Ancillary Tools Helpful for a Successful ProModel Discrete Event Simulation Project

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Prof. Scott Metlen Univ. of Idaho

Introduction

Using ProModel to teach process management inadvertently necessitates that students become more proficient with many tools centered on data and working with people. Of course, students learn many aspects of ProModel such as the need to understand parts of a process; these include locations, entities, arrival rates, process logic (LEAP), variables, and attributes.

They also learn about graphics, Statfit, batch/group, create, order, wait until, logic statements that operationalize business rules, and many other commands that help to model a process. However, when conducting a successful large process improvement project using discrete event simulation for an organization, students need to become proficient with many other tools to best utilize ProModel over the course of semester long projects.

Project Management Methodology

Understanding and being able to set up a project using project management methodology is critical to having a successful ProModel project. As in any project the scope and expected outcomes need to be delineated. To design the work break down structure for the project it is also critical to understand what tasks need to be accomplished to produce the final output, and when those tasks need to be completed.

Tasks include developing the scope and expected outcomes working with the project sponsor, analyzing and preparing data for entry into ProModel, base model construction, verification and validation of the base model, determining what treated models to build, statistical analysis of the outputs from each model relative to the base model, cost/benefit analysis, and a report delineating findings and recommendations. Each team in the class I guide has to complete a Project Execution Plan (PEP) and then discuss in their final paper how well they met their time gates, why they did or did not meet those dates, and what they did to catch up if they did not meet those time lines. There are times in the project where they learn the lesson of not utilizing the ‘student syndrome’.

Relationship Management

To do a good job of all the tasks mentioned above, students have to become accomplished at relationship management. They have to visit with their sponsor not only about the scope and expected outcomes, but what data and information is needed to complete the project. There will be missing data, acronyms that need to be explained, assumptions that have to be made and supported due to the missing data and information, uncertainty about the proper rule to guide the logic, and many other items to discuss on at least a weekly bases with the sponsor. Oftentimes it is being uncomfortable talking to a sponsor that leads to procrastination and missed time gates.

Data Sets and Simulation

Of course there is the ever present need to be able to make sense out of large sets of data and be able to convert them to information that ProModel can utilize. When dealing with nearly 12,000 different types of entities for one process, being processed through a job shop with 1400 unique process centers, the data sets become large. The route array that informs ProModel which machine which entity goes to when can become 12,000 rows and 200 columns, and the duration array can become too large and have to be split into four arrays, each with 3000 rows and 1400 columns.

There are many Excel tools that help the students explore their data sets. These tools include but are not limited to: filters, pivot tables, different types of lookup commands, find and replace, if statements, count statements, the ‘and’ function to build many lines of logic quickly, and different types of conditional formatting.

Once the base model and treated models are created and have generated 30 replications the students determine if the treatments actually made a difference by conducting a hypotheses test, if the null (the means of the samples have a high probability of being drawn from the same population) is rejected they proceed to the cost/benefit analysis. If the null is accepted, they try other treatments. If the treatment was successful, Statfit is utilized to determine the distribution of the output, at which point a Monte Carlo simulation is utilized to generate a larger sample of deltas between the base and a treated model to determine the distribution of deltas used to generate the net benefit.  That benefit could be number of extra units built, decrease in throughput time, time in system, net present value, or some other form of benefit.

Report Out

While the models are being built, the team is also working on their presentations and written reports. Thus, as they are discovering assumptions that they need to make, they are putting them into the oral and written report, thereby learning the value of parallel processing. By the time the last statistical analysis is completed, the presentation and paper are completed and ready for presentation for the teams that do a good job of following their PEP.

Conclusion

As demonstrated above there are many tools that ProModel users need to be proficient with when conducting a successful discrete event simulation using ProModel. However, perhaps it is not only ProModel and the ancillary tools that need to be taught and modeled when teaching a discrete event class, but the willingness to say, “I do not know how to do that, lets do some research and discover how”. That is the most important trait that modelers need to have, the willingness and perseverance to learn new tools and apply them in unique ways to capture unique opportunities.

Meet Professor Scott Metlen, Ph.D.

