08 May 2018

Data Science: Simulation Model (Definitions)

"A 'what-if' model that attempts to simulate the effects of alternative management policies and assumptions about the firm's external environment. It is basically a tool for management's laboratory." (Jae K Shim & Joel G Siegel, "Budgeting Basics and Beyond", 2008)

"Simulation models are formal representations of a portion of reality. Simulation models allow managers to share and test assumptions about problem causes and solutions." (Luis F Luna-Reyes, "System Dynamics to Understand Public Information Technology", 2008)

"A simplified, computer, simulation-based construction (model) of some real world phenomenon (or the problem task)." (Hassan Qudrat-Ullah, "System Dynamics Based Technology for Decision Support", 2009)

"A model that shows the expected operation of a system based solely on the model." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"An analytical technique that often involves running models repeatedly using a variety of inputs to determine the upper and lower bounds of possible outcomes. This simulation process is also sometimes used to identify the likely distribution of outputs given a series of assumptions around how the inputs are distributed." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"A representation of a system that can be used to mimic the processes of the system under varying circumstances. It is usually operated subject to stochastic disturbances." (Özgür Yalçınkaya, "A General Simulation Modelling Framework for Train Timetabling Problem", 2016)

"A model that represents an actual procedure over time." (Rania Tegou, "Excess Inventories and Stock Out Events Through Advanced Demand Analysis and Emergency Deliveries",  2018)

"technique that created a detailed model to predict the behavior of CI/service" (ITIL)

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IT Professional with more than 24 years experience in IT in the area of full life-cycle of Web/Desktop/Database Applications Development, Software Engineering, Consultancy, Data Management, Data Quality, Data Migrations, Reporting, ERP implementations & support, Team/Project/IT Management, etc.