In asset-intensive industries, spare parts represent more than mere inventory, they are critical enablers of operational continuity, safety, and cost efficiency.
Poor spare parts classification drives millions in hidden costs through excess inventory, misprocurement, and costly equipment downtime.
Spare parts classification forms the foundation for predictive maintenance, procurement optimization, and effective MRO inventory management.
Spare Parts Classification Categories
Spare parts can be classified based on multiple dimensions to streamline inventory management, maintenance planning and procurement decisions.
Effective classification ensures that parts are correctly prioritized, stocked, and managed according to their operational importance. The main categories used in practice include:
By Usage & Interchangeability
Usage-based classification organizes parts by how they are consumed or replaced in operations:
1. Consumable: These are single-use items that are consumed during operations.
2. Rotable/Interchangeable: These are repairable, high-value assemblies that can be swapped out for a pre-serviced unit to minimize downtime.
3. Service: Unlike consumables and rotables, this category refers to non-physical maintenance activities that must still be captured and standardized in the material master.
In asset-intensive industries such as Oil & Gas and Mining, many maintenance needs are fulfilled through services, which require the same structured classification, coding, and governance as tangible parts.
Why it matters:
Services are often entered into ERP/EAM systems as “service material codes” (non-stock), ensuring they are visible in procurement workflows but not mistaken for consumables or rotables.
Classifying services properly prevents errors in stock management (e.g., holding “calibration” as inventory).
It also improves spend visibility. Organizations can track how much is spent on outsourced services vs. physical spare parts.
Classifying parts by usage enables accurate stocking levels, optimizes inventory costs, and ensures rapid availability of critical assemblies.
Por ejemplo:
Industria | Consumibles | Rotables | Servicio |
Oil & Gas (O&G) | Seals, gaskets, lubricants | Pumps, gearboxes, turbine skids | Calibration, NDT, valve refurbishment |
Minería | Drill bits, wear liners | Haul truck engines, alternators | Condition monitoring, rebuilds |
Relevancy:
Treating Servicio as a distinct use category makes classification more comprehensive, ensures cleaner data harmonization, and helps finance, maintenance, and supply chain teams see the full picture of operational costs.
By Cost & Consumption
Cost and consumption-based classification ensures efficient capital allocation and procurement planning. Common frameworks include:
1. ABC Analysis: Categorizes items by consumption (concentration) value:
- A-items: High-value, low-volume parts requiring tight inventory control.
- B-items: Medium-value, moderate consumption items.
- C-items: Low-cost, high-volume items often managed with bulk ordering.
Typical pattern: A = 10–20% items / 70–80% value; B = ~30% / 15–25%; C = 50–60% / 5–10%.
In Oil & Gas and Mining, A-class often includes high-spec rotables and critical instrumentation.
As per a report by IBIMA Publishing, spare parts in a printing industry application were classified as:
At PT XYZ in Gresik, Indonesia, an ABC analysis with EOQ methods showed that 8.6% of items (Group A) accounted for 56.8% of the budget, 18.5% of items (Group B) for 24.2%, and 72.9% of items (Group C) for 10%. This allowed high-value items to be closely managed while lower-value items were handled with simpler controls.
2. HML Analysis: Focuses on unit cost for supplier negotiation and procurement strategy.
High/Medium/Low unit price is useful when consumption data is thin, common in brownfield sites.
3. SDE Analysis: Assesses procurement complexity and lead time.
Scarce: Items with limited global suppliers or import dependencies.
Difficult: Items with long manufacturing cycles or complex logistics.
Easy: Items readily available in local markets.
This is particularly relevant for deepwater subsystems in Oil & Gas or large haul truck components in Mining.
4. SOS Analysis: Identifies demand patterns tied to operational cycles or seasonal consumption, reducing obsolescence risk.
Season vs off-season usage especially relevant for Mining shutdown seasons and Oil Sands winterization kits.
5. VED Analysis: Evaluates spare parts based on criticality to operations and safety.
Vital (V): Parts whose failure immediately halts production or compromises safety. They must always be in stock (e.g., ESD valves in Oil & Gas, braking systems in Mining).