Dr. Scott Metlen earned his Ph.D. in Business Administration at the University of Utah in 2002 and is currently an associated professor of Production Operations Management at the University of Idaho. Dr. Metlen teaches Quality Management and Systems and Simulation, both are aspects of  Process Management. Prior to his academic carrier, Dr. Metlen spent 20 years managing products and processes in agriculture and food processing. Through a gift from the Micron Foundation, he has the resources to oversee at least twenty process improvement projects for various organizations per year through the classes he teaches. These projects provide meaningful experiential learning for the 40 to 80 students involved.

 

Brazilian Academic Simulation Awards Given in Honor of Rob Bateman

ProModel friends and associates, last October 12 we lost a dear friend, Rob Bateman and it is very hard to believe that a year has already passed.  Coincidentally, just a few days before the loss of our colleague, on October 6, 2015, the first ever ‘Rob Bateman’ award was delivered in the city of Joao Pessoa (north east coast of Brazil).  Here is the web site of the event:  http://www.abepro.org.br/enegep/2016/index.asp.  The Simula Brazil is a national award for simulation systems, organized and hosted by the portal “www.simulacao.net” which is sponsored by the Belge Consulting (www.belge.com.br). The award has institutional support of ABEPRO (www.abepro.org.br) and SOBRAPO (www.sobrapo.org.br) and is linked to the National Production Engineering Meeting (ENEGEP).

This award aims to encourage young students to use more simulation technology to develop projects and analyze real or fictitious situations through the use of the ProModel modeling and simulation technology (ProModel® or MedModel®) as well as assisting teachers with simulation education. The hope is that this practice will allow for better industrial engineering courses using ProModel and more simulation use in local companies, as well.  This year the award was given to the following recipients:

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Marcelo Fugihara of Belge presenting the award for originality to Jacyszyn Bachega of Universidade Federa de Goias

 

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Marcelo Fugihara of Belge presenting the award for complexity to Thiago Fernando Rosa Tedoro and Professor Jose Lazaro Ferraz of Universidade FACENS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Here is a photo of all of the students in attendance at the event, called Enegep –  Encontro Nacional de Engenharia de Produção

We hope that this award in some small way pays tribute to our friend Rob Bateman.

Your friend in Simulation,

Alain de Norman & Belge team.

Simulating Impatient Customers-Reneging

Dr. Farhad Moeeni - Professor of Computer and Information Technology

Dr. Farhed Moeeni – Prof. of Computer & Information Technology, Arkansas State University

GUEST BLOGGER – Dr. Farhed Moeeni

In an earlier blog I discussed some of the issues related to modeling customers’ behavior; such as impatience, especially the balking behavior.  Customers’ impatience can lead to certain behaviors such as balking, reneging and jockeying.  Ignoring the case of balking at arrival caused by queue capacity, balking happens when customers assess the waiting line, develop some opinion about the required waiting time (likely by scanning the queue length) and decide whether to join the queue or not. In short, balking happens when the person’s tolerance for waiting is less than the anticipated waiting time at arrival.  Jockeying happens when two or more service channels provide identical services, independently and with distinct waiting lines.  A customer who is presently waiting in one of the queues chooses to leave the current line and join another queue in the hopes of less waiting time.

Reneging, however happens after a person joins a queue.  In other words, the initial anticipated delay was tolerable.  But later, as time passes, the person becomes impatient and abandons the queue without receiving the service because the prospect of being served within the initially anticipated time diminishes or the predicted remaining waiting time exceeds the customer’s tolerance threshold for further waiting.  Other incidents such as receiving an urgent call may also cause abandoning of the queue.  However, this or similar scenarios are not caused by impatience.

Researchers believe the tolerance threshold is not a constant and can change over the course of the waiting period.  For example, studies indicate that the probability of reneging decreases as the person continues to wait, but only for a certain amount of waiting time after which the customer starts losing patience and the probability of reneging increases.  Another interesting conjecture proposed in the literature is that as the number of people behind a waiting customer increases, the likelihood of the customer reneging decreases.  The decision to renege is also influenced by other factors, for example, personal differences, prior experiences with similar situations, the critical nature of the service to be received, attributes and atmosphere of the waiting area’s surrounding (service-scape), and whether or not an alternative date or time for the same service or a similar service is available. Thus, the decision process for reneging is more complex than balking and modeling and simulating reneging is also more complex.

