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3 Loads but only 2 Trucks: Which Load is Left Behind?

A common strategy to combat demand uncertainty is the use of safety stock, but this also increases the risk of excess stock, increased obsolescence, and higher carrying costs.
By Richard Koury Rassey and Yong Zheng
January 23, 2017

Editor’s Note: Every year, 40 or so students in the MIT Center for Transportation & Logistics’ (MIT CTL) Master of Supply Chain Management (SCM) program complete one-year thesis research projects.  The students are early-career business professionals from multiple countries with 2 to 10 years of experience in the industry.  The research projects are sponsored by and carried out in collaboration with multinational corporations.  Joint teams of company people, MIT SCM students, and MIT CTL faculty work on real-world problems chosen by sponsor companies.  In this series, we summarize a selection of the latest SCM research.  The researchers for the project described below, Richard Koury Rassey and Yong Zheng, defined a method for prioritizing inbound loads when carriers are capacitated for their MIT Supply Chain Management Program master’s thesis.  The sponsor was a major retailer, and the project was supervised by Dr. Chris Caplice, Executive Director, MIT CTL and Dr. Francisco Jauffred, Research Affiliate, MIT CTL.

Retailers often face demand uncertainty due to seasonality and variable consumer shopping behavior, so supply chain robustness is critical to ensure sufficient product availability. 

A common strategy to combat demand uncertainty is the use of safety stock, but this also increases the risk of excess stock, increased obsolescence, and higher carrying costs. 

The thesis research sponsor company, ShopCo (a pseudonym), is a major retailer that wanted to define prioritization logic for inbound loads to determine which loads received priority, and when inventory would exceed carrier capacity.  Each load can comprise one or more purchase orders (POs).

To address these issues, we developed a method based on the Analytic Hierarchy Process (AHP) to assign priority scores to each load.

Lack of Control Created Opportunity

ShopCo places weekly orders for various types of merchandise throughout the United States.  Historically, ShopCo relied on its suppliers and carriers to determine the order of loads that were picked up. Unfortunately, these trading partners used logic that may not meet ShopCo’s needs.

The retailer is striving to improve product availability at the store level, and wants to ensure its supply chain is prioritized around this goal.  Currently, it uses an internally developed load allocation tool to assign PO’s to loads. However, the tool’s objective function is based largely on reducing costs rather than improving product availability.

The lack of control over which loads were shipped created an opportunity to improve the inbound transportation process. We developed prioritization logic based on ShopCo’s objectives to determine the order that loads were picked up. 

The challenge was overcoming the difficulty in comparing loads with different characteristics and aligning stakeholders with conflicting perspectives on priority.  The AHP provided a framework to prioritize loads with multiple criteria. In addition, a consensus was reached among the stakeholders on which priority values should be assigned to specific characteristics of each load. 

Application and Next Steps

After applying the AHP priority values to a sample of ShopCo’s load dataset, we segmented the priority scores to define thresholds to facilitate decision-making.  For example, the priority scores in the top 20 percent defined the thresholds for the “critical” segment.  This segment is required to ship that day, so if there were a capacity constraint, more carrying capacity was needed.  This segmentation equips ShopCo with thresholds to more efficiently manage the loads and make quicker decisions.

Since ShopCo uses a load allocation tool to assign PO’s to loads, we hypothesized that it is possible to increase the total priority scores shipped by reshuffling the PO’s using a Knapsack optimization model.  We took a sample of the loads and found that we could improve the total load priority score shipped by up to 8.3 percent as compared to the performance of the allocation method.  Although the existing tool appears to adequately optimize loads, there is an opportunity for improvement.

We believe this research benefits not only ShopCo, but also other companies and industries that manage their inbound transportation with carrier capacity constraints.  Although the factors used may differ, this underlying framework would align load priorities with company objectives. 

For further information on the SCM thesis authored by Rassey and Zheng titled Prioritizing Inbound Transportation contact Dr. Bruce Arntzen, Executive Director, MIT Supply Chain Management Program at: .(JavaScript must be enabled to view this email address).  For more information on the SCM program visit http://scm.mit.edu/program.


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