Optimizing Equipment Maintenance & Replacement Decisions

Course Overview

This comprehensive professional development Optimizing Equipment Maintenance & Replacement Decisions program is designed for managers who oversee maintenance operations, managers who oversee maintenance of company assets, professionals responsible for making decisions about when to replace equipment, engineers who use organization’s equipment, asset managers, maintenance engineers, maintenance policymakers, risk managers, and other professionals interested in maintaining their equipment responsible for implementing maintenance and replacement strategies across manufacturing, energy, infrastructure, and industrial contexts. Drawing from comprehensive maintenance optimization frameworks including predictive analytics mechanisms, reliability-centered maintenance approaches, and economic life analysis principles, this program addresses proven practices where industry studies show predictive maintenance delivers 18-25% overall maintenance cost reductions compared to traditional approaches with 8-12% savings over preventive maintenance alone and up to 40% savings over reactive strategies while McKinsey research demonstrates leading organizations achieve 30-50% downtime reduction and extend equipment lifespan by 20-40% with 10:1 to 30:1 ROI ratios within 12-18 months of implementation, while research on equipment replacement in context of lean manufacturing highlights how value stream mapping can visualize current production states and outline preferred future states furnishing essential insights for analyzing replacement challenges.

The curriculum integrates introduction defining maintenance and replacement with cost of failure and benefits, why equipment maintenance and replacement matters with consequences of poor maintenance, optimizing maintenance and replacement practices with RCM and condition-based monitoring, developing maintenance and replacement strategy with economic service life and defender-challenger comparison, implementing maintenance and replacement plan with phased implementation roadmap, evaluating maintenance and replacement programs with reliability metrics and cost-benefit analysis, maintaining and replacing equipment for optimal performance with root cause analysis and precision maintenance, and maintenance and replacement strategies including predictive maintenance and TPM to provide comprehensive coverage of technical, operational, and strategic domains for achieving maintenance optimization excellence.

Why This Course Is Required?

Equipment maintenance management represents critical competencies for cost optimization where industry studies show predictive maintenance delivers 18-25% overall maintenance cost reductions compared to traditional approaches with 8-12% savings over preventive maintenance alone and up to 40% savings over reactive strategies with McKinsey research demonstrating leading organizations achieve 30-50% downtime reduction and extend equipment lifespan by 20-40% with 10:1 to 30:1 ROI ratios within 12-18 months of implementation while 95% of organizations implementing predictive maintenance report positive returns and 27% achieve full payback within 12 months. Strategic replacement planning demands specialized knowledge where research on equipment replacement in context of lean manufacturing highlights how value stream mapping can visualize current production states and outline preferred future states furnishing essential insights for analyzing replacement challenges with organizations transitioning from batch-and-queue to lean cellular manufacturing using VSM data on expected cost reductions including inventory savings, reduced floor space, improved quality, enhanced flow, and better scheduling to justify replacing large monument machines with smaller flexible equipment. Risk mitigation requires professionals with maintenance expertise where evidence that predictive maintenance reduces unnecessary tasks with IBM research showing 30% of preventive tasks are unnecessary and forecasts failures based on actual condition empowering participants to schedule maintenance when needed rather than arbitrary timeframes while learning to interpret condition-based indicators including vibration, temperature, and oil analysis.

The essential need for comprehensive equipment maintenance and replacement training is underscored by its critical role in operational success where confidence in applying data-driven frameworks enables effective maintenance timing while delivering reduced costs and extended asset life. Maintenance professionals must master predictive maintenance fundamentals including condition monitoring and failure forecasting, understand comprehensive economic analysis including life cycle costing and replacement timing, and apply proper troubleshooting and proactive strategies to ensure organizations achieve superior cost reductions, enhanced equipment availability, improved safety performance, and competitive advantage through comprehensive understanding of MTBF, OEE, RCM, and economic service life methodologies.

Research demonstrates that equipment maintenance training is crucial for organizational success, with studies showing case examples such as steel manufacturing facility achieving $1.5 million in first-year savings and preventing $3 million transformer loss through predictive analytics demonstrating how early fault detection and proactive intervention prevent critical failures giving participants ability to troubleshoot equipment problems, develop preventive and predictive maintenance plans, and apply learned concepts to real-world scenarios and lean manufacturing replacement framework teaching professionals to evaluate both tangible benefits including inventory savings, reduced floor space, and improved quality and intangible factors including flexibility, enhanced flow, and better scheduling when deciding whether to replace high-volume legacy equipment with lean work cells equipping participants to select maintenance and replacement strategies that best suit their organization.

