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Consumption-based forecasting and planning : predicting changing demand patterns in the new digital economy / / Charles W. Chase
| Consumption-based forecasting and planning : predicting changing demand patterns in the new digital economy / / Charles W. Chase |
| Autore | Chase Charles |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021] |
| Descrizione fisica | 1 online resource (270 pages) |
| Disciplina | 658.40355 |
| Collana | Wiley and SAS Business Ser. |
| Soggetto topico |
Demand (Economic theory)
Business logistics Business forecasting |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-80987-8
1-119-80989-4 1-119-80988-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Acknowledgments -- About the Author -- Chapter 1 The Digital Economy and Unexpected Disruptions -- Disruptions Driving Complex Consumer Dynamics -- Impact of the Digital Economy -- What Does All This Mean? -- Shifting to a Consumer-Centric Approach -- The Analytics Gap -- Why Predictive and Anticipatory Analytics? -- Difference Between Predictive and Anticipatory Analytics -- The Data Gap -- The Impact of the COVID-19 Crisis on Demand Planning -- Closing Thoughts -- Notes -- Chapter 2 A Wake-up Call for Demand Management -- Demand Uncertainty Is Driving Change -- Challenges Created by Demand Uncertainty -- Ongoing "Bullwhip" Effect -- When Will We Learn from Our Past Mistakes? -- Why Are Companies Still Cleansing Historical Demand? -- Consumer Goods Company Case Study -- Primary Obstacles to Achieving Planning Goals -- Why Do Companies Continue to Dismiss the Value of Demand Management? -- Six Steps to Predicting Shifting Consumer Demand Patterns -- Closing Thoughts -- Notes -- Chapter 3 Why Data and Analytics Are Important -- Analytics Maturity -- Collecting and Storing Consumer Data -- Why Is the Data Ecosystem Important? -- Why Data and Analytics? -- Building Trust in the Data -- AI/Machine Learning Creates Trust Challenges -- Pursuit of Explainability -- Engage with Domain Experts and Business Specialists -- Why Is Downstream Data Important? -- Demand Management Data Challenges -- How Much Data Should Be Used? -- Demand-Signal Repositories -- What Are Demand Signal Repositories? -- Benefits of a Demand Signal Repository -- What Are Users Looking to Gain? -- Why Is It Important? -- What Is Consumption-Based Analytics? -- Closing Thoughts -- Notes -- Chapter 4 Consumption-Based Forecasting and Planning -- A Change of Mindset Is Required.
Why Consumption-Based Forecasting and Planning? -- What Is Consumption-Based Forecasting and Planning? -- Consumption-Based Forecasting and Planning Case Study -- Consumption-Based Forecasting and Planning Six-Step Process -- Understanding the Relationship Between Demand and Supply -- Why Move Demand Planning Downstream Closer to the Consumer? -- The Integrated Business Planning Connection -- Demand Management Champion -- Closing Thoughts -- Notes -- Chapter 5 AI/Machine Learning Is Disrupting Demand Forecasting -- Straight Talk About Forecasting and Machine Learning -- What Is the Difference Between Expert Systems and Machine Learning? -- Do Machine Learning Algorithms Outperform Traditional Forecasting Methods? -- M4 Competition -- M5 Competition -- Basic Knowledge Regarding Neural Networks -- Why Combine ML Models? -- Challenges Using Machine Learning Models -- Data Challenges and Considerations -- Black Box Effects -- Interpretation of the ML Model Output -- Case Study 1 -- Using Machine Learning to Enhance Short-Term Demand Sensing -- A Practical Application of Demand Sensing Using Machine Learning -- Converting Weekly Forecasts to Daily Forecasts -- Overall Results -- Weekly Forecast Results -- Daily Forecast Results -- Conclusions -- Case Study 2: Using Advanced Analytics to Adapt