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A wealth of data about a business
comes from the supply chain. But,
while the information is there,
companies are not yet capitalizing
on its real value as a source of
insight. Data is capable of shaping
the very future of the enterprise but
opportunities are currently being
missed. Most companies are sitting
upon a goldmine of untapped supply
chain data that has the ability to give
their organisations a competitive edge.
Supply chain data already runs the
day-to-day flow of goods around the
world and a small group of trailblazing
companies are utilizing it as a predictive
tool for accurate forecasting. These
companies are not making drastic and
sudden changes – they are slowly
realising that they are not restricted to
running their business by looking in
the rear-view mirror. By employing new
technologies and harnessing the power
of data-driven insights, companies
are starting to anticipate and even
predict the future, to get ahead of their
competitors and direct their global
operations accordingly. Here are three
ways that supply chain data science
can make a difference to your business:
1. Descriptive data:
Descriptive
analytics comprise business
intelligence systems, such as supply
chain dashboards and scorecards,
and enable ad hoc queries. They
also include data visualization and
geographic mapping, which help tell
a story with the data. Utilizing these
tools, companies can manage the
day-to-day operation of their supply
chain to become more agile and
cost-effective.
2. Internal data for prediction:
If you
are responsible for your company’s
smartphone sales in the United
States, for example, with good data
analytics you get up on Monday
morning, switch on your tablet
and see real-time analysis of your
sales in every state, along with
your shipping costs and your pain
points (e.g. lagging sales). Based
on this information, you dynamically
adjust your production schedule, the
marketing budget, sales promotions,
inventory position, stock locations
and transportation routing – and
intelligently decide to back off in
one area and ramp up in another.
As a result, you capture sales, avoid
excess inventory, improve service,
shrink product obsolescence and
improve your bottom line.
3. External data for prediction:
External sources incorporate
everything from news feeds and
weather predictions to social
media. You need to be able to
look not just at real-time sales but
also at industry trends, and even
what’s going on in the news for ‘X’
celebrity who wears a red headset
to an event. You need to follow and
mine social media for that celebrity,
because now everyone wants a
new headset in red, and we need to
immediately adjust our production
and distribution to capture that
opportunity.
Companies that are taking supply
chain data science and its potential
seriously are ahead of the curve in
two ways. They are innovating their
business operations and evolving as
an organisation to something more
advanced and superior than the
competition. They are also ahead of
the curve in terms of their operations
– they are not running their business
retrospectively. They are predicting
future trends and capitalising on them.
This marks a new era of business
operations entirely. The foundation
for this shift comes from the supply
chain and in today’s technology driven
environment, companies can have a
true 360-degree cockpit view of their
entire operations. This is not about
having a better supply chain. This is
about having a smarter enterprise.
Using Data Science
Your untapped supply chain data could be an invaluable predictive tool, says Gary Keatings of DHL.
16
Logistics Business Magazine | February 2016
DATA ANALYTICS