Lesson Overview
This lesson describes the central features of the business scenario SAP Forecasting and Replenishment (SAP F&R).
Lesson Objectives
After completing this lesson, you will be able to:
• Describe the replenishment process using SAP F&R.
• Explain the special features of a causal-based forecast.
• Name the systems that you require to use SAP F&R.
Business Example
You want to optimize replenishment at your stores or distribution centers by:
• Using a very good forecast that observes specific events
• Displaying purchase order proposals, which can be released at a very high percentage rate without any additional processing.
• Highly automate the process
• Monitor the process using the analyses in SAP NetWeaver BI.
To do so, use the SAP Forecasting and Replenishment component in SAP Supply Chain Management.
Figure 65: SAP Forecasting & Replenishment
SAP Forecasting and Replenishment is a component of SAP Supply Chain Management (SAP SCM, as of Release 4.1); an SAP Retail System (as of Release 4.6C, PI 2004.1) or any other SAP system can be connected as a master data system.
Main features:
• Exact forecast that observes specific events, such as public holidays and retail promotions, reduction in stocks and ranges of coverage.
• Short-term and long-term operative planning for products that are procured regularly or seasonally.
• High level of automation
• Data provision (sales, forecasts etc.) for vendors (Collaborative Planning, Forecasting and Replenishment).
SAP also offers a workshop on SAP Forecasting and Replenishment (W26FRW).
Figure 66: Possible system landscape
You can only use SAP Forecasting and Replenishment if you connect it to an ERP system providing SAP F&R with relevant data (master data, stock levels, etc.).
Inbound interfaces for SAP F&R are fully included. If you are using SAP Retail (from Release 4.6C, Plug-In 2004.1), the standard interfaces for providing SAP F&R with data and importing purchase orders are available.
POS data is also required during planning for stores. Many different types of connection are possible here. In terms of performance, SAP recommends loading the POS data to SAP F&R using a SAP Business Information Warehouse in conjunction with SAP POS Data Management, so that data does not first have to be updated in the SAP Retail System. However, it is also possible to reference sales from the SAP Retail System.
There is also the option of using SAP Business Intelligence to evaluate SAP F&R-relevant business quantities. To do so, data (such as master data, stocks) from the SAP Retail System and SAP F&R-specific data (SAP F&R master data and key figures) is extracted from SAP F&R. SAP F&R Business Content (as of BI CONT 3.53) is available to you for the evaluation.
Figure 67: Core Process in SAP F&R
The system performs a replenishment run in the SAP F&R business scenario, which consists of the main steps forecast, requirements calculation and requirements quantity optimization.
The requirements calculation involves calculating the safety stock and the net requirement. The basis for this calculation is data determined during scheduling, such as order, delivery and availability days, as well as actual stocks, target stocks and open goods receipt and goods issue quantities.
Requirements quantity optimization covers:
• Logistical rounding
• Observation of vendor restrictions (such as minimum order quantities)
• Determination of an optimum purchase order quantity according to capital tie-up (warehouse costs versus purchase order costs).
Figure 68: Causal-Based Forecast
Historical sales (POS data) on the article for the last two years is used as the basis for forecasting articles for a store.
The following can be used as the basis for forecasting:
• Goods issues from the DC
• Aggregated store purchase orders
• Aggregated sales data from stores
The forecast is carried out at location product level.
The causal-based forecast also takes account of the influence of events, such as public holidays, retail promotions or sales price changes.
The forecast contains the identification of seasonal behavior and trends.
The forecast horizon and the frequency profile can be defined flexibly according to whether the forecast is to be performed for a short-term replenishable article or an article with a longer replenishment lead time. The system also supports the forecast for articles that are temporarily not listed.
References, whose historical data is used as the basis for forecasting the new article, can be used for new articles that do not yet have a sales history. It is also possible to use an artificial history, which the system calculates from historical data for a group of references (such as the entire goods group).
In addition to the operative forecast, a tactical forecast can also be created, which can be made available to a vendor during collaborative planning (Collaborative Planning, Forecasting and Replenishment, CPFR). The tactical forecast has the same content as the operative forecast, but may have a different forecast horizon.
