ABC analysis for inventory is a classification method that ranks every stock item by its annual usage value (unit cost multiplied by annual consumption), then sorts items into three classes: A for the vital few high-value items, B for the moderate middle, and C for the trivial many low-value items. Each class receives a different control policy.
ABC analysis applies the Pareto principle, the observation that roughly 80% of value comes from 20% of items. In a maintenance storeroom, a small share of spare parts ties up most of your inventory investment, while thousands of cheap fasteners and seals barely register. Instead of managing every part with equal effort, you focus scarce attention where the money and risk actually sit.
Typical class boundaries look like this:
These percentages are guidelines, not laws. Adjust the cut points to fit your part mix and how many items you can realistically manage under tight control.
Start by building one row per stock-keeping unit with two figures: annual consumption (units used per year) and unit cost. Multiply them to get annual usage value, then rank descending. The formula is simple:
Annual usage value = annual quantity consumed × unit cost
Next, compute each item's share of total value and a running cumulative percentage. Where the cumulative curve crosses your chosen thresholds, you draw the A/B and B/C lines.
Imagine a five-item slice of a maintenance store. Total annual usage value is 100,000 euros.
Rank by value and accumulate: gearbox (42%), servo drive (cumulative 66%), bearing set (cumulative 86%), belt (95%), O-ring kit (100%). Using an 80/95 boundary, the gearbox and servo drive are Class A (2 items, 66% of value), the bearing set and belt are Class B, and the O-ring kit is Class C. Two items now command the bulk of your control effort.
The whole point of classification is to act differently on each group. Match the policy to the stakes.
Note that Fabrico supports asset and spare-parts tracking, but does not place orders for you. Automatic reordering stays a manual or supplier-side decision.
Pure usage value can mislead a maintenance team. A 20-euro sensor that halts a bottleneck machine is far more important than its price suggests. That is why many storerooms run a second axis, criticality, alongside value. A cheap but mission-critical part may be pulled up to A-level control regardless of its usage value.
Pair ABC with reliability data to sharpen the call. Parts on assets with poor MTBF and MTTR deserve tighter buffers, and a structured FMEA exercise helps flag which failure modes justify holding safety stock at all.
Done well, ABC analysis frees up working capital, cuts stockouts on the parts that matter, and directs counting labor where it pays off. A practical rollout looks like this:
Re-run it at least twice a year, and after any major change in production mix, machine fleet, or supplier pricing. Usage patterns drift, so an item classified as C last year can climb into B or A. A stale classification quietly misdirects your counting effort and safety stock, which erodes the whole benefit of the exercise.
ABC classifies items by annual usage value, answering how much money each part represents. XYZ classifies by demand variability, answering how predictable consumption is. X items are steady, Z items are erratic. Combining both (a nine-box AX to CZ grid) gives sharper policies, since a high-value item with erratic demand needs more buffer than a steady one.
Yes, but add a criticality axis. Spare parts differ from raw materials because a low-cost component can still cause expensive downtime if it fails. Run standard value-based ABC first, then promote cheap but mission-critical spares to tighter control. This hybrid protects uptime without over-investing in the many low-impact, low-value items in your store.
Want to see your spare-parts usage classified and controlled in one place? Book a Fabrico demo to see how real-time CMMS tracking and analytics turn raw consumption data into an actionable ABC view of your storeroom.
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