This training course is designed for all professionals working in the field of data analysis, oil and gas exploration, geology and reservoir modelling.
This training course is suitable for a wide range of professionals but will greatly benefit:
Other professionals involved in procurement, maintenance and operations of assets
Duration
5 Days
Programme Overview
No matter how expensive and robust the system or machine is, it will work for only so long if not maintained properly, more systems, processes, and machines you have maintenance cost will skyrocket, and the deadlines will come upon your company even before you realize it. Properly maintaining your systems and machines makes failure rates lower and production downtimes seldom and less expensive, however as the maintenance activities are costly, they need to be planned based on the accurate predictions as maintenance based solely on manufacturers manuals are usually not good enough as manufacturers have tested only in the laboratory environments and the environments where the systems are used are much different from the laboratory environments.
With Big Data and IoT maintenance planning and failure rate prediction is now much easier and the companies who use the benefits of these concepts are improving their maintenance schedules, reducing the costs and downtimes, therefore, winning over their competition. With the addition of agent-based simulation, the machine learning and deep learning algorithms could be expedited and the maintenance predictions made as close to the real-life as possible, as we can simulate the behaviour of ageing assets and new workforce behaviour, or the introduction of cutting edge technology to ageing workforce, something which is not in the user manuals, but it is omnipresent in today’s industry.
This training course will feature:
Maintenance principles, downtimes, and preventive maintenance applications,
Using Predictive Analytics to Optimize Asset Maintenance
Means and methods how to reduce/minimize/optimize asset life cycle costs
Big Data and IoT roles in maintenance planning and scheduling
Using Any Logic software for process design and predictive maintenance optimization
Advanced concepts: Aligning the industry needs with the workforce availability
Objectives
By the end of this training course, participants will be able to:
Understand the importance of maintenance planning and scheduling
Understand the capabilities of Agent-Based simulation
Acquire the knowledge of using AnyLogic software for maintenance planning and simulation
Import, analyze and interpret Big Data Through Predictive Analytics for Maintenance Optimization
Understand the benefits of IoT for automation of maintenance scheduling and downtime reduction
Perform the optimization of maintenance scheduling using AnyLogic simulation software
Methodology
This training course will utilize a variety of proven adult learning techniques to ensure maximum understanding, comprehension, and retention of the information presented. This includes the presentation of theoretical concepts, video lectures and many exercises that will be done through the guided work of the delegates themselves. Delegates will be guided through the real-life examples and will be provided with Personal Learning Edition of AnyLogic software, as well as introduced to Personal Learning Edition of AnyLogistix software and their capabilities.
Course Outline
Day One: Predictive Asset Maintenance
Reactive Maintenance
Maintenance Reliability
Contribution of Planning Coordination, and Scheduling
Symptoms of Ineffective Job Planning
Maintenance Deliverables
Exercise: Introduction to AnyLogic and AnyLogistix software
Day Two: Using Predictive Analytics in Maintenance Systems
Data management
Big Data Quality and sources
Dealing with large data sizes
IoT and adaptive maintenance: Integrated data collection
Uncertainty in implementation cost and Return on Investment
Exercise: Design the data collection and modelling and simulation tools
Day Three: Maintenance Planning Principles
Work order system
Maintenance requirement forecasting
Traditional forecasting methods
Downtime planning and mitigation
Costs of poor planning
Ripple and Bullwhip effects on production originating from poor maintenance plans
Exercise: Improving maintenance process with AnyLogic agent-based modelling
Day Four: Spare Parts Procurement and Inventory Planning
Procurement for maintenance
Spare parts inventory and availability
Development of Work Programs and the Maintenance Calendar
Sizing the Maintenance Staff
Exercise: Defining and optimizing supply chain process of spare parts in Any Logistic
Day Five: Proactive Maintenance Planning
Detailed Planning of Individual Jobs
Materials Support
Work Measurement
Analytical Estimating
Coordination with Operations
Exercise: Job Feedback, Close Out, Analysis, and Schedule Compliance using agent-based modelling
