Start with the material planning decision
Inventory analytics becomes useful when it supports a concrete choice: what to replenish, when to order, or which shortage to investigate. A stock balance alone cannot answer all three. Choose a synthetic manufacturer with raw materials, work in progress, and finished goods, then narrow the project to one replenishment decision.
Describe the planning environment. Is demand driven by confirmed production orders, a forecast, or both? Are suppliers constrained by minimum order quantities? Is the material perishable? These details determine whether a simple reorder rule is meaningful. State assumptions instead of presenting one inventory formula as universally correct.
Model movements before computing balances
Create a movement ledger with item, location, timestamp, quantity, unit, and movement type. Receipts, issues, returns, transfers, and adjustments should have explicit signs. A transfer between warehouses changes local availability without changing total company stock. Preserve both sides rather than treating one as fresh demand.
Reconstruct opening balance plus movements to obtain closing balance. Reconcile that result to a synthetic stock snapshot. Include a deliberate unit error, such as a receipt in cartons alongside issues in individual pieces. Document the conversion rule and show how the error changes both stock availability and apparent usage.
Separate consumption from unmet demand
Historical issues can understate demand when a material was unavailable. A line that stopped requesting stock after repeated shortages may look quiet rather than constrained. Track backorders or production requirements separately when the dataset supports them. If it does not, label consumption as observed usage rather than unconstrained demand.
Plot lead times from purchase order release to usable receipt, not merely supplier dispatch. Include quality inspection delays if they matter to availability. A mean lead time can conceal occasional long delays, so inspect the distribution and identify whether a few exceptional orders dominate the result.
Test a replenishment rule honestly
Build a simple simulation that advances one day at a time, subtracts demand, and receives previously placed orders after their lead times. Evaluate a stated reorder rule against a baseline. Declare whether shortages are backordered or lost, because those assumptions change service calculations and subsequent inventory levels.
Use clearly labelled hypothetical holding and shortage costs if actual commercial data are unavailable. Report service outcomes and average stock separately rather than hiding everything in one financial score. Vary lead time and demand assumptions to show where the policy fails. A simulation is evidence about assumptions, not proof of a future saving.
Distinguish value, movement, and criticality
ABC analysis based on annual consumption value can help prioritize attention, but a low-value component may still stop an entire production line. Pair economic importance with operational criticality and supplier constraints. Do not interpret a low-value category as permission to ignore quality, safety, or continuity requirements.
Accounting valuation is another distinct question. IAS 2 addresses inventory accounting, including cost and net realizable value; it does not supply a universal replenishment policy. Keep financial valuation fields separate from physical availability. An item can be physically present yet unusable because it is quarantined, expired, or allocated to another order.
Deliver an exception-led planning view
Build a dashboard showing projected shortages, overdue purchase orders, excess stock under your assumptions, and records that fail reconciliation. Each exception should contain an item, location, required date, evidence, and proposed owner. Give planners a route to inspect the underlying movements rather than only red and green status cards.
For the final exercise, introduce a late supplier delivery and a changed production schedule. Explain which recommendations change and which remain stable. Publish the assumptions, simulation steps, and limitations alongside the visuals. This demonstrates planning judgement without claiming that a portfolio prototype replaces an ERP system or an experienced production planner.
Sources & further reading
Last editorial review: 27 October 2026. Examples are educational, not live employer data or guaranteed outcomes.