What you’ll learn
- Analyze maintenance KPIs including MTBF, MTTR, availability, PM compliance, backlog, emergency work, OEE, and maintenance cost.
- Apply a structured maintenance work management process from work identification and planning to scheduling, execution, and close-out.
- Interpret CMMS/EAM data from work orders, asset history, failures, downtime, spare parts, and maintenance records.
- Identify bad actor assets, recurring failures, reliability issues, and weak maintenance processes using maintenance data.
- Design and interpret maintenance KPI dashboards that support better maintenance and reliability decisions.
- Use maintenance data analysis to recognize trends, support forecasting, and move toward proactive and predictive maintenance.
Requirements
- No programming or data science experience is required. All maintenance analytics concepts are explained step by step.
- Basic familiarity with maintenance, work orders, reliability, industrial operations, or Excel is helpful but not required.
Description
“This course contains the use of artificial intelligence.”
Turn maintenance data into better maintenance decisions.
Modern maintenance teams generate large amounts of data through work orders, CMMS/EAM systems, equipment history, downtime records, preventive maintenance activities, spare parts, and failure reports.
But collecting data is not the same as using it effectively.
A maintenance dashboard may contain dozens of KPIs and still fail to answer the most important questions:
Are our assets becoming more reliable?
Are we reducing reactive maintenance?
Is our preventive maintenance program actually working?
Is the maintenance backlog under control?
Which assets are causing repeated failures?
Are our maintenance resources being used effectively?
What is the data telling us to do next?
THE INTERACTIVE MAINTENANCE ANALYTICS LAB
Unlike traditional slide-only courses, enrollment includes free lifetime access to our proprietary, browser-based Maintenance Analytics Lab software toolkit (zero installation required, runs 100% offline and secure):
1. 18 Interactive KPI Calculators: Instant SMRP world-class benchmark comparisons and slide-warning diagnostics.
2. Machine Learning & Weibull Prediction Studio: Fit 2-parameter Weibull curves (Beta/Eta) and simulate our real-world 1,000 Utility Transformers J48 Decision Tree failure classifier.
3. 3-Way Pareto & Jack-Knife 2×2 Matrix: Dynamically isolate Acute (high-MTTR) vs. Chronic (high-frequency) bad actors.
4. MRO Inventory Optimizer: Wilson EOQ, dynamic safety stock, and reorder point (ROP) modeling across normal Z-score service levels.
5. Life Cycle Costing (LCC) Engine: Discounted cash flow NPV profiles and EUAC Defender vs. Challenger asset replacement economics.
6. Executive 360° SMRP Cockpit: Dynamic spider radar benchmark visualizer.
7. Real-Life Case Study Presets & JSON Data Backup/Restore.
What You Will Learn
You will learn how to understand, calculate, interpret, and use important maintenance and reliability indicators, including:
MTBF — Mean Time Between Failures
MTTR — Mean Time To Repair
MTTF — Mean Time To Failure
Equipment Availability
OEE — Overall Equipment Effectiveness
PM Compliance
Schedule Compliance
Planning Compliance
Maintenance Backlog
Emergency Work Percentage
Wrench Time
MRO and Spare Parts Availability
Maintenance Cost and Financial KPIs
The focus is not only on formulas.
You will learn what each KPI actually tells you, how it can be misinterpreted, and how it should support real maintenance decisions.
Understand the Complete Maintenance Work Management Process
Good analytics starts with good maintenance processes and good data.
For this reason, the course first explains the maintenance work management cycle, including:
Work Identification → Planning → Scheduling → Work Assignment → Execution → Close-Out
You will understand why incomplete work orders, poor failure descriptions, weak planning, and incorrect close-out practices eventually create unreliable KPIs and misleading dashboards.
This connection between maintenance execution and maintenance analytics is essential for anyone who wants to use data professionally.
Work with CMMS and EAM Data
CMMS and EAM systems contain valuable information about:
However, raw CMMS data does not automatically create useful information.
You will learn how maintenance data moves through a practical analytics process:
Problem Definition → Data Collection → Data Cleaning → Visualization → Analysis → Communication → Decision
You will also understand why data quality must be checked before performing analysis and why even sophisticated dashboards can produce poor decisions when the underlying maintenance data is weak.
Find Problems Hidden Inside Maintenance Data
Maintenance analytics becomes valuable when it helps us discover what requires attention.
The course explains how maintenance data can be used to identify:
Bad actor assets
Repeated failures
Excessive downtime
Reliability problems
High levels of emergency work
Growing maintenance backlog
Weak maintenance processes
Performance gaps
Opportunities for maintenance improvement
Instead of simply reporting historical numbers, the goal is to help you ask better questions about your maintenance operation.
Build Better Maintenance Dashboards
A good dashboard should do more than display charts.
You will learn how maintenance KPIs can be organized and interpreted so that dashboards support managers, engineers, planners, and reliability teams in understanding performance and identifying priorities.
The course also discusses a critical question:
What should a maintenance dashboard help you decide?
This distinction helps move maintenance reporting from simply presenting numbers toward supporting action.
From Reactive Maintenance to Data-Driven Maintenance
The final part of the course explores how historical maintenance information can support more advanced decisions.
You will understand the journey from:
Raw Data → Information → Analysis → Insight → Prediction → Decision
This provides a practical foundation for organizations that want to gradually move from reactive maintenance toward more proactive, reliability-focused, and data-driven maintenance strategies.
Who Is This Course For?
This course is designed for professionals who work with industrial assets, maintenance activities, reliability, or maintenance data, including:
Maintenance Managers
Maintenance Engineers
Reliability Engineers
Maintenance Planners and Schedulers
Maintenance Supervisors
Asset Management Professionals
CMMS/EAM Users and Administrators
MRO and Spare Parts Professionals
Operations and Engineering Professionals
Engineers who want to develop maintenance analytics skills
It is also suitable for professionals who already receive maintenance reports and dashboards but want to better understand what the numbers actually mean.
Do I Need Data Science or Programming Experience?
No programming experience is required.
Basic familiarity with maintenance, equipment, work orders, industrial operations, or reliability concepts is helpful, but the key concepts are explained step by step.
Basic Excel familiarity can also be useful.
This is a maintenance-focused course—not a programming or advanced data science course.
By the End of the Course
You will have a clearer understanding of how modern maintenance organizations connect:
Maintenance Work → CMMS Data → KPIs → Reliability Analysis → Dashboards → Better Decisions
You will be able to interpret maintenance performance more critically, recognize problems hidden in maintenance data, and use KPIs and analytics to support reliability, asset performance, and operational improvement.
If you want to move beyond simply reporting maintenance numbers and start understanding what those numbers mean and what to do with them, this course is designed for you.
Author(s): Amir Malekniazi ⭐| Asset leadership & Management
