{"product_id":"demanddriven-forecasting-9781118669396","title":"DemandDriven Forecasting","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eFrom a review of basic forecasting methods to the advanced and innovative techniques in use today, this book offers a fundamental understanding of the quantitative methods used to sense, shape, and predict future demand within a structured process. It is suitable for professionals who need to improve the accuracy of their sales forecasts.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eForeword xi\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAcknowledgments xix\u003c\/p\u003e \u003cp\u003eAbout the Author xx\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Demystifying Forecasting: Myths versus Reality 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Collection, Storage, and Processing Reality 5\u003c\/p\u003e \u003cp\u003eArt-of-Forecasting Myth 8\u003c\/p\u003e \u003cp\u003eEnd-Cap Display Dilemma 10\u003c\/p\u003e \u003cp\u003eReality of Judgmental Overrides 11\u003c\/p\u003e \u003cp\u003eOven Cleaner Connection 13\u003c\/p\u003e \u003cp\u003eMore Is Not Necessarily Better 16\u003c\/p\u003e \u003cp\u003eReality of Unconstrained Forecasts, Constrained Forecasts, and Plans 17\u003c\/p\u003e \u003cp\u003eNortheast Regional Sales Composite Forecast 21\u003c\/p\u003e \u003cp\u003eHold-and-Roll Myth 22\u003c\/p\u003e \u003cp\u003eThe Plan that Was Not Good Enough 23\u003c\/p\u003e \u003cp\u003ePackage to Order versus Make to Order 25\u003c\/p\u003e \u003cp\u003e“Do You Want Fries with That?” 26\u003c\/p\u003e \u003cp\u003eSummary 28\u003c\/p\u003e \u003cp\u003eNotes 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 What Is Demand-Driven Forecasting? 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTransitioning from Traditional Demand Forecasting 33\u003c\/p\u003e \u003cp\u003eWhat’s Wrong with The Demand-Generation Picture? 34\u003c\/p\u003e \u003cp\u003eFundamental Flaw with Traditional Demand Generation 37\u003c\/p\u003e \u003cp\u003eRelying Solely on a Supply-Driven Strategy Is Not the Solution 39\u003c\/p\u003e \u003cp\u003eWhat Is Demand-Driven Forecasting? 40\u003c\/p\u003e \u003cp\u003eWhat Is Demand Sensing and Shaping? 41\u003c\/p\u003e \u003cp\u003eChanging the Demand Management Process Is Essential 57\u003c\/p\u003e \u003cp\u003eCommunication Is Key 65\u003c\/p\u003e \u003cp\u003eMeasuring Demand Management Success 67\u003c\/p\u003e \u003cp\u003eBenefits of a Demand-Driven Forecasting Process 68\u003c\/p\u003e \u003cp\u003eKey Steps to Improve the Demand\u003c\/p\u003e \u003cp\u003eManagement Process 70\u003c\/p\u003e \u003cp\u003eWhy Haven’t Companies Embraced the Concept of Demand-Driven? 71\u003c\/p\u003e \u003cp\u003eSummary 74\u003c\/p\u003e \u003cp\u003eNotes 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Overview of Forecasting Methods 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderlying Methodology 79\u003c\/p\u003e \u003cp\u003eDifferent Categories of Methods 83\u003c\/p\u003e \u003cp\u003eHow Predictable Is the Future? 88\u003c\/p\u003e \u003cp\u003eSome Causes of Forecast Error 91\u003c\/p\u003e \u003cp\u003eSegmenting Your Products to Choose the Appropriate Forecasting Method 94\u003c\/p\u003e \u003cp\u003eSummary 101\u003c\/p\u003e \u003cp\u003eNote 101\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Measuring Forecast Performance 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e“We Overachieved Our Forecast, So Let’s Party!” 105\u003c\/p\u003e \u003cp\u003ePurposes for Measuring Forecasting Performance 106\u003c\/p\u003e \u003cp\u003eStandard Statistical Error Terms 107\u003c\/p\u003e \u003cp\u003eSpecific Measures of Forecast Error 111\u003c\/p\u003e \u003cp\u003eOut-of-Sample Measurement 115\u003c\/p\u003e \u003cp\u003eForecast Value Added 118\u003c\/p\u003e \u003cp\u003eSummary 122\u003c\/p\u003e \u003cp\u003eNotes 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Quantitative Forecasting Methods Using Time Series Data 125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding the Model-Fitting Process 127\u003c\/p\u003e \u003cp\u003eIntroduction to Quantitative Time Series Methods 130\u003c\/p\u003e \u003cp\u003eQuantitative Time Series Methods 135\u003c\/p\u003e \u003cp\u003eMoving Averaging 136\u003c\/p\u003e \u003cp\u003eExponential Smoothing 142\u003c\/p\u003e \u003cp\u003eSingle Exponential Smoothing 143\u003c\/p\u003e \u003cp\u003eHolt’s Two-Parameter Method 147\u003c\/p\u003e \u003cp\u003eHolt’s-Winters’ Method 149\u003c\/p\u003e \u003cp\u003eWinters’ Additive Seasonality 151\u003c\/p\u003e \u003cp\u003eSummary 156\u003c\/p\u003e \u003cp\u003eNotes 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Regression Analysis 