ProModel and Autodesk – Partners in Optimal Factory Design

Autodesk, the creator of AutoCAD® and Inventor® software, and ProModel have teamed up to bring you the best of both worlds of factory design and process optimization.  ProModel’s Process Simulator Autodesk Edition and ProModel Optimization Suite Autodesk Edition connect to Autodesk’s AutoCAD, Inventor, and Factory Design Utilities in order to streamline model building, process optimization and ultimately facility design.  You can get a 30-day evaluation version of both products from the Autodesk App Store

Process Simulator Autodesk Edition Eval

ProModel Optimization Suite Autodesk Edition Eval

Check out these great new Process Simulator Autodesk Edition videos:

Executive Overview – 2 min

 

Detailed Overview – 6 min


 

An Introduction to Manufacturing Process Simulation at Rose-Hulman Institute of Technology

Rose-Hulman McCormack_Jay

Dr. Jay McCormack; Rose-Hulman – Associate Professor Mechanical Engineering

I teach classes in both the mechanical engineering and engineering design programs at Rose-Hulman Institute of Technology in the areas of design and manufacturing. Rose is a small college in Terre Haute, Indiana focused on STEM. In the mechanical engineering program, one of the courses that we find differentiates our graduates is a junior level course on design for manufacturing. Instead of focusing on the sciences of manufacturing processes (which is clearly very valuable also), our course focuses on the application of design principles to facilitate the manufacturing of a given product, comparison of various manufacturing methods, and supporting design best practices related to manufacturing such as geometric dimensioning and tolerancing.

Two years ago, one member of the teaching team, the original creator of our design for manufacturing class, proposed integrating a process simulation project into the course. Our students are exposed to many manufacturing methods and work in depth with a few, but never had any exposure to manufacturing process. This is arguably appropriate content for any mechanical engineer but, a robotics minor is popular with our mechanical engineers, and many of the robotics students end up in positions related to manufacturing. Additionally, many biomedical engineers take the course and end up working as process engineers for medical device manufacturers. Therefore, the need was there for students to get a first exposure to manufacturing process and process simulation. I had some experience with process simulation more than a decade ago and experience with lean manufacturing so I was elected (appointed) by the teaching team to develop the project. I was familiar with ProModel products, but it had been a while since I used any simulation tools. I evaluated Process Simulator, a tool from ProModel that installs as a plug in to Microsoft Visio, and several other products, but found that Process Simulator allowed me to get students from zero to their first model quickly. After choosing Process Simulator and discussing the options to get the software from ProModel, I started developing the project.

Project Outcomes

My first objective was to develop the learning outcomes for the project. There were a few factors driving the learning outcomes:

  1. The students were almost exclusively novices. Virtually none of the 175 students in the course had any experience with any process simulation software, so the learning outcomes had to include low Bloom’s level items focused on both manufacturing topics and Process Simulator concepts.
  2. The design for manufacturing course itself has a number of bottlenecks involving other projects. Several hands on course projects involve specialized equipment and technician time. These projects require creative scheduling to get all students equal access to these resources in an eight-week period. Consequently, the learning outcomes and process simulation project were scoped to allow students to work in a self-directed manner with a given set of tutorial videos, feedback from their instructor, and a due dates that varied for project teams.

At the successful completion of this project, students will be able to:

  1. Define manufacturing process terms – batch, process, inventory, WIP, workstation, buffer, cycle time.
  2. Define fundamental Process Simulator concepts – entity, resource, activity, routing, arrival, setting simulation properties, batching, buffers, and priority.
  3. Apply Process Simulator to model a manufacturing process using the fundamental concepts.
  4. Redesign a manufacturing process using Process Simulator.

Even with just those basic concepts the students were able to create useful Process Simulator models of a given manufacturing process. Additionally, the model was sufficiently complex to require creative experimentation and exploration in order to make improvements.

