Quantitative Analysis for Decision Makers

Mik Wisniewski has almost five decades of experience in Quantitative Analysis. He has taught at a number of leading universities, worked in both industry and government and has extensive consultancy experience in the UK and across Europe, Africa, the Middle East and Asia.Dr Farhad Shafti is a senior academic with expertise in Management Science. He has extensive experience teaching in highly ranked universities, covering undergraduate, postgraduate and MBA studies in the UK and overseas.Dr Wee Meng Yeo is a senior lecturer at Adam Smith Business School, Glasgow. He has considerable expertise in the areas of operations management, forecasting and inventory control and has worked in the UK and the Far East.

• 1 Introduction Decisions, decisions and more decisionsData, information and analysisSo where does quantitative analysis fit in?So who uses quantitative analysis?What’s quantitative analysis got to do with managers and with me?Models in quantitative decision makingUsing the textSummary 2 Tools of the Trade Some basic terminologyFractions, proportions, percentagesRounding and significant figuresCommon notationPowers and rootsLogarithmsSummation and factorialsEquations and mathematical modelsGraphsLog graphsReal and money termsWorked exampleSummaryExercises 3 Presenting Management Information A business exampleBar chartsPie chartsFrequency distributionsPercentage and cumulative frequenciesHistogramsFrequency polygonsOgivesLorenz curves (Pareto diagrams)Time-series graphs (line charts)Z chartsScatter diagramsRadar chartsWhich chart to useGeneral principles of graphical presentationWorked exampleSummaryExercises 4 Management Statistics A business exampleWhy are management statistics needed?Measures of averageMeasures of variabilityUsing the statisticsCalculating statistics for aggregated dataIndex numbersWorked exampleSummaryExercises 5 Probability and Probability Distributions TerminologyThe multiplication ruleThe addition ruleA business applicationProbability distributionsThe Binomial distributionThe Normal distributionWorked exampleSummaryExercises 6 Decision Making Under Uncertainty The decision problemThe maximax criterionThe maximin criterionThe minimax regret criterionDecision making using probability informationRiskDecision treesThe value of perfect informationWorked exampleSummaryExercises 7 Statistical Inference: Making Sense of Sample Information Populations and samplesSampling distributionsThe Central Limit TheoremCharacteristics of the sampling distributionConfidence intervalsOther confidence intervalsConfidence intervals for percentages and proportionsInterpreting confidence intervalsHypothesis testsTests on a sample meanTests on the difference between two meansTests on two proportions or percentagesTests on small samplesInferential statistics using a computer packagep values in hypothesis testsc2 testsWorked exampleSummaryExercises 8 Quality Control and Quality Management The importance of qualityTechniques in quality managementStatistical process controlControl chartsControl charts for attribute variablesSpecification limits versus control limitsPareto chartsIshikawa diagramsSix sigmaWorked exampleSummaryExercises 9 Forecasting I: Moving Averages and Time Series The need for forecastingApproaches to forecastingTrend projectionsTime-series modelsWorked exampleSummaryExercises 10 Forecasting II: Regression The principles of simple linear regressionThe correlation coefficientThe line of best fitUsing the regression equationFurther statistical evaluation of the regression equationNon-linear regressionMultiple regressionThe forecasting processWorked exampleSummaryExercises 11 Linear Programming The business problemFormulating the problemGraphical solution to the LP formulationSensitivity analysisComputer solutionsAssumptions of the basic modelDealing with more than two variablesExtensions to the basic LP modelWorked exampleSummaryExercises 12 Inventory Control The inventory control problemCosts involved in inventory controlThe inventory control decisionThe economic order quantity modelThe reorder cycleRobustness of EOQ decisionAssumptions of the EOQ modelIncorporating lead timeSome technical insightsClassification of inventoryMRP and JITAI and inventory managementWorked exampleSummaryExercises 13 Project Management Characteristics of a projectProject managementBusiness exampleNetwork diagramsDeveloping the network diagramUsing the network diagramA technical pointGantt chartsUncertaintyProject costs and crashingWorked exampleSummaryExercises 14 Simulation The principles of simulationBusiness exampleDeveloping the simulation modelA simulation flowchartUsing the modelWorked exampleSummaryExercises 15 Financial Decision Making InterestNominal and effective interestPresent valueInvestment appraisalReplacing equipmentWorked exampleSummaryExercises