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Record Nr. |
UNINA9910139044003321 |
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Autore |
Chase Charles |
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Titolo |
Demand-driven forecasting [[electronic resource]] : a structured approach to forecasting / / Charles W. Chase, Jr |
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Pubbl/distr/stampa |
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Hoboken, N.J., : Wiley, c2013 |
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ISBN |
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1-118-73557-9 |
1-118-69186-5 |
1-118-73564-1 |
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Edizione |
[2nd ed.] |
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Descrizione fisica |
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1 online resource (386 p.) |
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Collana |
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Wiley & SAS Business Series |
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Disciplina |
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Soggetti |
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Economic forecasting |
Business forecasting |
Forecasting |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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 |
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 |
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DEMAND-DRIVEN FORECASTING PROCESS; KEY STEPS TO IMPROVE THE DEMAND MANAGEMENT PROCESS |
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 |
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 |
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 |
Analysis of the Autocorrelation Plots |
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Sommario/riassunto |
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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 |
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