The quality of your asset management data in IBM Maximo determines the quality of every maintenance decision your organization makes. Work orders, preventive maintenance schedules, inventory requirements, and reliability analysis all depend on a clean, consistent asset hierarchy backed by meaningful classification data. This guide covers the complete Maximo asset data model — from the fundamental distinction between locations and assets through failure code analysis and spare parts linkage.

Locations vs. Assets: The Core Distinction

Many Maximo implementations start with confusion about when to create a location record versus an asset record. Getting this distinction right from the beginning is critical because it affects how work orders are raised, how history is accumulated, and how costs are reported.

Locations in Maximo represent fixed physical places. They do not move. Examples include: Plant A, Building 3, Production Line 2, Pump Station Bay 4, Electrical Room 1A. Locations form the physical and functional map of your facility. They can represent geographic sites, buildings, rooms, systems, or functional positions on a production line. Locations are organized in a hierarchy using the Superior Location field, which allows costs and work orders to roll up from the equipment position level all the way to the plant or enterprise level.

Assets are maintainable physical items that can be installed at a location and later moved, replaced, or retired. A pump is an asset. A motor is an asset. A vehicle is an asset. Assets carry individual serial numbers, manufacturer information, warranty dates, and complete maintenance histories that follow the equipment regardless of which location it occupies. When you replace a failed pump with a spare, the old pump retains its failure history and the new pump begins accumulating its own.

The relationship between assets and locations is managed through the Install Asset transaction. When an asset is installed at a location, Maximo records the installation date, creating a location history that allows you to answer questions like: “What pump was installed at Pump Bay 4 during the period when we experienced the high vibration failures?”

A practical rule for deciding whether to create an asset or a location: if the item has a manufacturer, a model number, a serial number, and could be removed and replaced with an identical unit, create an asset. If it is a fixed physical position or system that defines where assets are installed, create a location.

Building Effective Asset Hierarchies

Asset hierarchies in Maximo are built through parent-child relationships between asset records. The structure typically mirrors the physical or functional decomposition of your equipment:

Enterprise → Site → Plant → System → Equipment → Component

For example: North America (Enterprise) → Houston Plant (Site) → Refinery Unit 2 (Plant) → Cooling Water System (System) → CW Pump P-101 (Equipment) → Mechanical Seal Assembly (Component).

Hierarchies serve multiple purposes. Cost roll-up reports aggregate maintenance spending at each level. PM schedules — managed through Maximo work orders — can be applied to parent assets and inherited by child assets. Failure analysis can be performed at the system level, not just the individual equipment level, which reveals systemic problems that individual equipment analysis misses.

The depth of the hierarchy should be balanced against maintainability. Deep hierarchies (seven or more levels) become difficult to navigate and maintain. Most organizations find that four to five levels provide adequate analytical detail without excessive complexity.

Rotating Assets and Spares are a specific hierarchy pattern in Maximo. A spare pump can be created as an asset record and linked to the operating position using the rotating spare relationship. When the operating pump fails, the swap transaction records the installation of the spare and the removal of the failed unit. The failed pump enters the repair workflow as its own asset, accumulating its repair history until it returns to the spare pool.

Classification and Technical Specifications

Classification in Maximo is the system for grouping assets by type and attaching type-specific attributes. Without classification, Maximo can tell you a pump had 15 work orders last year. With classification, it can tell you that all 100mm centrifugal pumps in your fleet average 15 work orders per year, and that the pumps handling corrosive liquids average 23 — insight that drives targeted PM improvements.

Classification is configured through the Classifications application. A classification is a hierarchical structure that mirrors an equipment taxonomy: Rotating Equipment → Pumps → Centrifugal Pumps → End-Suction Centrifugal Pumps. Each classification node can have its own set of Specifications — attribute definitions with data types, units of measure, and default values.

Common specifications for a centrifugal pump classification include: design flow rate (m³/h), design head (m), rated power (kW), impeller diameter (mm), number of stages, casing material, seal type, and bearing type. When a technician searches for a replacement pump with equivalent specifications, Maximo’s classification search returns all assets matching the specified attribute values.

For MAS Manage users, the classification framework connects to MAS Health’s asset scoring model, where equipment in the same classification can be benchmarked against each other for maintenance cost and condition trends.

Failure Code Configuration

Failure codes are configured in the Failure Codes application and assigned to assets through their Failure Class. The four-level hierarchy works as follows:

Failure Class is assigned to each asset record and defines which Problem, Cause, and Remedy codes are valid for that equipment type. For example, a “CENTRIFUGAL-PUMP” failure class would include problem codes for seal leakage, bearing noise, low flow, and vibration — but not codes relevant to electrical motors or heat exchangers.

Problem codes describe the symptom as observed, typically from the operator’s or requester’s perspective. They should be specific enough to be meaningful but not so granular that technicians struggle to choose. Twelve to twenty problem codes per failure class is a practical range.

