Predictive Maintenance Market: Introduction
- Predictive maintenance is also called condition based maintenance. It monitors the performance and maintenance as well as condition of machines and equipment during operations to avoid failure. Predictive maintenance depends on condition monitoring. The three facets of condition monitoring includes online, periodic, and report.
- Predictive maintenance solutions monitor the condition of assets by using sensor devices. The sensor devices supply the data in real time which is used to predict when the asset requires maintenance. Examples of using predictive maintenance sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation.
- There are various advantages of predictive maintenance which includes cost savings, minimizing planned downtime, maximizing lifespan of equipment, optimizing employee productivity, and more. However, the disadvantage is the amount of time it takes for assessing and implementing PDM schedules.
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Increasing need to reduce cost and downtime for maintenance
- Predictive maintenance offers organizations the ability to make a prediction 20 times faster than threshold based monitoring systems. In industries such as oil & gas, and industrial manufacturing, downtime costs a large amount of money, caused by machine failures. Hence, industrial customers are becoming more aware of predictive maintenance.
- In addition, organizations are leveraging AI and ML technologies for precision accuracy and speed over traditional tools to analyze the data. These factors are increasing the demand for predictive maintenance, which is fueling the growth of the market.
Rising concerns over data privacy issues
- Data generated by devices used in predictive maintenance are rising in volume as connected devices are growing in number. This causes misuse of data such as to determine the strategies of rival companies, and take control of machines. Also, a surge in privacy issues is seen due to unavailability of resources for implementation of AI on IoT devices. These factors may hamper the market growth of predictive maintenance.
Maximum Growth to be observed in the Asia Pacific Market
- In terms of region, the global predictive maintenance market can be divided into North America, Europe, Asia Pacific, South America, and Middle East & Africa.
- The predictive maintenance market in Asia Pacific is anticipated to expand at the maximum CAGR during the forecast period.
- This growth is attributed to a wide range of applications, adoption of advanced technologies, benefits of predictive maintenance, and growing manufacturing industry in developing economies such as Japan, Taiwan, and China. Thus, these factors are expected to increase the demand for predictive maintenance products.
- The predictive maintenance market in North America and Europe is expected to show high growth rate due to increasing competition among players. The market in Middle East & Africa is also likely to show high growth due to increasing demand from the oil & gas industry.
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Key Players in the Global Market
The global predictive maintenance market is highly fragmented due the presence of a large number of medium and large enterprises. Prominent players operating in the global market are focusing on product launch and technological developments to meet the growing demand.
Key players operating in the global predictive maintenance market include:
- IBM
- Microsoft
- SAP
- Hitachi
- Schneider Electric
- Others
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