Dr. Watson Says: ...The supply chain is a great potential source of new data, but the supply chain team may be letting the data disappear without capturing its value... What Do You Say? Click Here to Send Us Your Comments Click Here to See Reader Feedback

The book Big Data defines Big Data as the universe of data for a given topic—that is, in the past we had to rely on taking samples of data about a topic, and now we just capture the full set of data.  Once we have the universe of data we find correlations that we couldn’t see before.  And, the book goes on to state that having the universe of data can have real economic value.  Some experts are even starting to suggest that basic economic models should add ‘data’ to the list of labor and capital as basic inputs to economic production.

It is clear that companies like Amazon, Google, and Netflix are good at extracting the value from the data they are able to collect.  Some may argue that these firms are not like a traditional supply chain and the lessons from them are not applicable.  But, I think this is a mistake.  The lesson from these companies is that there is a lot of value in collecting and acting on new sets of data.  The supply chain is a great potential source of new data, but the supply chain team may be letting the data disappear without capturing its value.   

Here are three ways where the supply chain may be wasting Big Data:

One, not capturing and analyzing the details of load tendering.  Alex Scott (a Ph.D student in Penn State’s supply chain program and research associate at Opex Analytics) is doing research around Big Data in load tendering.  He is seeing that many firms are not capturing all the details of the load tendering process.  For example, a shipper will offer a load to its primary carrier, give the carrier time to accept the load, and if the carrier rejects the load, move down through the secondary carriers and on to the spot market.  All of this data can easily be captured.  For example, you could see how often and how fast the primary carrier accepts the load, at what price, and under what other market conditions.  You could also see the price paid to secondary carriers and the spot market.  The economic value of this data can be quite large—large shippers can understand and predict carrier behavior and adopt operational measures to counteract, for instance, freight rejections.  Also, they can evaluate when it is preferable to go to the spot market (e.g., when market conditions are loose) versus using backup carriers.  The savings associated with this can be quite large.

Two, not capturing and analyzing log data coming from your equipment.  More than ever, equipment in your factory, warehouses, on your trucks, or at your customer sites has the ability to provide detailed log information from its sensors.  This data is often underutilized.  By capturing and analyzing this data, you are in a better position to spot patterns before the equipment fails and better determine the expected life of various components.  This helps you build better predictive models to prevent a damaging failure or help schedule better preventative maintenance.

Three, not capturing and analyzing your eCommerce data.  The big eCommerce sites, like Amazon, excel at analyzing their on-line data.  But, many other companies with a growing on-line business are not taking advantage of the data on their users to make better recommendations to the users, present better search results, or present better content.  With online transactions, you know the sales history of the customer who is logged in, the click history, and about similar customers.  You can leverage this data to make your customer’s experience better and to increase revenues.

Final Thoughts:

The recent attention to Big Data, no matter whether you think it is over-hyped or justified, has drawn attention to the need to think creatively about collecting new sources of data and extracting value from it.