Cause codes represent the root mechanism. A symptom of “bearing noise” might have causes including: lubricant starvation, contamination, overloading, or installation damage. Cause codes are the most diagnostic level of the failure hierarchy and require the most training to apply consistently.

Remedy codes document what was done to resolve the failure. These are typically action-based: replaced bearing, cleaned and flushed lubrication system, adjusted shaft alignment, replaced mechanical seal.

The quality of failure data depends entirely on technician discipline at work order completion. Best-practice implementations make failure codes mandatory fields on corrective work orders — Maximo enforces this through conditional required fields configured at the Status Change level. Without mandatory enforcement, technicians under time pressure often leave failure codes blank, degrading the analytical dataset.

Asset Tracking and History

Every transaction in Maximo that references an asset generates a history record visible on the asset’s History tab. This includes:

  • All work orders (corrective, preventive, inspection) ever raised against the asset
  • All status changes (active, inactive, decommissioned, in repair)
  • All location changes (installations, removals, transfers)
  • All meter readings recorded at inspection or work order completion
  • All cost transactions (labor, materials, tools) charged to asset work orders

This accumulated history enables maintenance analysts to calculate:

Asset age and lifecycle position: comparing current age against design life and historical failure frequency to determine whether an asset is approaching end-of-economic-life.

Repair vs. Replace analysis: when an aging asset is failing frequently, the total cost of all work orders over its lifetime can be compared against the capital cost of replacement. Maximo’s asset cost reports provide this data directly.

MTBF (Mean Time Between Failures): calculated from the dates of consecutive corrective work orders against the same asset. A declining MTBF trend on a critical asset is an early warning that a significant failure is approaching.

Spare Parts Linkage

Linking spare parts to assets in Maximo creates a critical connection between the asset registry and the inventory and storeroom management module. When a planner creates a work order for a specific asset, Maximo can automatically suggest the spare parts linked to that asset’s job plan or BOM (Bill of Materials).

The Asset BOM (Bill of Materials) application allows maintenance engineers to document every maintainable component of an asset — each with its storeroom item number, quantity on the asset, and replacement frequency. When a planner opens a work order for pump P-101, the BOM provides the item numbers for bearings, seals, gaskets, and O-rings specific to that pump’s make and model.

For organizations using MAS, the spare parts linkage integrates with the MAS Health asset condition score. If an asset’s condition score drops below a threshold, MAS can automatically check storeroom availability for the asset’s critical spares and trigger a purchase requisition if stock is below the safety stock level — a closed-loop between condition monitoring and procurement that reduces both stockouts and overstock.

Reliability Analysis in Maximo

Reliability analysis in Maximo uses the accumulated failure code data, asset history, and meter readings to identify improvement opportunities. The primary tools available within base Maximo include:

Failure reports built in the Reporting module that aggregate problem, cause, and remedy codes by asset class, location, or time period. These identify the most frequent failure modes and the most effective remedies across the fleet.

Asset cost summaries that compare total maintenance cost against replacement value. Assets with a high maintenance-to-replacement cost ratio are candidates for capital replacement in the next budget cycle.

PM frequency analysis that compares the dates of PM work orders against the dates of corrective work orders on the same asset. When corrective failures consistently occur shortly after PM intervals, the PM frequency may need to be shortened or the PM task content may need to be revised.

For organizations that have deployed MAS, the Health and Predict applications extend this analysis with machine learning models trained on historical work order and sensor data, generating failure probability scores and remaining useful life estimates that would require specialist reliability engineering to calculate manually in Maximo 7.6.

Data Quality: The Make-or-Break Factor

Even the most technically correct Maximo asset configuration delivers little value if the underlying data is incomplete or inconsistent. The most common data quality problems in Maximo implementations are:

Missing or incorrect asset records: assets that exist in the field but are not in Maximo, or Maximo records that no longer correspond to physical equipment. A physical asset inventory — comparing Maximo records against what is actually on the floor — is the most effective remediation and should be conducted before going live on any new site.

Incomplete failure codes: as described above, failure codes left blank at work order completion. Enforce completion through Maximo’s conditional required fields and include failure coding accuracy in technician performance metrics.

Duplicate records: the same physical asset appearing in Maximo as multiple records, typically created by different technicians who could not find the existing record. Deduplication requires a systematic audit of assets with matching descriptions, manufacturer data, or serial numbers.

Stale meter readings: meters that have not been read within their expected frequency, undermining the reliability of meter-based PM triggers. Configure Maximo alerts for overdue meter readings to maintain meter data currency.

Organizations that invest in asset data quality from the beginning of their Maximo implementation consistently achieve better PM compliance rates tracked through reporting, lower emergency work percentages, and faster user adoption than those that defer data quality work until after go-live. The Industrie du Futur platform highlights how connected asset data ecosystems amplify the value of disciplined Maximo data management.