to Changing Consumer Demand Patterns -- Situation -- Approach to Short-Term Demand Sensing -- Data Investigation -- Analytics Approach -- Results -- Delivering Real-Time Results -- Closing Thoughts -- Notes -- Chapter 6 Intelligent Automation Is Disrupting Demand Planning -- What Is "Intelligent Automation"? -- How Can Intelligent Automation Enhance Existing Processes? -- What Is Forecast Value Add? -- Do Manual Overrides Add Value? -- Case Study: Using Intelligent Automation to Improve Demand Planners' FVA -- A New IA Approach Called "Assisted Demand Planning". Process Approach -- Process Steps -- Results -- Closing Thoughts -- Notes -- Chapter 7 The Future Is Cloud Analytics and Analytics at the Edge -- Why Cloud Analytics? -- What Are the Differences Between Containers and Virtual Machines? -- Why Cloud Analytics? -- Predictive Analytics Are Creating IT Disruptions -- Data Is Influencing Software Development -- Why Cloud-native Solutions? -- Why Does All This Matter? -- Cloud-Native Forecasting and Planning Solutions -- Why Move to a Cloud-Native Demand Planning Platform? -- Why "Analytics at the Edge"? -- Edge Analytics Benefits -- Edge Analytics Limitations -- Forecasting at the Edge -- Cloud Analytics Versus Edge Analytics -- Closing Thoughts -- Notes -- Index -- EULA. |
| Record Nr. | UNINA-9910554842503321 |
Chase Charles
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Consumption-based forecasting and planning : predicting changing demand patterns in the new digital economy / / Charles W. Chase
| Consumption-based forecasting and planning : predicting changing demand patterns in the new digital economy / / Charles W. Chase |
| Autore | Chase Charles |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021] |
| Descrizione fisica | 1 online resource (270 pages) |
| Disciplina | 658.40355 |
| Collana | Wiley and SAS Business |
| Soggetto topico |
Demand (Economic theory)
Business logistics Business forecasting |
| ISBN |
1-119-80987-8
1-119-80989-4 1-119-80988-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Acknowledgments -- About the Author -- Chapter 1 The Digital Economy and Unexpected Disruptions -- Disruptions Driving Complex Consumer Dynamics -- Impact of the Digital Economy -- What Does All This Mean? -- Shifting to a Consumer-Centric Approach -- The Analytics Gap -- Why Predictive and Anticipatory Analytics? -- Difference Between Predictive and Anticipatory Analytics -- The Data Gap -- The Impact of the COVID-19 Crisis on Demand Planning -- Closing Thoughts -- Notes -- Chapter 2 A Wake-up Call for Demand Management -- Demand Uncertainty Is Driving Change -- Challenges Created by Demand Uncertainty -- Ongoing "Bullwhip" Effect -- When Will We Learn from Our Past Mistakes? -- Why Are Companies Still Cleansing Historical Demand? -- Consumer Goods Company Case Study -- Primary Obstacles to Achieving Planning Goals -- Why Do Companies Continue to Dismiss the Value of Demand Management? -- Six Steps to Predicting Shifting Consumer Demand Patterns -- Closing Thoughts -- Notes -- Chapter 3 Why Data and Analytics Are Important -- Analytics Maturity -- Collecting and Storing Consumer Data -- Why Is the Data Ecosystem Important? -- Why Data and Analytics? -- Building Trust in the Data -- AI/Machine Learning Creates Trust Challenges -- Pursuit of Explainability -- Engage with Domain Experts and Business Specialists -- Why Is Downstream Data Important? -- Demand Management Data Challenges -- How Much Data Should Be Used? -- Demand-Signal Repositories -- What Are Demand Signal Repositories? -- Benefits of a Demand Signal Repository -- What Are Users Looking to Gain? -- Why Is It Important? -- What Is Consumption-Based Analytics? -- Closing Thoughts -- Notes -- Chapter 4 Consumption-Based Forecasting and Planning -- A Change of Mindset Is Required.
