In the aftermath of the COVID-19 pandemic, businesses, particularly those in asset-intensive industries, are actively attempting to strike a balance between cutting costs and optimizing asset availability. Maintenance operations are one area that is being extensively investigated in this regard, as in asset-heavy enterprises, maintenance is a crucial component of profitability. While conventional approaches to maintenance were reactive—i.e., fixing equipment as and when it fails—and preventive—where machine parts are replaced, repaired, or recharged preemptively—the recent trend has been to move towards what is referred to as predictive maintenance (PdM).
Monitoring the functioning and status of machinery and equipment in real time in order to foresee anomalies, breakdowns, or failures is referred to as predictive maintenance. Although the history of this form of maintenance goes back to the 1990s , several recent developments in technology as well as the rising need for cost-effective solutions in manufacturing – in order to respond appropriately to ongoing supply chain crises – have reignited interest in and implementation of this approach in maintenance. PdM has quickly evolved from a risky niche market to an enterprise solution with strong ROI. The streamlining of IoT asset connection, developments in cloud services, and advancements in ML frameworks have all led to the growth of this market.
When it comes to reducing expenses, manually inspecting assets on a regular basis is insufficient and counterproductive. Today, utilizing IoT-enabled sensors to monitor assets is far more cost-effective than using human resources to carry out periodic audits as these sensors are able to assess assets by continuously monitoring various elements, such as temperature, vibration, and oil quality, every second. Additionally, this innovative approach equips businesses to address issues before they become more serious, avoids unplanned downtimes, and drastically reduces costs, thus helping navigating supply chain disruptions, manufacturing delays, and more.
In spite of its initial overhead costs, manufacturers who embrace IoT-based PdM stand to gain several benefits:
The advantages of IoT-based PdM are not simply hypothetical. According to a survey conducted among manufacturers, 91% of those who have employed PdM in their companies reported a decrease in repair time and unplanned downtime, while 93% saw improvement in the life preservation of equipment . It is no surprise, then, that the global IoT PdM market is predicted to reach $111 billion by 2030, with over 80% of businesses planning to invest in it or already doing so . For example, a large American food manufacturer was able to remotely monitor selected critical assets using PdM tech and sensors. Detecting faults like overheating of fuse holders and oil quality degradation ahead of time helped prevent shutdowns in snack production, reducing losses in sales and unplanned downtimes .
Figure: A hot fuse holder was discovered via infrared inspection of the main pole for a plant's automated warehouse using IoT-based sensors, preventing the warehouse from shutting down. Source: plantservices.com
Any and all sectors where assets create data and require maintenance can benefit from IoT-based PdM application; this includes sectors such as consumer packaged goods, textiles, chemicals, and pharma. Vehicle repair and malfunction concerns, for example, can be diagnosed early through PdM in the airline, automotive, and railway industries. The adoption of IoT-based PdM in the energy and oil and gas industries even saves lives by detecting possible failures of oil mining equipment and power plants.
Businesses are now beginning to see maintenance as an investment opportunity rather than as an unnecessary cost. The various enumerated benefits of PdM, such as improved uptime, increased efficiency, reduced expenses, and enhanced asset performance through anticipatory and proactive corrective actions, are most effectively unlocked with the help of IoT-based technologies and its advanced analytics capabilities.
As a result, IoT-based predictive maintenance is now a critical investment for any manufacturing business. To ensure that this strategy is implemented in the best possible manner, companies can leverage advanced technologies such as integration software in order to revert to normal operations as quickly as possible.
Keep an eye out for our upcoming blog post in this PdM series where we'll walk you through how to deploy PdM-sensing infrastructure in your industrial setup and enable world-class maintenance practices.
Shamia is a researcher and writer focused on technology, data analytics and AI. She holds a post-graduate degree in literature, so it goes without saying that she is enthusiastic about all things social and cultural. In her free time, she enjoys reading up about world history, engaging in lively discussions about varying theological perspectives, and exploring the city where she currently resides.