Step-by-Step Statistical Forecasting Using SAP APO
- by Rajesh Ray, Senior Managing Consultant and SCM Product Lead, IBM Global Business Services
- Sangeeth K. Parvatam, Senior Consultant, IBM Global Business Services
- June 22, 2012
Learn a 15-step methodology for executing forecasting projects in SAP Advanced Planning and Optimization. Understand the most common methods of statistical analysis. Learn best practices for implementing these methods in practice.
Forecast strategies are used in SAP Advanced Planning and Optimization to decide how forecast values are calculated. Selecting a method depends on characteristics of the data, including seasonality, linear trends, and which data is emphasized.
Statistical forecasting is a strong feature of the Advanced Planning & Optimization (APO) Demand Planning (DP) suite and a lot of companies look at this capability of APO for an effective demand planning process. The recent version of APO (SCM 7.0) covers a wide range of statistical forecasting models. However, mere availability of models does not ensure the best forecast result unless they are used effectively. The first few questions that probably come to mind for any company looking for such a tool are:
- What are the best practices for using the APO statistical forecasting tool?
- How do I know which model best meets the needs of my business (as there are lots of models)?
Based on our experience of executing such statistical forecasting projects for clients from different industries, we have put together a methodology for executing such projects. The methodology is broken into 15 logical steps. We also provide a set of tips and tricks for effective use of this tool and a set of case studies.
Step 1. Finalize the Scope of the Statistical Forecasting Project
In any statistical forecasting project, the common tendency is to do statistical forecasting for every possible stock keeping unit (SKU) that the organization sells. However, it is important to finalize the scope of the project for two reasons.
- Statistical forecasting does not give the desired result in certain cases
- Sometimes being selective gives quicker results
- Forecasting does not give the desired result for certain SKUs, including these:
- New SKUs for which very little history is available and which do not closely mimic the sales behavior of existing SKUs (where like modeling cannot be used).
- SKUs that the organization wants to discontinue in the next few months.
- Purely promotional SKUs that are sold for a very short period during the year, such as Christmas.
- Highly seasonal SKUs for which very little history is available. Ideally a statistical forecasting tool needs at least 24 to 36 months of history of such SKUs to identify seasonality.
- SKUs for which there is a permanent capacity constraint (i.e., the organization always sells less than the original demand of the SKU as it has a constraint in production capacity).
- SKUs with highly variable or unpredictable supplier lead time and production lead time. Variability during replenishment skews the actual demand and makes forecasting unreliable.
From our experience, it is also important to be selective while starting such a project. A quick ABC analysis of SKUs based on sales volume can be handy here. Identify those SKUs that contribute 80 percent of sales and put most of the effort of model selection in these SKUs. If by better statistical forecasting, the forecast accuracy for these SKUs can be improved, it will have a positive effect on the overall business and can deliver quicker results. While in the long run, forecasting needs to be extended to all SKUs, it is always better to start with A and B category SKUs.