Big data and predictive analytics are
quickly changing the way most industries do business. By collecting all
industry-related data, even the little pieces of data that might seem insignificant,
companies can predict trends, find patterns and gain an edge in what might be the
most competitive field we’ve ever seen. Pharmaceutical supply chains process
massive amounts of data every day and can benefit from tools that big data
provides. If you’re managing a supply chain, how can you implement big data to
assist in your daily tasks?
Special guest post by Megan Ray Nichols
Supply
Chain Analytics
Big data provides predictive analytics. It
utilizes algorithms to make sense of the enormous amount of collected data and use
it to make predictions about future events. Individuals and businesses can
make informed decisions about supply chains, purchases, sales and other related
factors using the predictions.
Supply chains have almost always been driven
by a form of analytics, based on past experience and a variety of performance
indicators. And with the implementation of big data, supply
chain managers can make quantifiable predictions which they can later act
upon rather than making changes to the chain after the fact.
First
Steps
Pharmaceutical
supply chains are complicated, as they’re rife with multiple distribution
levels and regulations. In many ways, it’s like a Jenga tower. If one piece is
moved wrong, the whole thing could topple over. This is why big data will be
one of the most vital tools in the industry.
Predict
Not React
Even when you consider all of the
information that can be collected after-the-fact in regards to pharmaceutical
supply chains, managers are left reacting to situations that arise and often
scrambling to fix problems before they interrupt the supply chain.
Implementation
Besides collecting as much information as
possible, you should remember to:
·
Involve
everyone. Big data is not something that you can
simply drop on your crew and expect it to work.
It will require a concerted effort from everyone — the workers up to the
CEO.
·
Invest
in quality hardware. Don’t skimp on the
hardware that will handle your big data system. DIY big data systems can be built using open-sourced software like
Hadoop, but if the device can’t stand up to the load, your hard work goes
down the virtual tubes.
·
Ask
for help. If you’re not technologically inclined,
you will need more than a YouTube tutorial. Look into big data consulting firms
to help you get set up. Consider hiring someone specifically to maintain your
big data system.
No comments:
Post a comment
Pharmaceutical Microbiology Resources