Course Objectives

Upon successful completion, participants will be able to:

• Determine the best time to schedule maintenance
• Determine when to replace equipment
• Estimate the economic life of equipment
• Select the most cost effective replacement option
• Minimise downtime and maximise production
• Develop a comprehensive maintenance and replacement plan
• Apply concepts learned to real world scenarios and make sound decisions
• Learn how to troubleshoot equipment problems
• Understand different types of maintenance and replacement strategies
• Explain the differences among reactive maintenance, preventive maintenance, predictive maintenance, and reliability centered maintenance and when each approach is appropriate for different asset criticality levels.​
• Use condition monitoring information such as vibration, temperature, and oil analysis to determine optimal maintenance timing and avoid unnecessary preventive tasks that IBM research shows can account for about thirty percent of traditional preventive work.​
• Perform basic economic life and replacement timing analysis using concepts such as equivalent annual cost defender challenger comparison and total cost of ownership to decide when to retain existing assets versus invest in new equipment.​
• Evaluate the impact of maintenance and replacement decisions on key reliability and performance indicators including mean time between failures mean time to repair and overall equipment effectiveness.​
• Develop and implement a phased maintenance and replacement roadmap that combines predictive maintenance technologies reliability centered maintenance practices and lean replacement thinking based on value stream mapping insights.

Master maintenance optimization excellence and drive equipment reliability. Enroll today to become an expert in Maintenance & Replacement Leadership!

Training Methodology

This collaborative Optimizing Equipment Maintenance & Replacement Decisions course comprises the following training methods:

The training framework includes:

• Lectures with instructor whiteboarding and presenting materials
• Computer lab with projector showing videos and slides
• Combination of instructor lecturing and video presentations
• Hands-on activities
• Case studies analyzing real-world maintenance scenarios
• Workshops developing maintenance strategies and replacement plans
• Practical exercises covering preventative maintenance timing and repair vs. replace decisions

This immersive approach fosters practical skill development and real-world application of maintenance optimization principles through comprehensive coverage of predictive analytics, economic analysis, and reliability-centered maintenance with emphasis on measurable cost reductions and downtime improvements.

This program follows the Do-Review-Learn-Apply model with expert instructors ensuring industry-relevant content through practical case studies and maintenance examples, creating a structured learning journey that transforms traditional maintenance approaches into professional excellence.

Who Should Attend?

This Optimizing Equipment Maintenance & Replacement Decisions course is designed for:

• Managers who oversee maintenance operations
• Managers who oversee maintenance of company assets
• Professionals responsible for making decisions about when to replace equipment
• Engineers who use organization’s equipment
• Asset managers
• Maintenance engineers
• Maintenance policymakers
• Risk managers
• Other professionals interested in maintaining their equipment
• Reliability engineers and planners
• Facility managers and operations managers
• Professionals seeking maintenance optimization certification

Organizational Benefits

Organizations implementing equipment maintenance and replacement training will benefit through:

• Significantly enhanced cost reduction through comprehensive training delivering measurable returns where industry studies show predictive maintenance delivers 18-25% overall maintenance cost reductions compared to traditional approaches with 8-12% savings over preventive maintenance alone and up to 40% savings over reactive strategies with McKinsey research demonstrating leading organizations achieve 30-50% downtime reduction and extend equipment lifespan by 20-40% with 10:1 to 30:1 ROI ratios within 12-18 months of implementation while 95% of organizations implementing predictive maintenance report positive returns and 27% achieve full payback within 12 months
• Better replacement decision optimization through research on equipment replacement in context of lean manufacturing highlighting how value stream mapping can visualize current production states and outline preferred future states furnishing essential insights for analyzing replacement challenges with organizations transitioning from batch-and-queue to lean cellular manufacturing using VSM data on expected cost reductions including inventory savings, reduced floor space, improved quality, enhanced flow, and better scheduling to justify replacing large monument machines with smaller flexible equipment directly supporting developing comprehensive maintenance and replacement plans and selecting cost-effective options
• Improved maintenance efficiency through evidence that predictive maintenance reduces unnecessary tasks with IBM research showing 30% of preventive tasks are unnecessary and forecasts failures based on actual condition empowering professionals to schedule maintenance when needed rather than arbitrary timeframes while learning to interpret condition-based indicators including vibration, temperature, and oil analysis giving tools to determine best time to schedule maintenance, decide when to replace equipment, and estimate economic life
• Strengthened competitive advantage through comprehensive understanding of predictive analytics, economic service life analysis, RCM methodologies, and condition monitoring frameworks that enable superior maintenance optimization excellence

