Supply Chain Brain - November 2010

problematic to be exclusively relying on the quantitative-model forecasting software.

don’t really have anything to say, we need to rely on quantitative methods. My experience is that that isn’t really the case. Experienced managers who know their industry have a lot of insight. They come to conferences, they hear the buzz, they know what’s happening. So, we really can’t just

SUPPLYCHAINBRAIN EXCLUSIVE

Q: Do you have an example you can
share?
Sanders: In fact, Nike, just a few years
ago, in a highly publicized case—they had
They are objective. They always give you
the same results. They can process tremen-
dous amounts of information and data. We
as humans can’t do that, but they’re only as
good as that data that they are based on.

purchased an i2 software package, highly promoted, but what happened is that after they used the package, six months later they were in court. There was too much slow-moving inventory, not enough of the fast inventory that was needed. When they looked back, what they found is that managers were exclusively relying on the quantitative automated forecasting software and not enough on managers who actually knew about their products.

Q: So are you saying we should rely on
managerial expertise instead?
Sanders: Here is the tricky thing. In
academics, we often say that managers
rely on automated packages. We need to
find a way to incorporate what managers
know, what’s the latest they’ve heard. A
quantitative model or software package
can’t incorporate the very latest, what you
as a manager have just found out in the last
three minutes when you were on the
phone with your vendor.

Q: So it’s both technology and people that make for success?

Sanders: Absolutely. Keep in mind both approaches have their strengths and weaknesses. The trick is to combine them in some kind of way.

Quantitative methods are consistent.

on Monday morning or Friday afternoon, so our forecasting won’t be the same. Our ability to consider a lot of factors isn’t very strong. However, unlike an automated forecasting model, we are privy to a lot of insight into the industry that we’re in, so we need to find a way to harness the strength of both methods. That’s really the way forward in terms of forecasting.