Introduction Capstone is a business simulation designed to teach strategy‚ competitive analysis‚ finance‚ cross-functional alignment‚ and the selection of tactics to build a successful and focused company. In each round‚ we have to meet the buying criteria for our customers in each segment. We have five different products: Traditional Segment‚ Low-End Segment‚ High-End Segment‚ Performance Segment‚ and Size Segment. Each round is a year in the company’s life and decisions are made in research and
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Thomas Shewman Eaton MKT 300 9 December 2012 Simulation Paper In the first quarter and second quarter‚ I had to develop my brand name‚ the target segment‚ the responsibilities as Vice President‚ and location of where I will sell my product. I chose the name Pacers for my brand name. When I think of selling computers‚ I want a brand name that sounds fast and reliable; I came up with a few names and chose Pacers because it makes the consumers or buyers think the computer is fast and reliable. When
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2 Change Orders 3 Lessons 3 Appendix A: Simulation Comments 4 Appendix B: Simulation Results 6 Consensus versus Average Forecasting The consensus forecasts worked well for quick insight into estimated demand for each month. In our first year we used the consensus demand because we did not know the dynamics of the group‚ and we were relying on their expertise to guide us toward a more accurate forecast. As we progressed through the simulation we came to the realization that the consensus
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TITLE: MODELING AND SIMULATION PROPORTIONAL-INTEGRAL (PI) CONTROLLER FOR A DC MOTOR WITH IMPROVEMENT PERFORMANCE. SUMMARY This report is about a simulation study of modeling classical control technique of dc motor drive with improvement in proportional-integral (PI) control which is used closed-loop operation to control speed of motor. The speed regulator uses a PI controller in order to obtain the electromagnetic torque needed to reach the desired speed. Current controller controls the
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ICS 2307 SIMULATION AND MODELLING Course Outline Systems modelling – discrete event simulation Design of simulation experiments simulation Language probability and distribution theory Statistical estimation‚ inference and random number generators Sample event sequences for random number generation Translation of models for simulation application References Simulation modelling and analysis Introduction Computers can be used to imitate (simulate) the operations of various kinds of real
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conflicts between organizations. Particularly in international industrial marketing‚ when “big-ticket” and/or high technology products are involved‚ sales are most often negotiated. The General Medical MRI Negotiation Simulation (GM/MRI) has been developed specifically to provide a context for experiential learning and practical discussion of international business negotiations. JAPANESE NEGOTIATION STYLE INSTRUCTIONS The simulation becomes much more interesting for everyone if a little culture is brought
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Monte Carlo Simulation Using RiskSim 10 10.1 RISKSIM OVERVIEW RiskSim is a Monte Carlo Simulation add-in for Microsoft Excel 2000–2010 (Windows) and Microsoft Excel 2004 (Macintosh). RiskSim provides random number generator functions as inputs for your model‚ automates Monte Carlo simulation‚ and creates charts. Your spreadsheet model may include various uncontrollable uncertainties as input assumptions (e.g.‚ demand for a new product‚ uncertain variable cost of production‚ competitor reaction)
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1. INTRODUCTION Simulation history is viewed in different angles including: -simulation uses e.g. analysis‚ training‚ research. - types of simulation models e.g. discrete-event‚ continuous‚ combined discrete - simulation programming languages or environments e.g. Arena‚ SIMSCRIPT‚SLAM and - application domains or communities of interest e.g. communications‚ manufacturing‚ military‚ transportation). The objective of this history is to highlight people‚ places‚ and events that have marked
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Computer Simulation * A software program that runs on any computer that attempts to simulate some phenomenon based on a scientist’s conceptual and mathematical understanding of the given phenomenon. * The scientist’s conceptual understanding is reduced to an algorithmic or mathematical logic‚ which is then programmed in one of many programming languages (Fortran‚ C‚ C++‚ etc.) and compiled to produce a binary code that runs on a computer. * Have become a useful part of modeling
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Summary…………………………………………………………………………………2. Introduction……………………………………………………………………………………………3. Preparation of Everest simulation………………………………………………………………….3 Critical Analysis of Attitudes / groups and teams of Everest simulation 1 and 2 Group Experience and result……………………………………………………………………….. Individual experience and result……………………………………………………………………. Critical Analysis of Leadership of Everest Simulation 1 and 2 Group experience and result……………………………………………………………. Individual Experience and result…………………………………………………
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