Stock variances can be frustrating, but they often point to specific issues that can be identified and resolved. Whether it’s missing sales, unrecorded deliveries, miscounts, or incorrect recipes, understanding the root cause is key to maintaining accurate inventory records.
In this guide, we’ll walk through the most common (and simple to fix) stock variance issues:
Incorrect recipes: When what’s configured doesn’t reflect what’s truly being used.
Waste events: Food or drink loss that isn’t logged or accounted for correctly.
Inventory setup issues: Items being purchased under one name but used in recipes under another.
Operational behaviour: Unapproved ordering, overproduction, or poor portion control.
Topics
Core Assumptions & Investigative Process
Before we can meaningfully diagnose a variance, we must ensure that all controllable variables are correct. The theoretical usage (what the system expects to be used) is only as good as the assumptions feeding it.
Getting Ready: Establish Certainty in Known Input Data Points:
Closing Stock Count
This is the most crucial input into your reconciliation.
Ensure stock counts are accurate, consistent, and completed within the same window across all sections.
2. Deliveries Logged Correctly
Double-check that all deliveries have been entered with the correct quantity and matched to the right SKU.
3. Transfers Recorded
Any stock transferred between locations or sections must be logged.
Missing transfers are a common cause of unexplained variances.
4. Waste Logged and Categorised
Waste must be logged with clear reasons—this is essential for separating controllable vs uncontrollable losses.
Now what? Identify the Unknowns
With all the above solidified, the remaining “unknown” becomes theoretical recipe usage. This is where potential discrepancies lie, including:
Shrinkage during batch prep
Over-portioning
Substitutions in production
Prep wastage
Step-by-Step Diagnostic Process
Step 1. Analyse Recipes in Detail
Is the correct recipe linked to the correct POS button?
Is the portion size accurate? Check if the recipe assumes 150g but 180g is actually plated.
Are batch yields configured realistically? Trim, evaporation, or over-production during prep can introduce error.
Have you reviewed the true cost impact of the recipe?
If a recipe or batch yield is inaccurate, it inflates the theoretical usage and leads to underreported cost. This affects your margin.
→ Variance analysis can reveal that certain items cost more than expected, which may lead you to reconsider the menu item’s viability.
→ A dish may show strong theoretical GP on paper, but operational GP tells the real story.
→ Action: Update recipe inputs, validate yields, and reassess if the dish is commercially viable in practice.
Step 2: Revisit Recipe Mapping & POS Integration
Ensure the POS sales data is tied directly to the intended recipe and modifier logic.
Step 3: Assess for Hidden Operational Gaps
If recipe data and inventory reconciliation are solid, and yet variances remain, you're likely facing operational issues that are not captured explicitly in the system.
This includes:
Unlogged waste
Under-the-radar substitutions
Poor prep practices (e.g., lack of standardized portioning tools)
Non-compliance with stock management workflows
Use Nory to spot these:
Look at weekly variance trends: is it the same items showing up repeatedly?
Then zoom out to a monthly view: This can give a more accuaret view as any mistakes made between weekly stocktakes is usually corrected over a longer period.
Highlight top 5 high-value variances and focus coaching or action here first.
Step 4: Reduce the Stock Window for Sharper Visibility
One of the most effective tactics for identifying root causes is to shorten the window of stock analysis.
Here's why:
A full months’s stocktake can mask fluctuations.
By counting every week or even a few times a week, you get:
Clearer signals on when the issue occurs (early week vs weekend)
A shorter feedback loop to validate if your corrective actions are working
Less “noise” in the data due to cumulative issues compounding
This creates an operational microscope- helping managers and chefs react quicker, rather than waiting 7+ days to see if something worked.
Waste: The Hidden Driver of Variance
Many issues behind variance are rooted in waste, often unseen but highly damaging to profitability.
This includes:
Shrinkage
Over-production
Poor processing or batch control
Over-ordering
Burnt or mishandled food
Gaps in operational training
While waste might show up in your variance report as a negative number, it doesn’t explain why it happened. That’s where waste logging becomes critical.
Logging Waste with Reasons:
Logging why waste occurred gives you qualitative insight.
Did it happen due to:
Poor ordering?
Overproduction during service?
Product expiry or misrotation?
These reasons help distinguish where processes need fixing.
Why It Matters:
It turns guesswork into action.
You’ll know whether to coach teams on portioning, adjust prep quantities, or rethink par levels.
