With SAP Integrated Business Planning for Supply Chain (SAP IBP), demand management functionality spans the entire planning spectrum from more strategic planning like Sales and Operations Planning to operational demand planning, including Demand Sensing for short-term demand planning. Statistical models allow you to develop accurate mid-term forecasts while Demand Sensing allows you to react to those near term demand changes that occur while SAP Integrated Business Planning provides the platform to collaborate between groups (for example, Marketing, Sales, and Supply Chain) to add business intelligence to the forecast for the most accurate forecast you can get. All of this allows you to ensure that you are reacting to changes in demand and deploying the product to the right place to satisfy that demand.
The table shows the tools that are available in SAP IBP for demand.
| Tool | Description |
|---|---|
| Statistical Forecasting | If you want to create long-term forecasts, you need a forecast model with pre-processing, forecasting and post-processing algorithms, an application job template to run the forecast and the SAP IBP, add-in for Microsoft Excel to see the results |
| Demand Sensing | If you want to create short-term forecasts based on the Demand Sense, your planning area and forecast model need to fulfill special requirements |
| Promotion Integration | If you want to create a more accurate demand plan, you can integrate trade promotions data from external trade promotions planning systems. That is, you can run statistical forecasting on a data basis that doesn't contain the impact of promotions on the historical sales figures. At the end of the forecast process, you can include the planned future promotions in your final demand plan |
| ABC/XYZ Segmentation | If you want to focus on a set of items in demand planning, you can categorize the selected planning objects based on various segmentation measures and thresholds |
| New Product Introduction | If you want to create forecasts for new products, for which no historical values are available, you can identify and consider products that have a similar historical sales pattern and assign them as reference products to the new product |
Time Series Analysis | If you want to identify patterns in individual time series data such as Trend, Seasonality, Continuous, and Irregular as well as significant changes, for example, level shifts where the mean of the time series values alters significantly or trend changes where the direction or slope of a trend alters significantly. Based on these insights, you can select the most suitable forecast algorithms |
Driver-Based Planning | If you want to capture business drivers such as risks or opportunities. You can capture qualitative and quantitative information for them by evaluating their likely effects and expressing those effects in key figure values for multiple planning levels. You can also consider the drivers in your supply chain plan |
SAP provides a predefined model for demand planning in IBP – SAP6 standard planning area for demand.
In the standard statistical forecasting process, demand forecasts can be created or modified for a longer period such as 2-3 years for long-term forecasting or 12-18 months for mid-term forecasting.
As a demand planner, you run a forecast each week or month for multiple products or product groups in the background with mass processing, then review and adjust the figures interactively using the SAP IBP, add-in for Microsoft Excel.
As an analyst or data scientist, you can create and edit forecast models as required to achieve a variety of forecasting results.
There are a lot of different statistical models in SAP IBP which will be described further.
The figure shows the standard statistical forecasting process.
