A Decision Support System for Empty Hopper Car Management

Nickolas K. Freeman (), Arunachalam Narayanan () and Gary P. Burns ()
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Nickolas K. Freeman: Culverhouse College of Business, University of Alabama, Tuscaloosa, Alabama 35487
Arunachalam Narayanan: C. T. Bauer College of Business, University of Houston, Houston, Texas 77204
Gary P. Burns: LyondellBasell Industries, N.V., Houston, Texas 77010

Interfaces, 2019, vol. 49, issue 3, 173-188

Abstract: Chemical manufacturers in the United States use rail hopper cars to store and transport solid products. Customers also use the producer’s hopper cars as storage vessels, holding the car until the contained product is consumed. Uncertainty in customer hold times and transit times makes predicting the return of empty hopper cars to production sites challenging. This prediction difficulty results in increased costs due to the transfer of empty cars to and from external storage yards, and on rare occasions, production curtailment. We develop a data-driven decision support system to improve the management of empty hopper car inventory for a large U.S. chemical manufacturer. The company’s previous approach for the described task provides only reasonable estimates for cars that are in transit to the shipping facility. Our decision support system (DSS) provides a rolling forecast for a 40-day planning horizon, which improves daily and cumulative predictions by 57% and 13%, on average, when compared with the previous method. Ultimately, the DSS provides the company with increased visibility into its supply chain and allows it to make probabilistic business decisions while eliminating several data retrieval and manipulation tasks.

Keywords: railcar inventory management; chemical industry; decision support systems; simulation (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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