Predictive maintenance is a technique that helps in analyzing and determining the condition of any equipment in order to efficiently estimate the timeframe for the performance of maintenance. The effective use of the technique helps in saving cost as well as time. The technique helps in the effective detection of any failure patterns. Predictive maintenance focuses on predicting and determining device/ equipment failure and when it will happen so that preventive measures can be taken. There are several benefits associated with the predictive maintenance technique. These benefits include savings on production time, and expenses related to equipment parts and other associated raw material. The predictive maintenance technique also minimizes the overall time for maintenance and repair of industrial equipment. Predictive maintenance can minimize several issues with reliability and quality. Overstocking can also be reduced in the inventory with the usage of the predictive analytics technique.
Predictive maintenance is offered as a solution by solution vendors in the market. The solution helps reduce unforeseen downtime with optimized scheduling and failure prevention. It also boosts overall equipment effectiveness by optimizing the production conditions of each machine and process. It minimizes the overall throughput by effectively identifying process bottlenecks. Therefore, the solution acts as a very effective tool for almost all industry verticals.
The predictive maintenance market is driven by a rise in the need among industries such as energy, utility, manufacturing, and automotive among others to minimize the overall operational cost. Moreover, a rise in the use of sensors among these industries is expected to drive the predictive maintenance market. Sensors are being used at the plant site, during distribution, etc.. These sensors generate a significant amount of data, which can be effectively utilized for decision making. Companies operating across various industry verticals need to analyze the data for operational efficiency. Predictive maintenance can effectively use these data to analyze trends. In the coming years, companies will utilize the solution in order to make efficient use of real-time data. Also, companies operating in these industries have extensive pressure of time-to-market. It is, therefore, crucial for them to identify the cause of the fault or failure before they actually occur. Therefore, this aggressive need to be competitive and on schedule in the predictive maintenance market is expected to increase demand for predictive maintenance solutions and services in the coming years. Also, the introduction of a new business model for delivering predictive maintenance techniques to the predictive maintenance market is estimated to propel the predictive maintenance market in the coming years. However, low level of awareness among enterprises about the uses and benefits of predictive maintenance solutions is expected to restrain the predictive maintenance market in the near future. Nevertheless, with time, the effect of the aforementioned restraint is expected to diminish as more and more companies realize the importance of the solutions.
The predictive maintenance market can be segmented based on component, industry, and region. Based on component, the predictive maintenance market can be bifurcated into solutions and services. In terms of industry, the market can be classified into utility, oil & gas, manufacturing, automotive, health care, agriculture, and others. The oil and gas industry segment can be further divided into upstream, mid-stream, and downstream. Based on region, the predictive maintenance market can be categorized into North America, Europe, Asia Pacific, Middle East & Africa, and South America.
Players operating in the market are analytics providers, software platform providers, and software as a service providers. These players are continuously investing in research and development (R&D) in order to provide the most affordable and comprehensive solutions to end-use industries.
Key players operating in the predictive maintenance market include IBM Corporation, Software AG, SAP SE, Siemens AG, Microsoft Corporation, General Electric Company, C3, Inc., Uptake, Seebo Interactive Ltd., and Space Time Insight, Inc.
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