Predictive analytics to improve pricing and sourcing in third-party logistics operations

Christopher Lindsey*, Andreas Frei, Hani Mahmassani, Young Park, Diego Klabjan, Michael Reed, Gregory Langheim, Todd Keating

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Pricing shipments and sourcing capacity for a third-party logistics (3PL) provider operating in a spot market requires real-time decision making that is ripe for computer-based support driven by analytics. A decision support system outlined here leverages the 3PL provider's historical shipment data along with its knowledge of both sides of the shipping process to increase profits and to better perform the pricing and sourcing tasks. At the core of the system are discrete choice models for shippers and carriers along with a profit maximization model. The discrete choice models predict the acceptance or rejection of an offer for a shipment to shippers and a bid for capacity to carriers. The profit maximization model determines the shipper price that maximizes the 3PL provider's expected profit. In addition to those models are procedures for determining a list of potential carriers for an incoming shipment and also for ranking those carriers. As its main outputs, the system produces a shipper price and a ranked carrier list. The system is applied to real-world data provided by a 3PL company, with excellent results. The system is able to produce competitive yet profitable prices and to select potential carriers that would increase the 3PL provider's profits.

Original languageEnglish (US)
Pages (from-to)123-131
Number of pages9
JournalTransportation Research Record
Volume2410
DOIs
StatePublished - Dec 1 2014

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering

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