Maintenance Teams: Research Backed 90 Day Pilot to Cut Spare Parts Use

In maintenance, consumo de peças is the measured rate at which spare parts are used, and the simplest way to cut total cost is to measure that usage straight from your CMMS, segment parts by criticality, and then apply targeted stocking policies and operational fixes. Getting the data model right first is essential according to spare parts management research. Everything else, from ABC segmentation to Kanban pilots, depends on it.

TL;DR:

• A reliable data model linking work orders, asset IDs, and spare parts is essential for accurately measuring consumption and optimizing stock policies.
• Demand for spare parts is often bursty and unpredictable, requiring specialized models like METRIC for emergency shipment planning instead of traditional forecasting methods.
• Moderate, strategically accepted stockout risks for low-criticality parts can minimize total costs better than aiming for zero stockouts everywhere.
• Using segmentation by cost, demand predictability, and criticality improves stocking decisions and service levels without unnecessary inventory increases.
• Implementing a single system with integrated identifiers and automated KPI tracking accelerates pilot projects and enhances ongoing spare parts management efficiency.

Table of Contents

• Why spare-parts consumption behaves differently
• The data model behind measuring consumo de peças
• How to measure and segment parts consumption
• Operational levers to reduce consumption without raising downtime
• Turning the method into a working pilot
• Author checklist: first 90 days to start reducing consumo de peças
• Speed up your consumo de peças pilot with the right platform
• Sources
• FAQ

Why spare-parts consumption behaves differently

Spare-parts demand does not behave like retail demand. Most industrial and facility parts sit idle for months, then get consumed in a burst when a bearing fails or a valve seizes. This intermittent, low-volume pattern breaks standard forecasting methods built for steady, high-frequency sales data, and it is why so many maintenance teams end up either drowning in obsolete stock or scrambling for expedited shipments.

Corrective demand tends to follow arrival patterns closer to a Poisson process, rare, largely random events rather than a smooth trend. Multi-item network models such as METRIC and its extensions were built specifically to handle this, modelling lateral and emergency shipments across locations rather than treating each part in isolation.

The real driver behind every stocking decision is not the part’s cost. It is the cost of the asset sitting idle while you wait for it. That is why system availability and downtime cost, not unit price, should set your service-level targets.

Research on spare-parts inventory modelling consistently points to a counterintuitive conclusion: a moderate, deliberately accepted stockout risk on low-criticality items usually minimises total net cost better than chasing near-zero stockouts everywhere, according to spare parts inventory literature from TU Delft. Perfect availability on every SKU is not efficient. It is expensive.

The data model behind measuring consumo de peças

You cannot manage what your systems cannot connect. Most maintenance organisations track work orders in one module and parts issues in another, with no reliable link between the two, and that gap is the single biggest obstacle to measuring real consumption.

An empirical data model built specifically for spare parts management shows the fix requires linking preventive maintenance (PM) and materials management (MM) modules through three consistent identifiers: the asset ID, the work-order ID, and a standardised spare-part number. In the underlying case study, this linkage connected numerous spare parts to their bills of materials across tens of thousands of recorded consumptions link, built as a read-only business intelligence layer sitting on top of the existing CMMS rather than replacing it.

Pro Tip: A single, disciplined spare-part numbering standard that maps cleanly to bills of materials is often the highest-leverage change a maintenance team can make. It costs nothing to implement and unlocks every analysis that follows.

Once the identifiers are consistent, the fields that actually matter for consumption analysis are:

• Bill of materials (BOM) links connecting each part to the specific asset or equipment model it serves
• Lead time by supplier and part, not a single blanket assumption across the catalogue
• Part status (active, obsolete, superseded) to stop analysing dead stock as if it were live demand
• Storage location, including technician vans and satellite depots, not just the central warehouse
• Repair and failure records tying consumption events back to specific failure modes

An analytical layer built on these fields turns scattered transaction logs into a dataset you can actually segment, forecast, and act on. Without it, every downstream decision, from reorder points to criticality ranking, rests on incomplete data.

How to measure and segment parts consumption

Turning raw consumption data into a working stocking policy takes four steps, and skipping any one of them tends to produce plans that look sound on paper and fail on the shop floor.

1. Track the right KPIs first. At minimum, monitor consumption rate per part per period, fill rate (orders fulfilled from stock without delay), days of supply on hand, stockout risk over the relevant lead-time window, and repair turnaround time for repairable spares.
2. Run ABC segmentation by cost or volume. Rank parts by their annual consumption value, not unit price alone. A cheap gasket used constantly can outweigh an expensive sensor used rarely.
3. Layer in XYZ segmentation for demand predictability. X parts show steady, forecastable demand; Y parts show variable but patterned demand; Z parts are genuinely erratic. Standard smoothing methods struggle with intermittent Z items, and bootstrapping or Smart-Willemain-style approaches tend to outperform them for this segment.
4. Assign service targets by criticality, not habit. A parts room does not need one blanket service level. It needs several, mapped to what each part actually protects.

ISO 55000’s asset management framework offers a useful structure for documenting these risk-based decisions so they survive audits and staff turnover.

Operational levers to reduce consumption without raising downtime

Cutting consumption does not mean cutting availability. Several proven levers reduce holding costs and waste while keeping critical assets running, and the right one depends on the part’s segment, not a blanket policy.

