Data identification and data collection methods in simulation
Data identification and data collection methods in simulation
There seems to be paucity in the research into the collection of data for use in simulation. This is rather unfortunate since data quality and availability are two of the most challenging issues in many simulation projects. This paper discusses how simulation practitioners identify and collect data for simulation projects. The data was collected from ORH Ltd, a management consultancy company using participant observations method and an experiment using a mock case scenario. From the observation, we produce a generic data identification and collection method. The method is evaluated using a real project conducted for a UK Ambulance Service.The experiment reveals variations in the data identification process that are influenced by the role and the level of experience of modellers.
simulation modelling, Data collection, data, Data Identification
195-205
Onggo, B.S.S.
8e9a2ea5-140a-44c0-9c17-e9cf93662f80
Hill, J.
061b7c5e-5bdc-496a-b783-fa4e7f831921
2014
Onggo, B.S.S.
8e9a2ea5-140a-44c0-9c17-e9cf93662f80
Hill, J.
061b7c5e-5bdc-496a-b783-fa4e7f831921
Onggo, B.S.S. and Hill, J.
(2014)
Data identification and data collection methods in simulation.
Journal of Simulation, 8 (3), .
(doi:10.1057/jos.2013.28).
Abstract
There seems to be paucity in the research into the collection of data for use in simulation. This is rather unfortunate since data quality and availability are two of the most challenging issues in many simulation projects. This paper discusses how simulation practitioners identify and collect data for simulation projects. The data was collected from ORH Ltd, a management consultancy company using participant observations method and an experiment using a mock case scenario. From the observation, we produce a generic data identification and collection method. The method is evaluated using a real project conducted for a UK Ambulance Service.The experiment reveals variations in the data identification process that are influenced by the role and the level of experience of modellers.
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Accepted/In Press date: 25 November 2013
Published date: 2014
Keywords:
simulation modelling, Data collection, data, Data Identification
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Local EPrints ID: 425081
URI: http://eprints.soton.ac.uk/id/eprint/425081
ISSN: 1747-7778
PURE UUID: a6e1b73d-0570-4d98-aaee-1e29acddc215
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Date deposited: 10 Oct 2018 16:30
Last modified: 16 Mar 2024 04:38
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Author:
J. Hill
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