Aggregated Information in Supply Chains

52 Pages Posted: 20 Feb 2018

See all articles by Vladimir Kovtun

Vladimir Kovtun

Independent

Avi Giloni

Independent

Clifford M. Hurvich

Stern School of Business, New York University; New York University (NYU) - Department of Information, Operations, and Management Sciences

Date Written: February 08, 2018

Abstract

We study a two-stage supply chain where the retailer observes two demand streams coming from two consumer populations. We further assume that each demand sequence is a station- ary Autoregressive Moving Average (ARMA) process with respect to a Gaussian white noise sequence (shocks). The shock sequences for the two populations could be contemporaneously correlated. We show that it is typically optimal for the retailer to construct its order to its supplier based on forecasts for each demand stream (as opposed to the sum of the streams) and that doing so is never sub-optimal. We demonstrate that the retailer’s order to its supplier is ARMA and yet can be constructed as the sum of two ARMA order processes based upon the two populations. When there is no information sharing, the supplier only observes the retailer’s order which is the aggregate of the two aforementioned processes. In this paper, we determine when there is value to sharing the retailer’s individual orders, and when there is additional value to sharing the retailer’s individual shock sequences. We also determine the supplier’s mean squared forecast error under no sharing, process sharing, and shock sharing.

Suggested Citation

Kovtun, Vladimir and Giloni, Avi and Hurvich, Clifford M., Aggregated Information in Supply Chains (February 08, 2018). NYU Working Paper No. 2451/41679. Available at SSRN: https://ssrn.com/abstract=3125303

Vladimir Kovtun (Contact Author)

Independent

No Address Available

Avi Giloni

Independent

No Address Available

Clifford M. Hurvich

Stern School of Business, New York University ( email )

44 West 4th Street
New York, NY 10012-1126
United States

New York University (NYU) - Department of Information, Operations, and Management Sciences

44 West Fourth Street
New York, NY 10012
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
8
Abstract Views
93
PlumX Metrics