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Enterprise Resource Planning (ERP)
Predictive Maintenance using ERP Data Training Course
Introduction
In today’s competitive industrial landscape, leveraging data for operational excellence is no longer optional it’s imperative. Predictive Maintenance using ERP Data empowers organizations to anticipate equipment failures, reduce unplanned downtime, and optimize asset utilization. Predictive Maintenance using ERP Data Training Course integrates ERP-driven insights with advanced predictive analytics, IoT connectivity, and machine learning models, enabling maintenance professionals, operations managers, and data analysts to transform raw enterprise data into actionable maintenance strategies. Participants will gain a deep understanding of real-time data extraction, trend analysis, and performance monitoring from ERP systems to drive operational efficiency, minimize costs, and enhance decision-making.
This course is designed for professionals seeking to embrace Industry 4.0 standards, smart manufacturing practices, and AI-driven maintenance strategies. Through hands-on sessions, case studies, and interactive workshops, participants will explore predictive algorithms, KPI tracking, and risk-based maintenance planning using ERP datasets. By the end of this program, learners will be equipped to implement proactive maintenance schedules, reduce downtime, and extend the lifecycle of critical assets, positioning their organization as a leader in data-driven maintenance optimization.
Programme Curriculum
Predictive Maintenance using ERP Data Training Course
Introduction
In today’s competitive industrial landscape, leveraging data for operational excellence is no longer optional it’s imperative. Predictive Maintenance using ERP Data empowers organizations to anticipate equipment failures, reduce unplanned downtime, and optimize asset utilization. Predictive Maintenance using ERP Data Training Course integrates ERP-driven insights with advanced predictive analytics, IoT connectivity, and machine learning models, enabling maintenance professionals, operations managers, and data analysts to transform raw enterprise data into actionable maintenance strategies. Participants will gain a deep understanding of real-time data extraction, trend analysis, and performance monitoring from ERP systems to drive operational efficiency, minimize costs, and enhance decision-making.
This course is designed for professionals seeking to embrace Industry 4.0 standards, smart manufacturing practices, and AI-driven maintenance strategies. Through hands-on sessions, case studies, and interactive workshops, participants will explore predictive algorithms, KPI tracking, and risk-based maintenance planning using ERP datasets. By the end of this program, learners will be equipped to implement proactive maintenance schedules, reduce downtime, and extend the lifecycle of critical assets, positioning their organization as a leader in data-driven maintenance optimization.
Course Duration
5 days
Course Objectives
Understand the fundamentals of Predictive Maintenance and its impact on operational efficiency.
Analyze ERP data for maintenance insights and actionable recommendations.
Apply machine learning algorithms to forecast equipment failures.
Integrate IoT sensors with ERP systems for real-time monitoring.
Develop risk-based maintenance strategies to minimize downtime.
Optimize asset lifecycle management using predictive analytics.
Create KPIs and dashboards for maintenance performance monitoring.
Implement condition-based maintenance models using ERP data.
Utilize data visualization tools for trend analysis and decision-making.
Reduce maintenance costs through data-driven planning.
Identify critical assets and prioritize maintenance interventions.
Conduct root cause analysis for recurrent equipment issues.
Enhance organizational digital transformation initiatives with ERP-driven predictive insights.
Target Audience
Maintenance Managers
Operations Managers
Production Engineers
ERP Analysts
Data Scientists in Manufacturing
Reliability Engineers
Plant Managers
IT Professionals supporting ERP systems
Course Modules
Module 1: Introduction to Predictive Maintenance
Definition and benefits of predictive maintenance
Maintenance strategies
Industry 4.0 and smart manufacturing context
ERP data relevance in predictive maintenance
Case Study: Downtime reduction in a chemical plant
Module 2: Understanding ERP Data for Maintenance
Key ERP modules
Data extraction and integration techniques
Maintenance work order analysis
Historical maintenance data cleaning
Case Study: Leveraging SAP PM data for predictive insights
Module 3: Predictive Analytics and Machine Learning Basics
Overview of machine learning algorithms
Regression, classification, and anomaly detection
Time-series forecasting for equipment failure
Feature selection and data preprocessing
Case Study: Predicting motor failures in a manufacturing unit
Module 4: IoT Integration with ERP Systems
Sensors and IoT device connectivity
Real-time monitoring of asset health
Data streaming into ERP systems
Alerts and predictive triggers
Case Study: IoT-enabled predictive maintenance in a power plant
Module 5: Condition-Based and Risk-Based Maintenance
Difference between condition-based and risk-based approaches
Identifying critical assets and failure modes
Risk prioritization matrices
Maintenance scheduling optimization
Case Study: Risk-based maintenance planning in automotive manufacturing
Module 6: KPI Development and Dashboarding
Key maintenance KPIs
ERP dashboard configuration
Real-time monitoring of predictive alerts
Data visualization for actionable insights
Case Study: KPI-driven decision-making at a steel plant
Module 7: Root Cause Analysis and Continuous Improvement
Identifying recurring failures
5 Whys, Fishbone Diagram
Linking root causes with ERP maintenance data
Continuous improvement loops
Case Study: Reducing repetitive downtime in a food processing plant
Module 8: Implementation Strategy and Digital Transformation
Roadmap for predictive maintenance adoption
Change management and stakeholder alignment
ERP customization for predictive analytics
ROI measurement and benefits tracking
Case Study: Digital transformation in predictive maintenance at a pharmaceutical company
Training Methodology
This course employs a participatory and hands-on approach to ensure practical learning, including:
Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.
Register as a group from 3 participants for a Discount
Upon successful completion of this training, participants will be issued with a globally- recognized certificate.
Tailor-Made Course
We also offer tailor-made courses based on your needs.
Key Notes
a. The participant must be conversant with English.
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
c. Course duration is flexible and the contents can be modified to fit any number of days.
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
e. One-year post-training support Consultation and Coaching provided after the course.
f. Payment should be done at least a week before commence of the training, to FINESKILL TRAINING CENTER account, as indicated in the invoice so as to enable us prepare better for you