Implement an RCM-based approach to define and update maintenance strategies, including a configurable risk matrix, FMECA analysis, and flexible RCM decision tree.

Developed and tested in close collaboration with reliability engineers and consulting teams. It is designed for frequent use and rigorous analyses.

Maintenance Strategy takes RCM one step further and provides a tool to consolidate failure mode-level strategies into equipment-level maintenance plans.

Centralize all RCM analyses to support maintenance plans. Standardize analyses using a common equipment hierarchy, risk measures, catalogs, etc.

It can be integrated seamlessly into RMES Suite, using digital models, settings, and time model definitions. Also, maintenance plans can be exported to ERP systems.

Share knowledge and best practices across areas and assets. Empower Maintenance Excellence teams by giving visibility of each asset’s maturity and needs.

Implement Hazard and Operability analysis methodology, and provide a systematic approach to hazard evaluation for industrial systems.

Use standardized equipment codes and hierarchies, failure mode catalogs, and time models, ensuring consistency and standardization across analyses and processes.

Implement Hazard Identification analysis for early identification and characterization of industrial process hazards.

Build, reuse, and share a library of hazard evaluations for similar systems and processes.

All analyses are centralized and readily available to everyone in the organization.

Leave behind spreadsheets and ad-hoc slide decks, and use a specialized tool designed for hazard analyses.

At the core of RCA is a structured problem-solving methodology that aligns each analysis with clear priorities, objectives, and countermeasures.

RCA keeps track of improvement plans created after the identification of root causes, reporting the status and progress level of each initiative.

Implement industry best practices such as 5-Whys analysis, Fishbone diagram, Tree Analysis, and SMART objectives, among others.

Root-cause analysis can be automatically triggered from online data. Each analysis request can be automatically assigned to specific users for further development.

RCA integrates with the RMES Ecosystem, importing event data, catalogs, models, etc. This lets users explore new improvement opportunities or assess the effectiveness of countermeasures.

RCA provides a centralized source of analysis and countermeasures, using standardized data and categories. Best practices and knowledge can be shared between different areas and assets.

KPIs from different equipment and assets are compared using standardized criteria, including the impact of process redundancies.

Event data is standardized and homologated using RMES models and catalogs. This translates into KPIs calculated with the same criteria and scope.

Online integration of data sources provides updated KPIs and on-demand analysis.

KPI comparisons for processes, equipment, and subsystems, ensuring equivalent comparison scopes. Driver analysis that includes failure modes, time to failure, and time to repair.

In contrast to static benchmark reports, Observatory is a web application that can be explored in different dimensions.

Support and simulate changes in KPI definitions by using Observatory’s category translation capabilities.

Visual tools indicate which parts should be ordered to anticipate the requirements of maintenance plans, making it easier than navigating the ERP interface or using ad-hoc spreadsheets.

Unplanned spare part requirements are estimated from equipment reliability analysis, enabling granular information about the sources of order requirements.

SPRisk provides suggestions on the optimal stock level and reorder points, using flexible algorithms that adapt to the specific ordering policy in place.

Assess spare part criticality by combining stock variables with the risk of lost production, uncovering new opportunities to optimize stock levels.

A spare part directory lets users visually search for specific parts information, and drill down into specific stock and reliability levels.

Online connection to ERP systems ensures timely information and updated risk assessments.

VDT breaks a process’s production down into the contribution of performance KPIs, including utilization, availability, and rate.

Process-level KPIs can be broken down into specific drivers, including equipment-level KPIs, linking technical performance to process outcomes.

Drill down into specific events and loss categories, visualizing the impact of shift, day, and week events.

Each tree can be adjusted, changing its structure, KPI definitions, colors, etc.

VDT uses online data from source systems, including data historians, APM, ERP/CMMS, and dispatcher systems, among others.

Export trees into Excel or show them online.

Simulate stoppage events impacting runtime and process rate, using probability distributions and a Monte Carlo simulation engine.

Model performance parameters at the equipment and process level, and use RMES digital models to calculate system performance using bottom-up algorithms.

Define and evaluate different performance scenarios, comparing the impact of optimizing physical asset management plans.

Use the historical data available to RMES Suite, simplifying the simulation effort and aligning the simulation model to the corporate metric definition.

Automatic suggestions for probability distributions using historical data and a high degree of flexibility to adjust distributions and their parameters.

Perform bottom-up full-potential analysis, evaluating the individual and combined impact of improvement initiatives.

Online bottleneck analysis that identifies production losses with respect to the maximum process rate, and drills down into their main drivers.

Use online data and RMES Operational Logics Classifier to automatically assign rate losses to a standard loss category.

Asset stoppage events have a relevant impact on process rate losses. Performance Analytics isolates this contribution as a specific loss cause.

Visualize equipment operating status in real-time, and estimate the opportunity cost of lost production because of downtime.

Performance Analytics provides a centralized system to manage production loss and bottleneck data, thus leaving behind spreadsheets and ad-hoc solutions.

As part of RMES Ecosystem, Performance Analytics can import classified stoppages from Datafill, visualize bottlenecks using VDT and OEE, and evaluate scenarios with LCC.

Combine advanced analytics models with reliability analysis and systemic analysis, extending the reach and scope of risk evaluations.

Connect to multiple data sources, including data historians, ERP/CMMS, and APM, among others.

Combine equipment signal data, performance KPIs, and maintenance strategy status into risk metrics at the machine and process levels.

Analyze decision scenarios combining predictive analytics, vulnerability metrics, and stochastic simulation.

Integrate other existing models and data into the product for a unified risk perspective and decision-support tool.

Optimize inspection frequency based on failure mode risk and the maintenance plans in place.

Analyze fleet, equipment, and subsystem performance and priorities using time or usage-based metrics.

Analysis of component duration, reliability, and time between overhauls.

Identification of deviations in actual/planned maintenance plans, and quantification of their impact on asset performance.

Advanced models to optimize the maintenance strategy.

Advanced analytics-based suggestions to enrich data from dispatcher systems.

Analyze MTBF variations, identify premature failures, and correlate failure mode trends.