Continuous review, or (Q,r), and periodic review, or min-max, sit at opposite ends of the same trade-off. An applied study on MRO inventory in an oil and gas operation found that switching class A items from a blanket min-max policy to a (Q,r) model with explicit stockout and backorder costs cut average inventory investment by over half while improving service level notably link. Min-max still works well for low-value, predictable C and Y items where the administrative simplicity outweighs the precision loss.

• Pilot Kanban for technician vans or local depots on 10 to 20 SKUs over roughly 30 days before scaling; visual replenishment triggers cut both stockouts and overstock for high-turnover consumables, leveraging field service software for plumbing, HVAC & electrical to optimize technician vehicle stocking.
• Use lateral transshipment between sites to cover a shortfall from a neighbouring depot instead of holding duplicate safety stock everywhere, a core recommendation from multi-item spare parts modelling research.
• Expedite repairs on repairable spares rather than over-stocking spares as insurance. Field tests combining stockout risk estimation with repair expediting produced savings of up to 8% alongside forecasting accuracy around 63% at 15 days and 83% at 45 days link.
• Evaluate 3D printing and IoT-driven predictive prepositioning for slow-moving, high-lead-time parts where a printable geometry or a predictive failure signal can replace weeks of held stock.

Turning the method into a working pilot

The academic models matter less than whether your team actually runs them. A practical rollout fits inside a 90-day window if you keep the scope tight.

1. Weeks 1 to 2: select a pilot scope of 10 to 20 SKUs tied to a single critical asset group, and clean the master data on those parts specifically.
2. Weeks 3 to 4: map the three identifiers (asset, work order, part number) and link every BOM record for the pilot SKUs.
3. Weeks 5 to 8: enable full work-order traceability so every issue and return ties back to a specific job, then start the Kanban trial.
4. Weeks 9 to 12: measure the KPI deltas against your baseline and decide what scales.

Software support matters most in three places: BOM linkage, a clean parts master, and work-order traceability that captures consumption automatically instead of relying on manual logging. Automated reporting closes the loop by surfacing the KPI shifts without a spreadsheet rebuild every month.

Author checklist: first 90 days to start reducing consumo de peças

Start narrow. Pick one asset group, clean its parts data, and link the identifiers before touching stocking policy at all. Weeks 1 to 4 belong to data hygiene, not to reorder points. Weeks 5 to 8 belong to running the Kanban pilot and watching fill rate and days of supply move. Weeks 9 to 12 belong to comparing the numbers against your baseline honestly, including the failures.

The most common pitfall is skipping the identifier work because it feels slow. It is the whole foundation. Measure, adjust, repeat, and resist the urge to declare victory after one good month.

— Pedro

Speed up your consumo de peças pilot with the right platform

Spreadsheets and disconnected systems can run a 20-SKU pilot, but they buckle once you try to scale ABC segmentation, work-order traceability, and automated reporting across a full parts catalogue. Fullyops is built for exactly that transition: a single platform where BOM links, parts master data, and work-order history live together, so the identifier work described above happens inside the system rather than across three spreadsheets and a shared drive. Its work order management tools tie every parts issue back to the job that consumed it automatically, and its inventory and reporting features surface the KPI deltas a 90-day pilot needs without manual rebuilding. Plans are structured around Basic, Professional, and Advanced tiers depending on how many technicians, admins, and integrations you need. If you are ready to run the pilot rather than just plan it, start with a demo and bring your first 10 to 20 SKUs.

Sources

• Chapter on multi‑item spare parts models (TU Delft / scholarly publication)
• Advance stockout risk estimation for repairable spare parts
• Improved MRO inventory management system in oil and gas company
• ISO 55000: asset management — overview, principles and terminology

FAQ

What is consumo de peças in maintenance operations?

It refers to the measured rate at which spare parts are used to repair or maintain assets, typically tracked as units consumed per period per part or asset group. Measuring it accurately requires linking work orders to parts issues through consistent identifiers, as described in empirical spare parts data modelling.

How do I reduce spare parts consumption without increasing downtime risk?

Segment parts by criticality and demand pattern first, then apply the matching policy: (Q,r) for high-value predictable items, min-max for low-value stable ones, and Kanban pilots for high-turnover van stock. Lateral shipments and repair expediting cut local safety stock further without adding risk, based on findings from stockout risk and repair expediting research.

What KPIs should I track for parts consumption?

Track consumption rate, fill rate, days of supply, stockout risk, and repair turnaround time as a minimum set. These give you both the demand-side picture and the supply-side responsiveness needed to set realistic service targets.

How does ISO 55000 relate to spare parts management?

ISO 55000 provides the asset management framework for aligning parts provisioning decisions with organisational risk tolerance and documenting why a given service level was chosen. It does not prescribe specific stocking formulas but supports the governance layer around those decisions.

Can a CMMS alone measure consumo de peças accurately?

A standard CMMS captures the transactions but rarely links them into an analysable model on its own; most need an added layer connecting asset, work-order, and part identifiers. Platforms like Fullyops address this by tying work order traceability directly to inventory records rather than treating them as separate systems.

Recommended

• Spare parts management for maintenance managers: a practical system
• Workflow for Preventive Maintenance: Cut Costs by 30% in 2026
• Cut Maintenance Stockouts in 90 Days Using Asset Linked Spare Coding
• Maintenance Planning Step by Step: Cut Downtime by 30%