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Standard solutions allow for a greater level of design and refinement – if a solution is going to be used multiple times then the benefit of good design is multiplied and amplified.
It requires a change in the skill sets required in laboratories: scientists who develop skills in equipment engineering and coding or hardware and software engineers who develop skills in science.The current education system does not produce cross-fertilised disciplines (although skills like coding are becoming more endemic in the cohorts entering the workforce today).. Perhaps a more pressing problem is the fact that the new workforce of the 2020s is not keen to travel into an office or laboratory to work, preferring working remotely.. For research work and smaller more specialised laboratories, the automation story is different.
Without the scale, the investment in robotised systems against simple improvements in efficiency does not add up.The released value of scientists being freed up to spend more time analysing, discussing, collaborating and thinking is not well quantified.Islands of automation may be seen as investable to allow new science.. Added to this there is not a joined-up ecosystem that looks at today’s smallscale testing as tomorrow’s large-scale roll-out, meaning that testing protocols are often developed in ways that inhibit or slow future automation.. Change, adaptation and flexibility.
A combination of emerging factors drive change in the activities and operations in a laboratory function..In research laboratories, the work and team can change rapidly as new discoveries are made, equipment and technologies change, and there is the need to reduce or cease some operations while others are expanded..
The people and skills change and there are changes in social and environmental demands.
This uncertainty drives a need for a combination of flexibility and adaptability..The first is a web app to accelerate the design of precision manufactured housing in London for the Greater London Authority (GLA).
It allows users to create intelligent models within a 3D context, based on government space standards and DfMA rationale.Adaptability and feedback on the suitability of certain DfMA construction systems are just some of its features.
Cresser-Brown notes that because designs are generated at speed, it allows users to ‘make objective decisions based on far greater exploratory studies than possible using traditional feasibility workflows.’.Phil Langley points out that ‘the app encodes... ‘best practice’ design guidance from across the manufacturing and systemisation industry and will be made freely available and open source.’.