Improve Planning Results by Forecasting at the Product Characteristic Level

  • by Alok Jaiswal, Consultant, Infosys Limited
  • September 28, 2015
Learn the process flow of characteristic-based forecasting (CBF) in SAP Advanced Planning and Optimization (SAP APO). Follow a step-by-step procedure to configure the master data, create CBF-relevant configuration, release the forecast at the characteristic level from demand planning, and interpret the results.
Learning Objectives

By reading this article, you will learn how to:

  • Configure master data required in SAP Advanced Planning and Optimization (SAP APO) to use characteristic-based forecasting (CBF)
  • Create and configure Demand Planning (DP)-relevant objects to be used in CBF
  • Use CBF-related configuration
  • Use a consumption group
  • Release a forecast from DP at the CBF level
Key Concept

Demand Planning (DP) in SAP Advanced Planning and Optimization (SAP APO) helps to generate forecasts based on historical values. By using characteristic-based forecasting (CBF) in DP, you can forecast not only at the product but also at the characteristic level. For example, in the automobile industry you can have several variants of a product (e.g., a motorized bicycle) based on color, engine, or transmission. Therefore, the planner needs to forecast demand not only at product level but also at the characteristic level. In SAP terminology, different features of a product such as a motorcycle are called characteristics (e.g., color or engine), and each characteristic has different values assigned to it known as characteristics values

SAP Advanced Planning and Optimization (APO) Demand Planning (DP) helps you to carry out the forecasting process. You can use statistical forecasting techniques, such as the constant, trend, and seasonal models with different forecasting models. The output of the DP process is the demand plan (forecast). The demand plan is used to generate planned independent requirements at required locations for specific time periods.

Characteristic-based forecasting (CBF) is a feature of DP with which planners can not only define their forecast at the product level but also can segregate the forecast for a product at its characteristic level.

In demand planning without CBF, planners perform planning at the stockkeeping unit (SKU) or product level, but they cannot see the product’s forecast at the detail characteristic level. Therefore, CBF has been introduced to incorporate this feature in DP. This is particularly useful in the automobile and high-tech industries in which you can have multiple variants of the same product and the number of combinations can go from a few hundreds to thousands. Obviously, not all combinations are relevant for the forecasting process. CBF provides you with the flexibility to restrict the characteristics used in CBF to those that are important for planning purposes.

Business Scenario

Consider an example of a motorcycle manufacturer that builds motorcycles with various colors, with different engine capacities, and with different wheel types. In this case one of the models of the motorcycle is the product, but color, engine, and wheel types are its characteristics. With the help of the CBF feature, planners can see the demand not only at the motorcycle level but also at the segregated demand level in which various colors, engine capacities, and wheel types help them complete an accurate forecast.       

Alok Jaiswal

Alok Jaiswal is a consultant at Infosys Limited.

He has more than six years of experience in IT and ERP consulting and in supply chain management (SCM). He has worked on various SAP Advanced Planning and Optimization (APO) modules such as Demand Planning (DP), Production Planning/Detailed Scheduling (PP/DS), Supply Network Planning (SNP), and Core Interface (CIF) at various stages of the project life cycle.

He is also an APICS-certified CSCP (Certified Supply Chain Planner) consultant, with exposure in functional areas of demand planning, lean management, value stream mapping, and inventory management across manufacturing, healthcare, and textile sectors.

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10/2/2015 2:31:25 PM
Vinod Ramchandani


Job well done. Very Good !


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