ASSET PERFORMANCE MANAGEMENT
Specialized Asset Performance Management products that extend RMES Suite’s capabilities
Maintenance strategy and root cause analysis
Advanced production loss analytics
Spare parts optimization and criticality analysis
Predictive analytics and simulation
Additional APM Products
*Includes products in the development phase. Please get in touch with our specialists to obtain more information.
Streamline a Reliability-Centered Maintenance process.
Product designed to update, standardize, and analyze the maintenance strategy of a process using a Reliability-Centered Maintenance (RCM) approach. As an output, Maintenance Strategy produces a consolidated maintenance plan that can be loaded into the ERP/CMMS system.
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.
Systematize hazards and risk analysis.
Risk Analysis implements tools to standardize and centralize HAZOP and HAZID studies, contributing to data governance and data integrity policies. Risk Analysis integrates with the RMES Ecosystem, making use of existing online data access, digital models, and cause catalogs from other RMES products.
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.
Trigger, manage, and standardize Root Cause Analysis across the organization.
RCA guides, standardizes, and improves the quality of root-cause analyses across the organization. It provides a unified workflow to trigger, prioritize, and analyze the cause of failure events, using standardized data and cause categories. RCA visualizes the progress and status of each improvement plan to ensure that their benefits are realized.
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.
Fair asset-performance benchmarking between assets of a mining group
Observatory provides fair equipment performance comparisons between areas and assets of the same company. It uses RMES digital models and algorithms to homologate and normalize event data, generating transparent systemic KPIs.
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.
Use asset reliability and maintenance plan data to optimize spare part inventories.
SPRisk is a product that analyzes and optimizes spare part inventories, combining supply chain data with equipment reliability analysis. Using information from maintenance plans and equipment reliability, SPRisk evaluates each equipment’s stock readiness and vulnerability, and identifies opportunities to optimize the stock level and reorder point.
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.
Driver analysis for industrial processes.
VDT provides a tree-based breakdown of a process’s production into utilization and rate-loss components. Each metric can be drilled down into specific drivers and events, letting managers and engineers see the link between events, performance KPIs, and production. VDT can be integrated into different data systems, providing online loss analysis from trusted sources.
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.
Advanced Life-Cycle Cost Simulation
LCC is a stochastic simulation software that forecasts the life-cycle performance of a process, including its throughput, runtime, and asset availability. Using RMES digital models, users can modify equipment-level parameters, and simulate system-level scenarios. Common use cases include maintenance strategy optimization, reliability analysis, dynamic maintenance plan analysis, and full potential analysis.
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.
Performance Analytics is an advanced product for analyzing throughput bottlenecks in industrial processes. It uses digital models and online process data to monitor asset stoppages and rate losses. Each loss event is quantified in terms of throughput impact and the opportunity cost of lost production, providing users with an accurate operational status of the process and its bottlenecks. Loss events can be classified by combining sensor signals and operational logics and importing stoppage event records from historians.
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.
Predictive analytics for equipment vulnerability.
Predictive Maintenance uses different data sources to assess a machine’s overall failure vulnerability. This combines advanced analytics for failure prediction, reliability analysis, KPI analysis, and systemic analysis to estimate failure and throughput risk metrics. These risks are then analyzed using simulation-based scenario analysis, in order to recommend the best actions in the context of maintenance plans.
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.
Expert tools to increase fleet uptime and reduce life-cycle costs.
Fleet Analytics is a product designed to analyze fleet equipment performance and optimize the maintenance strategy. It covers the gap between operational systems and generic performance analysis tools, delivering features to enrich asset performance data, analyze life-cycle performance, and optimize maintenance strategy and costs.
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.