Simulation: Information Flow

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Part III Simulation Information Flow for
Performance Enhancement with Case Studies
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Systems have grown in complexity over the years
mainly due to the increased striving for
performance enhancing combined with a greater
degree of informational uncertainty and
imprecision in system's external and internal
environments. This complexity, always present in
real life systems, makes the application of
quantitative tools as problem solvers
questionable in many instances. This issue was
extensively discussed in some previous Chapters
of this book. In this Section discrete computer
simulation technique is proposed as an additional
analysis tool that proved to be an adequate,
effective and economically efficient problem
solver in a number of cases of information flow
and management related to scheduling and resource
allocation and utilisation for complex real life
servicing, processing and manufacturing system.
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SIMULATIONTO SIMULATE MEANS TO DUPLICATE THE
ESSENCE OF THE SYSTEM OR ACTIVITY WITHOUT
ACTUALLY ATTAINING REALITY ITSELF.
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COMPUTER SIMULATIONESTABLISHMENT OF A
MATHEMATICAL-LOGICAL MODEL OF A SYSTEM AND THE
EXPERIMENTAL MANIPULATION OF IT ON A COMPUTER.
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• MAIN ADVANTAGES
• TIME
• COST
• SAFETY

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SIMULATION FOR
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• Modern simulation
• platforms

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Visual SLAM AweSIM
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SIMULATION FOR PERFORMANCE ENHANCEMENT
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PROJECTS

• steel processing
• manufacturing
• hospital operation
• mining
• preventive maintenance
• servicing

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SOLVING COMPLEX PROBLEMS
COMPLEX SYSTEM
DECOMPOSITION
REPRESENTATION
INTEGRATION
INTEGRATED SOLUTION
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Skill level needs and service requirements at
different locations
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The average number of sampling
positions and their location
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Attribute Description
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• File Description

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• Description of Duration Times

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• Resource Description

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Global Variables Description
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Data Collection Description
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• MODELLING RESULTS
• The presented model identifies problem areas that
  need considering for the staffing arrangements.
• It shows some of the capabilities that AWESIM has
  as a simulation language and its ability in
  modelling of the management process for condition
  monitoring.
• One of these capabilities is an easy
  implementation of 'What if' scenarios ranging
  from changes in a process to changes in the
  system itself by way of altering duration times,
  resources, conditions, etc.

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• The changes that were implemented for one of the
  'What if' scenarios involve the setting up of the
  resources such that all the vibration has only
  one skill level, capable of performing all
  duties necessary. In this way the pressure on
  the system for the vibration side of the
  processes was reduced by shortening the queue
  lengths and the average wait times for the
  resources concerned. Also, the overall times in
  the system for the vibration were reduced.
• In optimising the resources, the results were
  able to show that instrumentation was sufficient
  and the only difficulties with resources were the
  staffing levels of the oil analysis process.
  'Average wait times' and 'total times' were too
  high on some occasions and needed addressing by
  implementing assistance. On the vibration side of
  the system the staffing levels were above
  requirements. Thus exploiting that the staffing
  numbers for the oil process is the deciding
  component in scheduling and staff allocations.

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• The model can be used to simulate the monitoring
  service and optimise the manning scale and
  instrument resources for this service. It
  provides an adoptable, flexible management
  framework that may be applied to any company with
  a condition monitoring workforce that is varying
  in versatility, expertise, and availability.

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