Project Overview

The objective given to students was to use Process Simulator to model the performance of a factory, suggest improvements to the factory, and measure the impact of the improvements. Excerpts from the project description follow and a link to the complete project description is located at the end of this article.

Scenario

You are an engineer at HOBO Inc. (Hands On Bottle Opener, Inc.), producers of a line of extruded, one-handed bottle openers (The Blue Collar, Figure 1) that appeal to customers through durability, reliability, and functionality. You were on the new product development team that designed a new, beautiful, and refined one-handed bottle opener (The Executive, Figure 2) that will allow you to enter an untapped market. The new design is fabricated using an investment casting process that fits well with the geometric complexity and modest volume of production planned for the new model. Because investment casting is not part of HOBO’s core competency, you will outsource the casting. HOBO will receive a shipment of boxes of unfinished casting trees from the fabricator every morning. Each bottle opener will be sawn from the casting tree then tumbled to remove burrs and to produce better surface finish. Sawing, tumbling, and the subsequent inspection step are among our core competencies, so we plan to perform these operations in house on an existing production line. Additionally, we have two workers that are available on the day shift to be used as much as they are needed. (Note that they will not be fired if they are not used all day. We have work for them elsewhere in the factory.) 

This seems like a great opportunity to try the new process simulation software that you are evaluating for purchase. You gathered some baseline data about the operation (see the Factory Description) based on the verbal description by other engineers and managers. The information that you gathered is in the section called Baseline Factory Description.

Baseline Factory Description

There are four workstations. In order, they are:

  1. Receiving
  2. Sawing
  3. Tumbling
  4. Inspection

There are buffers to store work in process (WIP) located before sawing and before tumbling. A flowchart representing the process is shown in Figure 3. A process box for each workstation and the buffers is shown in Table 1. A more complete description of each is found after the table.

Rose-Hulman_Material Flow

Figure 3. Flow of Materials through the Baseline Factory

Deliverables

In order to earn a C Use Process Simulator software to provide a baseline estimate of net income (revenue – expenses) for the first year of operation. The Baseline Factory must follow all the process rules and procedures outlined in the Baseline Factory Description section. Write a memo summarizing the findings.
In order to earn a B Design meaningful improvements to the Baseline Factory. Describe the Improved Factory in the memo by capturing each of the suggested improvements.
In order to earn an A Use Process Simulator to model the Improved Factory. Report the improvement in yearly net income in the memo.

Details about the baseline factory are provided in subsequent sections, as is a set of tutorial videos that guide students through basic concepts. The videos are a mix of guided examples recorded by me and videos provided by ProModel.

Takeaways

This project was first developed and used in academic year 2018-2019. We were pleased with the enthusiasm that students approached the project and engaged in competition to produce the most profitable manufacturing process compared to their peers. We revised the project for 2019-2020 to include a grading scale that further encouraged exploration and a set of tutorial videos walking students through a given omelet station Process Simulator model.

All of the students received a base level exposure to process simulation, but we were pleased to see that a number of students dove deeper into manufacturing process issues. Students challenged the notion that inspection was required to wait until the last process step, unknowingly suggesting the use of quality at the source, a fundamental lean concept. Those students were able to see the positive impact of quality at the source in their Process Simulator models. Other students had insights about the impact of batch work and how batches served as mechanisms for covering root cause process issues. Those students reduced batch sizes where possible and identified the root cause problems.

The complete project description, scoring rubric, and tutorial video list is linked here. You are welcome to reuse it, modify it, make it better, and/or fix mistakes. If you do, let me know at mccormac@rose-hulman.edu. We look forward to featuring Process Simulator as part of our design for manufacturing course in future years and finding new ways to challenge students to explore manufacturing processes and process simulation.

