Collaboration between scientists and engineers requires acknowledging their different mindsets. Scientists operate with a 'freedom of thought' to prove a novel concept works once. Manufacturing engineers must translate that concept into a robust process that works consistently every time.
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The most effective innovators combine two seemingly contradictory traits: a boundless imagination to envision novel solutions and a ruthless pragmatism that rejects ideas that can't be translated into reality. One without the other leads to either fantasy or stagnation.
In complex systems (e.g., electromechanical devices with software), problems often arise not within a single discipline but in the interactions between them. Engineers must adopt a systems-level view to anticipate and address these "undefined requirements" where different components intersect.
Engineering often defaults to a 'project mindset,' focusing on churning out features and measuring velocity. True alignment with product requires a 'product mindset,' which prioritizes understanding the customer and tracking the value being delivered, not just the output.
The common product development process is a sequential handoff model. A better approach is a "jazz band" model where cross-functional teams collaborate harmoniously from the start. This fosters creativity and reduces rework by including engineers in early ideation, rather than treating them as a final step.
Transitioning a biotech from discovery to development is not just a scientific step but a cultural one. According to Ron Cooper, it requires moving from a flexible "innovation and ideation culture" to a rigorous "engineering culture" focused on process and precision in areas like clinical trials and large-scale manufacturing.
To ensure a smooth transition from development to production, an operations or manufacturing SME must be part of the design process from the start. Otherwise, products are developed without manufacturability in mind, leading to expensive, reactive fixes and subjective quality control during scale-up.
A 'healthy tension' exists between research teams, who want to continually iterate on a therapy's design, and manufacturing teams, who need a finalized process to scale production for trials. Knowing precisely when to 'lock down' the design is a critical, yet difficult, decision point for successful commercialization.
The idea for a living computer came not from biologists, but from engineers with backgrounds in signal processing. This highlights how breakthrough innovations often occur at the intersection of disciplines, where outsiders can reframe a problem from a fresh perspective.
Unconventional AI operates as a "practical research lab" by explicitly deferring manufacturing constraints during initial innovation. The focus is purely on establishing "existence proofs" for new ideas, preventing premature optimization from killing potentially transformative but difficult-to-build concepts.
Technical tools are secondary to building a successful design system. The primary barrier is a lack of shared vision. Success requires designers to think about engineering constraints and engineers to understand UX intent, creating an empathetic, symbiotic relationship that underpins the entire system.
