventing goods and supplies from reaching their intended destination on time.
Many tools and technologies promise to prevent downtime and today’s enterprises adopt layered strategies that encompass everything from regularly scheduled maintenance to backup systems and even alternative suppliers. None of these solutions are foolproof, however, and they can be expensive or cumbersome to implement.
The good news? Downtime can be dramatically reduced, as many businesses have uncovered a powerful strategy for preventing operational issues: AI-driven predictive maintenance. Making the most of powerful algorithms and a wealth of data, today’s predictive maintenance solutions enable businesses to stay proactive, addressing potential issues before they even arise.
While implementing AI-powered systems may seem daunting, they are quickly becoming more common and the return on investment is often significant. There’s no better time to adopt cutting-edge solutions and optimize all aspects of equipment maintenance. AI-powered predictive maintenance holds huge potential for minimizing downtime in today’s modern supply chain.
What is AI Predictive Maintenance?
In the supply chain, AI-powered predictive maintenance leverages machine learning algorithms and extensive data analytics to help determine whether equipment is functioning optimally or whether repairs will soon be required. This brings a more proactive, yet nuanced approach to maintenance, ensuring that equipment receives attention the moment it is required, eliminating unreliable maintenance schedules.
The underpinning of predictive maintenance arrived several decades ago, with many businesses having used reliability engineering to determine the potential cost of downtime, along with the strategies that could reduce the potential for downtime. Incorporating advanced statistical methods, early reliability engineering solutions provided powerful insights into machine failure rates and the weak points responsible. This, in turn, influenced monitoring, ensuring that potentially vulnerable components received the attention they required at just the right time.
Through the years, techniques such as vibration analysis helped pinpoint potential mechanical problems, while technological breakthroughs such as the Internet of Things (IoT) made it possible to gather a wealth of sensor data. Exciting advancements have also involved radio frequency identification (RFID) tags, which are valuable not only for location monitoring, but also for facilitating insights into equipment usage and maintenance history.
More recently, artificial intelligence has been integrated to enhance monitoring and analysis, making it possible to leverage a higher volume of data and even collect insights in real time. Today’s systems leverage advanced machine learning algorithms to make accurate predictions.
AI predictive maintenance could be a key player in the Industry 4.0 movement (also known as the fourth Industrial Revolution), in which AI solutions integrate with sensors and robotics to improve transparency and efficiency throughout the supply chain. By ensuring that repairs and other interventions occur before downtime becomes a problem, the Industry 4.0 version of predictive maintenance can reduce operational expenses associated with unnecessary maintenance and equipment failures.
How AI-Powered Predictive Maintenance & Machine Vision Can Reduce Downtime in the Supply Chain
While AI-powered predictive maintenance promises many advantages, one of the most noteworthy involves the potential for dramatically reducing downtime. This begins with continuous monitoring, in which advanced sensors and machine vision constantly observe and report on equipment health. Machine vision, in particular, plays a crucial role by detecting misalignments, cracks, and other types of damage on critical devices—issues that may otherwise go unnoticed by the human eye—while sensors capture data on factors like excessive vibration and operating temperatures. When these two technologies are combined and powered with AI, they provide significant insights that enable even stronger predictive maintenance, allowing issues to be identified much faster.
Powered with AI algorithms and real-time data, sensors and machine vision can determine when, where, and even how anomalies occur. After all, sudden changes could be indicative of future failures and may suggest that additional attention is required. If certain thresholds are breached and conditions are deemed severe, these AI-powered systems can send automatic alerts indicating that maintenance is needed or a potentially catastrophic breakdown is near. Beyond this, historical data can reveal when certain maintenance procedures are typically required. Alongside real-time insights into how equipment is actually functioning, this can produce a nuanced maintenance strategy that is both proactive and sufficiently responsive.
How Does AI Enable Predictive Maintenance?