Essential (E): Parts that affect performance but allow operations to continue at reduced efficiency. Lead time can be tolerated to some extent.
Desirable (D): Low-impact parts that do not stop operations if unavailable. Stocking can be minimized or deferred.
This classification is widely used in industries where equipment uptime and safety are critical, such as Oil & Gas, Mining, and Power.
In a packaging company (as per a case study published by The UWS Arcade),
A VED analysis of spare parts classified ~32% of items as Vital, ~57.6% as Essential, and ~10% as Desirable. By combining this with an ABC‑VED matrix, around 37.4% of parts fell into the highest-priority category, representing ~82% of the annual inventory expenditure.
This prioritization enabled the company to focus inventory management, ensure critical parts were always available, reduce stockouts, optimize procurement spend, and improve overall maintenance planning.
6. XYZ Analysis: Categorizes items based on demand variability and predictability.
X Items: Demand is very stable and predictable. Stock levels can be tightly controlled with minimal safety stock. Example: Standard fasteners in manufacturing.
Y Items: Demand shows moderate variability or seasonal trends. Stock monitoring and forecasting are needed to avoid shortages. Example: Cooling system filters used more in summer.
Z Items: Demand is highly irregular or unpredictable. Often emergency-use or rarely consumed items. Higher safety stock or “order-on-demand” strategies are required. Example: Spare parts for older machinery or legacy equipment.
XYZ classification is particularly useful when combined with ABC analysis (value-based) to form an ABC-XYZ matrix, helping organizations prioritize inventory management efforts by balancing cost, availability, and risk.
This approach is widely applied in asset-intensive industries such as Oil & Gas, Mining, and Utilities, where aligning inventory levels with demand variability improves operational efficiency and reduces working capital tied up in spare parts.
By combining ABC, HML, SDE, VDE, XYZ and SOS, organizations can optimize inventory policies and ensure cost-effective MRO operations.
Accurately classified and managed spare parts data ensures that each classification is reflected consistently across ERP and MRO systems, enabling data-driven decisions and predictive analytics.
Relevancy:
ABC/HML standardizes value tiers across ERPs; SDE highlights procurement and lead-time risks for critical BOMs; SOS maps seasonal or operational demand cycles; VED ensures stocking aligns with operational criticality and safety priorities; and XYZ flags demand variability, helping optimize safety stock and reduce excess inventory.
By Source
Source-based classification addresses procurement strategy, supplier selection, and cost-quality balance:
1. OEM (Original Equipment Manufacturer): Ensures exact specification compliance but may carry higher costs.
2. Aftermarket: Provides cost savings but requires careful quality verification.
3. Remanufactured: Eco-friendly, lower-cost option for high-value components.
4. Used/Second Hand: Useful in emergencies or for legacy equipment where new parts are unavailable.
According to McKinsey,
Remanufacturing can reduce component costs by 40–60% for many parts vs buying new.
Understanding source implications allows procurement teams to balance cost, quality, and risk effectively.
Examples of Source-Based Classification in O&G and Mining
Source Category | Oil & Gas Industry Examples | Mining Industry Examples |
OEM (Original Equipment Manufacturer) | Safety-critical valves and precision instrumentation. | High-spec drill automation components, proprietary control modules. |
Aftermarket | Replacement piping components, gaskets, and non-critical fittings. | Robust market for GET (Ground-Engaging Tools) such as buckets, teeth, and cutting edges. |
Remanufactured | Compressors and rotating equipment through OEM-approved remanufacturing programs. | Engines, transmissions, and major drivetrain assemblies where remanufacturing is standard. |
Used/Second Hand | Legacy pipeline spares or discontinued skids used in emergency scenarios. | Older haul truck parts salvaged to maintain legacy fleets. |
Relevancy:
Consistent “source” flags prevent duplicate records (OEM Vs remanufactured Vs Aftermarket) and enable policy-based sourcing.