Bio

Dr. Jay McCormack is an Associate Professor of mechanical engineering at Rose-Hulman Institute of Technology. Dr. McCormack’s teaching and professional development interests are in the areas of design and manufacturing. He teaches courses for the mechanical engineering and engineering design programs as well as the institute’s multidisciplinary design course. Before joining Rose-Hulman, Dr. McCormack was a faculty member at the University of Idaho where he worked with the state’s manufacturing extension partnership. He co-founded Pittsburgh-based CAD tool developer DesignAdvance Systems Inc. after graduating from Carnegie Mellon with a PhD in mechanical engineering.

Predictive Variables, Artificial Neural Networks and Discrete Event Simulation in Manufacturing

Rebecca Santos

Rebecca Santos ProModel Tech Support Engineer

During my Master’s program at BYU, I worked with a team to complete my thesis.  The work turned into a published article by the International Journal of Modelling and Simulation!  Since I work for ProModel, it’s only appropriate that I share it with my fellow simulation enthusiasts.  Please let me know what you think by commenting below.

Discrete event simulation (DES) is a powerful tool that can help users make better decisions. Over the years tools such as ProModel and Process Simulator have been developed to simplify the application of simulation, decreasing the learning curve and increasing its use. One of the advantages of simulation is that it is able to create lots of data with valuable information. However, the data analysis process can be challenging and relevant information may not be fully analyzed.

Observing the opportunity to more fully learn from the data, we decided to use data mining algorithms to help in the data analysis process, and more than that, to guide the modeler to the variables that most impact the outcome of the system being modeled.

The data mining algorithm picked was Artificial Neural Networks (ANNs) which has been good at learning from the data and making accurate predictions, according to the scientific literature. We applied ANNs to the data generated by the simulation model and we were able to create ANN models that could predict the simulation model results. The ANN models created were then interpreted and through the interpretation it was possible to rank variables according to their impact on the output results. This makes it possible for decision makers to know how to prioritize and where to place their investments.

Here is the abstract from the article:

This research used a discrete event simulation to create data on a shipment receiving process instead of using historical records on the process. The simulation was used to create records with different inputs and operating conditions and the resulting overall elapsed time for the overall process. The resulting records were used to create a set of predictive artificial neural network models that predicted elapsed time based on the process characteristics. Then, the connection weight approach was used to determine the relative importance of the input variables. The connection weight approach was applied in three different steps:

(1) On all input variables to identify predictive and non-predictive inputs

(2) On all predictive inputs and

(3) After removal of a dominating predictive input.

This produced a clearer picture of the relative importance of input variables on the outcome variable than applying the connection weight approach once.

You are welcome to access the published article at the journal’s website or the full original paper at the ProModel website.

 

Univ. Texas Master of Public Health Program Teaches Lean Concepts With Process Simulator

headshot_prof michael kennedy 10-14-18

Michael H. Kennedy PhD, MHA, FACHE Assoc Prof & Chair Dept of Healthcare Policy, Economics and Management University of Texas Health Science Center at Tyler

I was first exposed to ProModel simulation products as an alternative to GPSS-H when earning my doctorate in Decision Sciences and Engineering Systems at Rensselaer Polytechnic University from 1988 – 1992.  I taught one simulation course as a graduate elective for the U.S. Army-Baylor University Graduate Program in Healthcare Administration soon after graduation.  Since then, I have employed MedModel as a component of several health administration courses, with simulation comprising approximately one-third of the course content.  Simulation modeling is a wonderful tool to reinforce concepts from statistics and probability such as the effects of random variation, practical application of probability distributions, and employment of goodness-of-fit testing.

After I arrived in January 2017 at the University of Texas Health Science Center at Tyler to begin teaching in our Master of Public Health Program, I decided to revise my approach to teaching quality.  Previous iterations of my quality course had generally proceeded as follows:

image_kennedy blog chart 1

Content was taught face-to-face and students used their laptops to create process analysis tools and control charts with me in class.

