The
Course Content
Day
One: Predictive Asset Maintenance
Reactive
Maintenance
Maintenance
Reliability
Contribution
of Planning Coordination, and Scheduling
Symptoms
of Ineffective Job Planning
Maintenance
Deliverables
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
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
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
Day
Five: Proactive Maintenance Planning
Detailed
Planning of Individual Jobs
Materials
Support
Work
Measurement
Analytical
Estimating
Coordination
with Operations
Why
Choose this Training Course?
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.
What
are the Goals?
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
Who
is this Training Course for?
This
training course is designed for all
professionals working in the field of data
analysis, oil and gas exploration, geology and reservoir modelling.
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 asset