Normalize pack sizes so your numbers tell the truth
Convert cases, formats and units to a common basis before comparing prices or costs.
Two invoices list mozzarella at €89 per case and €94 per case. The first looks cheaper until you notice it holds 6×2 kg blocks while the second holds 10×1 kg. Per kilo, the 'expensive' one costs €9.40 against €7.42 — a 27% gap hiding inside identical-looking paperwork. This is why pack normalization precedes every serious analysis: price variance detection, supplier comparison, recipe costing. Skip it and you are not measuring your kitchen; you are measuring formatting differences between vendors' order systems.
Choosing the right reference unit
- Cookable unit beats purchase unit. Price per kilo of usable product, per litre, or per portion — whatever survives into the pan.
- One unit per family. All cheeses in €/kg, all oils in €/L. Mixed units inside a family make comparisons impossible.
- Record the conversion chain. Case → packs → net weight, each step written down. Mental math is where errors breed.
- Yield belongs in the number. If product A needs trimming and product B doesn't, their per-kilo prices are lying until yield adjusts them.
A worked example
You receive olive oil in three formats across two months: 12×1L cases at €54.00, 6×3L tins at €76.50, and a 5L bag-in-box at €39.90. The case prices differ wildly; the litres tell one story.
| 12×1L @ €54.00 | €4.50 / L | reference format |
|---|---|---|
| 6×3L @ €76.50 | €4.25 / L | 5.6% cheaper per litre |
| 5L BIB @ €39.90 | €7.98 / L | premium format — check spec! |
The bag-in-box looks cheapest as a package but costs nearly double per litre — usually a different product grade. Normalization surfaces exactly these traps.
Interactive example · price spread
Spread between your highest and lowest quoted unit price: €6.30 (€39.90 → €46.20). Compare the quoted prices with each supplier. This is a price difference, not a savings projection.
Building the habit
- 1Define units for your top 30 lines. Cover 80%+ of spend first: assign each recurring product a reference unit and write down its current conversion.
- 2Convert at entry, not at analysis. The moment an invoice line enters your tracking, reduce it to per-unit price. Analysis-time conversion guarantees inconsistency.
- 3Flag pack changes immediately. When a supplier switches from 12×1kg to 6×2kg at the same case price, the unit price just moved 0% — but most systems record nothing. Make pack changes visible events.
- 4Recheck conversions quarterly. Suppliers change pack sizes without announcements. A quarterly audit of conversion factors catches silent drift before it corrupts months of data.
FAQ
- Do I need software for this?
- No. A spreadsheet with columns for case price, pack config, net units, and computed unit price covers most kitchens. Software earns its keep by automating conversion at invoice entry and flagging pack changes for you.
- What about items sold per piece, like steaks?
- Count pieces per case and use price per piece, adjusting for trim if you buy unportioned cuts. The principle is identical: express everything in the unit your kitchen actually consumes.
- How does this interact with price variance checks?
- Variance detection compares unit price over time for the same product. Without normalization, a pack change masquerades as a price jump (or hides a real one). Normalized units are what makes variance signals trustworthy.
- Who should maintain the conversion table?
- One named person — usually the chef or purchaser — with changes announced to everyone who reads the numbers. Unowned tables rot; owned ones get corrected.
Keep reading
- Catch invoice line-price drift before it eats your margin
Compare each invoice line against expected unit prices and act on real variances, not noise.
- Compare prices across the suppliers you already use
A fair comparison framework for recurring restaurant products and current suppliers.
- Building purchase history from invoices you already have
Turn past invoices into a decision record: what to keep, what to normalize, and how to spot recurring patterns without inventing certainty.
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