How to Measure Forecast Accuracy with SAP APO and SAP BW

  • by Rishi Menon, Specialist Master, Deloitte Consulting LLP
  • Satish Vadlamani, President, SAPsquad
  • November 13, 2014
Learn about the key considerations in designing processes for measuring forecast accuracy. See how forecast accuracy reports can be implemented using SAP APO Demand Planning (DP) and SAP Business Warehouse (BW).
Learning Objectives
Reading this article, you will learn:
  • The business imperatives for measuring forecast accuracy and where it fits in the demand planning process
  • How forecast accuracy measurement can be implemented in SAP using SAP Demand Planning and SAP Business Warehouse
Key Concept
Forecast accuracy is a measure of the difference between a prediction and what actually happens. It is a crucial metric in any enterprise that performs a demand planning function. Defining a well-structured measurement process is critical to improve not only forecast accuracy but also the demand-planning process itself. As the adage goes, you cannot improve what you don’t measure.
Forecast accuracy has a major impact on how well a manufacturing organization performs in terms of inventory levels, customer service, and capacity utilization. However, measuring forecast accuracy is not so obvious or easy as it first appears. There are many business and technology aspects that need to be considered during the implementation stage of the demand planning tools.

From a business perspective, several questions to consider are:

  • Which metrics should be used?
  • What are the advantages and limitations of these metrics?
  • At what aggregation level should the metrics be measured?
  • What forecast types (such as statistical or consensus) should be measured?
  • What lag, horizon, or buckets to consider? (We define lag as the number of periods before the actual demand the forecast has been generated — for example, a three-week lag is the forecast that is generated this week for three weeks out. Horizon as used in this article is the period of time over which to average accuracy metrics for reporting. Buckets identify the time granularity used for measuring forecast accuracy - for example, weeks or months.).
  • Should you measure forecast accuracy in units or dollars?

From a technology perspective, consider the following:

  • How do you capture sales history?
  • How do you capture historical (versioned) forecasts so that lagged accuracy can be measured?
  • How do you provide users with flexible yet quick report output?

We first discuss the critical business considerations and questions that need to be answered with respect to the forecast accuracy measurement process. We also describe how such a forecast accuracy measurement process can be implemented using SAP Advanced Planning and Optimization (APO), SAP Demand Planning (DP), and SAP Business Warehouse (BW). That includes high-level architecture as well as specific tricks to version forecasts, measure forecast lag, and calculate accuracy metrics using SAP BW queries.


Rishi Menon

Rishi Menon is a specialist master at Deloitte Consulting LLP, with more than 17 years of supply chain and enterprise application consulting experience. He specializes in supply chain planning and order fulfillment. He is SAP SCM, APICS (CPIM, CSCP & CIRM) and PMI (PMP) certified.

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Satish Vadlamani

Satish Vadlamani (Ph.D., PMP) is a senior independent SAP Advanced Planning and Optimization (APO) consultant and president of SAPsquad Inc. He has more than 15 years of SCM consulting experience in various industries. He specializes in SAP SCM consulting and service offerings.

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