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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 training course enables maintenance and reliability professionals to make informed, data-led asset management decisions. It provides a structured framework based on the Decision Making Grid (DMG). Participants explore how Big Data capabilities support established approaches, including Condition-Based Maintenance (CBM) and Reliability Centered Maintenance (RCM). The course also covers Total Productive Maintenance (TPM) and Design Out Maintenance.

Participants use existing operational data and apply relevant tools and techniques within practical maintenance situations. The course addresses the reduction of unplanned downtime, improvement of Overall Equipment Effectiveness (OEE), and increased value from CMMS data. Its practical approach moves beyond a purely theoretical examination of predictive maintenance. Participants develop how they assess, prioritise and act upon maintenance decisions using Big Data.

This Big Data Analytics for Predictive Maintenance Strategies training course will highlight:

  • Applying the Decision Making Grid to maintenance decisions
  • Using Big Data to support asset management
  • Applying Condition-Based Maintenance and Reliability Centered Maintenance
  • Integrating Total Productive Maintenance and Design Out Maintenance
  • Using operational data in real-world maintenance contexts
  • Reducing unplanned downtime and improving OEE and CMMS value
Why Choose Big Data Analytics for Predictive Maintenance Strategies
Course Goals

What are the Goals?

The Big Data Analytics for Predictive Maintenance Strategies Course aims to transition participants from reactive thinking towards a fully strategic, data-driven approach to managing maintenance.

  • Recognise the relationship between Big Data characteristics and decision-making within the DMG framework
  • Establish and use key performance indicators (KPIs) applicable to maintenance management
  • Make effective use of CMMS data to inform maintenance strategy selection
  • Determine and prioritise suitable maintenance strategies through multiple criteria decision-making
  • Implement best-practice benchmarking and maintenance audit techniques
  • Employ reliability modelling tools, including FMEA, RPN, FTA, RBD, and MCS
  • Enhance equipment performance by applying TPM, OEE, and root cause analysis
  • Restructure and reconfigure maintenance arrangements to achieve long-term operational excellence

 

Who is this Training Course for?

This Big Data Analytics for Predictive Maintenance Strategies training course is designed for:

  • Predictive Maintenance Engineers
  • Reliability Engineers
  • Maintenance Engineers
  • Condition Monitoring Engineers
  • Asset Management Professionals
  • Maintenance Planners
  • Data Analysts
  • Data Scientists
  • Industrial Engineers
  • Operations Engineers
Learning Approach

How will this Training Course be Presented?

This course uses a progressive, practical learning format that blends established frameworks with direct application to data. Each day develops from the one before, moving from the selection of foundational strategies to the complete reconfiguration of maintenance systems.

Participants are invited to bring data from their own organisations so they can use the Decision Making Grid and reliability tools within a relevant, practical setting.

Delivery approaches include:

  • Instructor-led sessions addressing essential concepts, frameworks, and methodologies
  • Analysis of case studies based on industry examples of predictive maintenance being applied
  • Practical data exercises that apply Big Data analytics to CMMS and maintenance datasets
  • Group discussions and peer-based learning for sharing operational challenges and best practices
  • Reliability modelling workshops applying RCM methods including FMEA, FTA, and RBD
  • Root cause analysis activities based on the Ask Why 5 Times approach
  • Integration exercises linking TPM, OEE, and KPIs with actual maintenance decisions
  • Implementation planning designed to support direct workplace application

 

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

  • • Wallstreet Development Academy Certificate of Completion for delegates who attend and complete the training course.

Frequently Asked Questions

The course covers the use of big data analytics in the context of predictive maintenance strategies. Its stated emphasis is on identifying appropriate maintenance strategies. No more detailed topic list or agenda is provided in the course information.
The course connects big data analytics with the development or selection of predictive maintenance strategies. It focuses on applying an analytics perspective to maintenance decision-making. The provided information does not specify particular data platforms, analytical models, or software.
The course aims to build understanding of how big data analytics relates to predictive maintenance strategy. It also focuses on considering which maintenance strategies are appropriate. The available description does not identify specific technical, software, or hands-on skills.
No prerequisites are stated in the provided course material. The title suggests that the course addresses both analytics and maintenance strategy concepts. However, no required level of prior knowledge or professional experience is given.
You will learn about the relationship between big data analytics and predictive maintenance strategies. The subtitle indicates a focus on determining appropriate maintenance strategies. Specific learning objectives, tools, techniques, and case studies are not listed in the provided course material.
Yes, selecting appropriate maintenance strategies is an explicit focus of the course subtitle. The course places this topic within the broader context of big data analytics and predictive maintenance. The exact selection criteria or decision frameworks covered are not specified.
The intended audience is not specified in the provided course information. The subject matter is centred on big data analytics, predictive maintenance, and appropriate maintenance strategies. No particular job roles, experience levels, or industries are identified.
Specific tools or technologies are not identified in the available course information. The stated focus is big data analytics for predictive maintenance strategies and the selection of appropriate maintenance approaches. No software platforms, programming languages, sensors, or analytical methods are listed.

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