Studies show that organizations implementing comprehensive equipment maintenance training achieve significantly enhanced cost reduction as McKinsey research confirms 18-25% maintenance cost reductions and 10:1 to 30:1 ROI ratios, better organizational outcomes through multiple sources demonstrating 30-50% downtime reduction and 20-40% equipment lifespan extension, and improved competitive positioning as research establishes 95% of organizations report positive returns with 27% achieving full payback within 12 months while organizations benefit from optimizing maintenance and replacement decisions, proper decisions made from data analytics, reduced cost of equipment ownership, risk management of risks associated with equipment replacement and maintenance, well-developed risk mitigation strategies, improved efficiency of equipment operation and decision-making process, reduced machine downtime, improved safety measures, increased equipment availability, reduced loss of equipment due to break, and reduced maintenance and replacement costs.

Empower your organization with maintenance optimization expertise. Enroll your team today and see the transformation in equipment reliability and cost performance!

Personal Benefits

Professionals implementing equipment maintenance and replacement training will benefit through:

• Confidence in applying data-driven frameworks to determine optimal maintenance and replacement timing through evidence that predictive maintenance reduces unnecessary tasks with IBM research showing 30% of preventive tasks are unnecessary and forecasts failures based on actual condition empowering participants to schedule maintenance when needed rather than arbitrary timeframes with learning to interpret condition-based indicators including vibration, temperature, and oil analysis giving professionals tools to determine best time to schedule maintenance, decide when to replace equipment, and estimate economic life matching course objectives
• Practical skills in troubleshooting and implementing proactive maintenance strategies through case examples such as steel manufacturing facility achieving $1.5 million in first-year savings and preventing $3 million transformer loss through predictive analytics showing how early fault detection and proactive intervention prevent critical failures with participants gaining ability to troubleshoot equipment problems, develop preventive and predictive maintenance plans, and apply learned concepts to real-world scenarios directly aligned with course objectives on minimizing downtime, maximizing production, and managing risk
• Understanding of economic and qualitative factors in replacement decisions through lean manufacturing replacement framework teaching professionals to evaluate both tangible benefits including inventory savings, reduced floor space, and improved quality and intangible factors including flexibility, enhanced flow, and better scheduling when deciding whether to replace high-volume legacy equipment with lean work cells with broader perspective equipping participants to select maintenance and replacement strategies that best suit their organization and negotiate service contracts effectively as emphasized in course personal benefits
• Advanced expertise in predictive maintenance technologies and IoT sensor integration
• Enhanced career prospects and marketability in manufacturing, energy, infrastructure, and industrial sectors with professionals gaining skills in condition monitoring, economic analysis, and reliability planning
• Improved confidence and performance
• Greater competency in knowledge of developing preventative maintenance plan
• Increased capability in ability to troubleshoot equipment problems
• Enhanced understanding of process of negotiating service contracts
• Superior qualifications for ability to select equipment replacement and maintenance strategies that best suit organization
• Advanced skills in saving on replacement and maintenance cost
• Enhanced professional recognition through track and manage equipment maintenance and replacement schedules, use data and analytics to optimize decisions, and track and manage risks of equipment replacement and maintenance

Course Outline

Module 1: Introduction: Defining Maintenance & Replacement

• What is equipment replacement?
• What is equipment maintenance?
• What are some common maintenance and replacement tasks?
• Defining the difference between replacement and maintenance
• When to perform maintenance or replacement
• Why is it important to perform maintenance and replacement?
• The cost of maintenance
• The benefits of maintenance and replacement
• The cost of failure
• The cost of replacement
• Understanding maintenance philosophies: breakdown (run-to-failure), preventive (time-based), predictive (condition-based), reliability-centered maintenance (RCM), and total productive maintenance (TPM)
• Analyzing economic life vs. physical life: optimal replacement timing based on cost minimization, not just asset condition
• Implementing key performance indicators: Mean Time Between Failures (MTBF), Mean Time To Repair (MTTR), Overall Equipment Effectiveness (OEE = Availability × Performance × Quality)
• Establishing failure cost components: direct repair costs, production loss, safety incidents, environmental impacts, reputational damage

Module 2: Why Does Equipment Maintenance & Replacement Matter?

