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Chapter 22 / Exercise 22.8
Statistics for Management and Economics + XLSTAT Bind-in
Keller
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Operations ManagementContents1Week 121.1Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21.2EXCEL Skills: The Basics for Excel Simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21.2.1Range Names. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31.2.2Data Array and Matrix Notation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31.2.3Random Variables, PDF, and CDF. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41.2.4Generate Random Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41.2.5Simulation, sampling, sample, sample path. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .51.2.6Statistics and Histogram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61.2.7Conditional Probability and Markov Chain. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61.3EXCEL Tips. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .71.3.1How to enter an array formula. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .71.3.2Data Table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .71.3.3How to Install Solver on Mac. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .71.3.4How to Speed Up Excel Execution:. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .81.3.5Excel shortcuts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .82Week 292.1Flextrola Case. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .92.1.1Excel Hints. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .92.2EXCEL Skills: Data table, Indicator. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .102.3The Newsvendor Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .111
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Chapter 22 / Exercise 22.8
Statistics for Management and Economics + XLSTAT Bind-in
Keller
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1Week 11.1IntroductionIn this class, we introduce backbone models and tools for operations management.The basicmodels are newsvendor, EOQ, revenue management, contracting, and auctions. The basic toolsare statistics, simulation, and optimization (linear and nonlinear programming). We will use Excelextensively.1We start with simulation. Simulation is a systematic way of thinking. It tells stories in a logicalfashion. Just as every story has actors, plots, and contexts, so does simulation. In a story, we wantto know who are the actors, how they interact, under what context, and what are the outcomes oftheir interactions. In simulation,random variablesare our actors, and we seek to understand howand why their interplay leads to certain outcomes.The central idea of simulation is simple: Theexpectationof a random variableXcan be approximat-ed byaveragingthe realizations{x1, x2,· · ·, xN}ofX, and theprobabilityof an event (e.g.,(X >0))can be approximated byaveragingits indicators1*(xi>0):E[X]Ni=1xiN,P(X >0)Ni=11*(xi>0)N.For each simulation, we must first specify the relevant random variables. Like actors, they eachhave names and behaviors (personalities, characters). For example, Bernoulli, Binomial, and Nor-mal are the names of typical random variables.Their behaviors are uniquely defined by theirdistribution functions—probability distribution function (PDF)f, or cumulative distribution func-tion (CDF)F. Depending on the problem, either one can be pleasant to work with. Their relationisF(x) =x−∞f(t)dtfor continuous case, andF(x) =tixf(ti)for discrete case.Second, we care about the context of the simulation. This is done by specifying the business andother environmental parameters.For example, the time horizon, the production cost, and themarket price. Along with the parameters of random variables, they constitute the INPUT of thesimulation.Third, we need to figure out the logic—the plot—of the simulation.That is, how the randomvariables interact in each context. This is the fun part of the simulation: as the logic depends onthe problem specifics, we must analyze each on its own merit.

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