Failure modes hiding in plain sight. PM schedules based on assumptions, not evidence.
Use controlled exports from SAP PM, Maximo, IFS, or another CMMS. Extract candidate failure modes and causes from unstructured text while scoring source quality. Compare work orders with manuals, P&IDs, drawings, and functional locations. Rank bad-actor candidates using customer-approved cost, frequency, and downtime logic.
Map functions to components and propose failure modes from function loss with cause-effect chains across system boundaries. Draft controls, mitigating tasks, and work instructions remain subject to reliability, safety, and document-control review. Support SAE JA1011 decision criteria and controlled SAP handoff.
Apply Weibull analysis, P-F curves, and scenario modeling where data quality and sample size support them. Compare strategies side by side and prepare spares proposals using approved frequency, criticality, and lead-time assumptions. Reliability engineers approve every recommendation.
Search approved work orders, manuals, and FMEAs in natural language. Answers cite the underlying document and location. Request Weibull analysis, P-F curves, or specific FMEA changes in the same workspace; analytical assumptions and row-level proposals remain reviewable.
Group equipment by approved class while preserving manufacturer, model, duty, configuration, environment, and site differences. Propose confirmed content for comparable instances; local owners accept or reject applicability. Keep the standardized hierarchy and taxonomy as customer-owned deliverables.
Store reviewer decisions, corrections, citations, and applicability rules as structured customer knowledge. Propose confirmed content for comparable equipment without silently copying it. Each receiving engineer reviews the evidence and differences before acceptance.
Source-linked draft content structured for customer-selected IEC 60812 or SAE J1739 workflows. Failure modes, causes, mechanisms, ratings, and assumptions remain visible to engineers.
Top failure modes ranked by RPN, frequency, and downtime impact. Your team knows exactly where to focus effort and budget.
Use Weibull and P-F analysis where the data supports them. Compare scenarios, expose assumptions, and prioritize tasks by customer-approved risk and criticality criteria.
Score work orders for completeness, consistency, asset identity, operating context, and failure-data richness. Surface systemic capture gaps before relying on the risk analysis.
Spares recommendations based on failure frequency, lead time, and criticality. Reduce stockouts without inflating inventory cost.
Prepare reliability block diagrams and Monte Carlo scenarios from agreed boundaries and inputs. Review availability, redundancy, and spares implications with model assumptions visible.
Each FMECA row linked to the work order, manual, or inspection report it came from. When data changes, affected rows are flagged. Audit-ready.
Equipment classes, functional locations, and failure taxonomies aligned across sites. Corrections on one class propagate to all instances. The hierarchy is a standalone deliverable.
New work orders and failure events matched to FMECA rows. Recurring failures flagged. PM tasks marked ineffective when failures persist. Gaps surfaced as new failure-mode suggestions.
Pass your engineers' review → scale site-wide.
From no structured risk strategy to a fully auditable, cross-functional FMEA that feeds design changes, work orders, and inspections.
Bring one manual. We show you what yours missed. 30 minutes.
