Even though the people responsible for building models are often the “data collection people”, I know very few associates who think this is a particularly enjoyable part of their job. But data collection is a necessary part of most simulation projects. An early task in each simulation project should be to identify what data will be needed and how that data will be obtained.
Identify Data. There are many different types of data that you will potentially need. Like other aspects of simulation, the identifying required data is best done iteratively. Start by looking at the major areas of your model: arrival sections, processing sections, storage areas, departure areas, internal movement and similar aspects. For each area, then consider the key parameters necessary to describe it. For example, in an arrival area: What is arriving? Are there many different types of entities? Do they each have descriptive attributes that are important? Do you expect the arrivals to follow some type of a time-based pattern? Considering questions such as these will also help you define the model and modeling approach and iteratively, more detail on the exact data required.
Locate Data. With the current level of automation and electronic tracking, the availability of data has become more prevalent. If it’s an existing system, there may already be data that is routinely collected. If it is a new system, the vendor may have access to data collected on similar systems. In either case, the existence of data does not necessarily make your job easy. For example, perhaps you are interested in a processing time on an operation, and that processing time is automatically captured. But what may not be obvious is exactly what that number represents. Does it (sometimes) include time when the process was failed (perhaps short failures are imbedded but long failures are not)? Does it (sometimes) include time when an operator went on break and forgot to properly log out? Detecting and cleaning such situations can be a tedious and frustrating part of using existing data.
Create Data. If the data you need does not exist or cannot be appropriately cleaned, you must often create it. On an existing system, the most accurate method is to electronically capture the data or have manual studies done to determine it. Either of these can be very expensive. An alternate approach is to get estimates from people who know – people running or managing the operation. Although fast and inexpensive, this may introduce bias and inaccuracy. Likewise on a system that does not yet exist, you may need to rely on specifications provided by a vendor, again possibly introducing bias and inaccuracy. More on dealing with this situation later.
This was a quick overview of some initial steps to consider in data collection. Next week I will discuss some additional steps on what to do next with that data. Until then, Happy Modeling!
Dave Sturrock
VP Products – Simio LLC
Tags: automation, data collection, electronic tracking, identify data, simio, simulation
I think the most difficult part is to identify data. When I face real world projects, I am often confused which data is needed.
As mentioned before , with the current automation of data collection it is usually extremely difficult to really understand the data you are looking at. There is so much data but usually little explanation of what the numbers really mean. This effect is multiplied across different divisions/locations of company if no standards have been put in place.
As we all known, data is significant in simulation. When the data we need does not exist or it not available, creating data is the only way. However, how to create data and make it exactly represent the real world are a big issue for IE engineers.
I think if the relationship between process is unknown, in order to create date, we can make some assumptions and to prove the assumptions by simulation. It likes two sides of coin. Data is important to simulation and simulation is also a good method to inspect data.
Data collection is the most important step in simulation model, the results is highly dependent on data, it will reflect the behavior of the system. Wrong data means wrong model . First, we have to identify our data by knowing the major areas in the model. Sometimes the data is not obvious to collect it directly. In other cases the data doesn’t exist, in which we have to carefully create our data.
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