Weighted Average Net-Dry Calculation for Physical Inventory
Grain companies use average net dry calculations when receiving grain for several important reasons related to quality control, pricing, and inventory management. Here's how and why grain companies use these calculations:
1. Quality Control: The calculation allows grain companies to assess the moisture content and quality of incoming grain. Moisture content is a critical factor because it affects the grain's storage stability, shelf life, and suitability for various uses. By measuring moisture levels accurately, grain companies can take appropriate actions to prevent spoilage, mold growth, and other quality issues during storage.
2. Pricing Determination: The moisture content of grain can significantly impact its weight and, consequently, its market value. Grain buyers often pay for dry weight, which means that grain with higher moisture content contains more water weight and less actual grain. To determine fair pricing, grain companies use calculations to adjust the weight and value of grain based on its moisture content. This ensures that both buyers and sellers are compensated fairly for the actual grain content.
3. Inventory Management: Accurate moisture content measurements are essential for managing grain inventory effectively. Grain elevators and storage facilities need to know the exact moisture levels of the grain in storage to prevent quality deterioration and to blend grains with different moisture contents to meet market specifications. These calculations provide this critical information, helping grain companies optimize their storage practices.
4. Compliance with Standards: Grain quality standards and contracts often specify maximum allowable moisture levels. Grain companies use calculations to ensure that incoming grain meets these standards. If the moisture content exceeds the specified limit, the grain may need to be dried, blended, or otherwise processed to comply with contractual obligations.
5. Risk Mitigation: Moisture variation in grain can lead to financial risks. For example, if grain with high moisture content is not adequately managed, it can result in spoilage or degradation during storage, leading to financial losses. By using these calculations, grain companies can identify and manage this risk by taking appropriate actions such as drying, aeration, or blending.
6. Consistency and Transparency: Standardized calculations provide consistency and transparency in grain transactions. Buyers and sellers can rely on these calculations to ensure fairness and accuracy in pricing and quality assessment. This consistency helps build trust in the grain industry.
7. Regulatory Compliance: In some regions, regulatory authorities may require grain companies to report moisture content accurately for tax, quality control, and compliance purposes. The calculations provide a standardized method for meeting these regulatory requirements.
SAP Agricultural Contract Management and the weighted average calculation
The weighted average calculation is a common business practice to determine the physical net inventory. The weighted average calculates the weighted average quality factor of a given Discount Premium Quality Schedule (DPQS) characteristic.
These are the quantities that agri-companies want to hedge - net-dry.
The weighted average calculation is a process which is automatically triggered for inbound processes through Load Data Capture (LDC), and calculates the weighted average quality factor of a given DPQS characteristic. This feature also provides tools to monitor, and if needed, to correct the average calculated by the system.
Automatic Weighted Average Calculation of Quality Characteristics
When processing an incoming shipment using LDC, SAP Agricultural Contract Management provides the ability to calculate and store the weighted average per characteristic in the given storage location. This process happens automatically in the background without manual action by the end user.
A workcenter is provided to perform the following:
- Evaluate and review calculated weighted average values per storage location.
- Review gross and new calculated net quantity per storage location. Net quantity is calculated leveraging the weighted quality characteristics.
- Manually correct the calculated weighted average values. For example, after samples have been analyzed by a lab.
- Recovery if processing ended with errors.
- Recalculations after changes have been made to loads. For example, quality characteristics of one or multiple loads were retroactively altered.
Analysis Reports
Analysis reports include the following:
- A summary report to provide total calculated results per characteristic for each material/location.
- A detailed report to provide detail on the actions (that is, LDC unload event) that are impacting the weighted average calculation.
In this example, it is evident that every unloading operation into a storage location or bin triggers the automatic generation of calculated weighted qualities and quantities for each bin. End users can perform necessary validations by reviewing either the summary report or the detailed report.
Furthermore, if a sampling process reveals the necessity for adjustments to any of these characteristics, the system offers a dedicated work center for manual adjustments. Subsequently, the system will automatically update the weighted averages based on the provided adjustments.
Note
It is important to note that these weighted averages are continuously recalculated as new loads are continuously received.Example I - Starting Situation
Moisture Quality Schedule
| Range | Shrink |
|---|---|
| 15% - 20% | 0.80% shrink for each 1% moisture |
| 20% - 22% | 1% shrink for each 1% moisture |
| 22% - 25% | 1.5% shrink for each 1% moisture |
Foreign Material Quality Schedule
| Range | Shrink |
|---|---|
| 0.1% - 100% | 0.1% Shrink for each 0.1% foreign material |
Let's begin with an example where we have an empty storage location, meaning the bin is completely empty. When we perform the initial unloading, we employ these schedules to compute the net weight.
We start by recording the moisture content, and the system utilizes predefined ranges within the schedule to calculate shrink based on the percentage of moisture. Additionally, we reference the foreign material schedule to assess the quality of the load.
Continuing with the example, let's consider the first truck that arrives for unloading. It records the following characteristics: moisture at 22% and foreign materials at 0.8%. Utilizing the provided schedules, the system computes the net quantity at 694.8 bushels, given an initial load of 900 bushels.
The system automatically generates the weighted average in the background, considering these values as it is the first load in the bin. Consequently, the gross value for this load is 900 bushels.
Now, when the second truck arrives with 850 bushels, its characteristics are different, with moisture at 16% and foreign material at 0.5%. According to the schedules, this results in a calculation of 736.95 bushels for this load.
The system continues to automatically compute the weighted average in the background. As a result, the moisture value is now averaged with the first load, resulting in an average of 19.08%, while the foreign materials' average with the first load is 0.65%.
With these updated values, the weighted average now reflects as 1,471.50 bushels.
In SAP Agricultural Contract Management, it is possible to use either the weighted average per bin or the individual unloaded net quantities for the position report.