Artificial intelligence is a key component in predictive maintenance. While businesses have long enjoyed access to considerable data, many have struggled to make full use of this information. Simply put, older computing systems were not powerful enough to fully leverage this data. And as the amount of information continues to increase (sparked by the escalating use of sensors), data overload becomes more of a risk. As a result, it may be more difficult for systems to secure actionable insights.
With AI built into the process, however, huge volumes of data can be analyzed in real-time, leading to swift and accurate insights that have a demonstrable impact on how and when equipment is serviced. Integrating data from numerous sources, today’s AI systems overcome previous data fragmentation issues, limiting silos and offering a cohesive approach to predictive maintenance.
Use Cases for These Technologies
Businesses across numerous industries are increasingly turning to AI-powered predictive maintenance to solve persistent downtime challenges. Relevant use cases across various sectors include:
- Monitoring conveyor belts: From bearing failures and belt breakages to general wear and tear, heavily-used conveyors can be prone to many problems. If strategically placed along conveyor belts, sensors can provide valuable insight into potential issues, ensuring that early signs of trouble are addressed promptly — and before such problems lead to breakdowns or safety hazards. These monitoring solutions can even determine how conveyor system usage contributes to wear and tear, providing data-driven insights into flow rates and wear patterns that reveal opportunities for improvement.
- Planning forklift maintenance: Forklifts are a mainstay in the modern warehouse, but they are prone to damage due to frequent use and harsh working conditions. This could involve bent forks, hydraulic system failures, engine overheating, or dangerous brake failures. Through predictive maintenance, it is possible to determine when forklifts require attention and which tune-ups or repairs are needed.
- Inventory management for supply chain components: Certain components are more prone to failure than others — but if they are not readily available when replacements are required, unplanned downtime remains a strong possibility. Predictive systems can help determine not only when replacements are likely, but also the extent to which certain parts need to be kept in stock. Better yet, AI can facilitate just-in-time storage through predictive parts ordering, in which detailed insights into equipment wear reveal how soon replacements are anticipated and when it’s time to secure these components.
- Automated storage and retrieval systems (ASRS): Capable of automatically retrieving items, ASRS promises powerful advantages, including opportunities to optimize smaller spaces. Because these systems are so heavily used in the modern supply chain, however, they call for frequent component replacements or sensor calibrations. Predictive models help to forecast when failure-prone components are most likely to show trouble but also facilitate condition-based maintenance to keep ASRS running smoothly.
Future of Machine Vision & Predictive Maintenance with AI
AI-powered predictive maintenance represents a dramatic step forward for supply chain operations, but additional advances can be expected in the near future. As the technology that underscores preventative maintenance continues to improve, further functionalities will be built into already impressive predictive maintenance systems to ensure that they leverage information from all available data sources.
Machine vision promises to elevate this process so that seemingly minor issues (which can quickly spiral if not promptly addressed) are detected with ease. These systems can conduct thorough visual inspections to reveal otherwise missed signs of trouble. For example, MV systems may highlight problems such as corrosion or cracks. Furthermore, by monitoring product quality, they can determine whether specific equipment issues could be responsible for patterns of defects.
Integrated with AI systems, MV can augment predictive systems’ abilities to produce real-time insights by using visual cues alongside other metrics to reveal signs of wear and tear. As MV systems take over, however, the algorithms that support them will need to be even more sophisticated — and this is where deep neural networks will come into play. Relying on multiple layers of nodes to help leverage insights from raw data, deep neural networks excel when applied to tasks involving images or video footage.
Peak Technologies is a Leader in Predictive Maintenance with AI
Predictive maintenance has the potential to transform the supply chain, but such solutions must be implemented strategically. This is where the experts at Peak Technologies come into play; we can help enterprises determine what role AI-powered solutions may have in your operation and how they can be integrated with existing systems to ensure seamless adoption.
As an industry leader in machine vision solutions Peak embraces AI-powered solutions and encourages supply chain leaders to do the same.
Ready to embrace the power of AI-driven predictive maintenance? Look to Peak Technologies for guidance. Reach out today to learn more about our end-to-end automation solutions and other advanced opportunities for boosting supply chain efficiency.
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