LEADER 01011nam0-22002891i-450- 001 990001711200403321 005 20051117151330.0 035 $a000171120 035 $aFED01000171120 035 $a(Aleph)000171120FED01 035 $a000171120 100 $a20030910d1937----km-y0itay50------ba 101 0 $aita 200 1 $a<>fecondazione artificiale degli animali domestici$econtributo tecnico-sperimentale della provincia di Pavia 210 $aPavia$cIstituto Pavese di Arti Grafiche$d1937 215 $a42 p.$d29 cm 300 $aIn testa al front.: P.N.F., Federazione pavese dei fasci di combattimento, Comitato intersindacale provinciale, Sezione agricola forestale 610 0 $aInseminazione artificiale 610 0 $aZootecnica 676 $a636.082 45 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990001711200403321 952 $a60 636.082 4 A 1$b14324$fFAGBC 959 $aFAGBC 996 $aFecondazione artificiale degli animali domestici$9359585 997 $aUNINA LEADER 06763nam 2200553 450 001 9910830942503321 005 20231110223717.0 010 $a1-119-80987-8 010 $a1-119-80989-4 010 $a1-119-80988-6 035 $a(CKB)4100000011991596 035 $a(MiAaPQ)EBC6686364 035 $a(Au-PeEL)EBL6686364 035 $a(OCoLC)1263025982 035 $a(CaSebORM)9781119809869 035 $a(EXLCZ)994100000011991596 100 $a20220413d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aConsumption-based forecasting and planning $epredicting changing demand patterns in the new digital economy /$fCharles W. Chase 210 1$aHoboken, New Jersey :$cJohn Wiley & Sons, Inc.,$d[2021] 210 4$d©2021 215 $a1 online resource (270 pages) 225 1 $aWiley and SAS Business 300 $aIncludes index. 311 $a1-119-80986-X 327 $aCover -- 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. 327 $aWhy 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". 327 $aProcess 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. 330 $a"Helps companies understand the short-term changes in consumer demand patterns as a result of the digital economy, and COVID-19. Also, what is driving those changing consumer demand patterns (price, sales promotions, in-store merchandizing, epidemiological, economic and other related factors like unplanned events related to the pandemic crisis). Provides real case examples using real data, and how to apply advanced analytics and machine learning to solve current business problems. Provides a framework for changing the way demand forecasting and planning are done, as well as the change management requirements for sustainability"--$cProvided by publisher. 410 0$aWiley and SAS business series. 606 $aDemand (Economic theory) 606 $aBusiness logistics 606 $aBusiness forecasting 615 0$aDemand (Economic theory) 615 0$aBusiness logistics. 615 0$aBusiness forecasting. 676 $a658.40355 700 $aChase$b Charles$0967720 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830942503321 996 $aConsumption-based forecasting and planning$94098946 997 $aUNINA