For necessary background, please preread Forecasts That Need Attention Explained before proceeding with this article.

Purpose

This article explains how to use the Variance panels and reports on the Forecast screen to identify forecast exceptions that require review. It also describes how to interpret variance data across different timeframes and use drill-down reports to prioritize which forecasts to adjust.

Step 1: Access the Variance Panels

Navigate to the Forecast screen.
Locate the two exception panels:
- Sales Exceeds Forecast: Highlights items where sales are running higher than forecast, which increases the risk of stockouts.
- Forecast Exceeds Sales: Highlights items where sales are running lower than forecast, which increases the risk of excess inventory.
These panels enable exception-based forecasting by focusing attention on outliers instead of every item.

Step 2: Interpret Panel Metrics

Each panel displays the top 5 items contributing to the largest forecast deviations.

For each item, the following information may appear:

Order Value: Calculated as Purchase cost × Recommended order quantity. If no purchase cost exists, the app uses Average cost × Recommended order quantity.

Variance %: Calculated as Variance future units ÷ the lesser of forecast units or sales units.

Variance Value: Calculated as Variance future units × cost.

Click an item code to drill into the Inquiry screen for a deeper investigation.

Windows: Press CTRL + Click

Mac: Press COMMAND + Click

Step 3: Choose an Evaluation Timeframe

Use the Month selector at the bottom of the Variance panels to choose a timeframe that matches the nature of the item’s demand. The default is 3 months, which compares the last three months of sales history with the next three months of forecast. Selecting the right timeframe helps you distinguish between short-term noise and meaningful forecast trends.

Available options:

Month to Date (MTD)
MTD sales represent the total quantity sold from the start of the current month up to today. This view compares that partial sales total to the full-month forecast to show whether sales are ahead or behind expectations.
Use MTD to identify immediate risks within the current month. It helps you act quickly to prevent stockouts or excess stock before the month closes.

1 Month
Ideal for reviewing newly launched or fast-moving items where short-term performance can reveal immediate forecast inaccuracies.

3 Months
Best for stable, high-volume items that should track forecasts closely. Use this view to spot consistent over- or under-forecasting across recent months.

6 Months
Suitable for moderately variable or irregular items. It balances short-term volatility with a broader trend to show whether the forecast remains directionally correct.

12 Months
Essential for seasonal products. This view smooths out monthly fluctuations and shows whether forecast accuracy holds across a full cycle. Always confirm results here before adjusting forecasts for items with known seasonality.

💡Tip:

If an item is consistent, large short-term variances (1–3 months) are red flags.
If an item is seasonal, short-term variances may be misleading. Check 12 months before making changes.

Examples:

If a product has already achieved 80 percent of its monthly forecast halfway through the month, it indicates that the forecast is too low and the item is at risk of a stockout.
If a seasonal product such as sunscreen appears to be selling significantly less in winter when using a 3-month selection, review its 12-month variance before adjusting. Over the full year, the forecast may still align with the true demand pattern.

Step 4: Access the Variance Reports

To view the full list of items where sales or forecasts differ significantly, click the total value displayed on the panel. This opens a detailed drill-through report showing all affected items.

Step 5: Interpret the Month-to-Date (MTD) Report

The Month-to-Date (MTD) report shows how items are performing against their forecasts for the current month. It allows you to act before the end of the month to prevent stockouts or excess stock.

The report contains two key perspectives:

Sales Exceeds Forecast: Items selling more than forecasted, which increases the risk of stockouts and negatively affects your fill rate KPI.

Forecast Exceeds Sales: Items selling less than forecasted, which can create excess inventory and negatively affect your stockholding KPI.

Click any column heading to sort the report in ascending or descending order. Sorting by variance value or units helps you identify the highest-impact items first.

The report displays the following information for each item:

Item details: Product code, description, classification, and groups.
Cost: The item's current cost price.
Fill rate: Average achieved fill rate over the selected period.
Sales: The quantity sold month-to-date.
Prorated sales: The projected sales for the full month based on the current sales rate. This is calculated by dividing the month-to-date sales by the number of elapsed days, then multiplying by the total number of days in the month.
- Example: If sales are 10 units on the 8th of March, the prorated calculation is:
  10 ÷ 8 = 1.25
  1.25 × 31 = 39 units (rounded)
  The prorated sales figure for this item on 8 March would therefore be 39 units.
Forecast: The current forecast for the month.
Variance units: Forecast minus prorated sales.
Variance value: Variance units × cost.
Variance %: Variance units ÷ the lesser of forecast units or prorated sales.
Man: Indicates whether the item has a frozen forecast. A snowflake icon means the forecast was manually.

This report enables early detection of forecast errors before the end of the month. If sales continue at their current rate, items with large negative variances may stock out before the next replenishment cycle.

Step 6: Interpret other Variance Reports

The reports present both historical and future variance comparisons across your selected timeframe.

Each row in the report shows:

Item details: Product code, description, classification, and groups.
Cost: The item's current cost price.
Fill rate: Average achieved fill rate over the selected period.
Sales: Average sales over the selected period.
Forecast: Average forecast for the same period.
Forecast History: The average of past forecasts over the preceding 12 months.
Variance Future Units: Forecast minus Sales.
Variance History Units: Forecast history minus Sales.
Variance Value: Variance units × Cost.
Variance %: Variance units ÷ the lesser of forecast units or sales units.
Man: Indicates whether the item has a frozen forecast. A snowflake icon means the forecast was manually.

Sort the data to identify items requiring intervention:

Variance Future Value prioritizes items where poor forecasting most affects stock value.

Variance Future Units highlights items with the greatest physical shortages or surpluses.

This allows planners to address the items that most affect fill rate and stockholding KPIs.

⚠️ Watchouts

Interpret timeframes correctly: A short-term variance can be misleading, especially for seasonal items. Always confirm findings against the 12-month view before changing forecasts.

Frozen forecasts: A snowflake icon in the Man column indicates a manual adjustment. These forecasts are frozen and will not be overwritten by the system forecast.