My decision to include Lean in the quality course provided the opportunity to learn and teach Process Simulator, another ProModel product.  Process Simulator operates as an add-in to Visio.  I couldn’t think of a better way to teach flow than to have students build process flow diagrams in Visio and then to model the flow of entities through the system using Process Simulator.  The revised course proceeded as follows:

image_kennedy blog chart 2

The first process model was built after an introduction to M/M/1 queuing formulas in an exercise to establish the correspondence between queuing and simulation.  I modified a problem provided by Ragsdale (2004) to model Acme Pharmacy which has one pharmacist with the capacity to fill prescriptions from 12 customers per hour.  The pharmacy averages 10 customers per hour seeking to fill prescriptions.  Students used an M/M/1 Excel template to compute and characterize Acme Pharmacy operations and to record queuing formula and simulation results.  The Process Simulator model looked like this:

image_kennedy blog chart 3

The comparison of the first two columns of Table 1 between queuing results and simulation results confused me until I remembered that the M/M/1 queuing formulas represented a steady state solution.  An 8-hour run with 20 replications shown in the third column produced results more closely aligned, but not as close as expected.

image_kennedy blog chart 4

Table 1.  Comparing Queuing and Process Simulator Results                                                        Note – Expected times have been converted from hours to minutes.

An extended 2080-hour run and an 8-hour run after an 8-hour warm-up were also subsequently modeled to determine if those efforts to move the system towards steady state produced results more closely aligned with queuing formula results.  The results in the last two columns showed that to be true.

During the remainder of the course, Lean principles were reinforced with a variety of Process Simulator models.

Reference

Ragsdale, C. T. (2004). Spreadsheet modeling and decision analysis: A practical introduction to business analytics (4th ed.). Mason, OH: Thomson South-Western.

About Dr. Michael H. Kennedy

Dr. Michael H. Kennedy, FACHE, arrived at the University of Texas Health Science Center at Tyler on January 23, 2017 to begin service as an Associate Professor in the Department of Healthcare Policy, Economics and Management in the School of Community and Rural Health. He has 42 years’ experience in teaching and health services administration that have been divided between academic positions and operational assignments in the military health system as a human resources manager, equal opportunity advisor, ambulatory care administrator, and other positions of leadership culminating as the Chief Operating Officer of a small military hospital.

In past academic assignments, Dr. Kennedy has served as Director of the Health Services Management Program at East Carolina University, Director of the Doctor of Health Administration Program at Central Michigan University, and Associate Professor in the Health Services Administration Program at Slippery Rock University. Dr. Kennedy was also Deputy Director of the U.S. Army-Baylor University Graduate Program in Healthcare Administration where he was twice selected by the students as Instructor of the Year.

Dr. Kennedy is a Fellow in the American College of Healthcare Executives.  He was appointed as Chair of the Department of Healthcare Policy, Economics and Management on September 1, 2018.

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.

Dr. Linda Ann Riley’s (Univ. New Haven) Innovative Teaching Approach for ProModel and Process Simulator (1 of 2)

Headshot_Linda Riley UNH

Dr. Linda Ann Riley Ph.D. UNH

I have been teaching ProModel for the past 20 years and most recently in the capstone simulation course in the University of New Haven’s (UNH) M.S. in Engineering and Operations Management (MSEOM) program.  In the course immediately preceding simulation, Six Sigma Quality Planning, I use both Microsoft Visio and Minitab as the software programs for course delivery.  This brings me to the topic of this blog post: my experiences this past Spring introducing Process Simulator into the ProModel /Visio/ Minitab mix.

 

In a traditional semester setting, teaching four different software programs in addition to subject matter content could be achievable. But the University of New Haven’s MSEOM is delivered in an accelerated format with each class meeting six hours on one evening each week over seven weeks. The final two classes in the program are Six Sigma Quality Planning and Simulation Techniques and Applications.  During these final two classes over 14 weeks, students also undertake a technical capstone project that fulfills a graduation requirement.

The UNH MSEOM is directed to individuals with technical undergraduate degrees presently holding middle to upper level management positions.  Almost all of the participants in the cohort work in engineering related jobs at large organizations such as General Dynamics, Amgen, Pratt & Whitney, Lockheed Martin, Pfizer and General Electric to name a few.  Fortunately, I have always had highly motivated students in each cohort I have taught over the past 10 years.

