TRAINING COURSE ON MAINTENANCE SCHEDULING USING BIG DATA IOT AND AGENT BASED SIMULATION

Course Date Location Course fee: Registrations
Start Date: 16/12/2019 End Date: 20/12/2019 Nairobi $ 1,000 Register to attend
Start Date: 13/01/2020 End Date: 17/01/2020 Nairobi $ 1,000 Register to attend
Start Date: 03/02/2020 End Date: 07/02/2020 Nairobi $ 1,000 Register to attend
Start Date: 02/03/2020 End Date: 06/03/2020 Nairobi $ 1,000 Register to attend
Start Date: 06/04/2020 End Date: 10/04/2020 Nairobi $ 1,000 Register to attend
Start Date: 04/05/2020 End Date: 08/05/2020 Nairobi $ 1,000 Register to attend
Start Date: 01/06/2020 End Date: 05/06/2020 Nairobi $ 1,000 Register to attend

INTRODUCTION

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.

COURSE OBJECTIVES

By the end of the course, participants should 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

DURATION

5 Days

WHO SHOULD ATTEND

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 to a wide range of professionals but will greatly benefit:

  • Procurement Planners, Maintenance Planners, Asset Managers
  • Data Scientists and Data Analysts
  • Logistics and Supply Chain Planers
  • Other professionals involved in procurement, maintenance and operations of assets

COURSE CONTENT

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 modeling 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 modeling

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 modeling

GENERAL NOTES

·         This course is delivered by our seasoned trainers who have vast experience as expert professionals in the respective fields of practice. The course is taught through a mix of practical activities, theory, group works and case studies.

·         Training manuals and additional reference materials are provided to the participants.

·         Upon successful completion of this course, participants will be issued with a certificate.

·         We can also do this as tailor-made course to meet organization-wide needs. Contact us to find out more: training@data-afriqueconsultancy.org

·         The training will be conducted at DATA-AFRIQUE TRAINING CENTRE, NAIROBI KENYA.

·         The training fee covers tuition fees, training materials, lunch and training venue. Accommodation and airport transfer are arranged for our participants upon request.

·         Payment should be sent to our bank account before start of training and proof of payment sent to: training@data-afriqueconsultancy.org