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SCDigest Expert Insight: Supply Chain by Design

About the Author

Dr. Michael Watson, one of the industry’s foremost experts on supply chain network design and advanced analytics, is a columnist and subject matter expert (SME) for Supply Chain Digest.

Dr. Watson, of Northwestern University, was the lead author of the just released book Supply Chain Network Design, co-authored with Sara Lewis, Peter Cacioppi, and Jay Jayaraman, all of IBM. (See Supply Chain Network Design – the Book.)

Prior to his current role at Northwestern, Watson was a key manager in IBM's network optimization group. In addition to his roles at IBM and now at Northwestern, Watson is director of The Optimization and Analytics Group.

By Dr. Michael Watson

November 4, 2014

Three Ways the Supply Chain Wastes Big Data

Big Data can have Real Economic Value and Supply Chains Often Miss the Chance to Capture this Value

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?

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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.

Previous Columns by Dr. Watson

The Three Use Cases for Data Scientists

Learn Python, PuLP, Jupyter Notebooks, and Network Design

EOQ Model and the Hidden Costs of Fixed Costs

CSCMP Edge - Nike Quote: "It is All an Art Project Until you Get it on Someone's Feet"

Supply Chain by Design: Why Business Leaders should think of AI as an Umbrella Term


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.

Recent Feedback

I continue to see evidence to support the notion that simply not everyone sees the potential for application.  The data is there but because many do not consider themselves to "tech", "scientific" or "strategic" enough, they do not see necessarily understand the potential value of the information.  They seem to lack the ability to translate the "bigness" of the data because they simply do not think that way. 

Nov, 09 2014

I agree that Big Data is extremely important for decision-making in the supply chain field. I wouldn't say that SC hasn't been utilizing big data at all, but I do agree that the information passed on from the MIS field should be applied to the SC areas. Many larger companies can afford to hire supply chain specialists to analyze the past orders or purchases for their company and are able to manipulate and use the historical data to implement better sourcing or logistical decisions, or they're able to analyze the data of shipping / freights as you suggested. Using the data from equipment and analyzing ecommerce data is important as well.

Using the big data, SC will be able to predict the trends in the market as well as make more informed choices with the extra data available. 

Of course the larger companies would be able to afford to hire more employees in order to make sense of the collected data, but I wonder if these kinds of data analytics would be feasible by the smaller or even medium-sized companies. Do you think companies will start developing more software programs to help supply chain departments of any size, analyze the big data?

Stephanie Kao
The University of Texas at Austin
Nov, 11 2014