{"product_id":"demand-driven-forecasting-isbn-9781118669396","title":"Demand-Driven Forecasting","description":"\u003cb\u003eAn updated new edition of the comprehensive guide to better business forecasting\u003c\/b\u003e \u003cp\u003eMany companies still look at quantitative forecasting methods with suspicion, but a new awareness is emerging across many industries as more businesses and professionals recognize the value of integrating demand data (point-of-sale and syndicated scanner data) into the forecasting process. \u003ci\u003eDemand-Driven Forecasting\u003c\/i\u003e equips you with solutions that can sense, shape, and predict future demand using highly sophisticated methods and tools. From a review of the most basic forecasting methods to the most advanced and innovative techniques in use today, this guide explains demand-driven forecasting, offering a fundamental understanding of the quantitative methods used to sense, shape, and predict future demand within a structured process. Offering a complete overview of the latest business forecasting concepts and applications, this revised \u003ci\u003eSecond Edition\u003c\/i\u003e of \u003ci\u003eDemand-Driven Forecasting\u003c\/i\u003e is the perfect guide for professionals who need to improve the accuracy of their sales forecasts.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eCompletely updated to include the very latest concepts and methods in forecasting\u003c\/li\u003e \u003cli\u003eIncludes real case studies and examples, actual data, and graphical displays and tables to illustrate how effective implementation works\u003c\/li\u003e \u003cli\u003eIdeal for CEOs, CFOs, CMOs, vice presidents of supply chain, vice presidents of demand forecasting and planning, directors of demand forecasting and planning, supply chain managers, demand planning managers, marketing analysts, forecasting analysts, financial managers, and any other professional who produces or contributes to forecasts\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eAccurate forecasting is vital to success in today's challenging business climate. \u003ci\u003eDemand-Driven Forecasting\u003c\/i\u003e offers proven and effective insight on making sure your forecasts are right on the money.\u003c\/p\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 Management 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\u003e\u003cb\u003eIntroduction to Quantitative Time Series Methods 130\u003c\/b\u003e\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 R 2 180\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    \u003cp\u003e\u003cb\u003eCHARLES W. CHASE J\u003csmall\u003eR\u003c\/small\u003e.\u003c\/b\u003e is the Chief Industry Consultant in SAS's Manufacturing \u0026amp; Supply Chain Global Practice, where he is the principal architect and strategist for delivering demand planning and forecasting solutions to improve SAS customers' supply chain efficiencies. He has more than twenty-six years of experience in the consumer packaged goods industry, and is an expert in sales forecasting, market response modeling, econometrics, and supply chain management.     \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDEMAND-DRIVEN FORECASTING\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eTo become demand-driven, your business needs to identify the right market signals, build demand-sensing capabilities, define demand-shaping processes, and effectively translate demand signals to create a more effective response. Doable? Now it is, with \u003ci\u003eDemand-Driven Forecasting, Second Edition.\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003eDistinctive for its attention to practical demand forecasting challenges, \u003ci\u003eDemand-Driven Forecasting, Second Edition\u003c\/i\u003e is completely updated with a detailed look at improving the forecasting process to better meet customer demands. Author and demand forecasting leader Charles Chase presents both comprehensive coverage of statistical methods as well as how to apply them in practice within a demand-driven forecasting process using actual data and examples. \u003c\/p\u003e\u003cp\u003eFeaturing new case studies and examples, \u003ci\u003eDemand-Driven Forecasting, Second Edition\u003c\/i\u003e includes the contributions of the latest theoretical developments, while presenting current empirical findings and technology advancements. The new edition features new coverage on demand-shifting, nonseasonal and seasonal ARIMA models, transfer functions, and cross-correlation function plots. \u003c\/p\u003e\u003cp\u003ePlus, the \u003ci\u003eSecond Edition\u003c\/i\u003e explores: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eWeighted combined modeling\u003c\/li\u003e \u003cli\u003eNew product forecasting using structured judgment\u003c\/li\u003e \u003cli\u003eApplication of additive and multiplicative Winters methods\u003c\/li\u003e \u003cli\u003eUsing graphical methods and plots to understand statistical output\u003c\/li\u003e \u003cli\u003eThe most recent developments in demand-driven forecasting\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eEssential reading for every professional responsible for forecasting and demand planning, \u003ci\u003eDemand-Driven Forecasting, Second Edition\u003c\/i\u003e provides you with proven processes, methodologies, and performance metrics you can apply immediately for significant improvement in forecast accuracy.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989036089573,"sku":"NP9781118669396","price":79.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118669396.jpg?v=1761782532","url":"https:\/\/k12savings.com\/es\/products\/demand-driven-forecasting-isbn-9781118669396","provider":"K12savings","version":"1.0","type":"link"}