This is a touchy subject, but want to start this thread and see if we get any hits.
There is considerable time spent where scientists and automation meet and iterate to “automating there assay”
Usually people develop their own like checklist of questions to ask when in this discussion phases from experience gained over the years.
So I ask you, what’s your checklist. What’s your ace in the hole of preliminary questions you like to ask to help you down the line when it comes to automating!
In my experience, automation in general is a nice idea, but best implemented in conditions where the method can sit and be happy for multiple months. The more complicated the answers to the above questions, the less likely I feel automation is the answer.
Why: Just define the goal… if this is automation for the sake of laziness; no dice. If this is to improve a function, what are we looking to improve → is this a realistic expectation?
Throughput: There is a breakeven point at which automation is worthwhile. This changes depending on what the process is, but typically I reserve automation for projects where the automation can be supported by more than “walk-away” time. For things like NAP, I generally concider 16 samples per run as the breakeven point, above which I expect processing time and tedium to affect the results (human error).
What’s the timeline: Similar to why, I like to ask “Is this even worthwhile?” If there are 5000 samples to run and its due in a month… what is the point of automation? Honestly any sort of discussion into the possibility of automation usually evolves into a reality check for both parties involved.
Couple of additions:
What does are the plans for data analysis / processing / storage and is that part of the scope of the project?
Then including some workshopping for the success/validation criteria. I think this overlaps with “why” but defining what success of the project looks like is super important to making sure everyone is happy with the completed process.
More meta: I wonder if we should strive to have these conversations while building new organizations. Perhaps communication is ideal when the automation engineer is the scientist?
That employee can be extremely rare to find though!