3 Loads But Only 2 Trucks: Which Load is Left Behind?

3 Loads But Only 2 Trucks: Which Load is Left Behind?

A new framework for allocating truck loads could help companies to streamline their freight operations

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.

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.

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.

This article was written by Richard Koury Rassey and Yong Zheng Rassey, whose SCM thesis, Prioritizing Inbound Transportation, was supervised by Dr. Chris Caplice, Executive Director, MIT CTL and Dr. Francisco Jauffred, Research Affiliate, MIT CTL. For more information on the research please contact Dr. Chris Caplice at: caplice@mit.edu. This article first appeared on the Supply Chain Management Review web site.



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