University of Tennessee

Business Analytics Publication: Real-time order tracking for supply systems with multiple transportation stages

July 17, 2014

Dr. Nana Bryan and Dr. Mandyam M. Srinivasan

European Journal Operational Research

Volume 236, Issue 2, 16 July 2014, Pages 548–560

Read full article here.


This paper presents a stochastic model that evaluates the value of real-time shipment tracking information for supply systems that consist of a retailer, a manufacturer, and multiple stages of transportation. The retailer aggregates demand for a single product from end customers and places orders on the manufacturer. Orders received by the manufacturer may take several time periods before they are fulfilled. Shipments dispatched by the manufacturer move through multiple stages before they reach the retailer, where each stage represents a physical location or a step in the replenishment process. The lead time for a new order depends on the number of unshipped orders at the manufacturer’s site and the number and location of all shipments in transportation. The analytic model uses real-time information on the number of orders unfulfilled at the manufacturer’s site, as well as the location of shipments to the retailer, to determine the ordering policy that minimizes the long-run average cost for the retailer. It is shown that the long-run average cost is lower with real-time tracking information, and that the cost savings are substantial for a number of situations. The model also provides some guidelines for operating this supply system under various scenarios. Numerical examples demonstrate that when there is a lack of information it is better for the retailer to order every time period, but with full information on the status in the supply system it is not always necessary for the retailer to order every time period to lower the long-run average cost. Read full article here.

Bryan, N., & Srinivasan, M. M. (2014). Real-time order tracking for supply systems with multiple transportation stages. European Journal of                Operational Research, 236(2), 548-560.