Wastage in food manufacturing rarely comes from a single failure. It is the predictable outcome of execution gaps across inventory, planning, and dispatch—where each team makes locally “reasonable” decisions that create system-level loss. If you want a 30% reduction, treat wastage as a decision-system problem: tighten visibility, align plans to real demand and inventory, and enforce dispatch rules that consistently move the right stock. Wastage is not one problem Most plants try to fix wastage in isolation—running a one-off expiry push, tightening receiving checks, or telling scheduling to “produce less.” Those actions can help, but they usually don’t hold because the drivers are connected. In practice, wastage typically shows up as a mix of: - Expiry-driven write-offs (inventory sits too long because it isn’t visible or prioritized) - Overproduction (plans are built on forecasts, not on what’s already on hand and at risk) - Poor dispatch decisions (the wrong batch ships because selection is manual or optimized for speed, not shelf life) When these three reinforce each other, the plant can be “working hard” and still losing margin daily. Where to focus to cut waste Inventory management: make expiry risk operational, not informational If you cannot see expiry risk at the batch level, you cannot manage it. Many organizations know expiry by SKU “in general,” but execution requires knowing which batches are at risk, where they are, and what decisions they are flowing into. Focus on: - Expiry tracking that is accurate enough to drive daily decisions (not a monthly report) - Batch-level visibility across locations (warehouse zones, cold storage, staging, 3PL, in-transit) What “good” looks like: - Near-expiry inventory is automatically flagged and quantified - Planners can see at-risk batches while creating schedules - Dispatch can select by batch with enforced rules (not tribal knowledge) Production planning: align output to demand and inventory risk Waste reduction is not “produce less.” It is produce what should be produced next, given demand and what is already aging. Align production with: - Demand (what customers will actually take within shelf-life constraints) - Inventory (what is already available and what will expire if not moved) Common execution gap: - Production plans built on forecast and capacity, while ignoring on-hand near-expiry stock. The result is fresh inventory produced while older inventory becomes unsellable. Dispatch optimization: make FEFO non-negotiable Dispatch is where inventory decisions become irreversible. If the plant does not consistently ship near-expiry stock first, it is effectively choosing to scrap it later. Prioritize: - Near-expiry stock via FEFO (First Expired, First Out) Where FEFO fails in real life: - Batch selection is manual and time-pressured - Pickers optimize for shortest travel path, not expiry - Customer constraints (minimum shelf life on delivery) aren’t encoded into dispatch logic The core shift: from discipline to decision systems Wastage is not caused by poor discipline. It is caused by poor decision systems. If teams need heroic effort to do the right thing, the system will revert under pressure. The goal is to build execution where the default decisions—planning, picking, allocating—naturally reduce expiry risk. A step-by-step approach to reduce wastage This approach is designed to be implemented incrementally. Each step reduces loss on its own, but the biggest gains come when all four steps run together. Step 1: Identify high-risk inventory Start by making expiry risk visible and measurable. Track: - Near-expiry batches by SKU, batch, quantity, and location Implementation checklist: - Define “near-expiry” thresholds by product family (e.g., 15/30/45 days) - Create a single view of at-risk inventory (including off-site and 3PL) - Add daily monitoring cadence: what moved, what didn’t, and why Output of this step: - A prioritized list of batches that must move or be consumed in production to avoid write-off Step 2: Align production to avoid creating new risk Use the high-risk view to prevent schedules that increase the problem. Avoid producing: - Low-demand SKUs that will compete with existing inventory and extend days on hand Practical planning rules: - Don’t schedule runs that increase on-hand coverage beyond realistic sell-through - Prefer producing SKUs that draw down at-risk ingredients or WIP - Use expiry risk as a constraint, not a post-plan clean-up Output of this step: - A schedule that protects service levels while reducing inventory aging Step 3: Optimize dispatch with enforced FEFO Make shipping decisions consistent and auditable. Ensure: - FEFO-based movement at batch level Execution details that matter: - Encode customer shelf-life constraints (e.g., “minimum 70% remaining shelf life”) into allocation - Prevent “easy picks” from overriding FEFO without a logged reason code - Track FEFO adherence daily (exceptions, root causes, and corrective actions) Output of this step: - Near-expiry inventory is automatically pulled into outbound flow instead of accumulating Step 4: Systemize decisions to reduce manual intervention Manual control does not scale, especially with high SKU counts, variable demand, and multiple storage locations. Reduce manual intervention by: - Standardizing decision rules (planning, allocation, picking) - Defining exception workflows (what requires approval vs. what is automatic) - Creating feedback loops so the plan learns from what actually shipped Output of this step: - Lower coordination cost, fewer last-minute firefights, and sustained waste reduction rather than temporary clean-ups What to measure to prove the 30% reduction To sustain improvement, measure both outcomes and execution quality. Outcome metrics: - Write-offs from expiry (value and units) - Waste as a percentage of production output - Inventory aging profile (units/value by remaining shelf-life bands) Execution metrics: - FEFO adherence rate (% shipments compliant) - Near-expiry inventory clearance rate (how fast risk inventory moves) - Plan stability (schedule changes driven by inventory risk vs. shortages) These metrics make it clear whether waste is falling because the system improved—or because a one-time push temporarily hid the problem.