Sunday, 29 January 2017

How to Implement Big Data for Pharmaceutical Supply Chain Management



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

Most pharmaceutical supply chains aren’t set up to jump into a big data implementation plan for one primary reason: the first step toward a functional big data system is data collection. Tagging the drugs with barcodes or RFID-enabled tags and tracking them from production to prescription allows pharmaceutical companies to curb product loss and reduce the number of fake or unsafe drugs on the market. They can collect all the data necessary to build the foundations for a big data and analytics network.

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.

With supply chain analytics and a stable big data system at their command, supply chain managers can predict with relative accuracy where problems will occur. This enables them to move from a reactive model to a predictive one, preventing supply chain interruptions. Predictions aren’t perfect, but they become more accurate as the system collects more data. Then, the predictions become more accurate and useful to supply managers. 

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.


Big data is changing the way we look at information. For pharmaceutical supply chains, in particular, it may change the way we keep track of our products. Supply chain analytics for pharmaceuticals is a relatively new field, but it may not be optional for long.