INDUSTRY SPOTLIGHTS Logistics
There it is in black and white.
Right? One local professor says:
“Maybe not.”
The University of Texas at
Arlington (UTA) is ramping up
new technology this year to
help with the critical decisionmaking, research that will
help businesses make better
decisions all along the supply
chain. Dr. Kay-Yut Chen,
professor in the Information
Systems and Operations
Management Department at
UTA, is a renowned behavioral
and experimental researcher
who will merge the classroom
with the boardroom, showing
how scientific analytics can
be used at every level, from
entry to executive, and improve
decision-making within a firm.
“I always focus on the theme
of human behavior. It has
transformed it all, from how
you run a supply chain from
the tip of your finger -- but
then you cannot get away from
human information and human
decisions. You want a computer
at one point and, at another,
you want human judgment.
The key is understanding
what a computer can do and
what human behavior will do
and where you need to rely
on both.”
Chen explains that any major
company will have a wealth
of data. However, data is just
data. If you have a lot of data
then it’s more difficult to make
a decision. Using statistical
and scientific methods to
understand what that data says
is very important. You have
to look at the whole chain
and understand the system of
supply chain, how components
flow like a network. In some
networks, computers are very
good at handling a component,
like freight, or selling, where
computers can look at
historical data and provide the
information needed to produce
the right amount.
However, with new designs
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Dr. Kay-Yut Chen.
and business-to-business
decisions, historical judgment
cannot always be relied on.
Utilizing behavior operation,
which includes human decision
and knowledge in every
component, is important. The
key is quantification – the
measurement of data and
human data, by measuring
the whole ray of learning and
treating the whole system,
you can meet it and measure
it enough.
Chen also notes that there
is a “sweet spot” in regard to
human purchasing behavior.
What do people want to buy?
How many do you want to
make? How much do people
value each of the features of