LEADER 05562nam 2200757 a 450 001 9910815808903321 005 20200520144314.0 010 $a1-118-73557-9 010 $a1-118-69186-5 010 $a1-118-73564-1 035 $a(CKB)2550000001102599 035 $a(EBL)1315864 035 $a(SSID)ssj0000917900 035 $a(PQKBManifestationID)11483966 035 $a(PQKBTitleCode)TC0000917900 035 $a(PQKBWorkID)10893234 035 $a(PQKB)10562601 035 $a(DLC) 2013017551 035 $a(Au-PeEL)EBL1315864 035 $a(CaPaEBR)ebr10734639 035 $a(CaONFJC)MIL505082 035 $a(CaSebORM)9781118735572 035 $a(MiAaPQ)EBC1315864 035 $a(OCoLC)841518549 035 $a(OCoLC)869214322 035 $a(OCoLC)ocn869214322 035 $a(EXLCZ)992550000001102599 100 $a20130805d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDemand-driven forecasting $ea structured approach to forecasting /$fCharles W. Chase, Jr 205 $a2nd ed. 210 $aHoboken, N.J. $cWiley$dc2013 215 $a1 online resource (386 p.) 225 1 $aWiley & SAS Business Series 300 $aIncludes index. 311 $a1-118-66939-8 311 $a1-299-73831-1 320 $aIncludes bibliographical references and index. 327 $aDemand-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 327 $aChapter 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 327 $aWHY 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 327 $aSTANDARD 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 327 $aSIMPLE 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 327 $aAnalysis of the Autocorrelation Plots 330 $aAn updated new edition of the comprehensive guide to better business forecasting Many companies still look at quantitative forecasting methods with suspicion, but a new awareness is emerging across many industries as more businesses and professionals recognize the value of integrating demand data (point-of-sale and syndicated scanner data) into the forecasting process. Demand-Driven Forecasting equips you with solutions that can sense, shape, and predict future demand using highly sophisticated methods and tools. From a review of the most basic forecasting methods to the most a 410 0$aWiley and SAS business series. 606 $aEconomic forecasting 606 $aBusiness forecasting 606 $aForecasting 615 0$aEconomic forecasting. 615 0$aBusiness forecasting. 615 0$aForecasting. 676 $a330.01/12 700 $aChase$b Charles$0967720 712 02$aWiley Online Library (Servicio en línea) 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910815808903321 996 $aDemand-driven forecasting$93925524 997 $aUNINA