Lab Automation
References 1 “The new hire: How a new generation of robots is transforming manufacturing”. Price Waterhouse Coopers, September 2014. 2 Robots. Macquarie Research. September 2016. 3 IFRWorld Robotics 2016. 4 Ales Vysocky, Petr Novak, Human – Robot Collaboration in Industry, MM Science, June 2016. 5 The Future of Employment: How Susceptible are Jobs to Computerisation? Carl Benedikt Frey and Michael A. Osborne, September 2013. 6 The Impacts of Robots on Employment, IFR April, 2017. 7 Design and Control of Warehouse Order Picking: a literature review, René de Koster, Tho Le-Duc and Kees Jan Roodbergen, January 2006. 8
http://news.berkeley.edu/ 2010/04/02/robot/. 9 Robotics in Logistics, DHL Trend Research, March 2016. 10 IBID. 11 “The new hire: How a new generation of robots is transforming manufacturing”. Price Waterhouse Coopers, September 2014. 12 The Robotics Market – Figures and Forecasts, The Boston Consulting Group, September 2016. 13
https://robotenomics.com/ 2016/01/11/the-facts-about-co- bot-robot-sales/. 14 Zhang, Andie. “Collaborative Robots”. EU VPC presentation, April 2017. 15 The Robotics Market – Figures and Forecasts, The Boston Consulting Group, September 2016.
The coming business impact of collaborative robots in the drug discovery space is clear. Cobots will reduce the unit capital costs required to bring automation to processes (less guarding, sharing of automation through mobility and higher uptime during workflows), expand the reach of automa- tion into scientific areas previously not possible, and overall reduce drug discovery costs. We believe the penetration of automation, led by collaborative robots, into both clinical and research lab areas will be very significant over the next 10 years. Beyond the general trends across all industries, labs are prime candidates for automa- tion penetration through robot collaboration. They are highly structured environments. They benefit from precision and repeatability. They require data capture and, at times, traceability. They are expensive environments with a high level of repetitive work. All of these are key criterion for driving suitability for collaborative robots. Drug discovery costs are substantial, and automa- tion can help. As the next generation of robotics continues to develop, we believe human-robot col- laboration can play a major role in reducing the overall discovery cost through increasing productiv- ity, quality and yields in labs. Ultimately, collabora- tive robots can help scientists be scientists by both eliminating their need to manage or control automa- tion and by removing the tedious and repetitive aspects of lab work and letting them drive research forward as only a human can.
DDW
Peter Harris is the CEO of HighRes Biosolutions. He has previously held a range of other positions, most recently Vice-President and Managing Director of Axel Johnson, Inc. Prior to joining Axel Johnson, he spent 15 years at Cadence, Inc, a high-technology medical device manufacturing and engineering firm enabling medical companies to bring better devices to market faster, and was
President & CEO for most of that period. Peter has been a Visiting Executive Lecturer at the Darden School of Business at the University of Virginia for more than 10 years and holds an AB in Mathematics from Middlebury College and an MBA from the University of Virginia.
Advanced Cell Diagnostics Agilent Technologies, Inc Analytik Jena AG
BioTek Instruments, Inc BMG Labtech GmbH
Charles River Laboratories, Inc
9,11
16,63 28 38
21,51 23-25
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Essen Bioscience, Inc
Eurofins Pharma Discovery Services IntelliCyt Corporation Labcyte, Inc
PerkinElmer, Inc Select Biosciences Ltd
6 3
32
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SLAS Source Bioscience Taconic Biosciences, Inc Wagner Medizin und Pharmatechnik
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Drug Discovery World Fall 2017
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