• The benefits of equipment maintenance
• The process of equipment replacement
• The importance of regular equipment maintenance
• Consequences of poor equipment maintenance
• Importance of routine maintenance
• The cost of equipment maintenance
• Understanding asset criticality: classifying equipment by consequence of failure (safety, production, environment, cost) and failure probability to prioritize resources
• Implementing life cycle costing (LCC): total cost of ownership = acquisition + installation + operation + maintenance + disposal
• Analyzing hidden costs of deferred maintenance: accelerated deterioration, cascading failures, reduced asset value, increased risk exposure
• Establishing benchmarking metrics: maintenance cost as % of replacement asset value (RAV), reactive vs. proactive maintenance ratio, maintenance backlog

Module 3: Optimizing Maintenance & Replacement Practices

• Benefits of optimizing maintenance and replacement practices
• Best practices for maintaining and replacing equipment
• How to reduce downtime with optimized maintenance and replacement practices
• Benefits of performing scheduled maintenance
• How to optimize maintenance and replacement activities
• Cost of savings of optimized maintenance and replacement
• Implementing Reliability-Centered Maintenance (RCM): systematic approach identifying failure modes, consequences, and cost-effective prevention tasks
• Using Failure Mode and Effects Analysis (FMEA): ranking failure modes by Risk Priority Number (RPN = Severity × Occurrence × Detection) to target high-impact issues
• Establishing condition-based monitoring: vibration analysis, thermography, oil analysis, ultrasonic testing, motor current signature analysis (MCSA) to detect incipient failures
• Implementing Pareto analysis: identify the 20% of equipment causing 80% of maintenance costs or downtime for focused improvement

Module 4: Developing a Maintenance & Replacement Strategy

• Determining when to replace equipment
• Installing replacement equipment
• Developing a replacement strategy
• Developing a budget for replacement and maintenance
• Determining what maintenance is needed on equipment
• Finding the replacement equipment
• Tracking maintenance and replacement activities
• Tools and techniques of replacement and maintenance
• Scheduling maintenance and replacement
• Applying replacement analysis methods: defender-challenger comparison using Present Worth, Annual Worth, or Internal Rate of Return (IRR) to time optimal replacement
• Understanding economic service life: point where equivalent annual cost (EAC) is minimized, accounting for increasing maintenance costs and decreasing salvage value over time
• Implementing asset management standards: ISO 55000 framework for strategic, risk-based asset management aligned with organizational objectives
• Using CMMS/EAM systems: Computerized Maintenance Management Systems or Enterprise Asset Management software for work orders, scheduling, inventory, history tracking, and analytics
• Establishing preventive maintenance task intervals: using P-F curve (potential failure to functional failure), manufacturer recommendations, reliability data, and operating context

Module 5: Implementing a Maintenance & Replacement Plan

• Defining maintenance & replacement plan
• How to implement a maintenance & replacement plan
• Determining the best time to implement a maintenance and replacement plan
• Who is responsible for maintenance and replacement?
• Developing a phased implementation roadmap: pilot testing on critical assets, scaling proven practices, continuous monitoring and adjustment
• Establishing roles and accountability: asset owners (strategic decisions), maintenance planners (scheduling), technicians (execution), procurement (parts availability), finance (budgeting)
• Implementing change management: training programs, standard operating procedures (SOPs), performance incentives, stakeholder buy-in at all levels
• Using resource leveling: balancing maintenance workload across periods to avoid peaks, optimize labor utilization, and minimize overtime costs

