Expiry isn’t an exception in food manufacturing. It’s a predictable output of the way planning, dispatch, and inventory decisions are made under shelf-life constraints. When the system can’t consistently prioritize what expires first, write-offs are the logical end state. Expiry loss is predictable, not accidental Shelf-life turns inventory into a perishable liability. Every day a batch sits, its value decreases and its urgency increases. Treating expiry as “bad luck” or “a few operational misses” hides the real issue: most plants are running decision logic that is not designed to prevent expiry. Expiry losses show up when: - demand and supply plans change faster than execution rules - inventory is visible at an aggregate level, but risk exists at the batch level - dispatch decisions optimize convenience (what’s easy to pick/ship) instead of what must move first If your environment includes multiple SKUs, batch/lot constraints, multiple storage locations, and shifting customer allocations, expiry risk changes daily. A monthly review and a manual “watch list” will always be late. Where the decision system breaks down No batch-level decisioning Most inventory views answer “how much do we have?” not “which specific lots are most at risk?” Expiry is batch-specific. Two pallets of the same SKU can have very different remaining shelf life based on production date, holds, or time spent in transit. Without batch-level decisioning, execution defaults to behaviors that create write-offs: - Wrong batches are used because pickers select what’s closest, easiest, or most accessible - Older stock is ignored because it is buried across locations, lanes, or third-party sites - allocation decisions are made without understanding which lots can actually support the promise window Batch-level intelligence isn’t just traceability for recalls. It’s the minimum input required to make rational dispatch and replenishment decisions. Dispatch decisions are manual and convenience-led In many plants, dispatch and shipping teams optimize for: - speed of loading - availability on hand - minimizing touches and rework Those priorities are understandable—but they are not aligned to shelf-life economics. When dispatch is manual, the system can’t consistently enforce expiry-aware rules across shifts, sites, and changing constraints. Manual decisioning also creates variability: - different planners and dispatchers apply different “rules of thumb” - exceptions become the norm during peak weeks - the organization learns about expiry risk only after aging inventory becomes obvious Lack of real-time expiry visibility Expiry risk is dynamic. It changes when: - orders cancel or move out - holds are released - production schedules shift - inventory moves between locations - customer shelf-life requirements vary by channel If systems don’t reflect these changes in near real time, teams are forced to make decisions using stale snapshots. The result is predictable: inventory that should have been prioritized earlier becomes unshippable later. Why people can’t solve this manually A common response is to “tighten process” by adding more manual monitoring. That approach fails at scale. Tracking expiry manually requires: - monitoring thousands of batches - across multiple locations (plant, DCs, co-packers, 3PLs) - with different remaining shelf-life windows and customer requirements - while demand signals and allocations change daily This isn’t a discipline problem. It’s an information and execution problem. Humans can’t continuously compute the best move sequence across all lots and constraints—and then enforce it consistently through picking and dispatch. The fix: make FEFO a system behavior The core shift is simple: expiry performance improves when the business moves from “policy” to system-enforced decision logic. Implement FEFO at the system level FIFO (first in, first out) is not enough in food environments because entry date is not the same as expiry risk. FEFO (first expiry, first out) prioritizes the earliest-expiring inventory—regardless of when it arrived or where it’s stored. To make FEFO real, you need the system to: - rank inventory by remaining shelf life at the batch/lot level - respect customer minimum shelf-life requirements by channel - factor in location constraints so “hard-to-reach” lots don’t become invisible - surface trade-offs explicitly when FEFO conflicts with other constraints Automate dispatch logic Dispatch should be system-driven, not dependent on individual judgment. That means: - automatically suggesting (or enforcing) which lots to pick and ship - making the “right” choice the default choice for warehouse execution - minimizing exceptions and requiring a reason code when FEFO is overridden The goal is not to remove operational flexibility. It’s to ensure flexibility doesn’t quietly create expiry exposure. Enable real-time expiry visibility Teams need a live view of: - what inventory is at risk (by SKU, batch, location) - how many days remain before it becomes unshippable by channel - what orders and lanes can consume it first - what will expire if nothing changes When this visibility is real-time, the organization can intervene early—before inventory becomes a write-off. When it’s delayed, every “save” becomes an expedited, high-friction scramble. Expiry is a decision problem, not an inventory problem Inventory write-offs are often blamed on execution: a missed rotation, a bad pick, a planning error. Those things happen, but they’re symptoms. The root cause is that the operating system for decisions—how batches are selected, allocated, and dispatched—doesn’t consistently optimize for shelf-life. If expiry is material for your business, treat it like any other high-cost failure mode: redesign the decision system so the correct action happens by default, across every shift and every site.