Maintenance Scheduling using Big Data, IoT and Agent Based Simulation

Training course 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 lose to the real life as possible, as we can simulate the behavior of aging assets and new workforce behavior, or the introduction of cutting edge technology to aging 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