Nory enables this culture of accountability with intuitive, fast waste logging, ensuring operators don't just record loss—they learn from it.
Conclusion: Build a Culture of Visibility and Discipline
The core of Nory’s value is turning ambiguity into accountability. Variances highlight what’s slipping through the cracks. But with a methodical approach- starting from validating knowns, then testing recipes, and finally tightening your operational lens- you create the ability to act fast and accurately.
Think of variances as a feedback loop. With the right processes in place, they can become your best tool for continuous improvement.
Quick Reference: Full Diagnostic Checklist
Use this checklist when reviewing variances to ensure a complete, step-by-step investigation.
Step 1: Validate Core Inputs
Closing stock counts are completed and accurate
Counts are done in consistent units and timeframes
All deliveries are entered with correct quantities and items
Transfers between locations/sections are fully recorded
Waste is logged daily with clear reasons (e.g., End of Day, Expired, Damaged)
No gaps in any operational data sources
Step 2: Review Recipe Accuracy
Recipes are correctly mapped to POS buttons
Portion sizes reflect actual usage
Batch yields are tested and realistic (e.g., accounting for trim or evaporation)
Modifiers and sub-recipes are included and linked correctly
Duplicate SKUs or product versions are merged
POS integrations are functional and recipe mapping is complete
Recipe costings are reviewed: is the actual GP close to theoretical GP?
Menu items are assessed for profitability after recipe corrections
Step 3: Investigate Operational Gaps
Are the same variances occurring week after week?
Are high-variance items lacking corresponding waste logs?
Are teams using standard portioning tools?
Are undocumented substitutions or swaps happening in prep?
Is there compliance with prep guides, par levels, and batch processes?
Are behaviours and gaps observable across specific shifts or days?
Step 4: Narrow the Stocktake Window
High-variance items are being line counted every 2–3 days
Mid-week or daily counts trialed for sharper visibility
Trend patterns identified by day or shift (e.g., variance spikes on weekends)
Corrective actions tested mid-week, not just after a full cycle
Feedback loop shortened to respond faster and validate fixes
Step 5: Implement Weekly Variance Review
Weekly variance review meeting is scheduled (30–45 min)
Top 5 high-impact variances reviewed and prioritized
Root causes discussed and documented
Assigned actions (e.g., recipe updates, staff coaching, system clean-up)
Progress reviewed in next week's meeting
Now, let's see some examples:
Missing Sales Example
In the screenshot below there is a variance of -2.95 on Parmesan 1/8 Block. The stock was counted correctly but there were not sale of the item connected to a recipe in Nory, this is shown in the column POS sales.
If you see a 0 Sales Qty but you know you sold menu items using that ingredient, it probably means you haven't assigned that Recipe to the POS ID.
Missing Deliveries Example
In the screenshot below Red Chilies is up 1 kg. The closing count is correct, but there are no deliveries of this product.
2 Kg were delivered but the received on date was not within the stock period.
💡The same process could be followed for missing waste entries and transfers that have not been completed.
Miscounts Example
If the math doesn’t add up, double-check how items were counted.
In this example, there’s a variance on Brined Chicken Fillets, showing as -5.25 units (£8.00). After reviewing the count, it was discovered that a storage area was missed during the stocktake.
How to fix it:
You can reopen the stock count and update the quantity for the item based on what’s in the missed storage area. Once updated, the variance will recalculate to reflect the correct closing quantity.
Incorrect Recipe Setup: Positive Variance Example
You’re seeing a positive variance of 16 loaves of gluten-free bread. The delivery records and closing count are both accurate. However, in the POS sales column, it shows 18 loaves sold that week - which the team knows isn’t right, as gluten-free options sell in very low volume.
What caused the variance?
After checking the item setup, it was found that the recipe or POS mapping had the loaves set up as 1 each instead of reflecting how they’re actually used or portioned in the system. This incorrect setup caused the system to deduct more from stock than it should.
How to fix it:
Check the recipe or POS mapping for the item and ensure the correct unit of measure and portion size are being used. Updating this will prevent over-deducting and help keep your variances accurate.
❗ Permission to edit inventory items is needed to access the inventory set up.
📖 Learn more about Permissions in Nory
The recipe for gluten-free bread was for 2 each, assuming 2 slices of bread would be deleted. Instead two loaves were being deducted per order.
To rectify this problem the item was updated to 10 each (10 slices of bread per loaf).