Mastering SAP Forecast Accuracy

Your Guide to Improving Forecast Outcomes‍

Chances are, you have heard the saying: “Forecasting is at its best inaccurate, at its worst completely unusable.”  If that is true, precisely what is its use and value?

In supply chain management, forecasting has been and will continue to be a valuable tool. By predicting demand and pricing, a business can provide signals for supply planning. That doesn’t imply that it is a perfect tool. To ensure your data is accurate and reliable as possible, it is crucial to look carefully at influencing factors that could impact your future outcomes. Let’s consider a few of them:

‍Look at the Quality and Granularity of Your Data

The quality and accuracy of your historical consumption data can play a significant role in forecasting. It is important to make sure your data is complete, consistent, and free from errors. Data cleansing and validation processes can help improve the reliability of your historical data.

The level of detail in your historical data also plays a role in ensuring accuracy. Depending on your industry and products, you may need to analyze data at various levels, such as SKU (stock-keeping unit),product category, product hierarchy, product characteristics, location, or time buckets (daily, weekly, monthly). Granular data can raise the accuracy of your insights.

Identify and Account For Seasonality, Trends, and Cyclicality

All of these enter into the big picture of analyzing your historical data. Seasonal fluctuations, such as holiday spikes or summer slumps, can significantly impact quantity and timing of demand. Trends, both short-term and long-term, should also be considered when forecasting.

Some products or markets may have cyclic patterns that are not strictly seasonal. These cycles can result from economic factors, industry trends, or other external influences. By recognizing these cycles and incorporating them into your forecast result one can generate a more viable and appropriate supply plan to meet the market demand.

Consider External Factors That Can Influence Demand

There are several to take into account, such as economic conditions, market trends, competitor actions, and regulatory changes. Technology change should also be carefully considered. These factors can have a significant impact on consumption patterns and Product Lifecycle Management (PLM). PLM is a topic of conversation of its own and will not be addressed here.

Determine If Promotions and Events Are Creating Demand Spikes

Special promotions, marketing campaigns, or events can cause short-term spikes in demand. Ensure that your forecasting models account for these promotional activities and their expected impact.

Enhancing Forecasting Accuracy: Beyond Data

Now that we have covered some of the external and data-driven factors that influence forecasting, it is time to take a deeper dive into how we can hone our forecasting even further.

Ask yourself: have you factored in your lead times for procurement and production when going through your supply planning? Longer lead times may require more accurate forecasting, since supply adjustments can’t be made quickly. Also, have you analyzed the effectiveness of demand-shaping strategies, such as pricing changes, product bundling, or discount? These strategies can influence consumer behavior and should be considered in your forecasts.

If the answer is yes to both questions, let’s move on to five additional ways to complement quantitative data and provide a more comprehensive view of demand drivers.

1. ‍Customer Feedback and Insights: Gather insights from customer feedback, surveys, and market research. ‍
2. Statistical Models: Utilize various forecasting methods and statistical models, such as time series analysis, moving averages, exponential smoothing, and regression analysis. Combining multiple models and assessing their accuracy can improve forecasting reliability.‍
3. Collaboration: Involve key stakeholders, including sales, marketing, and production teams, in the forecasting process.Their domain knowledge and input can enhance the accuracy of forecasts.‍
4. Continuous Improvement: Regularly monitor and evaluate the accuracy of your forecasts. Adjust your forecasting methods and models based on past performance and changing market conditions.‍
5. Scenario Analysis: Consider conducting scenario analysis to account for different possible outcomes. This approach helps you prepare for unexpected events and assess the impact of various factors on your supply chain.

In summary, while forecasting may never be perfect, a thoughtful and data-driven approach that considers both historical consumption, market and industry knowledge and incorporates the influencing factors discussed above and applies the techniques to compliment the quantitative data can significantly improve the accuracy forecasting and thus the viability of supply planning. Continuously refining your forecasting processes and staying responsive to changes in your market environment will help you adapt and optimize your supply chain management strategies.