| Summary: | In real-life environment, 80% of business processes are
dynamic whereby each process is dependent on individual
conditions of execution and at the same time contains a large
amount of parameters that makes them difficult to model. A selfadaptive,
agent-based simulation model for dynamic processes
enables reduction of costs, resources and efforts in designing new
models. This paper presents a workflow for modelling dynamic
processes that consist of key parameters needed for the design and
refinement of the simulation model, which are data collection and
data analysis. Three dynamic processes are chosen as case studies;
crime investigation, new student registration, and transportation
requests processes. The workflow of each case study is analyzed
using cross-case analysis, directed approach, and grounded
theory. The findings showed similarity of key parameters shared
by three dynamic processes and thus required to refine the selfadaptive
agent-based simulation model.
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