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Big Data Analytics for Predictive Maintenance Strategies

Appropriate Maintenance Strategies

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Why Choose Us

The Big Data Analytics for Predictive Maintenance Strategies Course gives maintenance and reliability professionals a structured, data-driven framework for making smarter asset management decisions. Built around the Decision Making Grid (DMG), this course connects Big Data capabilities with proven maintenance methodologies — from Condition-Based Maintenance (CBM) and Reliability Centered Maintenance (RCM) to Total Productive Maintenance (TPM) and Design Out Maintenance.

Participants work directly with existing operational data, applying tools and techniques to real-world maintenance contexts. Whether the goal is reducing unplanned downtime, improving Overall Equipment Effectiveness (OEE), or getting more value from your CMMS, this course provides a practical, results-focused path forward.

This is not a theoretical overview — it is a hands-on course designed to shift how professionals think about, prioritise, and act on maintenance decisions using Big Data.

 

Why Choose Big Data Analytics for Predictive Maintenance Strategies
Course Goals

What are the Goals?

The Big Data Analytics for Predictive Maintenance Strategies Course is designed to move participants from reactive thinking to a fully strategic, data-informed approach to maintenance management.

  • Understand how Big Data features and decision-making intersect within the DMG framework
  • Define and apply key performance indicators (KPIs) relevant to maintenance management
  • Utilise CMMS data effectively to support maintenance strategy selection
  • Identify and prioritise appropriate maintenance strategies using multiple criteria decision-making
  • Apply best practice benchmarking and maintenance auditing methods
  • Use reliability modelling tools including FMEA, RPN, FTA, RBD, and MCS
  • Improve equipment performance through TPM, OEE, and root cause analysis
  • Redesign and reconfigure maintenance structures for long-term operational excellence

 

Who is this Training Course for?

This Course is designed for professionals involved in maintenance planning, reliability engineering, and asset management who want to apply Big Data for better predictive maintenance outcomes.

This course is suitable for:

  • Maintenance managers and engineers seeking structured decision-making frameworks
  • Reliability engineers responsible for equipment performance and uptime
  • Asset managers working with CMMS and operational data
  • Plant and operations managers looking to reduce maintenance costs
  • Technical professionals involved in RCM or CBM implementation
  • HSE and quality professionals supporting maintenance excellence initiatives
  • Data and analytics professionals working within engineering or industrial environments
  • Professionals pursuing excellence in Total Productive Maintenance (TPM)

 

Learning Approach

How will this Training Course be Presented?

This Course is delivered through a progressive, hands-on learning structure that combines proven frameworks with direct data application. Each day builds on the previous — from foundational strategy selection through to full maintenance system reconfiguration.

Participants are encouraged to bring data from their own organisations to apply the Decision Making Grid and reliability tools in a relevant, practical context.

Delivery methods include:

  • Instructor-led sessions covering core concepts, frameworks, and methodologies
  • Case study analysis drawing on industry examples of predictive maintenance in action
  • Hands-on data exercises applying Big Data analytics to CMMS and maintenance datasets
  • Group discussions and peer learning to share operational challenges and best practices
  • Reliability modelling workshops using RCM techniques such as FMEA, FTA, and RBD
  • Root cause analysis activities using the Ask Why 5 Times approach
  • Integration exercises connecting TPM, OEE, and KPIs to real maintenance decisions
  • Implementation planning to support direct application back in the workplace

 

The Course Content

Introduction to Maintenance Strategies - The Decision Making Grid (DMG)

  • Maintenance decision making and features of Big Data
  • Key performance indicators for the DMG
  • Utilization of data in the Computerized Maintenance Systems Management (CMMS)
  • Methods of partitioning the DMG
  • Identification of available maintenance strategies
  • Prioritization of responsive decisions
  • Application of multiple criteria decision making in the DMG
  • Cost-Benefit analysis of the DMG

Keep the Best Practice - The OTF Decisions

  • Introduction to the concept of best practice ion reliability and maintenance
  • Maintenance standards
  • Maintenance auditing and benchmarkinG
  • Excellence awards in TQM
  • Reliability and Maintenance awards
  • Application to existing data

Investigative Strategies - The CBM Decisions

  • Common definitions and terminology
  • Standards in Reliability
  • Difference between maintenance and reliability
  • Reliability modeling approaches and decision making
  • Reliability Centered Maintenance (RCM)
  • Techniques related to RCM: FMEA, RPN, ICC, FTA, RBD, and MCS
  • Condition Base Maintenance technologies
  • Application to existing data

Human Factors - Skill Levels Upgrade Decisions

  • Key performance Indicators (KPIs)
  • Overall Equipment Effectiveness (OEE)
  • Total productive maintenance (TPM)
  • Ask Why 5 times concept
  • Learning from others
  • Application to existing data

Reconfiguration - Design Out Maintenance Decisions

  • Getting the best out of data in CMMS
  • Integrated framework of the Decision Making Grid (DMG)
  • Reconfiguration of the Maintenance and Reliability Structurers
  • Guidelines for successful implementation
Recognition

Certificate

  • • AZTech Certificate of Completion for delegates who attend and complete the training course.
Collaborations

In Partnership With

Anderson Coventry

Frequently Asked Questions

Absolutely. AZTech offers customised in-house training where this course can be tailored to your organisation's specific needs, objectives, and industry context. Please contact us directly to discuss your requirements.

Yes. This Big Data Analytics for Predictive Maintenance Strategies is offered in a classroom setting across multiple international locations as well as in a live online format, giving participants the flexibility to choose the option that best suits their schedule and learning preference.

AZTech courses are conducted in carefully selected 5-star hotels or 4-star premium hotels in the host city, featuring fully equipped, professional training rooms. Participants will enjoy coffee breaks and snacks throughout the day, as well as a complimentary lunch provided after each training session.

This Big Data Analytics for Predictive Maintenance Strategies training course is designed for professionals at all levels who wish to enhance their knowledge and skills in this subject area. It is suitable for managers, team leaders, specialists, and anyone looking to advance their career and professional development.

AZTech provides round-the-clock support to assist you with any enquiries. You can reach our team 24/7 by phone at +971 50 195 5668, by email at [email protected], or by visiting our website at www.aztechtraining.com. Our dedicated training consultants are always on hand to help you with course selection, registration, scheduling, and any other questions you may have.

There are no formal prerequisites for this Big Data Analytics for Predictive Maintenance Strategies course. It is open to all professionals regardless of their background or experience level, though a basic understanding of the subject area may be beneficial.

AZTech courses are delivered using a highly interactive and practical methodology, combining presentations, group discussions, real-world case studies, exercises, and workshops to ensure participants can immediately apply what they have learned in their professional environment.

Yes. Participants who successfully complete the course will receive an AZTech Certificate of Completion. This course may also be eligible for Training credits, which will be indicated in the course details.

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