Obviously, it is not possible to directly characterize the complex behavioral aspects of customers into the simulation model.  Each reneging incident is the result of complex interactions among many factors and the result of a thought process that eventually causes a customer to become impatient and abandon the queue. In other words, reneging cases are not random events; however, from the perspective of a simulation modeler or an observer who is watching the queue, the incidents may look like a random process.

In discrete-event simulation, the modeler has a number of mechanisms or tools available. For example, the modeler may apply some probability distribution to characterize the uncertain behavior of customers or formulate some rules to trigger reneging during the simulation runs.  In addition to probability distributions, the modeler may also identify and apply a number of observable predictor variables to fine tune the reneging behavior.  These variables may include the waiting time of each customer in queue before reneging incidents, the position of each customer in queue just before reneging, the number of customers in queue behind the reneging customer at the moment of abandoning the queue, etc.

Incorporating the above mechanisms is not too complex from the modeling and coding perspective. On the other hand, it is difficult to collect relevant data from the field if the required information is not available. A number of obstacles can contribute to these difficulties. First, sufficient observations are needed to estimate the probability distributions, the shapes and parameters with adequate precision.  Unfortunately, in many instances, reneging does not happen often enough to yield sufficient observational data from the field within a reasonable amount of time.

Second, accurate data collection for events such as reneging may need sophisticated data collection procedures because each customer should be tracked while in the system and the values of relevant state variables along with other important information such as the “waiting time in queue before reneging” and “the position of the customer in queue” before abandoning the queue should also be recorded.   Only the information related to those customers who renege is needed. However, those who eventually renege are not known in advance or at the time of joining the queue; thus everyone should be tracked.  Such elaborate data collection system may be costly to implement, intrusive to customers, or may need consent and collaboration from people.  The latter may also influence the behavior and contaminate the collected data.

I discussed some of the important issues surrounding customer’s reneging behavior in simulation.  The focus of the discussion has so far been on physical queues or waiting lines in which customers should be physically present to receive the service.  Some of the complex characteristics and thought processes associated with physical queues may or may not apply to virtual queuing systems such as call centers, web servers, e-commerce sites and the like.  One simplifying advantage of modeling human impatience in virtual queues is that much of the data needed to model reneging behavior may readily be collected electronically without facing the obstacles explained above. Virtual queuing systems have unique characteristics and deserve a  separate discussion.

Check -out more information about Dr. Moeeni.  If you would like more information about ProModel Simulation and simulation studies about queues check out the ProModel services industry information. Contact ProModel to learn more.

 

 

 

 

Teaching Supply Chain Management with ProModel

profshannonPatrick W. Shannon, Ph.D., is a professor of operations and supply chain management at Boise State University. He taught graduate and undergraduate courses in business statistics, quality management, lean manufacturing and other areas of operations and supply chain management. Professor Shannon developed a curriculum for his supply chain class, using ProModel Simulation which he used for over 10 years.

To provide you some insight into how you can use ProModel in the classroom, Professor Shannon was kind enough to allow us to share the materials he used.

Attached are PDFs of his course materials.

  1. Tri-Star Manufacturing: A Case Study in Lean Implementation
  2. The Tri-Star Simulation Model
  3. Project Requirements and Rules
  4. ProModel Instructions

Dr. Shannon served as dean of the College of Business and Economics from 2008-2014 and has lectured and consulted on statistics, lean manufacturing and quality management, project management, statistical modeling, and demand forecasting for over thirty years. He has co-authored 11 university level textbooks, and he has published numerous articles in such journals as Decision Sciences Journal of Innovative Education, Business Horizons, Transportation Research Record, Interfaces, Journal of Simulation, Journal of Production and Inventory Control, Quality Progress, and Journal of Marketing Research, Quality Management Journal, and The International Journal of Quality and Reliability Management.

He completed his BS and MS at the University of Montana and his Ph.D. in Statistics and Quantitative Methods at the University of Oregon. In 2015 he presented at the National Kidney Registry (NKR) Symposium in New York City. The presentation, authored by Shannon and Phil Fry, professor of operations management, is titled “Kidney Life Years” and describes the research Fry and Shannon have conducted with the NKR. The purpose of the research is to develop a statistical model to identify the donor characteristics that impact the length of time live donor kidney transplant will last.

