Predictive Maintenance Market Trends

Growth Drivers

• Rising Demand for Emerging Technologies - The rising adoption of technology for running the business efficiently is likely to increase the popularity of predictive maintenance technology. Around 65% of companies are looking forward to switching to various technologies and over 77%, who have already implemented the technology have expressed above-average satisfaction with it.
• Need to Reduce Maintenance Costs and Downtime - Predictive maintenance market predicts the time of equipment failure and prevents its further failure, thus reducing the downtime and maintenance cost of the company. For industrial businesses, downtime often costs between USD 30,000 and USD 50,000 per hour. This indicates that downtime can reasonably cost the average business between USD 10 and USD 25 million a year.
• Growing Adoption of IoT (Internet of Things) by the Company - Integration of IoT in predictive maintenance enables businesses to identify potential safety hazards, take action, and estimate problems before they have an impact on employees. In the next two years, starting from 2021, around 80% of businesses want to spend a considerable amount of money on at least one IoT project.
• Massive Generation of Data - Predictive maintenance uses the data-driven technique by using real-time data for finding any sort of anomalies in machines. Global data production, collection, transfer, and usage are all expected to rise sharply, reaching 64.2 zettabytes in 2020. Global data generation is anticipated to increase to more than 180 zettabytes over the following five years, up until 2025.
• Increased Prevalence of Digitization Among the Company - Digital transformation is expected to boost predictive maintenance market, by enhancing precision and blowing off downtime dramatically. Around 90% of all businesses have already embraced a digital-first company strategy or have plans to do so, while 55% of startups have done so.

Challenges

• Shortage of Expert Workers in the Market
• Constant maintenance and Upgradation of the System
• Escalating Challenges with Data Privacy - A significant amount of production and manufacturing data is gathered, transferred to, and stored in a database when using a predictive maintenance method. Moreover, the upgradation of industries to industry 4.0 generated a huge amount of data every hour. These industries are at higher risk of cyber-attacks, network breaches, and data theft. Therefore, the use of predictive maintenance can also lead to danger to the huge amounts of data, which in turn develops hesitancy among end users to adopt predictive maintenance.