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008			130429s2013 nju ob 001 0 eng
010			_a 2013017551
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019			_a869214322 _a880891537 _a961669378 _a962586748
020			_a9781118735640 _q(electronic bk.)
020			_a1118735641 _q(electronic bk.)
020			_a9781118735572 _q(electronic bk.)
020			_a1118735579 _q(electronic bk.)
020			_z9781118735510
020			_z111873551X
020			_z9781118669396 _q(cloth)
020			_z9781118691861
020			_z1118691865
020			_a1118669398
020			_a9781118669396
029	1		_aAU@ _b000050881356
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029	1		_aCHBIS _b010027049
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035			_a(OCoLC)841518549 _z(OCoLC)869214322 _z(OCoLC)880891537 _z(OCoLC)961669378 _z(OCoLC)962586748
037			_aCL0500000366 _bSafari Books Online
037			_a99CEEA45-A0DF-4EE8-B3F6-5F4AE9C76206 _bOverDrive, Inc. _nhttp://www.overdrive.com
042			_apcc
050	0	0	_aHB3730
072		7	_aBUS _x069030 _2bisacsh
082	0	0	_a330.01/12 _223
049			_aMAIN
100	1		_aChase, Charles.
245	1	0	_aDemand-driven forecasting : _ba structured approach to forecasting / _cCharles W. Chase, Jr.
250			_aSecond edition.
264		1	_aHoboken, New Jersey : _bJohn Wiley & Sons, Inc., _c[2013]
300			_a1 online resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aWiley & SAS business series
504			_aIncludes bibliographical references and index.
588	0		_aPrint version record and CIP data provided by publisher.
505	0		_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.
505	8		_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.
505	8		_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.
505	8		_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.
505	8		_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.
505	8		_aAnalysis of the Autocorrelation Plots.
520			_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.
650		0	_aEconomic forecasting.
650		0	_aBusiness forecasting.
650		0	_aForecasting.
650		4	_aBusiness forecasting.
650		4	_aEconomic forecasting.
650		4	_aForecasting.
650		7	_aBUSINESS & ECONOMICS _xEconomics _xTheory. _2bisacsh
650		7	_aBusiness forecasting. _2fast _0(OCoLC)fst00842699
650		7	_aEconomic forecasting. _2fast _0(OCoLC)fst00901942
650		7	_aForecasting. _2fast _0(OCoLC)fst00931721
655		4	_aElectronic books.
655		7	_aElectronic books. _2local
776	0	8	_iPrint version: _aChase, Charles. _tDemand-driven forecasting. _bSecond edition. _dHoboken, New Jersey : John Wiley & Sons, Inc., [2013] _z9781118669396 _w(DLC) 2013015670
830		0	_aWiley and SAS business series.
856	4	0	_uhttp://onlinelibrary.wiley.com/book/10.1002/9781118691861 _zWiley Online Library [Free Download only for SUST IP]
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