FDA Collection:
http://hdl.handle.net/2451/14093
Mon, 19 Mar 2018 09:17:29 GMT2018-03-19T09:17:29Z40.72596-73.998345NYU_OperationsManagementWorkingPapershttps://feedburner.google.comAggregated Information in Supply Chains
http://feedproxy.google.com/~r/NYU_OperationsManagementWorkingPapers/~3/yQqSNhSMG30/41679
Title: Aggregated Information in Supply Chains
Authors: Kovtun, Vladimir; Giloni, Avi; Hurvich, Clifford
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.<img src="http://feeds.feedburner.com/~r/NYU_OperationsManagementWorkingPapers/~4/yQqSNhSMG30" height="1" width="1" alt=""/>Thu, 08 Feb 2018 00:00:00 GMThttp://hdl.handle.net/2451/416792018-02-08T00:00:00Zhttp://hdl.handle.net/2451/41679Impact of Exponential Smoothing on Inventory Costs in Supply Chains
http://feedproxy.google.com/~r/NYU_OperationsManagementWorkingPapers/~3/hGeARGWErGQ/34464
Title: Impact of Exponential Smoothing on Inventory Costs in Supply Chains
Authors: Hsieh, Meng-Chen; Giloni, Avi; Hurvich, Clifford
Abstract: It is common for firms to forecast stationary demand using simple exponential smoothing due to the ease of computation and understanding of the methodology. In this paper we show that the use of this methodology can be extremely costly in the context of inventory in a two-stage supply chain when the retailer faces AR(1) demand. We show that under the myopic order-up-to level policy, a retailer using exponential smoothing may have expected inventory-related costs more than ten times higher than when compared to using the optimal forecast. We demonstrate that when the AR(1) coefficient is less than the exponential smoothing parameter, the supplier’s expected inventory-related cost is less when the retailer uses optimal forecasting as opposed to exponential smoothing. We show there exists an additional set of cases where the sum of the expected inventory-related costs of the retailer and the supplier is less when the retailer uses optimal forecasting as opposed to exponential smoothing even though the supplier’s expected cost is higher. In this paper, we study the impact on the naive retailer, the sophisticated supplier, and the two-stage chain as a whole of the supplier sharing its forecasting expertise with the retailer. We provide explicit formulas for the supplier’s demand and the mean squared forecast errors for both players under various scenarios.<img src="http://feeds.feedburner.com/~r/NYU_OperationsManagementWorkingPapers/~4/hGeARGWErGQ" height="1" width="1" alt=""/>Wed, 03 Feb 2016 00:00:00 GMThttp://hdl.handle.net/2451/344642016-02-03T00:00:00Zhttp://hdl.handle.net/2451/34464A Column Generation Algorithm for Choice-Based Network Revenue Management
http://feedproxy.google.com/~r/NYU_OperationsManagementWorkingPapers/~3/j0-HkvL1rKg/27726
Title: A Column Generation Algorithm for Choice-Based Network Revenue Management
Authors: Bront, Juan Jose Miranda
Abstract: In the last few years, there has been a trend to enrich traditional revenue management models built upon the independent demand paradigm by accounting for customer choice behavior. This extension involves both modeling and computational challenges.
One way to describe choice behavior is to assume that each customer belongs to a segment, which is characterized by a consideration set, i.e., a subset of the products provided by the firm that a customer views as options. Customers choose a particular product according to a multinomial-logit criterion, a model widely used in the marketing literature.
In this paper, we consider the choice-based, deterministic, linear programming model (CDLP) of Gallego et al. [6], and the follow-up dynamic programming (DP) decomposition heuristic of van Ryzin and Liu [16], and focus on the more general version of these models, where customers belong to overlapping segments. To solve the CDLP for real-size networks, we need to develop a column generation algorithm. We prove that the associated column generation subproblem is indeed NP-Complete, and propose a simple, greedy heuristic to overcome the complexity of an exact algorithm. Our computational results show that the heuristic is quite effective, and that the overall approach has good practical potential and leads to high quality solutions.<img src="http://feeds.feedburner.com/~r/NYU_OperationsManagementWorkingPapers/~4/j0-HkvL1rKg" height="1" width="1" alt=""/>Mon, 13 Oct 2008 20:26:02 GMThttp://hdl.handle.net/2451/277262008-10-13T20:26:02Zhttp://hdl.handle.net/2451/27726Dynamic Pricing for an M/M1 Make-To-Stock System with Controllable Backlog
http://feedproxy.google.com/~r/NYU_OperationsManagementWorkingPapers/~3/BqFDtC5dvFs/27725
Title: Dynamic Pricing for an M/M1 Make-To-Stock System with Controllable Backlog
Authors: Vulcano, Gustavo<img src="http://feeds.feedburner.com/~r/NYU_OperationsManagementWorkingPapers/~4/BqFDtC5dvFs" height="1" width="1" alt=""/>Mon, 13 Oct 2008 20:19:47 GMThttp://hdl.handle.net/2451/277252008-10-13T20:19:47Zhttp://hdl.handle.net/2451/27725