Depending on the type of study that you’re doing there are different step-wise approaches or workflows to follow. The first important distinction should be made between a data science and a simulation study.
Data science workflows
|1||Business understanding||Gather data||Frame the problem and look at the big picture|
|2||Data understanding||Clean data||Get the data|
|3||Data preparation||Explore||Explore the data to gain insights|
|4||Modeling||Model||Prepare the data to better expose the underlying data patterns with ML algorithms|
|5||Evaluation||Interpret||Explore many different models and short-list the best ones|
|6||Deployment||Fine-tune your models and combine them into a great solution|
|7||Present your solution|
|8||Launch, monitor, and maintain your system|
Simulation study workflows
The first question to ask is what simulation paradigm are you following? If you haven’t decided yet, take a look at the guides to select between System Dynamics, Discrete-Event Simulation and Agent-Based Modeling (see, e.g., [ 1]). With a modeling paradigm different approaches exist. Let’s take a closer look at them one by one.
- Behdani B. Evaluation of paradigms for modeling supply chains as complex socio-technical systems. InProceedings of the 2012 Winter Simulation Conference (WSC) 2012 Dec 9 (pp. 1-15). IEEE.
- Richardson GP, Pugh III AI. Introduction to system dynamics modeling with DYNAMO. Productivity Press Inc.; 1981 Jan 1.