Module 6: Evaluating Maintenance & Replacement Programs

• Identifying maintenance and replacement issues
• Evaluating the results of your maintenance and replacement programs
• Determining the most effective maintenance programs for your needs
• Comparing the cost of different maintenance programs
• Assessing the effectiveness of your maintenance and replacement program
• Assess the risks associated with a maintenance or replacement program
• Assessing the impact of a maintenance or replacement program on the budget
• Implementing key metrics for program evaluation: OEE trends, maintenance cost per unit produced, schedule compliance (%), emergency work ratio, asset availability
• Using reliability metrics: survival curves, Weibull analysis for failure distributions, reliability growth models to track improvement over time
• Conducting cost-benefit analysis: Net Present Value (NPV), Return on Investment (ROI), and payback period for major maintenance or replacement initiatives
• Establishing continuous improvement cycles: Plan-Do-Check-Act (PDCA) or Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) for ongoing optimization
• Applying risk-based inspection (RBI): prioritizing inspection frequency and rigor based on probability of failure and consequence of failure

Module 7: Maintaining & Replacing Equipment for Optimal Performance

• Troubleshooting equipment problems
• Performing routine maintenance according to the manufacturer’s recommendations
• Storing equipment properly to prevent damage
• Regularly cleaning and inspecting equipment for wear and tear
• Replacing equipment when needed or when it reaches the end of its useful life
• Using root cause analysis (RCA): 5-Whys, fishbone diagrams, fault tree analysis to eliminate recurring failures rather than treating symptoms
• Implementing precision maintenance techniques: laser alignment, dynamic balancing, proper lubrication practices, torque specifications to extend equipment life
• Establishing spare parts optimization: balancing inventory carrying costs against stockout risk using criticality analysis, lead times, and failure rates
• Applying age-replacement vs. block-replacement policies: deciding whether to replace items individually upon failure or in groups at fixed intervals based on cost trade-offs
• Understanding wear-out vs. random failure patterns: infant mortality (burn-in period), useful life (constant hazard), and wear-out phase (increasing hazard) for appropriate maintenance strategies

Module 8: Maintenance and Replacement Strategies

• Preventative maintenance
• Proactive maintenance
• Emergency maintenance
• Infrastructure replacement
• Schedule maintenance
• Equipment replacement
• Implementing predictive maintenance (PdM): using IoT sensors, machine learning algorithms, and real-time data analytics to forecast failures before they occur
• Applying prescriptive maintenance: beyond predicting failures, recommending optimal intervention actions (repair, replace, adjust) with cost-risk trade-offs
• Understanding run-to-failure (RTF): strategic application for low-criticality, low-cost equipment where planned maintenance is not economically justified
• Using Total Productive Maintenance (TPM): autonomous maintenance by operators, planned maintenance by specialists, quality maintenance, focused improvement, early equipment management
• Establishing capital replacement strategies: like-for-like replacement, technology upgrades, outsourcing (lease vs. buy), capacity adjustments based on demand forecasts
• Implementing opportunistic maintenance: coordinating multiple tasks during planned shutdowns to minimize total downtime and maximize labor/equipment utilization

Real World Examples

The impact of Optimizing Equipment Maintenance & Replacement Decisions Training is evident in leading implementations:

Steel Manufacturing Facility – $1.5 Million Savings and $3 Million Transformer Loss Prevention via Predictive Analytics

Implementation: Steel manufacturing facility implemented predictive analytics system across critical equipment assets deploying comprehensive sensor network with real-time condition monitoring to detect equipment degradation patterns with system analyzing vibration, temperature, oil quality, and electrical parameters continuously to identify gradual deterioration in transformer operations supporting proactive maintenance intervention to prevent catastrophic failure while analyzing historical performance data to optimize maintenance scheduling for other equipment assets across steel manufacturing operations supporting maintenance cost reduction while ensuring production continuity and asset reliability.
Results: The implementation achieved exceptional first-year savings demonstrating how comprehensive maintenance optimization training enables exceptional understanding that facility achieved $1.5 million in first-year savings through optimized maintenance scheduling, reduced emergency repairs, and extended equipment life with system successfully identifying gradual degradation patterns in transformer, delivered critical failure prevention where predictive analytics empowered maintenance teams to implement proactive intervention before critical failure occurred preventing potential $3 million transformer loss that would have resulted from catastrophic failure requiring emergency replacement, production downtime, and cascading system impacts, and established continuous improvement foundation demonstrating case illustrates how predictive maintenance detects faults early, optimizes maintenance scheduling, and compounds asset reliability improvements over time as system learns from historical performance data validating course emphasis on optimizing maintenance timing, minimizing downtime, and managing risk through data-driven decisions, showcasing how systematic predictive analytics with gradual degradation pattern detection and proactive intervention directly enables superior cost savings, enhanced critical failure prevention, and improved asset reliability in steel manufacturing operations.

