The Global Lab Automation market is valued at USD XX . XX billion in 2016 and is expected to reach a value of USD XX . XX billion by the end of 2022 , growing at a projected CAGR of XX . XX % during the forecast period of 2017 – 2022 . Automation is used in a wide variety of life science applications ranging from proteomics to systems biology . Automation is a dominant feature in the diagnostics market followed by the discovery and research labs . In clinical diagnostics , where profits are based on the number of samples , high throughput is the core driving factor . Total automation is generally preferred in such labs and manufacturing setups . Research labs and academic institutions are generally opting for modular automation , wherein they reduce the human intervention in tedious and repetitive tasks .
The emergence of robotics has transformed the typical workday for scientists around the world . With the advent of automation techniques , scientists are now able to set up , run and analyze the results of experiments in a fraction of the time they needed in the past . In today ' s world , pulling maximum efficiency out of every department , including the R & D labs , is a major issue .
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Automation of routine laboratory procedures , by the use of dedicated work stations and software to program instruments , allows associate scientists and technicians to think creatively about the implications of their experiments and to design effective follow-up projects or develop alternative approaches to their work instead of spending their days repeating tedious tasks .
Apart from reducing mundane tasks , the market for laboratory automation is also driven by the need for consistency in quality . Because the cost of an error is very high in a scientific paper or developing a drug , even fairly low error rates can have a profound impact on the conclusions you make downstream .
It is believed that taking out the human element helps in achieving more consistency . Great advances have been made in the molding of plastics that has reduced the volumes of reagents used and enabled handling of smaller liquid volumes easy . These advancements have worked in favor of the development of micro and nano-litre plates to fit in with the automated liquid handling equipment .
Validation and data quality play a major role in modern life science . Legal validation , patents , and clinical testing have become crucial issues . Automation enables a much higher
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