By Criticality
Criticality-based evaluation for spare parts aligns inventory with operational impact:
1. Vital (V): Parts whose failure immediately stops production or compromises safety. High-priority stocking and monitoring are essential.
2. Semi-Critical Parts (SC): These parts meet a certain minimum inventory level and a longer lead-time for semi-critical spares can be tolerated.
3. Non-Critical (NC): Components whose failure does not halt operations and can be deferred without significant consequences.
By linking criticality to MTTR, maintenance teams can prioritize stocking, inspection, and procurement, ensuring that vital items are never out of stock.
Examples of Criticality-Based Classification in O&G and Mining
Criticality Category | Oil & Gas Industry Examples | Mining Industry Examples |
Vital (Critical) | ESD (Emergency Shutdown) valves, fire/gas sensors, subsea control modules | Braking systems, collision-avoidance sensors, crusher liners |
Non-Critical | Office HVAC filters | Safety signages |
Relevancy:
A uniform criticality scale (e.g., V/S/N with definitions) standardizes min/max policies, safety stock, and approval workflows enterprise-wide
By Demand Pattern
Demand-based classification ensures alignment with usage frequency:
1. Fast-Moving: Frequently used parts that require continuous replenishment.
2. Slow-Moving: Infrequent use, managed with controlled stock levels to prevent capital lock-up.
3. Non-Moving/Obsolete: Items that rarely, if ever, see consumption. Strategic management of obsolete spare parts helps free up warehouse space and reduce carrying costs.
4. Dead Stock: Spares that haven’t been used at all in the past 12-24 months are typically tagged as “dead stock”.
A study by Deskera showed that,
FSN analysis shows fast-moving goods account for 10–15% of inventory, slow-moving 30–35%, and non-moving 60–65%. Optimizing this mix helps prioritize critical items, reduce excess stock, and free up working capital.
Analyzing historical consumption and categorizing parts by demand allows for data-driven inventory optimization.
Examples of Demand-Pattern-Based Classification in O&G and Mining
Demand Category | Oil & Gas Industry Examples | Mining Industry Examples |
Fast Moving | Filters, common seals | PPE (Personal Protective Equipment) |
Slow Moving | Specialty turbine blades | Specialized sensors |
Non-Moving/Obsolete | Obsolete skids | Discontinued drill parts |
Relevancy:
Studies depict that standardized movement codes enable automated policy selection (review frequency, reorder method) and expose disposal candidates.
By Function/Category
Functional classification groups parts based on technical or operational roles, a case of classic plant taxonomy:
MECH / ELEC / INST / SAFETY / CIVIL / IT.
Standardized functional classification supports nomenclature consistency, eases master data management, and facilitates analytics and automation. Maintenance teams can quickly locate parts and perform cross-functional reporting.
Relevancy:
It simplifies catalog browsing, aligns to maintenance skill sets, and enables role-based approvals.
By Storage Requirements
Storage-based classification ensures compliance and safe handling:
Hazardous: Requires specialized handling, adherence to Material Safety Data Sheets (MSDS), and regulatory compliance.
Perishable: Items with defined shelf life requiring rotation and monitoring.
Climate-Sensitive: Components sensitive to temperature or humidity, demanding specialized warehouse conditions.
Proper storage classification reduces risks of spoilage, regulatory violations, and operational disruption.
Examples of Storage-Requirement-Based Classification in O&G and Mining
Storage Category | Oil & Gas Industry Examples | Mining Industry Examples |
Hazardous | Chemicals, EX-rated spares | Cyanide (gold processing), explosive accessories |
Perishable | Sealants | Adhesives |
Climate-Sensitive | Humidity-sensitive instrumentation | Electronics |
Relevancy:
It enforces labelling, MSDS links, temperature/humidity rules across warehouses.
Multi-Criteria Classification
The most effective spare parts strategies combine multiple classification dimensions: usage, cost, criticality, demand, and storage requirements.
For example, a part may be A-class by cost, Vital by criticality, and Rotable by usage, enabling nuanced stocking and procurement decisions.