Click here to view his LinkedIn Profile.

If you are a professor interested in learning more about ProModel’s Academic offerings, please email cbunker@promodel.com for more information.  You may also check out the following: www.promodel.com/industries/academic

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Remembering Rob Bateman

Photo March 2009

Charles Harrell, Founder ProModel Corporation

Last month we lost a long-time member of the ProModel family and, for many of us, a beloved friend. Following a sudden incident of heart failure while working out in the gym, Rob Bateman passed away on October 11th 2015.  We at ProModel will remember him as a warm, energetic, impassioned leader and friend whose life was devoted to the pursuit of excellence and selfless service. His absence will continue to be profoundly felt in the months and years to come.

I first met Rob just over 25 years ago when he was doing graduate studies at BYU. He took a simulation class from me and I could tell he was excited about the potential benefits of simulation. So after completing a stint with the US state department as a foreign-service officer in 1990, Rob began working as a ProModel distributor. With his international background and grasp of simulation, Rob eventually become the Vice President of International Operations and later established an independent company (Dynamisis A.G.) for directing all international operations for ProModel.

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Robert Bateman

(April 4, 1958 – October 11, 2015)

Rob was an extraordinary individual with remarkable talents. He was one of those individuals who was always on-the-go and seemed to cram more into one day than most of us manage to accomplish in several days. At the same time, he maintained a zest for life and could frequently be seen buzzing around in his sports car with his signature driving cap or biking into work in his cycling shorts and helmet.

Here are a few of the many talents Rob displayed:

  • He was very knowledgeable…about everything. No matter what subject was being discussed, he always had something intelligent to contribute to the discussion. On top of his formal education, which culminated with a Ph.D. in Public Administration/Political Science, Rob filled his spare moments reading books or one of his 14 magazines he subscribed to.
  • As a consummate teacher he was passionate about getting people exposed to simulation. He wrote the first textbook on ProModel for use in college courses. For the past decade, when he wasn’t working with distributors to promote ProModel he was teaching at the local university.
  • He was an effective mentor and gave many individuals their first start in their careers. When several international distributors were asked what they remember about Rob, they invariably said he treated them as valued partners and became someone they could always turn to for advice.
  • He was resourceful and knew how to get by on very little sleep, food and comforts. When there wasn’t sufficient budget or resources to support an initiative he believed in, he somehow always managed to find the means needed to get the job done.
  • He was a real cosmopolitan and world traveler. If you ever called Rob, you would be just as likely reaching him at some airport as in his office. And there didn’t seem to be any country where he felt uncomfortable or couldn’t speak the language.
  • He was a friend to all and he never let business stand in the way of personal relationships. He took time to express an interest in others and always sensed if one was having a bad day or dealing with problems at home. He would do whatever he could to lift them up and help them keep things in perspective.
  • Finally, Rob was funny and had an infectious sense of humor. He could tell endless stories of his travel exploits where he encountered bizarre situations like returning to his car only to find all of his tires stolen. Though Rob took his commitments seriously, he never took himself too seriously.

Here are a few memories related by some of the distributers who worked with him.

A Distributor in Germany and Austria relates, “On my first trip to Utah to visit with Rob as a new ProModel rep, I had the feeling I was meeting with an old friend. I was impressed by his hospitality and the time that he gave me.”

A Brazilian distributor recalled meeting Rob the first time 21 years ago and thinking to himself, “Who is this guy who can conduct a meeting with high level business leaders, comfortably use legal and business terms in both German and English and then turn around the following day and teach a simulation course in Spanish to a group of engineers. How can one person have so many skills?”

As another of his co-workers commented, “I’ve been in rooms with him teaching and negotiating with Nigerians, Germans, Japanese, Brazilians, Mexicans, and more. No matter the nationality, Rob could relate and connect. He was confident, knowledgeable, and personable.”

On a personal note, one co-worker related: “This past year Rob joined the cycling team that I belong to called Team C4C (“Cycle for Cure”). The team was formed to raise money for health-related charities such as the Huntsman Cancer Center and the National MS Society. Rob immediately identified with the purpose of the group and quickly became one of the strongest riders on the team.”

