Big Data Applied to the Supply Chain

by Michael Watson

This week, Dan Gilmore of Supply Chain Digest wrote about very big data in the supply chain. It was a well-balanced article, covering both the hype and some nice solutions. Here is one quote:

“…For example, Ron Volpe of Kraft said his company (as are others) is working on tying together mountains of data from POS sales, promotional execution, social media and more to better understand their relationships, and if the resulting insight can lead to improvements in the ability to predict and then shape consumer demand.

Kraft did not know if this was possible, he said, but he believed the disadvantage a company in the consumer goods sector would be under from not breaking this code while its competitors did would be so huge that it was a major business risk to not pursue this Big Data effort.”

I was also able to address some of the confusion around the term “big data” in a follow-up article. Some of the confusion seems to be coming form the fact that different people are using the term for different purposes. No matter what your definition is for big data, it is clear that data is being used to drive more decisions. Here are some examples from the article:

• Using detailed ship-to data to better understand customer segments (to gain insight into different supply chain processes needed) and to understand what products ship together (to decide what to store where and where to put items in the warehouse).
• Using detailed truck sensor data to understand fuel efficiency and matching engine types to requirements.
• Using historical demand data along with outside data (like weather, demographics, competition, or the vehicle registration database if you are in the auto aftermarket) to create better forecasts.
• Using data on pricing and promotions to forecast change in demand — change for the promoted items, but also other items that may rise or fall with the promotion.
• Pulling supply chain data from multiple sources to create better cost-to-serve models.

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