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRegression Methods 160\u003c\/p\u003e \u003cp\u003eSimple Regression 160\u003c\/p\u003e \u003cp\u003eCorrelation Coefficient 163\u003c\/p\u003e \u003cp\u003eCoefficient of Determination 165\u003c\/p\u003e \u003cp\u003eMultiple Regression 166\u003c\/p\u003e \u003cp\u003eData Visualization Using Scatter Plots and Line Graphs 170\u003c\/p\u003e \u003cp\u003eCorrelation Matrix 173\u003c\/p\u003e \u003cp\u003eMulticollinearity 175\u003c\/p\u003e \u003cp\u003eAnalysis of Variance 178\u003c\/p\u003e \u003cp\u003eF-test 178\u003c\/p\u003e \u003cp\u003eAdjusted R2 180\u003c\/p\u003e \u003cp\u003eParameter Coefficients 181\u003c\/p\u003e \u003cp\u003et-test 184\u003c\/p\u003e \u003cp\u003eP-values 185\u003c\/p\u003e \u003cp\u003eVariance Inflation Factor 186\u003c\/p\u003e \u003cp\u003eDurbin-Watson Statistic 187\u003c\/p\u003e \u003cp\u003eIntervention Variables (or Dummy Variables) 191\u003c\/p\u003e \u003cp\u003eRegression Model Results 197\u003c\/p\u003e \u003cp\u003eKey Activities in Building a Multiple Regression Model 199\u003c\/p\u003e \u003cp\u003eCautions about Regression Models 201\u003c\/p\u003e \u003cp\u003eSummary 201\u003c\/p\u003e \u003cp\u003eNotes 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 ARIMA Models 203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePhase 1: Identifying the Tentative Model 204\u003c\/p\u003e \u003cp\u003ePhase 2: Estimating and Diagnosing the Model Parameter Coefficients 213\u003c\/p\u003e \u003cp\u003ePhase 3: Creating a Forecast 216\u003c\/p\u003e \u003cp\u003eSeasonal ARIMA Models 216\u003c\/p\u003e \u003cp\u003eBox-Jenkins Overview 225\u003c\/p\u003e \u003cp\u003eExtending ARIMA Models to Include Explanatory Variables 226\u003c\/p\u003e \u003cp\u003eTransfer Functions 229\u003c\/p\u003e \u003cp\u003eNumerators and Denominators 229\u003c\/p\u003e \u003cp\u003eRational Transfer Functions 230\u003c\/p\u003e \u003cp\u003eARIMA Model Results 234\u003c\/p\u003e \u003cp\u003eSummary 235\u003c\/p\u003e \u003cp\u003eNotes 237\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Weighted Combined Forecasting Methods 239\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Weighted Combined Forecasting? 242\u003c\/p\u003e \u003cp\u003eDeveloping a Variance Weighted Combined Forecast 245\u003c\/p\u003e \u003cp\u003eGuidelines for the Use of Weighted Combined Forecasts 248\u003c\/p\u003e \u003cp\u003eSummary 250\u003c\/p\u003e \u003cp\u003eNotes 251\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Sensing, Shaping, and Linking Demand to Supply: A Case Study Using MTCA 253\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLinking Demand to Supply Using Multi-Tiered Causal Analysis 256\u003c\/p\u003e \u003cp\u003eCase Study: The Carbonated Soft Drink Story 259\u003c\/p\u003e \u003cp\u003eSummary 276\u003c\/p\u003e \u003cp\u003eAppendix 9A Consumer Packaged Goods Terminology 277\u003c\/p\u003e \u003cp\u003eAppendix 9B Adstock Transformations for Advertising GRP\/TRPs 279\u003c\/p\u003e \u003cp\u003eNotes 282\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 New Product Forecasting: Using Structured Judgment 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDifferences between Evolutionary and Revolutionary New Products 284\u003c\/p\u003e \u003cp\u003eGeneral Feeling about New Product Forecasting 286\u003c\/p\u003e \u003cp\u003eNew Product Forecasting Overview 288\u003c\/p\u003e \u003cp\u003eWhat Is a Candidate Product? 292\u003c\/p\u003e \u003cp\u003eNew Product Forecasting Process 293\u003c\/p\u003e \u003cp\u003eStructured Judgment Analysis 294\u003c\/p\u003e \u003cp\u003eStructured Process Steps 296\u003c\/p\u003e \u003cp\u003eStatistical Filter Step 303\u003c\/p\u003e \u003cp\u003eModel Step 305\u003c\/p\u003e \u003cp\u003eForecast Step 308\u003c\/p\u003e \u003cp\u003eSummary 313\u003c\/p\u003e \u003cp\u003eNotes 316\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Strategic Value Assessment: Assessing the Readiness of Your Demand Forecasting Process 317\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStrategic Value Assessment Framework 319\u003c\/p\u003e \u003cp\u003eStrategic Value Assessment Process 321\u003c\/p\u003e \u003cp\u003eSVA Case Study: XYZ Company 323\u003c\/p\u003e \u003cp\u003eSummary 351\u003c\/p\u003e \u003cp\u003eSuggested Reading 352\u003c\/p\u003e \u003cp\u003eNotes 352\u003c\/p\u003e \u003cp\u003eIndex 355\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406902862167,"sku":"9781118669396","price":54.62,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118669396.jpg?v=1730497502","url":"https:\/\/bookcurl.com\/products\/demanddriven-forecasting-9781118669396","provider":"Book Curl","version":"1.0","type":"link"}