This same co-worker related how Rob was instrumental in helping him complete a grueling ‘Ultimate Challenge’ cycling event saying, “I will always cherish a picture I have of Rob and me crossing the finish line together at Snowbird after riding 100 miles and climbing 10,000 feet in one day. I could not have made it without his encouragement along the way.”

For all those who have been influenced by his exemplary life, Rob will always be remembered as a leader, mentor and friend. Perhaps it is fitting that he pursued a career in simulation modeling since he seems to have understood the impact that models can have, not only on organizations, but on the people around him. Those who knew Rob, know that he was a model of the best that a human being can be, and for that he will always be remembered.

One of ProModel’s Biggest Supporters Reflects on a Career in Academic Optimization!

ProModel Guest Blogger:  Linda Ann Riley, Ph.D., Adjunct Professor of Engineering, University of New Haven; former Associate Dean and recently retired professor from the School of Engineering, Computing and Construction Management at Roger Williams University.

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Linda Ann Riley, Ph.D.

When Sandra Petty, Academic Coordinator at ProModel Corporation, invited me to contribute to ProModel’s guest blog, it gave me an opportunity to reflect on an academic career with one ever present constant, the ProModel’s suite of simulation software products.  My Universities may have changed, yet each year for the past twenty or so I have taught at least one, (many times far more) discrete-event simulation courses to an undergraduate, graduate, corporate or government audience.  Regardless of the class, a Ph.D. or a freshman undergraduate, I have continued to use ProModel since its early days as one of the first Window’s based simulation products.  As ProModel Corporation has introduced new products, MedModel, ServiceModel, Process Simulator and Portfolio Simulator, my students have had an invaluable opportunity to be exposed to some of the best simulation products in the industry.

Each simulation class that I teach involves an external project where students work with non-proprietary data from industry, government or non-profit entities. Working only with the ProModel Student Package, I have seen some of the most impactful and innovative uses of ProModel simulation software. From modeling casino floor slot machine layout to nuclear reactor evacuation scenarios, the variety of applications for the software has been virtually limitless.  The simulation skillset acquired by students is one of the primary factors companies have cited when hiring students with ProModel experience.  Through the years, the aerospace, health care, automotive, logistics and defense industries have identified significant value in students graduating with exposure to ProModel’s suite of products.

I too, have benefited from using ProModel software.  For my entire career, my research has focused on productivity/process analysis and optimization.  For the past twenty years, ProModel software has played a central role as an application tool for this research.  As ProModel Corporation has evolved with additional products and capabilities, so too has my research.  In the early years, I focused on health care process and facility layout improvement using MedModel to simulate patient queuing alternatives, throughput strategies and identification of system waste. From there, my research moved to rare event simulation such as security breaches, hazardous materials transportation incidents and hybrid simulation that incorporated both a discrete-event and continuous element.  At that time, I used external code and output from other programs as inputs to ProModel. During this period of research, I also focused with Ph.D. students on new approaches to multi-objective evolutionary algorithms as well as meta-heuristics for optimizing large-scale discrete-event simulations using SimRunner as a starting point.

More recently, my research has concentrated on managing and controlling risk in complex infrastructure projects using discrete-event simulation for stochastic scheduling.   In the construction industry, traditional project management and scheduling approaches for highly-complex construction projects typically use methods such as CPM (critical path method), PERT (program evaluation and review technique) or Monte Carlo simulation. For the most part, these methods rely on deterministic, tractable mathematical models underlying the schedule. The ability to accurately predict project schedule outcome and manage performance volatility is essential to controlling risk.  Prior to ProModel Corporation introducing Project and Portfolio Simulator, I would simulate the stochastic nature of these schedules in ProModel.

Even though I have recently retired from a full-time academic career, I will continue to teach discrete-event simulation using ProModel in an adjunct faculty capacity.  Looking to the future, my research will focus primarily on the incorporation and design of intelligent interfaces that identify and apply algorithms for the optimization problem and constraints under study. This implies perhaps an additional layer of code incorporated into the optimization process. Ultimately, this intelligent interface could “learn” to recognize common optimization scenarios, select starting and stopping rules, and potentially also interface with the system improvement framework.