From day one of the Six Sigma class, three programs were introduced, Visio, Minitab and Process Simulator.  I use Lean Six Sigma and Minitab (5th Edition): The Complete Toolbox Guide for Business Improvement by Quentin Brook as the text for the class.  Teaching for six hours per class requires a pedagogical strategy of moving from content delivery to computer exercises and back multiple times during the class.  This strategy has been highly effective over the years and allows for reinforcement of the subject matter and in-class practice and troubleshooting using software.

Since most of the cohort had at least minimal exposure to Visio in the workplace, introducing Process Simulator proved to be rather seamless.  The Quickstart and How To videos were assigned for homework on the first class and by the second class, my expectation was that each student could create a fairly straightforward process flow diagram in Process Simulator as an in-class lab.

PS first model

Process Simulator – First Student Model

The mechanics of creating a process flow chart in the Process Simulator environment presented no challenges for the students.  However, one of my lessons learned involved the information needed to move from the purely Visio environment to the Process Simulator environment.  The level of detail needed with respect to the amount and type of information to accurately define properties for activities, routing rules and arrivals was of the same magnitude as needed for a simulation exercise using ProModel.

PS properties machine center

Process Simulator – Activity Properties Dialogue Box

Even though the students could apply simulation properties to their Process Simulator diagrams, the output results were far from the expected solution at first.

PM first model capacity graph

Output Viewer – Single Capacity Activity States Graph

Consequently, some time was spent troubleshooting the models.  Yet in the process, students developed a much richer understanding of how to use Process Simulator especially within the context of Lean and Six Sigma methodologies.  After a reasonable level of proficiency was developed with Process Simulator, we were able to export data and further analyze results in Minitab.  We will complete Linda’s story in the next post.

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.

Whirlpool and The University of Michigan Collaborate on a Simulation Project Using ProModel Software

Embarking on a simulation project can seem like a daunting task at times, especially if the project must be completed above and beyond one’s normal responsibilities.  During those times, it is beneficial to consider engaging a partner to help.

Of course ProModel provides professional model building and consulting services, but another alternative is to partner with a University that teaches ProModel, MedModel or Process Simulator.  This type of industry | academia collaboration is a win-win for both organizations.

Please check out this very successful simulation project by Whirlpool on which they partnered with the University of Michigan. The article was published in PlantServices.com.

Click here to see a list of colleges and universities using ProModel software products.  If you would like more information about our academic program, please contact us at education@promodel.com or 801-223-4601.

 

 

Get Ready for Fall Semester. Now is the Time to Add Simulation to your Curriculum!

Chrsitne Bunker Linked In

Christine Bunker Academic Program Director

Teaching simulation to your students will give them a head start when they reach industry. Learn some of the benefits of including simulation as you teach techniques for process improvement.

  • Accurate Depiction of Reality
  • Insightful System Evaluations
  • Dynamics for Predictive Analysis
  • Understand Interdependencies
  • Better Experimentation and Data
  • Animated Visualization
  • Advanced Optimization Techniques
  • Bottom Line Savings (Hard Dollar, Soft Dollar, and Labor Savings along with many Intangible Benefits)

For more details on any of these topics visit Justifying Simulation to understand the benefits of simulation.

Learn More about the ProModel Academic Program

To learn more about the academic program, please visit our website or review the ProModel Academic Overview.

If you’re interested in joining the growing ranks of ProModel educators or have any questions, please contact us at education@promodel.com to apply for a full professional license for academic use.

From Reality to Model

Adjunct Prof Mark Klee Headshot

Mark Klee; Adjunct Professor – Eastern Kentucky University

I know what you are thinking “From Reality to Model” shouldn’t that be the other way around? As an engineer at Toyota for the past 24 years I often encounter manufacturing processes that have slowly de-optimized. And now, just by walking by the processes on the floor, I can see waste (motion, waiting, over-processing). I know this means that the these processes need some work.

