tions. This can be anything from creating better forecasts
to predicting when a machine will break down, and from
estimating your chances of having to go to the spot market
for transportation capacity to predicting which products
your customers will likely buy.
Finally, prescriptive analytics refers to using your data and
your predictions to make recommendations on what action
to take. Prescriptive analytics is most often associated with
optimization technology. In supply chains, optimization
technology is commonly used to help managers decide such
matters as how many facilities they should have and where
they should be located, how to best route trucks, and how
to schedule warehouse or factory operations.
When these three definitions are presented, they often are
ranked in terms of their degree of complexity and strategic
importance. Descriptive analytics usually sits at the bottom
because it is considered to be the easiest to implement and
to provide the least amount of strategic value. Predictive
analytics is next, as it is a little more difficult to implement
but brings more benefits. And prescriptive analytics usually
sits on top, because it is the most complicated to implement
and provides the greatest value.
Not everyone agrees with this ranking. A descriptive
analytics project can be very complex to implement—it can
be difficult to clearly describe a large, complex global busi-
ness. Moreover, giving an entire management team a clear
picture of the organization may lead to significant strate-
gic change. In contrast, it may be very easy to implement
prescriptive analytics for truck routing, say, and while the
savings may be nice, it won’t lead to a significant strategy
shift. Instead of ranking the types of analytics, you should
think of each as having its own place and realize that each
company or organization will value different types of ana-
lytics at different times and places.
As a supply chain manager, it is important for you to
understand the three areas of analytics so you can make
sure you are properly addressing each one. Rather than
have a strategy for just one type of analytics, it’s necessary
to have a strategy for each. For example, do you have good
descriptive analytics in place to understand your sup-
ply chain? Are you using predictive analytics to forecast
demand and machine failures? Are you using prescriptive
analytics to determine where to make products, where to
locate facilities, and how to schedule resources?
Knowing these three definitions will also help you assess
proposals for analytics projects. The people presenting
these projects may not fully define what type of analytics
they are promoting. The definitions presented here will give
you a framework for determining exactly what the project
will do and how it fits into your overall analytics strategy.
