One of the best features that ProcessModel a business process improvement software boasts is Stat Fit, Stat Fit is a data fitting program available within ProcessModel Professional that allows the creation of custom distributions from your real life data, the distributions that are created from Stat::Fit can then be used in the various parts of your model and you can also use just the stat fit to get a distribution for your own use. There are literally millions of things Stat::Fit can be used for, let’s take an example of using it for creating an activity that has a dynamic capacity during process simulation.

Stat::Fit takes raw data (e.g. collected service times) and turns them into a single distribution that represents the collected data. For example, data collected on the length of breakdowns can be turned into a single distribution and be placed in a ProcessModel field.

Stat::Fit is accessed from the Tools menu of ProcessModel process simulation software. It allows you to improve the accuracy of your models by using collected data to determine the best distribution to use in order to reflect that data.

Continuous Distributions vs. Discrete Distributions

Distribution fittings are built-in functions that generate random numbers using predetermined patterns. Distributions may be discrete, randomly returning one value among a specified list of values, or they can be continuous and interpolate randomly according to the pattern provided by the input table or parameters. There are several steps in determining the best distribution to use given raw data from observations of the process being done for process mapping. First, you must determine whether the data is discrete or continuous, then follow the appropriate instructions. Stat::Fit is capable of much more than fitting data to distributions, but you need only take advantage of a few of its easy-to-use features when fitting your data to a ProcessModel distribution for process improvement.

Continuous Distribution

The following business process improvement example shows you how Stat::Fit can help you in process mapping more accurate models. A bank wants to model its teller operations, including the amount of time that it takes to serve each customer. Therefore, for a week, the time each customer spent with a teller is recorded. The data is entered in a text file which can be read by Stat::Fit. Using Stat::Fit, the data is analyzed and an activity time distribution is found that accurately reflects the amount of time required to serve a customer.

Discrete Distribution

The following business process simulation example shows how a restaurant could use ProcessModel to model its seating operation. The number of customers is a quantity of discrete entities. Therefore, the Stat::Fit component of ProcessModel would take data about the number of customers who enter in each group, create a discrete distribution to represent that data, and place the distribution in the Quantity field for Arrivals in the ProcessModel for the restaurant.