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Ram Ramesh's
Scholarly Papers
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1.
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Anna Ye Du
State University of New York - Management Science and Systems Jessica Pu Li
State University of New York - Management Science and Systems Ram D. Gopal
University of Connecticut - Department of Operations & Information Management Ram Ramesh
State University of New York - Management Science and Systems Giri Kumar Tayi
SUNY at Albany - School of Business
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02 Nov 07
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Last Revised:
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23 Mar 08
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145
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Abstract:
The emergence of globally distributed call-center networks, such as Dell International Support Service, has fundamentally increased the challenges to the management. This new trend of networking faces the risk of extensive demand fluctuation both over locations and over time. Existing literature in operation management uses pooling mechanisms to deal with demand fluctuations among different departments, while does not consider system-wide time-varying demand shifts. Queuing literature has developed algorithms to allow the number of servers to change in response to time-varying loads, however, a pooling configuration introduces additional business risk and is thus more complex to change. More sophisticated approaches are needed to accurately describe the reality of call-center operations. In this paper, we study a model generalized from the International Queue run in Dell's globally distributed call-center network and identify its operation risks from both demand fluctuation and pooling management. Then we propose an economic framework to use a Real-Options Approach (ROA) to systematically analyze those risks to help the management to hedge the risks to a pre-chosen level and hence secure the associated service quality and the costs.
Risk Management, Globally Distributed Call Center Networks
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2.
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Sanjukta Das
SUNY Buffalo Anna Ye Du
State University of New York - Management Science and Systems Ram D. Gopal
University of Connecticut - Department of Operations & Information Management Ram Ramesh
State University of New York - Management Science and Systems
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15 May 08
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15 May 08
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45
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Abstract:
Online storage service providers such as Amazon S3 grant a way for companies, particularly startups, to avoid spending resources on maintaining their own in-house storage infrastructure and thereby allowing them to focus on their core business activities. These providers however follow a fixed, posted pricing strategy which charges the same price in each time period and thus bear all the risk arising out of demand uncertainties faced by their client companies. We examine the effects of providing a spot market with dynamic prices and forward contracts to hedge against future revenue uncertainty. We derive revenue-maximizing spot and forward prices for a single seller facing a known set of buyers. We perform a simulation study using publicly available traffic data regarding Amazon S3 clients from Alexa.com to validate our analytical results. Our field study supports our analysis and indicates that spot markets alone can enhance revenues to Amazon but this comes at the cost of increased risks, due to the increased market share in the spot markets. Furthermore, adding a forward contract feature to the spot markets can reduce risks while still providing the benefits of enhanced revenues. While the buyers incur an increase in costs in the spot market, adding a forward contract does not cause any additional cost increase while transferring the risk to the buyers. Thus storage grid providers can greatly benefit by applying a forward contract alongside the spot market.
Online storage, Grid computing, Forward contracts, Market mechanism design
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3.
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Anna Ye Du
State University of New York - Management Science and Systems Xianjun Geng
University of Washington - Department of Management & Organization Ram D. Gopal
University of Connecticut - Department of Operations & Information Management Ram Ramesh
State University of New York - Management Science and Systems Andrew B. Whinston
University of Texas at Austin - Department of Information, Risk and Operations Management
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03 Apr 06
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Last Revised:
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25 Jul 07
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44
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Abstract:
With the rapid growth of rich-media content over the Internet, content and service providers (SP) are increasingly facing the problem of managing their service resources cost-effectively while ensuring a high Quality of Service (QoS) delivery at the same time. To address this problem, we consider a model where infrastructure resources are traded, cooperatively shared and accessed through coordination mechanisms. In this research, we conceptualize and model an economy of Internet based storage provisioning for rich-media content delivery. This is modeled as a Capacity Provision Network (CPN) where participants possess service infrastructures and leverage their topographies to effectively serve specific customer segments. A CPN is a network of SPs coordinated through an allocation hub. We first develop the notion of discounted QoS capabilities of storage resources. We then develop a market maker mechanism for optimal multilateral allocation in a network. The proposed CPN is closely tied to two fundamental properties of Internet service technology: positive network externality among cooperating SPs and the convexity property of capacity allocation with geographically distributed service sites. In conclusion, this study demonstrates the practical business viability of a cooperative CPN market.
Capacity Provision Network, Quality of Service, Market Maker mechanism, Optimization
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