Figure 69: Demand influencing factors
A demand influencing factor (DIF) is an external event with a significant influence on sales or on the demand for an article in a site. The following are a few examples of demand influencing factors:
• Public holidays, such as Easter or Christmas
• School holidays or the start of term
• Retail promotions
• Sales prices (and their changes)
• Local events, such as sports events
• Unusual weather, such as hurricanes or heat waves
• The number of stores supplied by one distribution center
A DIF occurrence is an actual occurrence of a DIF, such as Easter this year. Various assignments are made for this: time periods, sites, articles, etc.
If you enter the actual DIF occurrence for locations and location products in the system, the system analyzes the effect of historical occurrences on sales or consumption of a product. This is included in the forecast for future DIF occurrences.
The greater the frequency of a DIF occurrence in the past, the greater the accuracy of the forecast for a future occurrence of the same DIF.
Example: The system identifies an increase in consumption of a location product during a historical promotion. The system will then include this increase for the forecast if this location product is found in the promotion again.
In addition, SAP F&R can also analyze related sales dependencies. For example, a retail promotion for a certain product could reduce sales of competing products, or conversely, increase the sales of products that are often purchased with the advertised product or placed near it. Examples: During a promotion for a brand of cola it experiences increased sales (promotion effect), sales for the competing cola brands drop (cannibalization effect), and in turn, potato chips are also stronger in demand (affinity effect).
A forecast that observes the demand influencing factors is referred to as a causal-based forecast.
Demand influencing factors can be divided into various types, such as:
Boolean: The event takes place or does not take place. It does not have an assigned value. The system checks the influence of the boolean event on the historical sale or requirement, and calculates the forecast for a future occurrence of the event taking account of the increase or decrease factor determined. Example: Retail promotions and advertising campaigns, public holidays.
Variable Season: You can use this type for events with a longer duration, such as Christmas or Easter. The system determines a separate effect for every week. This means that variable trends can be represented.
Metric: The event has a value at all times. The system analyzes the connection between the level of the value and sales.
Value: Similar to metric, but with restricted validity periods. Example: heat waves Sales price: Metric DIF using sales prices that are obtained from the SAP Retail System master data.
Time series: Metric DIF using any time series (such as the number of stores supplied by a DC, temperature).
Correction factors:
• Multiplicative: Forecast values are multiplied by a correction factor, for example during the initial occurrence of an event (since the system has no previous experience of this event).
• Additive or absolute: The correction value is added to the forecast or replaces this, for example to represent planning data from retail promotions: the promotion quantities can also be procured using SAP F&R.
• Weighting factor: Use of profiles that represent the development of forecast values over time (for example, higher sales at the start of a retail promotion and reduction in sales towards the end of the promotion).
• Ignore history: Historical periods are not observed in the forecast. Example:
Store refit, or if planned quantities are made available by stock reduction during a promotion and no increased replenishment is to be calculated for the distribution center.
Figure 70: Multi-level replenishment based on aggregated order forecasts As of Release SAP F&R 5.1, you can also use multi-level replenishment (Multi-Echelon Replenishment, MER) in distribution centers as an alternative to the conventional replenishment. MER in SAP F&R means that replenishment is calculated at every level of a delivery network using the requirements that have been forecasted for all subordinate levels. The goal is to improve replenishment at the level of the supplying locations (central and regional distribution centers).
Example: Your company has a two-level supply network of distribution center and several stores. When you want to use multi-level replenishment for the distribution center, the system proceeds as follows:
• Besides calculating sales forecasts for the stores, it also calculates their order forecasts. Apart from considering the sales forecast for each location product, the system also takes into account order and delivery cycles, as well as forecasted stocks and a rounding to logistical units of measure.
• At the distribution center level the system totals the order forecasts of the supplied stores for each product to form an aggregated order forecast. The quantities are added up on the staging date of the distribution center, which means the system takes into account on which date the quantities to be delivered must be available.
• The aggregated order forecasts can be used for the replenishment of the distribution center in the following ways:
Indirectly, as a demand influencing factor, meaning the aggregated order forecast influences the forecast for goods issues
Directly, as a forecast replacement, which means that the aggregated order forecast serves as the basis for the requirements calculation, instead of the forecast for goods issues
You can also use the same principle in more complex networks that have more than two levels. Furthermore, you can also use aggregated order forecasts in connection with Collaborative Planning, Forecasting and Replenishment (CPFR) by making the data available to external vendors for a collaboration.
