Demand Forecasting
Optimizing Demand Planning or Demand Forecasting relies on core elements: quality data, minimizing bias, ensuring accuracy, and Forecast Value Add (FVA). This approach includes examining errors, identifying sources of inaccuracies, and focusing on factors that enhance forecast reliability. By addressing these, organizations can build a robust forecasting system that supports effective business decisions.
Quality Data
Demand forecasting, particularly in materials planning, starts with accurate sales order data. For effective forecasting and inventory management, forecasts should align with inventory locations. Simplement provides easy access to detailed data, enabling users to aggregate information as needed. This level of granularity makes it possible to refine analyses, manage outliers, and generate insights effectively.
Additionally, Simplement’s tools allow for financial alignment by easily converting material quantity forecasts into financial forecasts to check alignment for optimal decision-making.
Minimizing Bias
Bias reduction is essential to maintain forecast objectivity. Forecasts should avoid personal or financial motivations that may distort projections, such as sandbagging, where forecasts are intentionally set low to ensure they’re exceeded for potential bonuses. Sandbagging hurts sales and operations, leading to undersupplied inventory, expedited orders, and reduced profitability.
Tracking Signal, a KPI metric, can help monitor and reduce bias by providing visibility into forecast accuracy. Real-time data, easily accessible through Simplement’s Continuous Data Capture (CDC), ensures transparency and aligns all stakeholders around consistent metrics.
Ensuring Accuracy
Achieving accuracy in forecasts involves regular error measurement and understanding the underlying calculations. Forecasts function as operational plans, aiding in orderly production and purchasing.
A crucial component of accuracy is the “frozen period”—a time frame where forecasts remain fixed to allow stable planning, similar to setting fixed dinner plans. Changes to forecasts during this period should be limited, allowing manufacturing and distribution processes to proceed without disruptions. Proper locking mechanisms in forecasting software are recommended to prevent mid-cycle adjustments that could cause resource strain.
For accuracy measurements, excluding changes within the frozen period maintains data integrity and prevents last-minute adjustments from skewing forecast accuracy metrics.
Forecast Value Add
Adding value to demand forecasting involves focusing on changes that generate measurable improvements. Statistical forecasts, combined with manual adjustments, create data points for tracking Forecast Value Add.
When prioritizing forecasts for adjustment, ABC classifications combined with product/customer/market knowledge should drive forecasting actions. High-profit products and customers should receive priority, as well as items with low forecast accuracy due to volatility or inconsistencies. Does this mean ignore the rest? You know the answer to that. Detailed data aids in directing efforts, allowing for targeted inquiries with customers about potential issues such as pricing or order sizes, facilitating intelligent adjustments to forecasts.
Roundhouse by Simplement offers an all-encompassing solution for demand forecasting, providing organizations with access to clear, accurate SAP data. Connect with us to address your data challenges and see how we can help optimize your forecasting!
