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Top Trends in Global R&D Outsourcing Services Market - Technavio

Technavio’s latest market research report on the global R&D outsourcing services market 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.

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Technavio has published a new report on the global R&D outsourcing services market from 2017-2021. ( ...

Technavio has published a new report on the global R&D outsourcing services market from 2017-2021. (Graphic: Business Wire)

One of the key factors resulting in the growth of global R&D outsourcing services market is cost reduction and efficiency. Labor arbitrage is a result of globalization, which shifts business processes to less expensive locations and it is regarded as one of the prospective benefits of outsourcing processes. Firms that adopt the outsourcing strategy need not invest in resources nor be concerned about handling their limited available resources.

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The three emerging market trends driving the global R&D outsourcing services market according to Technavio research analysts are:

  • Rise in engineering analytics
  • Emergence of AI and machine learning
  • Evolution of industrial IoT

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Rise in engineering analytics

Engineering analytics (EA) derives insights from data collected by physical machines to design, produce, and deliver new products. The EA value chain includes all components such as sensors that generate information, networks that transmit information, and software that processes information. EA possesses the limitless potential to boost the evolution of technology. Therefore, numerous engineering service providers are investing in R&D to improve EA and understand its effective implementation.

According to Amrita Choudhury, a lead analyst at Technavio for ITO and BPO, “The US offers immense opportunities to expand the EA tools ecosystem. Although multiple analytical tools such as R, SPSS, and SAS exist, the shortage of EA-specific tools hinders the resolution of EA-based business problems. This has caused major enterprises to work with IT vendors to create an EA tool ecosystem, engineered with data access and integration layers.”

Emergence of AI and machine learning

Machine learning is basically a part of artificial intelligence (AI) systems. It is widely recognized in the engineering services industry. Machine learning solution integrated with analytical tools assists industrial manufacturers in the decision-making process.

The manufacturing sector has also implemented advanced software and IT systems such as military engineer services (MES), programmable logic controller (PLC), and supervisory control and data acquisition (SCADA). These systems help to generate a high volume of data at shop floor. Machine learning solutions are being applied to transform manufacturing data into a structured form for the key business decision-making process.

Evolution of industrial IoT

There has been a substantial increase in the implementation of industrial IoT in several industry verticals. Smart connected devices assist manufacturers in enabling M2M communications. Industrial IoT services help industrial manufacturers to design and develop products through real-time information exchange. Enterprises primarily outsource engineering research and development (ER&D) services for product engineering.

“Information that is retrieved through smart connected devices is analyzed and used in design and development phases. Industry verticals such as automotive need innovative technology features, which can help automotive design engineers to build smart and intelligent vehicles such as autonomous cars. IoT has an important role in building intelligent transportation systems,” says Amrita.

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