Figure 71: SAP F&R – Process Overview
Forecasting, requirements calculation and optimization all take place in the Forecasting and Replenishment processor. Purchase order proposals are generated for a requirement.
Exceptions can be generated in all steps. The exceptions are processed in the exception handling/manual release process.
The system can release purchase order proposals automatically in release management.
If serious exceptions have occurred or release conditions have not been met, release management blocks automatic release. You must release these types of purchase order proposal manually once the exceptions have been processed.
Purchase order proposals that have been released manually or automatically can be transferred to the SAP Retail System, where they are converted to purchase orders automatically.
To perform a qualitative evaluation of SAP Forecasting and Replenishment, you can perform analyses in SAP Business Intelligence or another legacy system. The SAP F&R Business Content in SAP NetWeaver BI contains a series of pre-defined evaluations.
During operative SAP F&R processing, the system determines key figures, which are used as the basis for the analyses in SAP NetWeaver BI . Two types of reports can be created:
• Exception-based reports: Exception situations such as understock and overstock, stockouts, lost sales and undelivered products are entered here.
• Non-exception-based reports: These reports trace development over a specific time period, such as variances of minimum stock level, stock history, service grade history, range of coverage, dead stock, forecast quality and history of manually changed purchase order proposals.
Depending on the business targets in place, the reports can be ordered according to categories, such as warehouse stock, forecast quality, stability of solution and manual access frequency. The objective here is to improve to optimize the quality of SAP F&R by adjusting parameters or changing demand influencing factors.
SAP F&R also provides optional data, which is made available to vendors to optimize their processes (procurement, production). This can be POS data, operative and tactical forecast data and forecast values, which are caused by demand influencing factors.
Figure 72: Example of transferring a promotion from SAP Retail
Retail promotions in the SAP Retail System can be handled in a variety of different ways in SAP F&R:
• Demand influencing factor of the boolean type (no transfer of planned quantities) You require at least one occurrence of the same type of historical promotion so that the system can determine the influence of this historical event on consumptions and use this for the forecast.
• Demand influencing factor of the absolute correction factor type. This means that the planned quantities are transferred to SAP F&R and replace the forecast values, assuming that the promotion quantities already contain the standard requirements.
• Demand influencing factor of the additive correction factor type. This means that the planned quantities are transferred to SAP F&R and added to the forecast values, assuming that the promotion quantities do not contain the standard requirements.
Alternatively, you can also ignore the promotion in SAP F&R by planning and procuring the entire promotion in SAP Retail, but observing the purchase orders as open purchase order quantities in SAP F&R, so that you do not need to additionally procure requirements in SAP F&R.
Figure 73: Planning Workbench in SAP F&R
The planning workbench in SAP F&R is the central tool for the stock planner. They can check purchase order proposals that were generated automatically, release any purchase order proposals that have not yet been released, and create manual purchase order proposals.
The stock planner receives detailed information for each purchase order proposal item:
Master data, movement data, demand influencing factors, costing results from the SAP F&R processor, logistical rounding settings. This data can also be used to support manual planning and requirements simulation and planned goods issues and receipts.
The stock planner also receives detailed information on commercial and technical exception situations (if information on the latter is required). They can for example be informed by the system that an article in a store is not found in a sufficient quantity to be available by the delivery date. Alternatively, they can be warned that the stocks were not updated during the replenishment calculation. These types of exceptions can be distributed between the different roles in the retail company and support data and system maintenance.
In addition to location-based requirements planning in the planning workbench, product-based requirements planning is also available to the stock planner in the product workbench. Here he or she can monitor the entire assortment and, for example, manually plan requirements for all products that were not automatically planned. In order to find these products, he or she can define criteria such as zero stock, backlogged purchase orders or specific exception messages, which the system can use to find critical products and suggest for manual planning.
Figure 74: SAP F&R Analyses
SAP F&R provides key figures and key performance indicators, which you can use to evaluate the quality of SAP F&R in any data warehouse (ideally with SAP NetWeaver BI). SAP NetWeaver BI contains comprehensive BI content for SAP F&R, which you can use to analyze the operative section of the business scenario. This includes standard analyses, which provide stock planners with important information on the replenishment process, the level of automation, the forecast quality and stock development etc.
Lesson Summary
You should now be able to:
• Describe the replenishment process using SAP F&R.
• Explain the special features of a causal-based forecast.
• Name the systems that you require to use SAP F&R.