As a further extension to the intelligent interface, dynamic algorithmic visualization capabilities might be incorporated into the optimization procedures.  Immersive technologies are used in many simulation arenas.  Incorporating immersive visualization into optimization would serve to bring a transparency between the modeling and optimization processes. This would allow users and decision makers to interactively view, and potentially redirect the optimization process. In essence, this feature would provide the decision maker the ability to immerse him or herself into the model, thus “directing” both the simulation and optimization processes.

In retrospect, discrete-event simulation and the ProModel Corporation have played a central role in my development as both a teacher and researcher.  I look forward to what the future holds for both the company and the field of discrete-event simulation.

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 recently retired as full professor from the School of Engineering, Computing and Construction Management at Roger Williams University (RWU) where she worked for twelve years. At RWU, she held the positions of Associate Dean, Engineering Program Coordinator and Professor of Engineering. She has over thirty years of teaching experience in both engineering and business and 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 the optimization of stochastic systems using simulation and evolutionary algorithms.

In addition, Dr. Riley is an active researcher with notable success in grant writing, grant and contract management, creating collaborative research partnerships and research administration. She is responsible for developing and writing over 150 competitive research/consulting proposals and has been awarded or procured contracts for clients in excess of twenty-five million dollars. Prior to her position at Roger Williams University, Dr. Riley spent 17 years at New Mexico State University (NMSU) holding positions as Director of the University Center for Economic Development Research and Assistance, Assistant Director for the Center for Business Research and Services and Director of the Advanced Modeling and Simulation Laboratory. She also held faculty positions in both the Colleges of Business and Engineering at NMSU.

In addition to teaching and research, Dr. Riley is active in consulting. She has extensive consulting experience in organizational productivity/process improvement implementing six sigma, lean, system dynamics, simulation and optimization approaches. She has extensive experience in the design, communication and implementation of strategic and economic development plans. Also, she worked for a number of years with the National Laboratories on technology commercialization strategies.

Dr. Riley is actively involved in attracting women and under-represented groups into science, engineering, mathematics and technology fields. She is a national speaker on the challenges of attracting women and under-represented groups into these fields and served as Chair of the American Society for Engineering Education Northeastern Section and National Chairperson of American Society of Mechanical Engineers Diversity Metrics Committee. Dr. Riley is a member of several professional business and engineering societies and has served as reviewer and/or editorial board member for business, healthcare and engineering journals.

Dr. Riley received her undergraduate degree from Boston University, earned an M.B.A from Suffolk University, completed a post-graduate fellowship at Brown University and earned her M.S. in Industrial Engineering and a Ph.D. in Business with a major field in Logistics from New Mexico State University. Also, for eleven years, Dr. Riley held the position of Vice-Chair of the Board for a large financial institution. In conjunction with this position, she completed 56 credits of Board of Directors courses and was awarded the Friedrich W. Raiffeisen and Edward W. Filene Awards.

ProModel Salutes Founder Charley Harrell for Years of Service…and Getting This Whole Thing Started!

Dr. Charles Harrell founded ProModel in 1988 and was the original developer of the Company’s simulation technology (ProModel PC). Today he serves on the Board of Directors and has been actively involved in new product development, acting as chief technology advisor.  Charley is also an associate professor of Engineering and Technology at Brigham Young University and the author of several simulation books.

Retirement PicsIn May, Charley officially retired from the company and to honor his innovative and productive career ProModel held two celebrations this summer at two of our locations in Orem Utah and Allentown Pennsylvania.  The events were attended by ProModel staff and many of Charley’s long time colleagues who have been with him from the start.

In recent years, Charley has written about his team’s original vision for ProModel back in 1988, “We set out to revolutionize the use of simulation in the business world by introducing the first graphically oriented simulation tool for desktop computers.  We were all convinced that we offered a unique product—a simulation tool that was developed and supported by engineers and specifically designed for engineers.”

Describing the success of ProModel Corporation, Charley writes, “In addition to the impressive growth in ProModel’s predictive simulation technology, it has also been gratifying to see the breadth of application of our technology, not just in fortune 500 companies, but also in the area of healthcare, education, homeland security, military readiness and humanitarian aid.”

The entire ProModel family would like to thank Charley for his years of service, guidance and friendship and we wish him all the best in the future! He has made ProModel what we are today.

In 2013 ProModel celebrated its 25th  Anniversary and Charley shared his memories and appreciation for ProModel in this thoughtful BLOG POST