Best-Before Management in Food Wholesale: FIFO, FEFO, and What Software Must Do

**Best-before management is more than a warehouse topic — it is a cash-flow, evidence, and customer topic.** Many distributors treat best-before as a pure warehouse task and only review write-offs after the month is closed. Best-before management connects food information, delivery evidence, inventory accuracy, and customer expectations. Treat all three together and shrinkage usually becomes easier to explain and reduce. Food-waste and ESG expectations are also rising, but reporting duties depend on company scope, customer programs, and national implementation. Documented waste rates can support tenders and supplier reviews; exact scoring, reporting format, and customer requirements should be validated with each customer before being used as a commercial promise.

**FIFO can be wrong in food — FEFO should be validated by SKU, temperature zone, and customer promise.** FIFO means: what was received first ships first. In food wholesale that does not always match the earliest expiry, especially when suppliers deliver batches with different remaining shelf life. FEFO means: the batch with the earliest expiry ships first. Fresh, frozen, ambient, and own-brand assortments can each need different rules depending on batch data, customer minimum shelf-life commitments, and QA risk. In practice, a platform should support batch-level rules and make the intended batch visible during picking. Running FEFO only as a sticker process can leave proof gaps when the picked batch differs from the intended batch. A pilot should compare the current process against local complaint, write-down, recall, and pick-evidence data before defining the final rule set.

**FIC, customer standards, and PPWR make batch-level evidence more important.** FIC (EU Regulation 1169/2011) requires food information and traceability discipline for packaged food, and many large B2B customers expect delivery-note or invoice evidence for batch/best-before data on relevant SKUs. The EU PPWR generally applies from 12 August 2026 and makes reusable-packaging and packaging-flow data more important, but exact obligations depend on packaging and operator role. On top: GoBD-oriented accounting expects traceable documentation for inventory write-downs caused by expiry. Simply writing off expired yogurt without a posted record with quantity, value, and reason is a tax-audit risk. Concretely: every material write-down should have document reference, quantity, value, and reason — annual lump-sum bookings are weak evidence.

**Five best-before mistakes to test before rollout — root cause is often master-data hygiene.** First: receipt without best-before capture, so pickers cannot reliably apply FEFO. Second: best-before data exists only on the carton, not in the system, which slows reviews and weakens evidence. Third: no alert when stock approaches a customer or internal shelf-life threshold. Fourth: expiry stock is not connected to sales or promotion workflows. Fifth: sales has no usable expiry overview for items that should move first. Common companion mistakes include sticker-only FEFO without scan evidence, stale master data, and complaints that never link back to the pick record. A pilot should inspect duplicate SKUs, wrong pack sizes, missing expiry fields, write-down reasons, and complaint links before promising software-driven savings.

**Working model: quantify expiry shrinkage before promising FEFO savings.** The useful calculation starts with local cost of goods, current expiry write-downs, affected SKU groups, customer shelf-life commitments, and how often the team can act on threshold alerts. FEFO, batch-level inventory, sales push lists, and promo pricing can reduce avoidable shrinkage only when master data, pick discipline, and sales execution are strong enough. A pilot should measure baseline shrinkage, alert coverage, action rate, and write-down movement before and after the process change. Finance should approve any annual saving range from actual postings and margin assumptions; insurance effects belong in a separate insurer validation step.

**Batch traceability should be treated as operational readiness, not a fixed response-time promise.** Food traceability and customer standards require reliable links between supplier batch, stock movement, delivery evidence, and affected customer lists. A modern platform should make this queryable: enter a batch, review which customers received which quantities when, and export a recall list for operations review. In a suspected incident, the target is not a marketing number; it is a rehearsed process with clear data ownership, reviewable evidence, and defined customer-notification responsibility. Validate response time, audit evidence, export completeness, and legal/QA handoff during the pilot.

**Minimum remaining shelf life is contractual — and the rule set must come from customer agreements.** Customers may define minimum remaining shelf life by SKU, category, delivery channel, and receiving process. Those values should be captured as customer-specific rules instead of copied from generic market examples. Good software can check remaining shelf life during order release or picking, block unsuitable batches, or suggest a better batch where the process supports it. The business case should measure avoided returns, re-picks, walking time, and customer dispute handling with local data. MRSL values can change through contracts or customer standards, so master-data maintenance needs an owner and review cadence.

**Frozen stock needs its own best-before review instead of assuming long shelf life removes risk.** Frozen items can look safe because their dates are longer, but customer remaining-life rules, storage cost, energy use, and seasonal demand can still create slow-moving stock. The right fix is site-specific: separate frozen thresholds, FEFO where relevant, regular aged-stock review, and customer-specific MRSL rules. Larger frozen warehouses should model write-down and energy effects from their own stock age, pallet occupancy, electricity contracts, and sell-through history. Any energy or cash-flow saving should be treated as a local finance model, not as a public benchmark.

**Common practical questions — answered concisely, with focus on RFP-relevant answers.** Do I need to scan every carton individually? No, pallet-level capture with batch and best-before is enough for most cases. For very heterogeneous mixed pallets, carton-level capture is worth it. How do I import best-before from EDI receipts? Via mapping of common EDIFACT segments — DESADV with DTM segment for best-before and batch. What about mixed pallets with multiple batches? A FEFO system splits them at receipt automatically and posts each batch to its own sub-stock. Do I need a dedicated best-before module or is the WMS enough? Pure ambient works with WMS; fresh needs batch-level inventory with FEFO logic at the pick. What about own-brand production? If you set best-before yourself, the system needs a calculation logic with shelf-life templates per SKU group. How often should you do a batch inventory? Quarterly cycle counts at batch level suffice; annual full inventory can be skipped if movement tracking is clean.

**LuniOps supports batch, lot, best-before, and FEFO workflows across the current web and iOS operations surfaces.** Concretely: lot receipt with expiry, FEFO pick evidence, threshold/expiry review surfaces, batch-aware delivery-note data where configured, and recall call-sheet exports from recorded lot movements. Promo pricing, MRSL rules, GoBD write-down documentation, DATEV handoff, and recall-time targets must be validated in a pilot with your current data and accountant. If you want to reduce expiry shrinkage in 2026, talk to us about a pilot. We start with an audit of your recent shrinkage postings, master data, and pick evidence, then identify the highest-leverage SKU groups before rollout.