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Production Control Policies in Supply Chains with Selective-Information Sharing
No full textWe consider a supply chain consisting of one manufacturer (capacitated supplier) and two retailers. We characterize the manufacturer’s optimal production policy under selective-information sharing, in which the manufacturer receives demand and inventory information from only one of the two retailers. We show that the manufacturer’s optimal production policy is a state-dependent base-stock policy and that the base-stock levels have a monotonic structure. We also perform an extensive numerical study to examine how system factors affect the benefit of information sharing and the relative values of information from each retailer. In addition, we identify cases where the cost saving due to receiving information from only one retailer captures most of the saving that can be obtained when the information is received from both retailers. Finally, we investigate the cost effectiveness of echelon-stock policies in systems with full-information sharing and introduce the “information pooling effect” as well as economies of scale with respect to information sharing
Burst Frame and Frequency Synchronization with a Sandwich Preamble
No full textPreamble schemes with periodic signal repetitions are often used for synchronization purposes in transmission over dispersive media. We describe a sandamble, being a splitted repetition preamble with reduced training overhead, and compare its frame and frequency synchronization performance in burst transmission to the performance of a conventional repetition preamble like standardized for IEEE 802.11a or HiperLAN/2 wireless modems. Optimum frame position and frequency offset estimation from a sandamble structure is treated and the Cramér– Rao lower bound for frequency estimation is derived. Though the fundamental idea is independent from the specific modulation scheme, we show results for Orthogonal Frequency–Division Multiplexing (OFDM)
Rank-modulation rewriting codes for flash memories
No full textCurrent flash memory technology is focused on cost minimization of the stored capacity. However, the resulting approach supports a relatively small number of write-erase cycles. This technology is effective for consumer devices (smart-phones and cameras) where the number of write-erase cycles is small, however, it is not economical for enterprise storage systems that require a large number of lifetime writes. Our proposed approach for alleviating this problem consists of the efficient integration of two key ideas: (i) improving reliability and endurance by representing the information using relative values via the rank modulation scheme and (ii) increasing the overall (lifetime) capacity of the flash device via rewriting codes, namely, performing multiple writes per cell before erasure. We propose a new scheme that combines rank-modulation with rewriting. The key benefits of the new scheme include: (i) the ability to store close to 2 bits per cell on each write, and rewrite the memory close to q times, where q is the number of levels in each cell, and (ii) efficient encoding and decoding algorithms that use the recently proposed polar WOM codes
02163. Carl F. Mela ([email protected]) is a Professor of Marketing, The Fuqua School of Business
No full textAbstract Central to a firm's growth and marketing policy is the revenue and profit potential of its customer assets. As a result, there has been a recent proliferation of work regarding customer lifetime value. However, extant research in this area is silent regarding how to assess the profitability of customers in a networked setting. In such settings, the presence of one type of customer can affect the value of another. Examples of such settings include job agencies (whose customers include both job seekers and listers), realtors (whose clients include home sellers and purchasers), and auction houses (whose customers include buyers and sellers). Customers such as buyers of an auction house pay no fees to the firm making their value difficult to compute. Yet these customers generate value to the firm because their presence attracts fee-paying sellers. In this paper we consider the value of a customer in these types of networked setting. We compute the value of customers by developing a joint model of buyer and seller growth. This growth comes from three sources -marketing actions (price and advertising), direct network effects (e.g., buyer to buyer effects), and indirect network effects (e.g., buyer to seller effects). Using this growth model we concurrently solve the firm's problem of choosing optimal pricing and advertising subject to constraints on customer growth. By relaxing constraints on growth by one customer, we can then impute their lifetime value to the firm. We apply our model to data from an auction house. Our results show that there are strong direct and indirect network effects present in our data. We find that in the most recent period buyers have a value of about 500. We also find that our approach leads to estimates of firm value that are more accurate than models that fail to consider network effects. Finally, price and advertising elasticities are low (-0.16 and 0.006) and decrease over time as network effects become increasingly important
