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IoT Market in Product Lifecycle and Asset Management - Top 3 Trends by TechnavioTechnavio's latest market research report on the global IoT market in product lifecycle and asset management provides an analysis of the most important trends expected to impact the market outlook from 2017-2021. Technavio defines an emerging trend as a factor that has the potential to significantly impact the market and contribute to its growth or decline. This press release features multimedia. View the full release here: http://www.businesswire.com/news/home/20171030006248/en/ 
Technavio has published a new report on the global IoT market in product lifecycle and asset management from 2017-2021.(Graphic: Business Wire) The global IoT market in product lifecycle and asset management is expected to grow at a CAGR of close to 20% during the forecast period. Developments in technology and the integration of IoT with cloud computing, Big Data analytics, augmented and virtual reality, predictive maintenance, and condition monitoring with respect to product lifecycle and asset management are increasing the demand for IoT solutions in product lifecycle and asset management. This report is available at a USD 1,000 discount for a limited time only: View market snapshot before purchasing Buy 1 Technavio report and get the second for 50% off. Buy 2 Technavio reports and get the third for free. The top three emerging market trends driving the global IoT market in product lifecycle and asset management according to Technavio research analysts are:
Looking for more information on this market? Request a free sample report Technavio's sample reports are free of charge and contain multiple sections of the report including the market size and forecast, drivers, challenges, trends, and more. Evolution of predictive maintenance with IoT Predictive maintenance is a technology that helps in reducing operational and capital costs. It facilitates the more efficient use of resources by proactive servicing and repair of assets. The process of predictive maintenance collects and analyzes massive quantities of data to predict faults and errors outside the normal tolerance range. The three major solution enhancements in predictive maintenance over traditional maintenance schedule are capturing sensors data, facilitating data communications, and making predictions. According to Raghav Bharadwaj Shivaswamy, a lead analyst at Technavio for automation research, "The major benefits of using predictive maintenance are reduced maintenance costs by predicting the future failures, reduction in the replacement of parts, reduced idle time of an asset, increased return on assets, reduced unplanned downtime, and improved overall equipment effectiveness." Evolution of cloud-based technologies Cloud is a virtual data center that stores, manages, and retrieves data, eliminating the infrastructure costs. The three types of clouds that are available are public, private, and hybrid clouds. Public cloud is accessible to the public, private cloud is restricted to only organizations, and hybrid cloud is a combination of public and private clouds. Integration of IoT, Big Data analytics, cloud computing, and predictive maintenance to product lifecycle and asset management adds value to the organizations that adopt IoT-enabled PLM and asset management. The deployment of IoT solutions can also be done through cloud technology. Cloud-based technologies are emerging as database driven tools, deriving information from unstructured data. "Many industries are accessing cloud services through wireless networks as Wi-Fi is emerging as the primary access method. Thus, it is expected that the emergence of cloud computing will drive the adoption of cloud services integrated with IoT, leading to the growth of the global IoT market in product lifecycle and asset management," says Raghav. Rising demand for Big Data analytics The emergence of IoT has led to the increased generation of data. However, the analysis of this data has become complicated. Hence, organizations are looking toward technologies, such as Big Data technology and solutions, to address such issues. Big Data analytics can analyze large volumes of data and provide valuable insights for organizations to make decisions. Taking optimized business decisions with the help of Big Data analytics leads to innovations in new business models, products, and service offerings. Asset-intensive industries, such as aerospace and defense, power, oil and gas, and automotive, use many sensors to monitor the condition of assets across the plant. This generated data is cross communicated among the machines through an industrial LAN network. Big Data analytics helps the industries to analyze the sensor data and initiate data collection for industrial equipment. Distributing machine learning algorithms with Big Data technologies, such as MapReduce, enable running algorithms to find correlations of comparatively lower statistical significance, which are not done by conventional technologies. Browse Related Reports:
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