Global standards such as UNSPSC or eCl@ss facilitate cross-system consistency, ensuring classification remains coherent across ERP, MRO, and procurement systems.
Multi-criteria classification also empowers automation, predictive analytics, and data-driven decision-making, creating a single source of truth for spare parts data.
Most of the companies combine 3 to 5 dimensions when it comes to classification of their data: e.g., Use + Criticality + ABC + SDE + Movement.
According to a study conducted by Salford University,
12 classes of spare parts were defined; through a hierarchical, multi-criteria classification, they achieved a ~20% reduction in total logistics cost while still meeting service target levels.
Why Classification is Non-Negotiable
Spare parts classification is more than an administrative exercise; it is a strategic enabler. By categorizing inventory correctly, organizations gain the following benefits:
1. Less Downtime, More Throughput:
Critical spares become both findable and forecastable, with stocking aligned to risk. DNV’s O&G study found that targeted spare optimization met availability targets without major system redesigns.
2. Lower Working Capital:
Cleansed MRO data has helped O&G operators reduce working capital by 10–20%, translating into multi-million-dollar savings.
3. Better Forecasting:
AI-driven approaches now outperform classical methods in predicting demand for fast-moving, slow-moving, and non-moving spare parts, provided the inventory is consistently classified.
4. Cost Reduction:
Proper classification identifies duplicate or redundant items, reducing overstocking and freeing capital for critical operations.
For example, recognizing high-value but low-demand parts (A-class in ABC analysis) allows businesses to invest selectively, minimizing inventory carrying costs.
5. Operational Efficiency:
Classification simplifies part identification, ensuring maintenance teams can locate required items quickly, reducing Mean Time to Repair (MTTR) and preventing unplanned downtime.
A clear hierarchy of fast-moving versus non-moving parts ensures timely replenishment without excess stock.
6. Strategic Sourcing:
Categorizing parts by source, criticality, and procurement complexity helps organizations negotiate contracts effectively, select suppliers with optimal lead times, and anticipate supply chain disruptions.
In essence, a structured classification system transforms MRO operations from reactive inventory management to data-driven strategic planning, aligning maintenance, procurement, and operational objectives.
How do the leading organizations achieve the most?
Oil & Gas:
- Criticidad (with supply risk) → standardizes stocking for safety/production-critical skids.
- Use Type (Consumable/Rotable/Service) → unlocks repair/return loops for rotables and eliminates service-as-stock errors.
- ABC + SDE → marries value focus with lead-time risk; crucial for turnarounds and import-dependent spares.
- Demand Pattern (FSM/NM) → enables predictive stocking and identifies write-off candidates; strengthens PdM analytics.
Minería:
- Criticidad (safety & production impact) → haulage, crushing, and fixed-plant bottlenecks dominate cost exposure. Mining maintenance is 30–50% of operating costs, so critical spares governance is non-negotiable.
- Demand Pattern → high divergence (fast-moving GET vs slow specialty controls). Forecasts work best once classes are clean.
- ABC + HML → aligns capital to high-value rotables (engines, transmissions) while controlling the flood of C-class consumables.
- Storage Requirements → hazardous/explosive handling and climate control (dust, temperature swings) make standardized storage attributes critical for compliance and shelf-life.
In Oil & Gas and Mining, classification is the operating system of your materials data. Get the definitions right, enforce them consistently across sites and systems, and the benefits cascade: fewer outages, safer operations, lower working capital, and clearer decisions.
If your teams are still arguing over what counts as “critical,” the ROI of harmonization is likely sitting in your storeroom.
Conclusión
Spare parts classification is the foundation of proactive MRO strategy. By systematically categorizing inventory across usage, cost, criticality, demand, function, source, and storage, organizations can reduce costs, improve operational efficiency, and strengthen strategic sourcing.
Investing in data governance and standardization ensures that classified spare parts data remains accurate, reliable, and actionable, enabling enterprises to achieve operational excellence and maintain uninterrupted production in asset-intensive environments.
