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  <titleInfo>
    <title>Demand-driven forecasting : a structured approach to forecasting</title>
  </titleInfo>
  <name type="personal">
    <namePart>Chase, Charles.</namePart>
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  <genre authority="">Electronic books.</genre>
  <genre authority="local">Electronic books.</genre>
  <originInfo>
    <place>
      <placeTerm type="code" authority="marccountry">nju</placeTerm>
    </place>
    <dateIssued encoding="marc">2013</dateIssued>
    <edition>Second edition.</edition>
    <issuance>monographic</issuance>
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  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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    <extent>1 online resource.</extent>
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  <abstract>An updated new edition of the comprehensive guide to better business forecasting Many 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. Demand-Driven Forecasting 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 a.</abstract>
  <tableOfContents>Demand-Driven Forecasting; Contents; Foreword; Preface; Acknowledgments; About the Author; Chapter 1 Demystifying Forecasting: Myths versus Reality; DATA COLLECTION, STORAGE, AND PROCESSING REALITY; ART-OF-FORECASTING MYTH; END-CAP DISPLAY DILEMMA; REALITY OF JUDGMENTAL OVERRIDES; OVEN CLEANER CONNECTION; MORE IS NOT NECESSARILY BETTER; REALITY OF UNCONSTRAINED FORECASTS, CONSTRAINED FORECASTS, AND PLANS; NORTHEAST REGIONAL SALES COMPOSITE FORECAST; HOLD-AND-ROLL MYTH; THE PLAN THAT WAS NOT GOOD ENOUGH; PACKAGE TO ORDER VERSUS MAKE TO ORDER; "DO YOU WANT FRIES WITH THAT?"; SUMMARY; NOTES.</tableOfContents>
  <tableOfContents>Chapter 2 What Is Demand-Driven Forecasting?TRANSITIONING FROM TRADITIONAL DEMAND FORECASTING; WHAT'S WRONG WITH THE DEMAND-GENERATION PICTURE?; FUNDAMENTAL FLAW WITH TRADITIONAL DEMAND GENERATION; RELYING SOLELY ON A SUPPLY-DRIVEN STRATEGY IS NOT THE SOLUTION; WHAT IS DEMAND-DRIVEN FORECASTING?; WHAT IS DEMAND SENSING AND SHAPING?; CHANGING THE DEMAND MANAGEMENT PROCESS IS ESSENTIAL; COMMUNICATION IS KEY; MEASURING DEMAND MANAGEMENT SUCCESS; BENEFITS OF A DEMAND-DRIVEN FORECASTING PROCESS; KEY STEPS TO IMPROVE THE DEMAND MANAGEMENT PROCESS.</tableOfContents>
  <tableOfContents>WHY HAVEN'T COMPANIES EMBRACED THE CONCEPT OF DEMAND-DRIVEN?Key Points; SUMMARY; NOTES; Chapter 3 Overview of Forecasting Methods; UNDERLYING METHODOLOGY; DIFFERENT CATEGORIES OF METHODS; HOW PREDICTABLE IS THE FUTURE?; SOME CAUSES OF FORECAST ERROR; SEGMENTING YOUR PRODUCTS TO CHOOSE THE APPROPRIATE FORECASTING METHOD; New Products Quadrant; Niche Brands Quadrant; Growth Brands Quadrant; Harvest Brands Quadrant; SUMMARY; NOTE; Chapter 4 Measuring Forecast Performance; "WE OVERACHIEVED OUR FORECAST, SO LET'S PARTY!"; PURPOSES FOR MEASURING FORECASTING PERFORMANCE.</tableOfContents>
  <tableOfContents>STANDARD STATISTICAL ERROR TERMSSPECIFIC MEASURES OF FORECAST ERROR; OUT-OF-SAMPLE MEASUREMENT; FORECAST VALUE ADDED; SUMMARY; NOTES; Chapter 5 Quantitative Forecasting Methods Using Time Series Data; UNDERSTANDING THE MODEL-FITTING PROCESS; INTRODUCTION TO QUANTITATIVE TIME SERIES METHODS; QUANTITATIVE TIME SERIES METHODS; MOVING AVERAGING; EXPONENTIAL SMOOTHING; SINGLE EXPONENTIAL SMOOTHING; HOLT'S TWO-PARAMETER METHOD; HOLT'S-WINTERS' METHOD; WINTERS' ADDITIVE SEASONALITY; Multiplicative versus Additive Seasonality; SUMMARY; NOTES; Chapter 6 Regression Analysis; REGRESSION METHODS.</tableOfContents>
  <tableOfContents>SIMPLE REGRESSIONCORRELATION COEFFICIENT; COEFFICIENT OF DETERMINATION; MULTIPLE REGRESSION; DATA VISUALIZATION USING SCATTER PLOTS AND LINE GRAPHS; CORRELATION MATRIX; MULTICOLLINEARITY; ANALYSIS OF VARIANCE; F-TEST; ADJUSTED R2; PARAMETER COEFFICIENTS; t-TEST; P-VALUES; VARIANCE INFLATION FACTOR; DURBIN-WATSON STATISTIC; INTERVENTION VARIABLES (OR DUMMY VARIABLES); REGRESSION MODEL RESULTS; KEY ACTIVITIES IN BUILDING A MULTIPLE REGRESSION MODEL; CAUTIONS ABOUT REGRESSION MODELS; SUMMARY; NOTES; Chapter 7 ARIMA Models; PHASE 1: IDENTIFYING THE TENTATIVE MODEL; Stationarity.</tableOfContents>
  <tableOfContents>Analysis of the Autocorrelation Plots.</tableOfContents>
  <note type="statement of responsibility">Charles W. Chase, Jr.</note>
  <note>Includes bibliographical references and index.</note>
  <subject authority="lcsh">
    <topic>Economic forecasting</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Business forecasting</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Forecasting</topic>
  </subject>
  <subject>
    <topic>Business forecasting</topic>
  </subject>
  <subject>
    <topic>Economic forecasting</topic>
  </subject>
  <subject>
    <topic>Forecasting</topic>
  </subject>
  <subject authority="bisacsh">
    <topic>BUSINESS &amp; ECONOMICS</topic>
    <topic>Economics</topic>
    <topic>Theory</topic>
  </subject>
  <subject authority="fast">
    <topic>Business forecasting</topic>
  </subject>
  <subject authority="fast">
    <topic>Economic forecasting</topic>
  </subject>
  <subject authority="fast">
    <topic>Forecasting</topic>
  </subject>
  <classification authority="lcc">HB3730</classification>
  <classification authority="ddc" edition="23">330.01/12</classification>
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      <title>Demand-driven forecasting</title>
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    <name>
      <namePart>Chase, Charles.</namePart>
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    <originInfo>
      <publisher>Hoboken, New Jersey : John Wiley &amp; Sons, Inc., [2013]</publisher>
      <edition>Second edition.</edition>
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    <identifier type="local">(DLC)  2013015670</identifier>
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      <title>Wiley and SAS business series</title>
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