Dr. Linda Ann Riley’s (Univ. New Haven) Innovative Teaching Approach for ProModel and Process Simulator (2 of 2)

Headshot_Linda Riley UNH

Dr. Linda Ann Riley Ph.D. Univ New Haven

As mentioned previously in the first post of this series, students in this final two-course sequence complete a technical capstone project. For the first time teaching in this program, I allowed the students to choose either ProModel or Process Simulator as the analysis program for their individual technical project.  In the Six Sigma class, the first sections of the technical paper were completed and involved identifying a process improvement opportunity in the student’s workplace, creating a project charter, value stream map, process flow chart, and undertaking extensive lean/six sigma statistical and qualitative analysis of the as-is process.  Once complete, students then propose process improvement scenarios.

The ultimate goal of the final class in the MSEOM program, Simulation Techniques and Applications, is to develop a moderate level of expertise using ProModel and then simulate both the as-is and the initial improved process design identified in the Six Sigma class.  Through iterations on process improvement using ProModel, the end result is an improved process that meets stakeholder requirements. One of the benefits of the students’ familiarity with Process Simulator was the number of common features between the two programs such as the Output Viewer environment, shared statements and functions and the Minitab interface.  But students did exhibit some push back when it came to building a model in ProModel.  To them, the Process Simulator environment was far easier to construct a model.

From my perspective, in Process Simulator, they relied too heavily on default values that are automatically inserted when simulation properties are applied, as well as the input and output buffers associated with each activity.  These features, e.g. input buffers, became the solution to any bottleneck in a process.  On the other hand, compared to learning ProModel, these built-in defaults caused far less frustration for the students when first running the model.  The models never became “gridlocked” because there were virtually unlimited buffers for each activity.

My intent in the simulation class was that every lab model and exercise undertaken in ProModel would be also completed in Process Simulator and vice versa.  This seemed at first to be a good reinforcement for using both programs.  Unfortunately, this strategy failed.  Relatively new to Process Simulator myself, I didn’t initially realize that several of the basic ProModel statements such as Group, Move With, Graphic and View, which had been required for my ProModel labs, were not available in Process Simulator.  In addition, the class used the student version of ProModel.  Consequently, when opening the Process Simulator exercises in ProModel, activities in Process Simulator, which correlate to locations in ProModel exceeded the student version limits.  One of the reasons this occurred was because a single activity in Process Simulator translated to three different locations in ProModel because of the input and output buffers.  The next time I teach the two-course sequence, I have a much clearer perspective of how to seamlessly construct the labs and exercises so they are interchangeable between the two programs.

After attempting this experiment, it is now far more evident to me that Process Simulator is a superior product for modeling processes defined using process flow charts as shown below.  It is ideal for use in a Quality, Six Sigma/Lean Process Optimization scenario.  I will definitely continue to use the product moving forward.

2018-10-17 14_48_01-Electronics Manufacturing.vsd [Compatibility Mode] - Visio Professional

ProModel best models highly dynamic scenarios where for example, detailed “scoreboards,” visualization of movement and external file reading and writing is required. In my opinion, it is best used for modeling large-scale systems with simulation as portrayed in the model screen shot below:

PM commuter transit model

I will also continue to use ProModel but I most likely won’t be using both Process Simulator and ProModel concurrently.  Process Simulator is ideal for an introduction to modeling while ProModel allows for far more complexity in all aspects of modeling.

In the end, approximately 20% of my students used Process Simulator as the modeling tool for their capstone project while the remainder used ProModel.  For most of my students, CAD drawings of workplace layouts and GIS mapping files that accurately reflect scale and travel times were used as background graphics for their technical models.  In addition, many of the students used external arrival files containing multiple attributes associated with each entity arrival.  Thus, ProModel was the software product of choice in these instances.

My final assessment is that Process Simulator is a phenomenal product.  With an expert level background in ProModel and Visio, the learning curve for me was practically non-existent.  For students, their familiarity with the Visio environment made the introduction to Process Simulator both fast and challenge-free.

About Dr. Linda Ann Riley Contact Information: linda.ann.riley@gmail.com

Linda Ann Riley, Ph.D. presently serves as an Adjunct Professor of Engineering for the University of New Haven’s graduate program in Engineering and Operations Management. She retired from full time teaching and administration in 2015.  Dr. Riley worked for 12 years at Roger Williams University (RWU) where she held the positions of Associate Dean, Engineering Program Coordinator and Professor of Engineering. Prior to RWU, she was a Professor and Program Director at New Mexico State University for 18 years.  Her teaching experience includes both engineering and business courses and she is the recipient of a number of corporate, university and national excellence in teaching awards. Dr. Riley is the author/co-author of over 100 articles, technical and research reports, and book contributions. Her area of scholarly interest involves stochastic system optimization using simulation and evolutionary algorithms.

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