Research and Development in Engineering

R&D rant. Don't shoot the messenger. 🙈 The federal government is good at R. It sucks at D. 😫 R, which stands for research, reduces the scientific and technical risks. We're figuring out if something is theoretically possible. That is done by amazing research scientists who focus on novelty, and pushing the boundaries of what we know about the world. D, which stands for development, is "systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products or processes or to improving existing products or processes." D reduces the production risks. This is preparing for scale by taking research and turning it into product. That is why it includes "construction of prototypes and operation of pilot plants." The focus is on HOW to produce, now that they've figured out what to produce. The 🇺🇸 Federal Government is still the largest funder of R in the world. When people talk about Big Tech dramatically outspending the government in R&D, they're talking about D. Look at the ratios in the chart below. Green is D. Notice anything? America's federally-funded research infrastructure is the best in the world. Imagine what would happen if our development infrastructure was also the best in the world. More R would make it across the valley of death, extending our technological advantages of others, deterring conflict and spurring development. R&D definitions from National Science Foundation (NSF): https://lnkd.in/g3WyZtum R&D outlays from SCIENCE COALITION INC: https://lnkd.in/gREkvzy2

It’s right there in the name: Research AND Development. If you haven’t tested anything in six months, you’re not doing R&D — you’re doing...something else. Real R&D is never finished. It’s a cycle — research, test, learn, adjust, repeat.  Companies that get R&D right know that: 1️- "Data" isn’t the same as "good data." Sometimes companies ask their own survey questions, collect responses, and think they’ve got usable insights, but none of it holds up. Good R&D means using validated assessments or designing research with scientific rigor. (And no, you don’t have to embed it into your product to do it right. There are faster, better ways.) 2. Iteration is basically the whole point. Companies can’t hesitate to retest or tweak without slowing their own growth. The fastest-growing companies are the ones that run small tests constantly, learn from them, and make changes in real-time (or are, dare I say, “agile.”). 3️. What customers say and what they do are not the same thing. People will say they "want this feature" or "like that design," but their behavior can say otherwise. Companies that combine qualitative research (what people say) with quantitative data (what people do) have a much clearer path forward. If your team has been running on six-month-old data, you should probably revisit the "R" in R&D. #researchanddevelopment #growthstrategy #fractionalcso

New pod episode with Jeremy Ridley, Senior Director of Engineering at Delve! He shares his engineering and development cheat codes you can't miss. Some of my favorite insights from this pod: → How Delve harmonizes hardware project arcs and software sprints → Budgeting for iteration cycles in development without blowing timelines → The FDA interaction strategy to avoid expensive design pivots and rework → Viewing regulatory requirements as innovation scaffolding vs. roadblocks → The future of AI/ML fueled simulations for FEA, biocomp, and human factors One of my favorite parts was unpacking the AI driven Dunning-Kruger trap that founders are falling into during product development (and how to combat it). If you're an engineer or product builder wanting the latest tactics and best practice, Jeremy gives a masterclass in product design and development. Link to the full episode in the comments below! #medtech #innovation #engineering #productdesign #productdevelopment #startups #medicaldevice #hardware

Introducing AI’s long-lost twin: Engineered intelligence by Brian Evergreen & Kence Anderson "There’s something missing With most scientific disciplines, breakthroughs are made in laboratories, then handed off to engineers to turn into real-world applications. When a team of chemical researchers discover a new way to form an adhesive bond, that discovery is handed over to chemical engineers to engineer products and solutions. Breakthroughs from mechanical physicists are transitioned to mechanical engineers to engineer solutions. When a breakthrough is made in AI, however, there is no distinct discipline for applied artificial intelligence, leading to organizations investing in hiring data scientists who earned their PhD with the aspiration of making scientific breakthroughs in the field of AI to instead try to engineer real-world solutions. The result? 87% of AI projects fail. Enter engineered intelligence “Engineered intelligence” (present participle: “intelligence engineering”) is an emerging discipline focused on real-world application of AI research rooted in engineering — the discipline of leveraging breakthroughs in science together with raw materials to design and build safe, practical value. This creates the capability for domain experts, scientists and engineers to create intelligence solutions without needing to become data scientists. The intelligence engineering approach for introducing engineered intelligence is: - Create a heatmap of the expertise across your existing processes; - Assess which expertise is most valuable to the organization and score the abundance or scarcity of that expertise; - Choose the top five most valuable and scarce expertise areas in your organization; - Analyze for ROI, feasibility, cost and timeline to engineer intelligent solutions; - Choose a subset of value cases and invest in execution." https://lnkd.in/e27xC3Bq