Improving forecasts with integrated business planning : from short-term to long-term demand planning enabled by SAP IBP /: from short-term to long-term demand planning enabled by SAP IBP. ([2019])

• Record Type: Book
• Further Information: Note: Ganesh Sankaran [and 3 others].
• Authors: Sankaran, Ganesh
• Contents: Intro; Preface; Reference; Contents; About the Authors; 1 Introduction; 1.1 The What and Why of Forecasting; 1.2 Planning Types; 1.3 Scope of This Book; 1.4 Statistical Forecasting Process Framework; 1.5 Purpose; 1.6 Key Challenges of Forecasting; 1.6.1 Seasonality; 1.6.2 Outliers; 1.6.3 Intermittent Demand; 1.6.4 Human Judgment; 1.6.5 Effectiveness and Efficiency; References; 2 Building Demand Planning Organization and Competencies; 2.1 Organizational Structures, Placement; 2.1.1 Some Insights on Organizational Structures and Roles; 2.1.2 Some Insights on Organizational Placement 2.2 Responsibilities, Interactions, Size of a Team2.2.1 Responsibilities (Incl. Job Profiles) and Impact; 2.2.2 Insights on Role Comparison and Size of the Team; 2.2.2.1 Insights on Role Comparison; 2.2.2.2 Insights on Size of the Team; 2.3 Concluding Remarks; References; 3 Efficient and Effective Usage of Out-of-the-Box Statistical Forecasting; 3.1 Model Class Selection; 3.2 Gathering, Analyzing and Cleansing Data; 3.2.1 SAP Use Case: Outlier Detection Variance Method; 3.2.2 SAP Use Case: Outlier Detection Interquartile Range; 3.3 Forecast Model Selection; 3.3.1 Averages 3.3.2 SAP Use Case: Moving Average, Moving Weighted Average3.3.3 Single Exponential Smoothing (SES); 3.3.4 SAP Use Case: SES; 3.3.5 Double Exponential Smoothing (DES); 3.3.6 SAP Use Case: DES; 3.3.7 Triple Exponential Smoothing (TES); 3.3.8 SAP Use Case: TES with Solver Parameters; 3.3.9 SAP Use Case: Statistical Forecasting on Aggregated and Product Level; 3.3.10 SAP Use Case: Automated Exponential Smoothing Within SES; 3.3.11 SAP Use Case: Automated Exponential Smoothing Within DES; 3.3.12 SAP Use Case: Automated Exponential Smoothing Within TES 3.3.13 SAP Use Case: Automated Exponential Smoothing with Best Results3.3.14 Adaptive Response Rate Single Exponential Smoothing; 3.3.15 SAP Use Case: Adaptive Response Rate Single Exponential Smoothing; 3.3.16 Brown's Linear Exponential Smoothing; 3.3.17 SAP Use Case: Brown's Linear Single Exponential Smoothing; 3.3.18 Croston Method; 3.3.19 SAP Use Case: Croston Method; 3.3.20 Multiple Linear Regression Model; 3.3.21 SAP Use Case: Multiple Linear Regression; 3.3.22 Auto-ARIMA/SARIMA; 3.3.23 SAP Use Case: ARIMA/SARIMA; 3.3.24 Automatic Model Selection; 3.3.25 SAP Use Case: Best Fit 3.3.26 SAP Use Case: Weighted Combined Forecast from SES, DES, TES3.4 Validate Model; 3.5 Make Predictions and Observations; 3.6 Evaluate a Model; 3.6.1 SAP Use Case: Tracking Signal; 3.6.2 SAP Use Case: Forecast with Prediction Interval; 3.7 Refine Model; 3.8 Concluding Remarks; References; 4 Custom Method to Forecast Seasonal Products; 4.1 Segmenting and Classifying Demand; 4.1.1 SAP Use Case: Classifying Demand; 4.1.2 SAP Use Case: ABC/XYZ Segmentation; 4.1.3 SAP Use Case: Use of CoV for Data Cleansing; 4.2 Identifying Seasonality
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2019
• Extent: 1 online resource
• Subjects: 658.4/038; Management information systems; Sales management; Business logistics; Business Process Management; Information Systems Applications (incl. Internet); Sales/Distribution; Supply Chain Management; Business Information Systems; Electronic books
• Languages: English
• ISBNs: 9783030053819; 3030053814
• Notes: Note: Includes bibliographical references.; Note: Description based on online resource; title from digital title page (viewed on April 12, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.