Siemens Wind Energy Operations – 25% Maintenance Cost Reduction through IoT Sensors and Condition-Based Scheduling

Implementation: Siemens wind energy operations examined predictive maintenance effectiveness to mitigate high costs associated with accessing remote turbine locations through systematic deployment of IoT sensors across wind turbine fleet enabling real-time condition monitoring with sensors tracking vibration, temperature, rotation speed, torque, and performance parameters continuously to identify early signs of wear or component degradation with system analyzing sensor data using machine learning algorithms to forecast maintenance needs supporting condition-based maintenance scheduling that triggers interventions only when sensor data indicates degradation across wind energy operations supporting maintenance cost optimization while avoiding wasted preventive tasks and emergency repairs in remote turbine environments.
Results: The implementation achieved substantial cost reduction demonstrating how comprehensive maintenance optimization training enables exceptional understanding that Siemens reports 25% maintenance cost reduction through IoT sensors enabling condition-based maintenance scheduling with predictive technologies optimizing maintenance schedules by triggering maintenance only when sensor data indicates degradation reducing unnecessary service visits particularly valuable in wind energy sector where accessing remote turbine locations involves significant helicopter, crane, and specialized technician costs, delivered operational efficiency enhancement where condition-based approach prevented costly component failures that would require emergency mobilization to remote sites while enabling planned maintenance during optimal weather windows and coordinating multiple maintenance tasks during single site visit maximizing technician efficiency and minimizing turbine downtime, and established scalable framework demonstrating solution applicable across distributed wind farm portfolios with centralized monitoring enabling fleet-wide optimization, predictive failure forecasting, and data-driven resource allocation validating course focus on selecting cost-effective strategies, tracking and managing schedules, and using data and analytics to optimize decisions, showcasing how systematic IoT sensor deployment with condition-based maintenance scheduling directly enables superior maintenance cost reduction, enhanced operational efficiency, and improved turbine availability in wind energy operations.

Oil and Gas Industry Companies – 36% Downtime Reduction and $34 Million Annual Savings through Data-Driven Predictive Approaches

Implementation: Multiple oil and gas industry companies examined predictive maintenance effectiveness through comprehensive 2024 Kimberlite study surveying more than 100 manufacturers globally at field service and IT decision-maker level in United States, United Kingdom, France, and Germany plus around 350 companies in other sectors globally including oil and gas, energy and utilities among others analyzing how companies using data-driven predictive maintenance approach compare to those with reactive maintenance strategies with study methodology evaluating downtime reduction, cost savings, maintenance efficiency, and asset reliability across diverse operational contexts in oil and gas sector supporting sector-specific validation of predictive maintenance benefits particularly important in high-stakes environments where unplanned outages can have severe production and safety consequences.
Results: The implementation achieved exceptional downtime reduction demonstrating how comprehensive maintenance optimization training enables exceptional understanding that 2024 Kimberlite study found companies in oil and gas industry using data-driven predictive maintenance approach see downtime reduced by 36% compared to those with reactive maintenance strategy with substantial operational improvement particularly significant in oil and gas operations where production continuity directly impacts revenue, contractual obligations, and market supply commitments, delivered massive annual cost savings where downtime reduction and improved maintenance efficiency resulted in $34 million in cost savings annually per company with savings comprising avoided production losses estimated at $260,000 per hour average across industries, reduced emergency repair costs, optimized spare parts inventory, and extended asset life through condition-based interventions preventing catastrophic failures, and established strategic imperative demonstrating sector-specific example underscores substantial financial and operational benefits of shifting from reactive to predictive models aligning with course objectives on reducing downtime, extending equipment life, lowering maintenance and replacement costs, and improving safety and availability validating data-driven predictive approaches in high-stakes oil and gas environments where safety, environmental protection, and operational continuity are paramount, showcasing how systematic data-driven predictive maintenance approach directly enables superior downtime reduction, enhanced annual cost savings, and improved operational reliability in oil and gas industry operations.

Be inspired by leading maintenance optimization achievements. Register now to build the skills your organization needs for equipment reliability excellence!