Category Archives: General Simulation

Successful Implementation of FutureFlow Rx® at Seattle Children’s – A New Predictive Patient Flow Technology

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Dan Hickman Avatar1

Dan Hickman ProModel CTO

I am jazzed about our FutureFlow Rx work with Seattle Children’s Hospital.  What a great group of people to collaborate with.  Check out the press release below and feel free to contact me if you’d like to learn more about this new product – dhickman@promodel.com

Thanks, Dan

Seattle Childrens Logo

Integrated Discrete Event Simulation Census Predictor, Developed by ProModel Corporation, Facilitates Inpatient Flow and Access Management

SEATTLE WA, May 14, 2019 – ProModel Corporation announced the successful implementation of a new predictive patient flow technology called FutureFlow Rx® at Seattle Children’s (SC).

Members of the Enterprise Analytics Community at Seattle Children’s will be presenting their use case at the Advanced Analytics for Children’s Hospitals Conference on June 5-6 at the Ann & Robert H. Lurie Children’s Hospital of Chicago. Seattle Children’s is a co-sponsor of this new analytics conference.

Seattle Children’s, like many other hospitals, faces challenges associated with capacity pressures and inpatient access. Demand for inpatient space often outstrips capacity, whether for physical or staffed beds.

SC leadership has established policy that scheduled elective surgeries must not be canceled due to capacity. Therefore, proactively managing inpatient capacity and staffing takes on even greater urgency in that context. In order to address this issue, they deployed a predictive-analytics solution for census estimation known as FutureFlow Rx®.

Seattle Children’s Quotes

“By providing accurate projections of staffing needs, FutureFlow Rx informs daily challenges faced by every hospital with timely and detailed information.” 

  • Susan Geiduschek, RN, DNP, Sr. Dir, Assoc. Chief Nurse, Seattle Children’s

 

“Integrating FutureFlow Rx into our Inpatient Access and Flow workstreams has really supported a smooth, functional, and most importantly effective process for proactively managing census during an especially challenging flu season. Diversions are down, and so is the general level of anxiety around decision-making. Fewer ad hoc huddles, more standard work with established guiderails and actions.”

  • Ruth McDonald, MD, Assoc. Chief Medical Officer, Seattle Children’s

 

Right Patient, Right Bed, Right Time

The functional goal of every hospital is to place the right patient in the right bed at the right time. Being the region’s only tertiary-care pediatric facility, it is paramount that SC maintain capacity to serve those children that cannot receive care elsewhere in the Pacific Northwest.

Thus, they must carefully maintain capacity and ensure as few diversions as possible. Combining the FutureFlow Rx® prediction with an application designed by SC Enterprise Analytics staff, they can accurately estimate their ability to manage daily and predicted census, allowing them to proactively make operational decisions that address challenges with foresight.

ProModel Quote

“We are very excited to be working with a hospital as visionary as Seattle Children’s.  They were willing to look beyond what existed today, to determine if a patient flow improvement platform could truly help increase safe inpatient access for their young patients”  Dan Hickman – CTO, ProModel Corporation

About ProModel Corporation

ProModel Corporation is a leading provider of simulation-based, predictive and prescriptive analytic decision support solutions. A Microsoft Gold Partner, ProModel specializes in custom and COTS (Commercial-Off-the-Shelf) software and services to help organizations optimize processes, policies and resource decisions to best align with their business strategy.

Founded in 1988, ProModel has tens of thousands of users of its software globally, focused across the Healthcare, Pharmaceutical, Government and Manufacturing & Supply Chain industries.

For additional information: ProModel Marketing 610-628-6842; healthcaresolutions@promodel.com

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

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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.

 

Process Simulator 2019 – Released!

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Aaron Nelson Product Development Manager Process Simulator

Aaron Nelson Product Development Manager Process Simulator

As ProModel’s new Process Simulator Product Development Manager, I’m really excited to bring you my first blog post in this position.

We launched Process Simulator 2019, our Microsoft® Visio ® plug-in, earlier this year which now supports both 64 and 32 bit Visio 2016.  We have added many other great features to this redesigned product. Check out the highlights below and get the full details on the What’s New page.

Visio 2016 64 and 32-bit support

Dockable Windows – We have redesigned all element property windows and tables and made them dockable within the Visio application work-space. This allows you to keep them open and out of the way so your diagram view space will be clear for quicker modeling changes.

Image-pcs2019-dockable-windows

Find & Replace – Process Simulator now has its own find and replace capability, separate from Visio, which will search across its elements and objects only. It even automatically brings the shape into view and opens the associated property or field where the searched text is found.

Image-pcs2019-find-replace

Model Compile – When you run your model, it is checked for errors prior to simulation. In this release, you are presented with a list of all errors, which allows you to quickly navigate to and resolve the issues.

Image-pcs2019-model-compile

Referenced Hierarchical Models – Submodels now have the ability to be referenced in addition to their current capability of unique instances. This means that activities linking to the same submodel at different points in your diagram will send their entities to that exact submodel (not just unique copies of the submodel).

Activity Multi-Entity Table – We have added Multi-Entity functionality. This allows you to convert your existing models that use multi entity, and create new ones as well, without having to code the functionality using logic. In Shape Properties you can select Multi Entity and then Define.

In this initial release for our US and Canadian customers with current Maintenance and Support, you can get the download in the Solutions Café.  You can also access the Process Simulator 2019 What’s New webinar recording from the solutions cafe.  If you are not a current customer, please contact your Account Manager for details.

Process Simulator 2019 will be available to everyone later this year.  Please let me know if you have any feedback by leaving a comment below or contacting me directly. Thanks!  anelson@promodel.com

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

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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.

Ingalls Develops Automated Unit Lay-Down ‘Advisor’ with Capacity Planning Tool

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Image_Ingalls from theSigal MagazineHuntington Ingalls Industries – Ingalls Shipbuilding (Ingalls) identified substantial savings potential in the lay-down placement and assignment process that had been previously utilized for managing asset location throughout the construction process.

Building four different hull forms in the tight shipyard footprint is a challenge. Ingalls Shipbuilding work instructions define the processes and responsibilities for the proper allocation and optimization of real estate (lay-down spaces) for structural units and assemblies under construction, while providing forward visibility for scheduled or potential overloads to capacity.

However, the old capacity planning processes were tedious and overly time-consuming. Resulting real estate allocations were seldom optimal and often required substantial rework to resolve space allocation conflicts, as the construction schedules for each hull form jockey for the same production resources.

The Ingalls team developed an automated process that optimizes unit layout and scheduling, and increases the construction of many units under a covered structure, significantly improving production rates—a plus in the hot southern climate.

“The new tool has taken a process that historically took 10 weeks to complete and can now finish the scheduling activity in less than an hour. Following project completion and full system implementation, we expect to reduce ‘real estate’ allocation processing time by 30% and place 20 more units ‘under cover’ annually, with an estimated cost savings of over $990K per year.”

 (Article Courtesy of “theSignal” and DefenseNews.com)

Click here to read the rest of the Ingalls story

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

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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)

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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

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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!

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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

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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 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