In his article “The Systems Engineering Mindset, Problem Solving and Critical Thinking,” James Lackey makes the case that the principles of Systems Engineering can and should be practiced by everyone. In this article I’d like to expand on his point by taking a Systems Engineering tool and applying it to everyday decision making: the trade study.
A trade study is method of tabulating pros and cons of options with the goal of making a decision easier. While used extensively in engineering, trade studies are useful for anyone who has to make a decision based on the available data, or has been tasked to provide a recommendation to their boss.
Here’s an example: what to have for dinner. Suppose my two choices are lasagna or grilled chicken. First, identify what factors should be considered for the trade. Choose factors that are quantifiable. For this example I’ll use cooking time, calories and tastiness. Cooking time is quantifiable, since any cook book will tell you that it takes longer to cook lasagna than grilled chicken. Calories is also quantifiable – a quick search on the internet will show that lasagna has more calories than grilled chicken. Tastiness is more subjective, since different people might prefer chicken than lasagna. But since this trade study is for me, and I prefer lasagna, I’ll keep it.
After conducting the research, I tabulate the results. I put a plus sign to show the winner for each factor and an extra row at the bottom which sums up the number of pluses:
Result: If I have the time and don’t mind the calories, I’ll have lasagna. Otherwise, chicken it is.
Of course, you probably don’t need a table for this example. Or more accurately, you probably do this intuitively without the need for a table. But how about if there are dozens of factors to be considered? And more than two options? Suppose I’m in the market for a house. I’ve narrowed my choices to three houses and now I need to determine which to purchase. What factors might be considered? Cost is quantifiable, as is proximity to good schools, square footage, and age of the house. Like the dinner trade study, subjective factors might weigh in as well. It’s hard to quantify floor layout or esthetic, but that certainly is a factor.
My research shows that house 1 is the most expensive, followed by house 2 and then house 3. House 3 is closest to a good school, while house 1 is farthest. The research continues for the objective factors. Esthetic is subjective, and I decide I like house 2 the best and house 1 the least.
Now it’s time for the table. Since there are three options I’ll rank each on a scale of 1-3, with 3 being the best (lowest cost, closest to school, most square footage, newest and nicest, respectively).
Result: Based on the available data and esthetic choices, house 2 is the clear winner.
Frequently some factors are more important than others. The factors can be weighted, but that’s beyond the scope of this article.
Esthetic factors aren’t considered for purely cost-based decisions. Manufacturers selecting an integrated chip should not factor the color of the chip in their decision without justification.
The trade study is a very useful tool for a Systems Engineer. I’ve used it for everything from deciding which GPS receiver to use on a satellite to purchasing my own house (yes, I did include floor layout as a decision factor).
The trade study – add it to your problem solving tool box.