Your Asset Maintenance Data Template

This attribute defines the steps taken during the lifecycle of the maintenance work order. It captures the distinct states the work order passes through, such as created, approved, in progress, on hold, and completed.

For accurate process mining, this field normalizes various system events into readable activities. It is essential for visualizing the process map, calculating transition times between stages, and identifying rework loops where a work order reverts to a previous status.

It defines the nodes in the process map and is required to reconstruct the workflow.

Derived from status history tables, transaction logs, or work order change logs.

This attribute provides the temporal context for every event in the maintenance log. It records the exact moment a status changed or a transaction was committed in the source system.

Timestamps are the basis for all duration-based metrics in process mining. They allow the calculation of cycle times, lead times, and resource idle times. High-precision timestamps are necessary to properly order events that occur in rapid succession.

It allows the sequencing of events and calculation of all duration metrics.

Found in transaction logs, history tables, or audit trails alongside the activity.

This attribute tracks the currency of the dataset used in the analysis. It helps analysts understand the freshness of the data and identify potential latency issues between the operational system and the process mining environment.

While not part of the physical process flow, it is crucial for data governance. It ensures that stakeholders know if they are looking at real-time data or a snapshot from a previous closing period.

It assists in validating data freshness and managing incremental loads.

Generated by the ETL pipeline or extraction script at runtime.

This attribute identifies the software environment responsible for generating the data. In complex maintenance landscapes, data might come from an ERP, a specialized CMMS, or an IoT monitoring platform.

In analysis, this field is useful for filtering data when multiple systems are ingested into a single process model. It helps verify data lineage and can be used to compare data quality or process variations between different legacy systems.

It ensures data traceability in multi-system environments.

Hardcoded during the extraction process or mapped from system configuration.

This attribute serves as the central case identifier for the process mining analysis. It represents a single end-to-end maintenance cycle, from the initial request or preventive trigger through to completion and financial closure.

In analysis, this field acts as the primary key for grouping all related events, costs, and resource logs. It enables the process mining engine to reconstruct the lifecycle of specific jobs, allowing analysts to trace the flow of work, identify bottlenecks at the case level, and correlate specific outcomes with order types or priorities.

It is the fundamental Case ID required to distinguish unique process instances.

Typically found in the work order header table (e.g., WONUM in Maximo, AUFNR in SAP).

This attribute aggregates the duration of all labor entries booked against the work order. It reflects the actual effort expended to complete the task.

This metric is essential for calculating workforce utilization and labor efficiency. Comparing actual hours against estimated hours reveals the accuracy of the planning process. Significant variances may indicate unclear job scopes or skill gaps.

It measures effort and is used to calculate utilization and planning accuracy.

Aggregated from Time Sheet or Labor Transaction tables.

This attribute categorizes assets based on the impact their failure would have on safety, environment, or production. It is often a static attribute on the asset master record but is frequently snapshotted onto the work order.

Using this attribute, analysts can determine if maintenance teams are correctly prioritizing work on critical assets. It helps in verifying that the most important equipment has the highest adherence to preventive maintenance schedules.

It helps align maintenance focus with business risk and operational impact.

Asset Master Data or Work Order Header if denormalized.

This attribute links the work order to the physical object in the field. It represents the tag number, equipment ID, or machine code for the asset undergoing maintenance.

This is vital for identifying 'bad actors' or assets that require frequent repairs. By aggregating work orders by Asset ID, analysts can calculate the Mean Time Between Failures (MTBF) and identify equipment that consumes a disproportionate amount of maintenance budget.

It allows performance analysis of specific equipment and MTBF calculations.

Work Order Header table (e.g., ASSETNUM in Maximo, EQUNR in SAP).

This boolean attribute simplifies the work order type into a binary classification: Preventive vs. Non-Preventive. It is often derived from the Work Order Type code.

This flag is the fastest way to filter dashboards to focus on PM compliance. It simplifies the calculation of the PM/CM ratio KPI, which is a standard industry metric for measuring the maturity of the maintenance organization.

It simplifies filtering and KPI calculation for Preventive Maintenance ratios.

Derived from Work Order Type or specific system flag (e.g., PPM flag).

This attribute identifies the team, craft, or department assigned to the work order, such as Electrical, Mechanical, Instrumentation, or Facilities.

This view enables performance benchmarking between different teams. Analysts can compare cycle times, backlog levels, and rework rates across departments to identify training needs or resource shortages in specific trades.

It enables benchmarking of performance and backlog across different teams.

Work Order Header (e.g., Work Center in SAP, Crew ID in Maximo).

This attribute indicates how critical the maintenance task is to operations. Values usually range from numerical codes (1, 2, 3) to descriptive labels (Critical, High, Medium, Low). It dictates the required response time and resource allocation.

In process analysis, this attribute is used to check SLA compliance. Analysts investigate whether high-priority items are actually moving through the process faster than low-priority ones, or if they are getting stuck in approval bottlenecks.

It enables SLA compliance analysis and resource prioritization checks.

Work Order Header table (e.g., PRIORITY in Maximo, PRIOK in SAP).

This attribute represents the due date planned by the scheduler or dictated by the SLA. It serves as the reference point for on-time delivery calculations.

Comparing the actual completion date against this target date allows for the calculation of Schedule Compliance and delay metrics. Large deviations indicate planning inefficiencies or resource constraints.

It is the baseline for calculating delays and schedule compliance.

Work Order Scheduling tab (e.g., GLTRP in SAP, SCHEDFINISH in Maximo).

This attribute sums up all financial postings related to the work order. It provides the final economic impact of the maintenance activity.

Cost analysis is a major driver for process mining in asset management. This attribute helps identify the most expensive asset classes to maintain and highlights outliers where repair costs exceed the replacement value of the asset.

It is central to financial impact analysis and budget variance reporting.

Cost summary tables or aggregated from GL postings linked to the Order.

This attribute classifies the nature of the maintenance work. Common values distinguish between planned work (Preventive) and unplanned outages (Corrective or Emergency). It is a standard field in almost all EAM/CMMS systems.

Analysts use this field to segment the process model. Comparing the workflow of Emergency repairs versus Preventive maintenance often reveals significantly different process paths, approval requirements, and cycle times. It is the primary dimension for calculating the Preventive-to-Reactive maintenance ratio.

It is critical for segmenting analysis between planned and unplanned work.

Work Order Header table (e.g., WORKTYPE in Maximo, AUART in SAP).

This attribute identifies the individual or lead technician responsible for executing the maintenance task. While departments track the group, this tracks the specific person.

This level of granularity is useful for analyzing workload distribution among staff. It can highlight if certain technicians are consistently overloaded or if specific individuals have higher rework rates, indicating a need for training.

It enables workload balancing analysis and individual performance metrics.

Work Order Assignments or Labor detailed tables.

This attribute defines the geographical or functional area where the asset resides. It might be a specific building, a floor, or a remote site.

Location data helps in analyzing travel time inefficiencies and logistics. If technicians spend significant time traveling between distant locations for low-priority jobs, process mining can highlight the opportunity for better route planning or grouping of work orders by location.

It assists in logistics analysis and identifying location-specific bottlenecks.

Work Order Header or linked Asset Location table.