Our typical method of improving these processes would be to employ the traditional Toyota Production System tools. We begin with observation and time study. Then we use video for motion analysis making these processes visual on paper with standardized work combination tables, standardized work charts, and production capacity calculations. Through these simple analysis tools, the waste in the process becomes more obvious and begins to generate ideas for improvement.

This is typically done one process or one zone of processes at at time. It is also usually done with paper, pencil, and stopwatch. The methods have proven time and again to be effective for process improvement and an effective method of developing engineers as well as manufacturing floor members in process improvement.

After the waste is discovered and the improvement ideas generated it is time to try some improvement ideas. The process visualization and capacity calculation documents are then modified to simulate the improvement idea. Then it is time to try the modified process on the production floor. The concept is tested in a controlled environment. After success is documented, the process standards are modified the team is trained to the new standard.

Using ProModel works very well with the Toyota Production System and as a method for developing manufacturing engineers, manufacturing floor members and students in manufacturing focused curriculum. In Eastern Kentucky University’s Applied Engineering and Management class, we follow this progression.

  • We first focus on learning process observation and visualization skill using the standard Toyota Production System tools.
  • Next we learn the processes of implementing controlled change in a mass production environment. We learn and practice these skills on the manufacturing floor to gain real world experience.
  • After learning the basics of observation and improvement, we come back to the classroom where we employ ProModel to fine tune our processes and learn if there are any opportunities for optimization that may have been overlooked.
  • With ProModel we can also test scenarios that may be difficult to test on the actual production floor like moving a piece of equipment, modifying a cycle time, changing a conveyor length or changing a delivery frequency.
  • These trials can be done as quickly as you can change the numbers in the model allowing for many more cycles of trial and error or trial and success in a shorter time.

As a result of the course and ProModel, students have deeper understanding of both the theory and application of process improvement allowing them to be an instant contributor to a manufacturing organization upon their graduation.

In the end, deeply understanding the current reality through observation, documentation, and modification of the current process helps us make a more accurate model. The result of the more accurate model is further optimization. This deepens learning and the improvement cycle continues.

Brief Bio:

Mark Klee, BS Eastern Kentucky University 1990, MS Purdue University 1992
Toyota Motor Manufacturing Kentucky 1994-Present
Eastern Kentucky University Adjunct Faculty 2012-Present

Real-World Simulation Examples for Student Learning

Rajaei_Hassan

Prof Hassan Rajaei Ph.D. – Bowling Green State University; Department of Computer Science

Objectives

Simulation is a powerful tool for teaching students about the techniques as well as providing deeper understanding of courses such as networking, operating systems, operational research, just to name a few. Simulation is a well-known technique for evaluating what-if scenarios and decision making in industry, defense, finance, and many others. Students quickly realize these values and want to learn how to master this technique.

Teaching simulation techniques often requires attractive problem assignments. Real-world has numerous examples that excite students to study and motivate them focusing on their learning objectives. Further, it challenges them to develop models to reflect the reality. Clear examples can teach students how to collect data, develop the base model, improve it to advanced models, analyze the obtained results, and think about the usability of their simulation results. These learning outcomes can clearly demonstrate valuable educational objectives.

A simulation tool like ProModel has numerous example models in its library, but the educational objectives can be best achieved through step-by-step experimental development of useful samples. ProModel can be a great help by exploring the details of similar examples.

This article, presents an example where a group of students developed a simulation model for the Bowling Green State University (BGSU) Students Union Cafeteria. Managing a university dining hall often exhibits challenges for the food services located in it. This study focused on reducing the average waiting time of the diners, while increasing overall efficiency of the services.

Simulating the Nest Cafeteria

This project focused on finding solutions for the Falcon’s Nest Cafeteria to increase the efficiency and decrease the average time of the customer spent in the system.

