A successful simulation project is one that delivers useful information or a result at the appropriate time to support a meaningful decision or a task. However, achieving that is not always an easy task. A simulation project is much more than building a model, so the skills that are required go well beyond knowing a particular simulation tool. The most important engineering habits when building a robust and reliable process models are as follows:

1. Defining good objectives

When the decision is made to conduct a simulation project, the first thing to define are the project objectives. This step cannot be highlighted enough because it will prepare and define your path from the step one to the very end of your project and be sure that without understanding the objectives in depth it is impossible to have a successful project. This includes having answers to these questions: 

•  Why will you simulate the system and what are your expectations to get out of it? 
•  To be more specific, you must determine who your interested parties and superiors are and how do they define the simulation project success and what their expectations are?
• Which are those questions and decisions the simulation project must give answers to? 
• What will be the role or purpose of the simulation project?

Although this step seems so basic and like something not worth mentioning – it is crazy how often exactly this comes as a main obstacle of a successful project. Be smarter than that – have your answers on time!

2. Understanding the process you will be describing with the simulation model

If you are lucky, you will be familiar with the process you are modeling. More typically, you do not know it well enough to accurately model it. Get to know your process and understand it before starting to build the model. While it is not reasonable to expect from a simulation engineer to know every process, an experienced engineer will know important questions to ask and will be able to understand the answers. Find out typical details about a process to be modeled from the book and process description. If possible, talk through a process with an engineer who knows it well.

3. Making the list of assumptions 

Summarize your understanding by making the list of assumptions and limit conditions that you will be taking into account so that you and all interested parties have a common understanding of how much detail will be modeled for each part of the process. For example, inlet and outlet flows and conditions, external influences details, simplifications of complex parts etc.  

4. Asking more questions

Ask. Ask. Ask. Ask more questions. Ask different people the same questions and don't be surprised that you get different answers. Your goal at this stage is not to solve the problem, but to understand the problem and the system well enough that you can describe and estimate the work. Part of this stage is to identify what you don't know so that you can allow time and risk in the project for that enlightenment.

5. Avoid any sort of urgencies 

Don’t allow the project to be pulled down by the sudden moment of urgency. While the best time to start a simulation study is very early in the associated project's lifecycle, that is unfortunately not the most common situation. It is far more common that simulation is first considered when problems are encountered and when there is just a short time planned before the final decisions must be made. At this point, everything becomes urgent, and you may even be "late" before you have started. If you don’t have enough time to do a simulation right, there is a little point starting it. Try to avoid the situations where the urgency will pull you down and the project is convicted to fail before it started just because the lack of time.

6. Start simple 

In the firsts developing cycle, the smart thing to do is to build the entire model or a major section of it with a minimal level of detail. Then start putting more details later on. You can then verify the model works before continuing on. This has the advantage of immediately generating a potentially useful model. Having the first draft of the model can also help you to communicate first results with all the interested parties and find out what your most critical parts are. 

7. Start verification with trickiest sections first

Although starting simple, ones you start going deeper in the analysis, resolve and verify trickiest sections first - A more experienced simulation engineers might implement the hardest or trickiest sections first to eliminate some project risk early on. A modeler with some "agile" background might do the highest priority or most important sections first. With this latter approach, at any stage, the most important aspects of the model have been completed. This helps reduce the risk of running out of time or budget without being able to produce any meaningful results.

8. Check the model in a wide variety of situations

A few quick tests of the model to see how it performs far from design conditions shouldn’t take too much of your time and jet it can give good feedback about its possibilities. Make test in a wide variety of situations, such as high capacity, low capacity, or recovering from a failure.

9. Don’t panic in case of getting unexpected results 

Unexpected results are not a problem – they are a primary reason for doing a simulation. Unexplainable results are a problem. Make sure to find the explanation for your unexpected results. They are an amazing ground for learning. If they cannot be explained, then go back and try to find the reason. In most cases that might lead to discovery of a bug that must be fixed. Are your input data accurate? Can you confirm that simulation software didn’t miss the calculation? Use controls in the software to allow you to step through a model or to "break" execution at a particular location, time, or condition. Often there will be a watch window that allows you to explore the detailed system state at any time or for any object to help further clarify what is happening. The verification process is certain to be an enlightening and quite necessary part of the project.

10. Make everyone aware: a model is only an approximation 

Don't over-represent the accuracy of the output data. Acknowledge and even emphasize to your decision-makers that the model is an approximation and it will not generate exact answers, however the value of this approximation is enormous if done right!