In other words, you can stuff all the variables you want in there, but you will likely still have error - especially with complex systems (regime switches, etc.) - that you did not put in your model. I think economists tried the "model stuffing" technique, and I'm not sure how much it helped in planning something as complex as the economy.
Resiliency isn’t invincibility.
People and companies do build in buffers, because efficiency != best-case efficiency. For example: https://en.wikipedia.org/wiki/Bus_factor If you have a meeting at 11:00, you won't plan to reach the office at 10:59. Personally I plan to reach by 10:30 so that I don't have to spend time worrying about being late, which is a waste of mental capacity. For me that's maximally efficient. So I think your definition of efficiency is wrong, if you get what I'm trying to say.
Even with this broader idea of efficiency, the drive for efficiency can cause trouble. How does one define maximally efficient? Do you optimize to handle cases that occur once a month, or once a year, or a decade, or century? Short-term profit makes it hard to optimize for these rarer cases, because you’ll look less efficient for a long time, and the average executive will have moved on before disaster strikes.
Going back to your example of a flight that's canceled, the airline should account for it. One simple way is to multiply the cost of that event by its probability. (And multiply it 5x or something, since passengers will be angrier at having an hour of their time wasted than happier at having an hour saved.) When you look at it with this framework, it makes sense why a pilot doesn't skip his checklist because the cost is very high, even if it's a 1 in 1000 case he's trying to prevent. In other words, it's not just how often it occurs, as you suggested.
Another example is how Bill Gates, in the early days of Microsoft, kept a year's worth of salary in the bank at all times. And how just-in-time supply chains broke when the 2008 financial crisis hit. Another way I heard this stated is that the rich are not the people with the most money, but with the most options. Or, hypothetically, a company with 100 engineers who builds iOS and Android native apps dedicating one engineer to building a hybrid app in Flutter because wasting 1% of engineering is time is better than betting the entire company in a wrong direction, thus wasting 100 engineers' time, though the "plan B" engineer's work is unlikely to yield a launchable app.
You also rightly point out issues like short-termism and misaligned incentives, but those have been discussed many times already, so I'm not going there.
For example, consider an airline. Maximum efficiency would involve every airworthy plane being fully occupied making flights that are 100% full. Ok, sounds good. Now, one of your planes hits a sign on takeoff and has to return to the airport and be grounded for repairs. How do you deal with all of these passengers who expect to get to their destination? You can't, your incredible efficiency means you’re fucked when something goes wrong. Now imagine that each of those people is part of some other enterprise that’s 100% efficient. The failure will cascade and before long, society’s only concern for efficiency will be about how to most efficiently crack each other’s heads open and feast on the goo inside.