Overview of the Nest: Students cafeteria at BGSU functions as an important part of the University’s dining service. This cafeteria serves thousands of students every day. During the rush-hours of lunch and dinner, this place gets really congested with long queues contributing to long waiting times. In this simulation, the Nest model consists of five main components: Customers, Servers, Locations, Queues, and Cashiers.

Using ProModel: This tool was selected for multiple reasons: a) the availability; b) the course used the tool and trained students; c) the tool supports discrete-event systems; d) large number of library models; e) statistical analysis and output results; f) animations.

Problems Encountered: The main problem faced was lack of statistics and accurate information. Other barriers included project time limit and lack of deeper familiarity of ProModel.

Possible Solutions: Based on primary analysis, two potential solutions were feasible:

1) Increase the attractiveness of other food stations which have lower waiting time;

2) Increase number of food servers.

Three approaches to reach the goals:

a) Ask the SME to provide all data and statistics;

b) Make a very detailed model over the actual system;

c) Combination of (a) & (b) methods.

Approach c was adopted for the study.

Simulation Models

Four models were developed: 1) base, 2) intermediate, 3) advanced, 4) final

Base Model: The base model had very basic setups with one food station and one cashier. The objective was to test the station service and the customers’ arrival, and their flow in the system.

Intermediate Model: All food stations were added according to the Nest along with the logic for entities to move through the system with a shared queue.

 Advanced Model: The advanced model includes all queues targeting to obtain realistic statistics using several scenarios (Figure 1).

Figure 1

Figure 1:  The Advanced Model improved from the intermediate one

Final Model: After developing three scenarios, obtaining good confidence, making sure they were on the right track, students moved towards developing the final model shown in Figure 2. It was implemented using a time schedule to simulate the rush hour and normal operating hours.

Figure 2

Figure 2:  Final simulation model for the Falcon’s Nest Cafeteria

Results and Analysis

In this simulation, students first aimed to find an ideal solution to demonstrate how to reduce the waiting time. It turned out that such a scenario would need more implementation time. Instead, students focused on two solutions:

1) To make other food stations more attractive;

2) Adding additional workers to the top three food stations. Test cases were developed for each solution.

The result shown in Figure 3 demonstrate a reduction in the average time compared to the baseline, except Case 3. The figure suggests an 11.1% decrease in average time spent in the Nest.

Figure 3

Figure 3: Solution 1 demonstrating reduction of Average Time in the system

Next method focused on improving the waiting time by adding food runners to 3 populated stations. This method was simulated and tested with 4 scenarios, and was compared with the baseline.

Figure 4

Figure 4: Solution 2, advocating one additional worker at each food station

As was expected, by adding a food-runner to each station the average time of the customers would decrease, however, certain stations would benefit most. If case 4 is adopted, there would be a 12.6% reduction in time spent by customers. If only 1 food runner is added, then the result yields only to 6.1% decrease in average time spent in the system by customers.

Concluding Remarks

This article presents an example of a real-world case study conducted by a group of students as a term project in a simulation techniques course shared by senior undergraduate students as well as graduate students. An important result of this study demonstrates how deeply the students were engaged in their learning objectives of the course. In a short period of time, they conducted a complete case study including: observation, gathering data, analyzing the problem at hand, developing models, confirming with the subject matter expert, documenting, and delivering the results. The full article is published in ASEE 2017 Annual Conference.

Professor Hassan Rajaei Ph.D.  

Hassan Rajaei is a Professor of Computer Science at Bowling Green State University, Ohio.  His research interests include Distributed Systems & IoT, Cloud Computing, High Performance Computing (HPC), Computer Simulation, Distributed Simulation, with applications focus on communications & wireless networks. Dr. Rajaei has been active in simulation conferences (e.g. SCS SprintSim, WSC) as organizer as well as research contributor. Dr. Rajaei received his Ph.D. from Royal Institute of Technologies, KTH, Stockholm, Sweden and he holds a MSEE from Univ. of Utah.