SAP IBP, advanced inventory optimization: Configuring and Preparing for Use

Creating an Inventory Planning and Optimization Process

The Power of Multi Echelon

Validating Inputs

Running an Inventory Optimization Operator

Analyzing the Results of an Inventory Optimization Run

Finalizing the Inventory Plan

Forecast Error Calculation

Objective

After completing this lesson, you will be able to create a forecast error calculation.

Forecast Error Calculation

Common use cases provide flexibility and visibility into forecast error or forecast accuracy measures. Inventory optimization and demand planning use cases are different. While inventory optimization use cases calculate forecast error as a primary input at the base planning level, demand planning use cases define error measures to monitor forecasting performance and track the forecast value-add by comparing different forecast key figures.

SAP IBP, advanced inventory optimization provides forecast error measurement functions via the Forecast Error Profiles - Inventory app, which was made available with SAP IBP 2405 and which replaces the deprecated Manage Forecast Error Calculation - Inventory Optimization app (not usable with 2411 and later).

The Forecast Error Profiles - Inventory app provides options to set up a forecast error calculation profile and to use it both from the Excel add-in and the Application Job app.

To use this app, your business role must contain the business catalog Profile for Forecast Error Calculations for Inventory (SAP_IBP_BC_INV_KPI_PROFILE_PC).

Usually, the forecast error-related process is based on the following steps:

Sample Use Case: Forecast Error Analysis

As part of the first steps to optimize the levels of inventory within the supply chain, we need to calculate the forecast error. The following figure presents a dashboard to understand the results of the algorithm:

The slide presents four graphs in a Dashboard:

Historical sales and historical lag 0 forecast (Eaches):
This is a line plot where two key figures (IO Sales and IO Forecast Lag) are grouped by weeks.
Historical Lag 0 forecast error coefficient of variation:
This is a heat map where customer groups and segments are displayed by location and product.
Historical sales and Historical Lag 1 Forecast (Eaches):
This is a line plot where two key figures (IO Sales and IO Forecast Lag) are grouped by weeks.
Historical Lag 1 forecast error coefficient of variation:
This is a heat map where customer groups and segments are displayed by location and product.

Calculate Forecast Error

Task 1: Forecast Error Calculation

Business Example

Inventory optimizations needs inputs regarding uncertainty of demands. This requires the determination of forecast errors. We will run forecast error calculation to have this information available for further processing.

Prerequisites

Authorizations to access and set up an Forecast Error Calculation - Inventory App.
Define a Planning Area where this segmentation should run, for example, IBP400.
Define Key figures which serve as input key figures, for example, IO Demand Forecast and IO Sales.
Master and key figure data loaded into the planning area.
Planning View used for this exercise: IO 210 Validate Inputs ##. For this exercise we will work with a planning filter FG1##. Therefore exercise Create a Planning View based on a template is a prerequisite for this exercise. No other previous exercises are prerequisites to be able to run this exercise.

Result

Results

Now you have executed a forecast error calculation which is needed to be able to run inventory operators with demand uncertainty as input.

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