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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

May 12, 2015

Using Transactional Data to Estimate Truckload Market Conditions in Near-Real-Time

This is a guest post from Alex Scott, Ph.D. candidate at Penn State and Senior Research Associate at Opex Analytics

Dr. Watson Says:

...those who can best utilize their resources and overcome risks will stay in power in the same day delivery business...
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A trend in analytics is the use of data to make better decisions.  This applies to the supply chain as well.  In this article, Alex Scott highlights how data from the transportation market could impact the freight market.

By Alex Scott

What is the status of the truckload transportation market?  Does demand outstrip supply?  Does supply outstrip demand?  Is the market relatively balanced?  These are hard questions to answer because, unlike commodities such as corn or beef, there is no centralized exchange for truckload transportation.

So if a shipper wonders why their contracted carriers are rejecting their freight, it’s hard to ascertain whether it is truly a “capacity issue” or whether their carriers are actually servicing other shippers’ freight (who may be paying more) instead of their own.  Or a carrier may wonder, should I allocate all of my capacity to a shipper with whom I negotiated a not-very-good rate?  Or is there likely freight out there for some of my assets that is more profitable?

In a recent project, I proposed a method to estimate truckload market conditions based on privately-observed spot bids from brokers and carriers.  The situation is thus: a large national shipper, when needing spot capacity (usually due to freight rejections), offers single loads to tens of brokers and carriers to bid upon via an auction mechanism.  When the shipper receives these bids, they observe the price that a carrier or broker is willing to accept to haul the freight.  Thousands of bids are received each week for various lanes around the country.  Some of these bids come from brokers, each of whom have relationships with hundreds or thousands of carriers.  Thus, if intelligently analyzed, these bids give us a signal of the status of the market.

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


The method to estimate market conditions requires multiple regression analysis using statistical analysis software (I use Stata).  It also requires a fair amount of operational data on each spot bid, such as the historical contract price, the lane, the pickup day-of-week and hour-of-day, among other variables.  But once these are all accounted for and I run an analysis, I then see a good estimate for the status of the market.  The advantage of this method over, say, the CASS index, is that CASS is run once a month and released about ten days after each month is over.  Also, it gives one number nationally, so it does not provide insight into regional conditions.  My method of estimation is near-real-time, to the point that (as of Monday) I know the conditions for last week.  Moreover, estimates are calculated regionally, providing deeper insight into the market.


Alex is an expert in supply chain modeling and applying new techniques to solve supply chain problems. Alex solves customer problems with new ideas and builds out the science for Opex Labs projects.

He is also currently working on his Ph.D. at Penn State where he is applying advanced analytics to solve supply chain problems. He is also on the editorial board for the Material Handling & Logistics magazine.

Previously, Alex was with the ILOG and IBM supply chain application team from 2007 to 2012 and prior to that was at J.B. Hunt and Kenco Logistics.




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