she had just begun to explore graduate
programs in O.R.
I tried to talk her out of it.
Bear in mind that some years ago, I
had been in the same place that these current St. Olaf students now were. Blessed
with a lot of good choices, I had chosen
to go forth to study operations research.
In fact, so too did my college classmates
Hai Chu and Karen Donohue, and I am
grateful to have had the chance to bask
in their (reflected) professional success
in operations research.
So why would I advise these youngsters not to follow in our glorious footsteps? Let’s start with some important
specifics. First of all, for both of these
students, the decision to go to graduate
school seemed to serve many purposes:
an opportunity to challenge themselves;
a chance to improve prospects for both
financially and intellectually rewarding
careers; and a socially acceptable path
with parents, peers and professors.
Also, from our conversations, it appeared that both had been initially attracted to operations research by my friend
Steve McKelvey, a professor who has
been inspiring Olaf math majors since my
own student days. Finally, it was quickly
apparent to me that the primary motivation was the chance to meaningfully apply their (current and future) skills, rather
than any particular passion for O.R. itself.
A NA L Y T I C S
Graduate programs in operations research certainly have many virtues, and I
will always be deeply indebted to the one
that took me in. There will always be some
for whom this is a clear and obvious right
next step, students who are passionate
about the methods and hungry to learn
more about them. Yet for the generally
quantitatively strong undergraduate who
is interested in applying her technical
skills within the business world, my postcollegiate recommendations are based
on a few simple premises:
1. The business world is increasingly
data-rich, but one must have the ability
to sort that data out before any of this
analysis can take place. This means getting comfortable with programming and
data preparation, which we know typically takes up more than 50 percent of the
time on most “real-world” projects.
2. Optimization is great, but really
good answers quickly are actually better,
especially if the environment is rapidly
changing or the objective itself lacks a
well-defined functional form.
3. You often can’t optimize a system
without first predicting future demand,
and that forecasting is itself a significant
challenge.
4. You are unlikely to do any of this great
work totally on your own, so developing the
skills needed to work effectively with others
is too important to be left to chance.
M A Y / J U N E 2 014
|
13