Data Collection Basics

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

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2 Responses to “Data Collection Basics”

  1. Sergio Paul Aguirre says:

    Thank you for this blog post, i really like what you said about comparing the data we require versus the data we will obtain. In my projects the main problem i always have is with the data, we always need more and more data! Sometimes measuring one single type of data can take a lot of time. I always try to solve my problems with estimates when that variable will not have a considerable effect in my results, but when its an important variable then i do all the measurements and validations to get that single data. It can be tricky at first but its a little fun thing, we get more “control” over our data that way, hehehe. 🙂

    Have a nice day!

  2. In mid-2014 Simio implemented new technology that lets the Simio automatically identify the relative importance of your input data streams as well as some optional analysis capabilities that helps you decide where to put additional data collection efforts (e.g. Is is better to collect more data on Input Parameter A or B?)
    It sounds like you will find these to be very helpful.

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