Why Consumption-Based Forecasting and Planning? -- What Is Consumption-Based Forecasting and Planning? -- Consumption-Based Forecasting and Planning Case Study -- Consumption-Based Forecasting and Planning Six-Step Process -- Understanding the Relationship Between Demand and Supply -- Why Move Demand Planning Downstream Closer to the Consumer? -- The Integrated Business Planning Connection -- Demand Management Champion -- Closing Thoughts -- Notes -- Chapter 5 AI/Machine Learning Is Disrupting Demand Forecasting -- Straight Talk About Forecasting and Machine Learning -- What Is the Difference Between Expert Systems and Machine Learning? -- Do Machine Learning Algorithms Outperform Traditional Forecasting Methods? -- M4 Competition -- M5 Competition -- Basic Knowledge Regarding Neural Networks -- Why Combine ML Models? -- Challenges Using Machine Learning Models -- Data Challenges and Considerations -- Black Box Effects -- Interpretation of the ML Model Output -- Case Study 1 -- Using Machine Learning to Enhance Short-Term Demand Sensing -- A Practical Application of Demand Sensing Using Machine Learning -- Converting Weekly Forecasts to Daily Forecasts -- Overall Results -- Weekly Forecast Results -- Daily Forecast Results -- Conclusions -- Case Study 2: Using Advanced Analytics to Adapt to Changing Consumer Demand Patterns -- Situation -- Approach to Short-Term Demand Sensing -- Data Investigation -- Analytics Approach -- Results -- Delivering Real-Time Results -- Closing Thoughts -- Notes -- Chapter 6 Intelligent Automation Is Disrupting Demand Planning -- What Is "Intelligent Automation"? -- How Can Intelligent Automation Enhance Existing Processes? -- What Is Forecast Value Add? -- Do Manual Overrides Add Value? -- Case Study: Using Intelligent Automation to Improve Demand Planners' FVA -- A New IA Approach Called "Assisted Demand Planning". Process Approach -- Process Steps -- Results -- Closing Thoughts -- Notes -- Chapter 7 The Future Is Cloud Analytics and Analytics at the Edge -- Why Cloud Analytics? -- What Are the Differences Between Containers and Virtual Machines? -- Why Cloud Analytics? -- Predictive Analytics Are Creating IT Disruptions -- Data Is Influencing Software Development -- Why Cloud-native Solutions? -- Why Does All This Matter? -- Cloud-Native Forecasting and Planning Solutions -- Why Move to a Cloud-Native Demand Planning Platform? -- Why "Analytics at the Edge"? -- Edge Analytics Benefits -- Edge Analytics Limitations -- Forecasting at the Edge -- Cloud Analytics Versus Edge Analytics -- Closing Thoughts -- Notes -- Index -- EULA. |
| Record Nr. | UNINA-9910830942503321 |
|
Chase Charles
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Demand-driven forecasting [[electronic resource]] : a structured approach to forecasting / / Charles W. Chase, Jr
| Demand-driven forecasting [[electronic resource]] : a structured approach to forecasting / / Charles W. Chase, Jr |
| Autore | Chase Charles |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2013 |
| Descrizione fisica | 1 online resource (386 p.) |
| Disciplina | 330.01/12 |
| Collana | Wiley & SAS Business Series |
| Soggetto topico |
Economic forecasting
Business forecasting Forecasting |
| ISBN |
1-118-73557-9
1-118-69186-5 1-118-73564-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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 |
| Record Nr. | UNINA-9910139044003321 |
|
Chase Charles
|
||
| Hoboken, N.J., : Wiley, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Demand-driven forecasting : a structured approach to forecasting / / Charles W. Chase, Jr
| Demand-driven forecasting : a structured approach to forecasting / / Charles W. Chase, Jr |
| Autore | Chase Charles |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2013 |
| Descrizione fisica | 1 online resource (386 p.) |
| Disciplina | 330.01/12 |
| Collana | Wiley & SAS Business Series |
| Soggetto topico |
Economic forecasting
Business forecasting Forecasting |
| ISBN |
1-118-73557-9
1-118-69186-5 1-118-73564-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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 |
| Record Nr. | UNINA-9910815808903321 |
|
Chase Charles
|
||
| Hoboken, N.J., : Wiley, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Demand-driven forecasting [[electronic resource] ] : a structured approach to forecasting / / Charles Chase
