How to Master Batch Mixing and FIFO for Short‑Shelf Products in Warehouses

1. Foundations of Batch Mixing

The core challenge of batch mixing is physical co‑location while keeping logical distinction, which requires clear, persistent batch identifiers for each product.

Key requirements include standardized batch identification, a batch‑level inventory ledger, and dynamic expiration calculation rules.

1.1 Standardized Batch Identification

Basic information: product name, specifications, etc.

Expiration information: production date, shelf‑life days, expiry date, batch number.

Extended information (optional): supplier batch number, storage conditions, inbound quality‑check results.

1.2 Batch‑Level Inventory Ledger

Record for each batch: inbound time, production date, expiry, quantity, precise storage location (shelf/row/level).

Real‑time inventory dynamics (inbound, picking, transfer, loss).

Expiration status tags (normal, near‑expiry, critical, expired).

1.3 Dynamic Expiration Calculation Rules

Standard calculation: expiry = production date + shelf‑life days.

Environmental correction: if storage conditions deviate (e.g., temperature), shorten effective shelf‑life accordingly.

Operational loss: damaged goods are marked unsellable and deducted from inventory.

2. Inbound Process: Precise Batch Recording

2.1 Standardized Batch Information Entry

Basic: product name, specs, supplier, production date, shelf‑life, batch number; for imports, include customs and quality‑report numbers.

Dynamic: inbound time, quantity, storage location (e.g., "B‑area‑03‑shelf‑2").

2.2 Binding Location and Batch

During inbound, scan the product barcode or batch number; the system binds each batch to a specific location and records the batch‑location‑quantity‑time relationship.

Every movement (transfer, replenishment) updates the location to keep record‑physical consistency.

2.3 Virtual Sub‑Zones in Mixed Areas

Even when items are physically mixed, the system can create virtual sub‑zones within the same shelf, sorted by expiration priority (e.g., earliest expiry first).

Example: three milk batches A, B, C share a shelf; the system marks A’s area as the priority picking zone.

3. Storage Phase: Dynamic Position Adjustment and Near‑Expiry Alerts

3.1 Dynamic Expiration‑Based Partitioning

Near‑term zone (e.g., ≤30 days to expiry).

Mid‑term zone (30‑60 days).

Long‑term zone (>60 days).

New inbound batches are placed into the appropriate zone based on remaining shelf‑life.

3.2 Batch Stacking Order Control

When stacking multiple batches in the same location, place the batch with the earliest expiry on the top or outermost layer to avoid hidden near‑expiry items.

3.3 Inventory Monitoring and Expiration Alerts

Tiered alerts: 30/15/7 days before expiry for long/mid/short‑life items; urgent alert 3 days before for short‑life items.

System blocks outbound of expired items (scan verification).

Link sales data to trigger early transfer or promotion for slow‑moving batches.

4. Outbound Process: Enforce Expiration‑Priority Picking

4.1 Outbound Strategy: Mandatory “Expiration First”

When picking, the system sorts batches by effective expiry and recommends the earliest‑expiry batch.

Pickers scan the item; the system displays all mixed‑batch expiry dates, highlights the optimal batch, and requires confirmation.

Visual aids such as color‑coded labels (red for ≤15 days, orange for 16‑30 days, green for normal) guide pickers to the near‑expiry zone.

4.2 Secondary Outbound Verification

Before shipping, staff re‑verify the batch’s expiry via scan or label check; mismatched picks are returned for re‑picking.

5. Regular Cycle Counting and Review

5.1 High‑Frequency Cycle Counting

Divide inventory into multiple zones; each day count one zone, reconciling system quantity, actual quantity, and remaining expiry. Discrepancies trigger immediate root‑cause analysis.

5.2 Near‑Expiry Batch Review

Periodically generate a “near‑expiry list”, analyze quantity, shelf placement, and loss reasons, then adjust procurement or promotion strategies accordingly.

5.3 Data‑Driven Procurement and Sales Optimization

Analyze historical sales and expiry loss data to fine‑tune order quantities (e.g., reduce order volume for items that consistently linger) and automatically apply tiered discounts as items approach expiry.

6. Personnel Management and Training

Train staff on batch identifier interpretation, system operations (especially expiration alerts and outbound rules), and near‑expiry handling procedures.

Incorporate expiration‑management metrics (e.g., near‑expiry handling rate, expired loss rate) into employee KPIs, trace responsibility via system logs, and conduct monthly reviews of expiry loss causes to continuously improve processes.