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
- Title:
- 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])
- Main Title:
- Improving forecasts with integrated business planning : from short-term to long-term demand planning enabled by SAP IBP
- 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 onIntro; 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 … (more)
- 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).
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- British Library HMNTS - ELD.DS.393797
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