| Demand-driven forecasting [[electronic resource] ] : a structured approach to forecasting / / Charles Chase |
| Autore | Chase Charles |
| Pubbl/distr/stampa | Hoboken, N.J., : John Wiley & Sons, 2009 |
| Descrizione fisica | 1 online resource (291 p.) |
| Disciplina | 658.40355 |
| Collana | Wiley & SAS Business series |
| Soggetto topico |
Economic forecasting
Business forecasting Forecasting |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-20361-9
1-282-36930-X 9786612369308 0-470-53099-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Demystifying forecasting : myths versus reality -- What is demand-driven forecasting? -- Overview of forecasting methods -- Measuring forecast performance -- Quantitative forecasting methods using time series data -- Quantitative forecasting methods using causal data -- Weighted combined forecasting methods -- Sensing, shaping, and linking demand to supply : a case study using MTCA -- Strategic value assessment : assessing the readiness of your demand forecasting process. |
| Record Nr. | UNINA-9910139791903321 |
|
Chase Charles
|
||
| Hoboken, N.J., : John Wiley & Sons, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Demand-driven forecasting [[electronic resource] ] : a structured approach to forecasting / / Charles Chase
| Demand-driven forecasting [[electronic resource] ] : a structured approach to forecasting / / Charles Chase |
| Autore | Chase Charles |
| Pubbl/distr/stampa | Hoboken, N.J., : John Wiley & Sons, 2009 |
| Descrizione fisica | 1 online resource (291 p.) |
| Disciplina | 658.40355 |
| Collana | Wiley & SAS Business series |
| Soggetto topico |
Economic forecasting
Business forecasting Forecasting |
| ISBN |
1-119-20361-9
1-282-36930-X 9786612369308 0-470-53099-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Demystifying forecasting : myths versus reality -- What is demand-driven forecasting? -- Overview of forecasting methods -- Measuring forecast performance -- Quantitative forecasting methods using time series data -- Quantitative forecasting methods using causal data -- Weighted combined forecasting methods -- Sensing, shaping, and linking demand to supply : a case study using MTCA -- Strategic value assessment : assessing the readiness of your demand forecasting process. |
| Record Nr. | UNINA-9910830497903321 |
|
Chase Charles
|
||
| Hoboken, N.J., : John Wiley & Sons, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Demand-driven forecasting [[electronic resource] ] : a structured approach to forecasting / / Charles Chase
| Demand-driven forecasting [[electronic resource] ] : a structured approach to forecasting / / Charles Chase |
| Autore | Chase Charles |
| Pubbl/distr/stampa | Hoboken, N.J., : John Wiley & Sons, 2009 |
| Descrizione fisica | 1 online resource (291 p.) |
| Disciplina | 658.40355 |
| Collana | Wiley & SAS Business series |
| Soggetto topico |
Economic forecasting
Business forecasting Forecasting |
| ISBN |
1-119-20361-9
1-282-36930-X 9786612369308 0-470-53099-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Demystifying forecasting : myths versus reality -- What is demand-driven forecasting? -- Overview of forecasting methods -- Measuring forecast performance -- Quantitative forecasting methods using time series data -- Quantitative forecasting methods using causal data -- Weighted combined forecasting methods -- Sensing, shaping, and linking demand to supply : a case study using MTCA -- Strategic value assessment : assessing the readiness of your demand forecasting process. |
| Record Nr. | UNINA-9910877077203321 |
|
Chase Charles
|
||
| Hoboken, N.J., : John Wiley & Sons, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Next generation demand management : people, process, analytics, and technology / / Charles W. Chase
| Next generation demand management : people, process, analytics, and technology / / Charles W. Chase |
| Autore | Chase Charles |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2016 |
| Descrizione fisica | 1 online resource (289 pages) : illustrations |
| Disciplina | 658.8 |
| Collana |
Wiley & SAS Business Series
THEi Wiley ebooks |
| Soggetto topico |
Business forecasting
Business logistics Supply and demand - Forecasting |
| ISBN |
1-119-22739-9
1-119-44959-6 1-119-22738-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910270946003321 |
|
Chase Charles
|
||
| Hoboken, New Jersey : , : Wiley, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||