In today’s competitive landscape, Original Equipment Manufacturers or OEMs face increasing pressure to optimise operations and enhance productivity. One of the most effective strategies for achieving these goals is predictive maintenance (PdM). This proactive approach leverages advanced analytics and real-time data to anticipate equipment failures before they occur, significantly reducing downtime and associated costs.
According to a recent report by MarketsandMarkets, the global predictive maintenance market is projected to reach $12.3 billion by 2026, growing at a compound annual growth rate (CAGR) of 28.4% from 2021. Talking purely about India, the Indian predictive maintenance market was valued at approximately $115 million in 2022 and is projected to grow significantly, reaching around $1.38 billion by 2027, with a compound annual growth rate (CAGR) of about 64.15% during the forecast period. This growth is driven by the increasing adoption of the Industrial Internet of Things (IIoT) and advancements in machine learning, enabling OEMs to monitor equipment health continuously and make data-driven decisions. Along with these things, the prominence of AI has also played a major role in the adoption of predictive maintenance.
The benefits of predictive maintenance are not just substantial but also financially rewarding. A study by Deloitte reveals that businesses implementing PdM can reduce maintenance costs by 25-30% and improve equipment uptime by 10-20%. For OEMs, this translates into significant cost savings and enhanced operational efficiency. By shifting from a reactive maintenance model to a predictive one, companies cannot only minimise unplanned downtime but also save manufacturers around $50 billion annually in the U.S. alone. These financial benefits should motivate OEMs to adopt predictive maintenance.
Real-world examples underscore the effectiveness of predictive maintenance in the OEM sector. For instance, GE Aviation’s use of advanced analytics to monitor its jet engines, predict potential failures and schedule maintenance accordingly. This approach has not only resulted in reduced maintenance costs but also improved aircraft availability, showcasing the tangible impact of PdM on operational efficiency. Moreover, the PdM also enables OEMs to find out any structural or fundamental flaw in their design which helps in designing a better and more efficient design, saving money in the process.
As the OEM industry continues to evolve, embracing predictive maintenance will be crucial for organisations seeking to stay ahead of the competition. By reducing downtime and maximising equipment efficiency, OEMs can enhance profitability and improve customer satisfaction by delivering products on time and meeting quality standards.
In conclusion, the transition to predictive maintenance represents a significant opportunity for OEMs. With the right investments and cultural shifts, companies can harness the power of data to optimise their operations and thrive in an increasingly demanding market.
