Education Using Simulation to Model Customer Behavior in the Context of Customer Lifetime Value Estimation Shahid Ansari, Alfred J. Nanni Accounting and Law Division, Babson College, Wellesley, Massachusetts 02457 {sansari@babson.edu, nanni@babson.edu} Dessislava A. Pachamanova, David P. Kopcso Mathematics and Science Division, Babson College, Wellesley, Massachusetts 02457 {dpachamanova@babson.edu, kopcso@babson.edu} T his article illustrates how simulation can be used in the
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[pic]M C Escher http://www.theorsociety.com/Science_of_Better/htdocs/prospect/index.asp What is Operational Research? The discipline of applying appropriate analytical methods to help make better decisions. By using techniques such as problem structuring methods (sometimes known as 'Soft O.R.') and mathematical modelling to analyse complex situations, operational research gives executives the power to make more effective decisions and build more productive systems based on: • More
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purchase would be made. JET Copies’ owners are putting together a simulation model to determine whether the purchase of another copy machine is necessary. They have the following information: • Time between breakdowns is 1- 6 weeks with probability of a breakdown increasing the longer the copier went without a breakdown • repair time probabilities Table 1: Probability of the days to repair copier Repair Time (days) Probability 1 0.20 2 0.45 3 0.25 4 0.10 • Loss of revenue during repair
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the process of management platform, Process simulation for the process simulation module is put forward based on probability analysis, and for the process modeling module, more collaborative process modeling technology is put forward. In the business application layer, process monitoring application based on application driven is proposed. Positioning in the implementation of enterprise business process management system for business process simulation software implementation or, as well as to
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numerical results with the Markov chain model which indicate that our model produces results comparable to a simulation model, but does so in a fraction of the computational time needed by the latter. This advantage of the analytical model becomes more pronounced in the context of optimization of the AGV’s capacity which without an analytical approach would require numerous simulation runs at each point in the capacity space. Note to Practitioners—This paper presents a model for determining the
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rather to continue with the price policy and strategy which risks the chance of competitors taking action and responding to the new changes to compete, or decide not to make any changes to the price policy instead increase the marketing budget. Research To make an accurate decision the company has researched the outcomes of both scenarios. The data collected included information from the top competitors price changes and marketing strategy results for the past three years. The most important
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1 PROBABILISTIC APPROACHES: SCENARIO ANALYSIS, DECISION TREES AND SIMULATIONS In the last chapter, we examined ways in which we can adjust the value of a risky asset for its risk. Notwithstanding their popularity, all of the approaches share a common theme. The riskiness of an asset is encapsulated in one number – a higher discount rate, lower cash flows or a discount to the value – and the computation almost always requires us to make assumptions (often unrealistic) about the nature of risk
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OPERATIONS RESEARCH Vol. 58, No. 3, May–June 2010, pp. 549–563 issn 0030-364X eissn 1526-5463 10 5803 0549 informs ® doi 10.1287/opre.1090.0780 © 2010 INFORMS A Stochastic Model for Order Book Dynamics Rama Cont Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027, rama.cont@columbia.edu Sasha Stoikov Cornell Financial Engineering Manhattan, New York, New York 10004, sashastoikov@gmail.com Rishi Talreja Department of Industrial
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MORTGAGE PORTFOLIO ANALYZER: A QUASI-STRUCTURAL MODEL OF MORTGAGE PORTFOLIO LOSSES TECHNICAL DOCUMENT 1 Mar 4, 2011 Roger M. Stein Ashish Das Yufeng Ding Shirish Chinchalkar ABSTRACT This document outlines the underlying research, model characteristics, data, and validation results for Mortgage Portfolio Analyzer, which is an analytic tool to assess credit risk measures, capital levels and stress scenarios for portfolios of residential mortgages. Mortgage Portfolio Analyzer comprises loan-level
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during the project early phase when only scarce information is available and contractual obligations are to be taken. In this context, both “internal” risk (e.g. probability of cost overrun) and “external” risk (e.g. probability of winning) must be taken into account. The paper presents the PRIMA (Project RIsk Management - IST-1999-10193) research project aiming at implementing such a “risk driven approach” to Project Management through the development of a Risk Management Corporate Memory and a Decision
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