Remaining Useful Life (RUL) can be defined as an estimation of time an item, component, or system is able to function before it requires any repair or replacement. The remaining useful life of any system or component is calculated by observing or average estimates of similar items, systems or components or a combination thereof. Remaining useful life (RUL) is a subset of predictive maintenance and is majorly used in manufacturing industry as RUL helps them to implement cost-effective predictive maintenance, which helps to reduce 10-35% in maintenance costs. It also helps to reduce the machinery downtime by 30-50%. Therefore, these factors are expected to aid in growth of the remaining useful life estimation software market during the forecasted period. 

Rising Focus on Industrial Automation is expected to Increase Adoption of Remaining Useful Life Estimation Software.

Remaining useful life estimation software aids in collecting information related to equipment, process it, and predict the remaining life of any component or system. Moreover, manufacturing, automotive, and aerospace and defence industries are focusing on industrial automation to improve the efficiency and performance of machineries. This factor is fuelling adoption of remaining useful life estimation software during the forecasted period (2019-27). For instance, in 2017, Mercedes Benz implemented predictive maintenance software with features of remaining useful life estimation software for real-time data gathering, which allows the company to acquire data from vehicle when it is being used by the customer. It aids the company to predict any incident or failure regarding the vehicle and helps to gather information related to remaining useful life of the vehicle components.

High Cost, Complex Installation and Implementation of Predictive Maintenance Solution Set-Up is Expected to Restrain the Market Growth.

Remaining useful life estimation software is a part of predictive maintenance solution. Moreover, predictive maintenance solution system consists of large number of connected IoT devices such as sensors, receivers and high end computer systems. These devices are used to gather the data of any system or components using AI algorithm. Moreover, artificial intelligent and machine learning technologies are also used in predictive maintenance solution to analyses the data. This makes the system and solution very costly and complex. Considering these factors, overall predictive maintenance set-up is high in cost, which requires high capital investment and therefore, are major factors restraining growth of remaining useful life estimation software market during the forecasted period.

Remaining Useful Life Estimation Software Market Taxonomy

On the basis of component, the global remaining useful life estimation software market is segmented into

  • Software
  • Services

On the basis of deployment, the global remaining useful life estimation software market is segmented into

  • On-premise
  • Cloud

On the basis of end-use industry, the global remaining useful life estimation software market is segmented into

  • Manufacturing
  • Aerospace & Defense
  • Automotive
  • Energy
  • Hospitals, Clinics & Diagnostic Laboratories
  •  Pharmaceuticals

On the basis of region, the global remaining useful life estimation software market is segmented into

  • North America
  • Europe
  • Asia Pacific
  • Latin America
  • Middle East
  • Africa

Major players operating in the global remaining useful life estimation software market include Aspen Technology, Inc., BigR.io, LLC, MainTech Systems GmbH, Merino Services Ltd., Ridgetop Group, Inc., SAP SE, Schaeffler AG, Senseye Ltd, SimuTech Group, and The MathWorks, Inc.

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