12 Ideas You Should Know From: 101 Things I Learned in Engineering School by Matthew Frederick and John Kuprenas
/The 12 Big Ideas:
To solve a problem, start with something you are familiar with and build from there
Favor accuracy over precision, especially early on in a project or new endeavor. Don’t be a perfectionist when it doesn’t matter
Understand trade-offs. There’s always a cost. Make sure you understand it. It may not be obvious or intuitive. But done pretend trade-offs don’t exist
Understand when physical laws no longer apply. Nature, humans, complex systems, etc will not follow pre-determined paths
Quantify whenever possible, but don’t make it the only thing you do
Another trade-off: no system is perfect. You can’t ignore all the flaws, nor can you find all the flaws. It’s a balance
It’s important to be patient in the early parts of a new commitment/project/idea. It’s early on when small decisions can result in big impacts – both on the negative and positive
Law of Diminishing Marginal Returns – whether an improvement drive a small or large increase in value depends on the condition of what is being improved. Look for the low-hanging fruit
Understand when multi-tasking works and when it fails
Be careful about adding size, complexity, components, features, etc, without understanding the often overlooked side-effects. There’s always a trade-off
Measurement is important to accurately track performance and judge results
The most important thing is to keep the most important thing the most important thing
My Highlights From the Book:
To solve a problem, start with something you are familiar with and build from there:
Every problem is built on familiar principles. Every problem has embedded in it a “hook”—a familiar, elemental concept of statics, physics, or mathematics. When overwhelmed by a complex problem, identify those aspects of it that can be grasped with familiar principles and tools. This may be done either intuitively or methodically, as long as the tools you ultimately use to solve the problem are scientifically sound. Working from the familiar will either point down the path to a solution, or it will suggest the new tools and understandings that need to be developed.
Favor accuracy over precision, especially early on in a project or new endeavor. Don’t be a perfectionist when it doesn’t matter:
Accuracy is the absence of error; precision is the level of detail. Effective problem solving requires always being accurate, but being only as precise as is helpful at a given stage of problem solving. Early in the problem solving process, accurate but imprecise methods, rather than very exact methods, will allow consideration of all reasonable approaches and minimize the tracking of needlessly detailed data.
Understand trade-offs. There’s always a cost. Make sure you understand it. It may not be obvious or intuitive. But done pretend trade-offs don’t exist:
Lightness versus strength, response time versus noise, quality versus cost, responsive handling versus soft ride, speed of measurement versus accuracy of measurement, design time versus design quality… it is impossible to maximize the response to every design consideration. Good design is not maximization of every response nor even compromise among them; it’s optimization among alternatives.
Understand when physical laws no longer apply. Nature, humans, complex systems, etc will not follow pre-determined paths:
Engineering follows the laws of science, but nature does not. As a system of understanding created by humans, science is contained within reality. Nature follows itself; science is our remarkable but imperfect attempt to explain it. Quantification is exact not unto reality, but unto itself.
Quantify whenever possible, but don’t make it the only thing you do:
You don’t fully understand something until you quantify it. But you understand nothing at all if all you do is quantify.
Another trade-off: no system is perfect. You can’t ignore all the flaws, nor can you find all the flaws. It’s a balance:
More inspections and fewer inspections both produce more errors. Inspection occasionally rejects a good item or fails to identify a defective item. A false positive error has little consequence other than the cost of replacing the item. But a false negative error can have great consequence, as the item may fail after being placed in service. More inspections are not necessarily the answer, however. Statistically, the addition of an infinite number of layers of inspection will cause nearly every item to be found defective for some reason. The optimal level of inspection balances the economics of replacing false positives with the human and moral consequences of failing to detect real errors.
It’s important to be patient in the early parts of a new commitment/project/idea. It’s early on when small decisions can result in big impacts – both on the negative and positive:
Decisions made just days or weeks into a project—assumptions of end-user needs, commitments to a schedule, the size and shape of a building footprint, and so on—have the most significant impact on design, feasibility, and cost. As decisions are made later and later in the design process, their influence decreases. Minor cost savings sometimes can be realized through value engineering in the latter stages of design, but the biggest cost factors are embedded at the outset in a project’s DNA.
Enlarge the problem space. Almost every problem is larger than it initially appears. Explore and enlarge it at the outset—not to make more work, but because the scope of the problem almost certainly will creep—it will grow larger—on its own. It’s easier to reduce the problem space later in the process than to enlarge it after starting down a path toward an inadequate solution.
Law of Diminishing Marginal Returns – whether an improvement drive a small or large increase in value depends on the condition of what is being improved. Look for the low-hanging fruit:
Customers notice and are willing to pay for improvements to low quality products more than high quality products. A 10% improvement to a low quality product will lend more than a 10% increase in the value of quality—the user’s perception of its quality. But as subsequent improvements are made, they add value at a decreasing rate. If a 10% quality improvement costs $10, a 20% improvement will cost more than $20. Eventually, the cost of improving quality increases at a faster rate than the improvement will be perceived. The optimal quality-cost state theoretically occurs when the slopes of the value and cost curves are equal. At this point, the rate of improving a product equals the rate at which costs to the producer will increase. Beyond this point, the producer’s cost for providing one more unit of quality will exceed the value the customer will perceive.
Understand when multi-tasking works and when it fails:
Be careful when asking a part to do more than one thing. It may seem desirable to minimize effort, material, and time by having one feature or part serve multiple purposes. However, this depends on the level of skill and care that can be expected during application. The greater the sophistication of the end user and the more controlled the user’s environment, the more one may rely on multi-functionality. But where an error would be catastrophic, it is usually better to have each part serve only one purpose.
Be careful about adding size, complexity, components, features, etc, without understanding the often overlooked side-effects. There’s always a trade-off:
A successful system won’t necessarily work at a different scale. An imaginary team of engineers sought to build a “super-horse” that would be twice as tall as a normal horse. When they created it, they discovered it to be a troubled, inefficient beast. Not only was it two times the height of a normal horse, it was twice as wide and twice as long, resulting in an overall mass eight times greater than normal. But the cross sectional area of its veins and arteries was only four times that of a normal horse, calling for its heart to work twice as hard. The surface area of its feet was four times that of a normal horse, but each foot had to support twice the weight per unit of surface area compared to a normal horse. Ultimately, the sickly animal had to be put down
Measurement is important to accurately track performance and judge results:
“The most important thing is to keep the most important thing the most important thing.” —DONALD P. CODUTO, Foundation Design