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Yasamin Salmani, PhD Candidate at Drexel University

Yasamin Salmani, PhD Candidate at Drexel University


Gerri C. LeBow Hall
3220 Market Street
Rm. 408
Philadelphia, PA 19104
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Title: Investment Decision Making in Improving Multiple Sales Channels

Abstract: A supply chain, employing multiple sales channels, needs to continually enhance these channels for achieving competitive advantage. The purpose of this dissertation is to provide a systematic procedure for optimal investment decisions towards improving existing sales channels in a multi-channel multi-product setting. The research provides a channel-level benefit-cost analysis, rather than the common product-level profitability, through the integration of operations and marketing perspectives. The motivation to undertake this problem is the critical decision for investment, on the part of many established firms that typically utilize multiple channel structures. This ultimately helps the firms to enhance their competitive position through improvements in the performance of sales channels. In pursuing our objective, initially, we investigate the customers’ criteria in choosing sales channels in a so-called omnichannel setting. Then, we suggest an analytic model that considers both customer priorities and demand correlations among channels, for quantifying customer input. We explicitly resolve the major issue of channel demand correlation that most manufacturers encounter in multichannel retailing. This model is tested and verified utilizing an experimental study in an omnichannel retailing environment. Next, we develop a mathematical model for analyzing benefits and relevant operational costs associated with each specific channel. Accordingly, we propose a novel measurement for budget allocation decision among already established, alternate sales channels. We further provide a comprehensive analysis of the effect of the firm’s “cycle service level” and “demand uncertainty” on the improvement investment decisions. Later, we implement the model using a company’s data and demonstrate the practicability of the proposed methodology.

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  • Faculty
  • PhD
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    • Current Students

Disciplines

  • Decision Sciences and MIS