An Intensive 5-day Training Course
Maintenance Analytics
Leveraging Analytics to Enhance Operational Efficiency
Scheduled Dates
Classroom
| 08 - 12 Jun 2026 | London - UK | $5,950 | RESERVE A SEAT |
| 19 - 23 Oct 2026 | London - UK | $5,950 | RESERVE A SEAT |
Would an alternative date be more suitable?
We offer a variety of tailored training options, customized to meet your organisation's needs. Delivered anytime, anywhere, we make it easy to bring expert training directly to your team.
Why Choose this Training Course?
Maintenance Analytics is the application of data-driven techniques to optimize maintenance operations and enhance asset performance. This PetroKnowledge Maintenance Analytics training course provides a comprehensive overview of maintenance analytics, covering data collection, analysis, and interpretation to drive informed decision-making.
Participants will learn how to leverage maintenance data to predict equipment failures, optimize maintenance schedules, and reduce downtime. Through a combination of theoretical knowledge and practical exercises, attendees will develop the skills necessary to implement maintenance analytics within their organizations.
What are the Goals?
Upon completion of this Maintenance Analytics training course, participants will be able to:
- Understand the fundamentals of maintenance analytics and its role in asset management.
- Identify and collect relevant maintenance data for analysis.
- Apply data analysis techniques to extract valuable insights from maintenance data.
- Develop predictive maintenance models to optimize maintenance schedules.
- Calculate key performance indicators (KPIs) to measure maintenance performance.
- Utilize maintenance analytics to improve asset reliability and reduce maintenance costs.
- Communicate analytical findings effectively to stakeholders.
Who is this Training Course for?
This PetroKnowledge Maintenance Analytics training course is designed for professionals involved in maintenance and asset management, including:
- Maintenance managers and supervisors
- Reliability engineers
- Asset management professionals
- Maintenance planners and schedulers
- Data analysts with an interest in maintenance
- Engineers and technicians with maintenance responsibilities
- Individuals looking to develop their data analytics skills for maintenance applications
How will this Training Course be Presented?
The training course will employ a blended learning approach, combining classroom instruction, hands-on exercises, and case studies. Participants will have the opportunity to work with real-world maintenance data using industry-standard analytics tools. The training course will be interactive, encouraging active participation and discussion among attendees.
Organisational Impact
By implementing maintenance analytics, organizations can expect the following benefits:
- Increased equipment reliability and availability
- Reduced maintenance costs through optimized maintenance schedules
- Improved decision-making based on data-driven insights
- Enhanced asset lifecycle management
- Improved overall equipment effectiveness (OEE)
- Enhanced risk management through predictive failure analysis
- Increased operational efficiency and productivity
Personal Impact
Participants who complete this Maintenance Analytics training course will:
- Develop a strong foundation in maintenance analytics and data-driven decision-making.
- Gain practical skills in data collection, analysis, and interpretation.
- Improve their ability to identify maintenance improvement opportunities.
- Enhance their problem-solving and critical thinking skills.
- Increase their value to the organization by contributing to cost savings and efficiency improvements.
Daily Agenda
Day One: Introduction to Maintenance Analytics and Data Collection
- Introduction to maintenance analytics and its benefits
- Importance of data-driven decision making in maintenance
- Identifying key performance indicators (KPIs) for maintenance
- Data sources and types relevant to maintenance (CMMS, ERP, IoT sensors, etc.)
- Data quality and cleansing techniques
- Data exploration and visualization using sample maintenance data
- Introduction to data visualization tools (e.g., Excel, Power BI, Tableau)
- Creating basic visualizations (charts, graphs, dashboards) to understand maintenance patterns
Day Two: Descriptive and Diagnostic Analytics
- Descriptive statistics for maintenance data (mean, median, mode, standard deviation)
- Data distribution analysis (histogram, box plot)
- Correlation analysis to identify relationships between variables
- Time series analysis for maintenance data (trend analysis, seasonality)
- Root cause analysis techniques (5 Whys, Pareto analysis)
- Failure mode and effects analysis (FMEA)
Day Three: Predictive Analytics and Machine Learning
- Introduction to predictive modeling and its applications in maintenance
- Data preparation for predictive modeling (feature engineering, normalization)
- Overview of machine learning algorithms for maintenance (regression, classification, clustering)
- Model evaluation metrics (accuracy, precision, recall, F1-score)
- Building a predictive maintenance model using a machine learning tool (e.g., Python, R)
- Model deployment and monitoring
Day Four: Prescriptive Analytics and Optimization
- Introduction to prescriptive analytics and optimization
- Optimization techniques for maintenance scheduling (linear programming, integer programming)
- Simulation modeling for maintenance planning
- Risk-based maintenance (RBM)
- Implementing prescriptive analytics to optimize maintenance operations
- Challenges and opportunities in applying prescriptive analytics
Day Five: Implementation and Organizational Change
- Developing a maintenance analytics roadmap
- Change management and stakeholder engagement
- Overcoming challenges in implementing maintenance analytics
- Return on investment (ROI) measurement
- Continuous improvement and monitoring of maintenance analytics
- Best practices for maintenance analytics
Certificate
- On successful completion of this Training Course / Online Training Course, a PetroKnowledge Certificate / E-Certificate will be awarded to the delegates.
