How to Find Slow-Moving Inventory in Excel
Start with the sample data
This example uses synthetic business data in USD. Download the CSV and import it through Excel’s Data → From Text/CSV. Set dates to Date, amounts to Decimal Number, and identifiers to Text. The examples use Excel for Microsoft 365 on desktop with English formula names and comma separators.
Download the sample CSV · Data dictionary
Keep an untouched copy of the input. These are educational examples, not customer records or measured customer outcomes.
The result to check
Units in low-sales SKUs: 180 units
SKUs C and E hold 180 units combined. The threshold is an explicit teaching assumption, not an industry standard.
Build the analysis in Excel
1. Prepare the source and scope
Import operations.csv into a new worksheet with headers in row 1. Leave the source columns in their original order for the formulas below. Review the data dictionary before choosing the reporting period. Keep identifiers as text and convert numeric columns explicitly. Save a working copy so you can return to the original fixture.
2. Build the calculation
For this exercise, define low sales as fewer than 20 units over the observation period. Filter units_sold and total the corresponding units_on_hand.
=SUMIFS(F2:F7,E2:E7,"<20")3. Reconcile and interpret the output
The expected check is units in low-sales skus: 180 units. SKUs C and E hold 180 units combined. The threshold is an explicit teaching assumption, not an industry standard. If your result differs, inspect the selected rows, data types and date filters before changing the formula.
4. Adapt the workflow to a recurring report
Replace the sample with a copy of your own source, retaining the same column meanings and units. Extend bounded ranges to include new rows, refresh PivotTables where used, and compare the result to an independent source total. Record the reporting period and any exclusions beside the output. Low recorded sales may reflect stockouts, a recent launch or incomplete demand history.
Checks before using your own data
- Low recorded sales may reflect stockouts, a recent launch or incomplete demand history.
- Keep blanks distinct from zero. Investigate missing records rather than hiding errors with a blanket IFERROR formula.
- Verify results after changing filters, sorting rows or appending a new period. The sample output is a check for this fixture, not a forecast for your business.
Tools and reference guides
Continue with your own data
Analyze your spreadsheetSee the supported workflow and upload your file when you are ready. The sample is not loaded automatically.
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