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

Simulating Impatient Customers

ProModel Guest Blogger: Dr. Farhad Moeeni, Professor of Computer & Information Technology, Arkansas State University

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

Dr. Farhad Moeeni 

Simulation is one of the required courses for the MBA degree with MIS concentration at Arkansas State University.  The course was developed a few years ago with the help of a colleague (Dr. John Seydel).  We use Simulation Using Promodel, Third Ed. (Harrell, et al., Ghosh and Bowden, McGraw-Hill) for the course.  In addition, students have access to the full-version of the Promodel software in our Data Automation Laboratory. The course has attracted graduate students from other areas including arts and sciences, social sciences and engineering technology who took the course as elective or for enhancing research capability.  Students experience the entire cycle of simulation modeling and analysis through                                          comprehensive group projects with a focus on business decision making.

Most elements of waiting lines are shared by various queuing systems regardless of entity types such as human, inanimate, or intangible.  However, a few features are unique to human entities and service systems, two of which are balking and reneging.  One of the fairly recent class projects included modeling the university’s main cafeteria with various food islands. Teams were directed to also model balking and reneging, which was challenging. The project led to studying various balking and reneging scenarios and their modeling implications, which was very informative.

Disregarding the simple case of balking caused by queue capacity, balking and reneging happens because of impatience.  Balking means customers evaluate the waiting line, anticipate the required waiting time upon arrival (most likely by observing the queue length) and decide whether to join the queue or leave. In short, balking happens when the person’s tolerance for waiting is less than the anticipated waiting time at arrival.  Reneging happens after a person joins the queue but later leaves because he/she feels waiting no longer is tolerable or has utility.  Literature indicates that both decisions can be the result of complex behavioral traits, criticality of the service and service environment (servicescape). Therefore, acquiring information about and modeling balking or reneging can be hard.  However, it offers additional information on service effectiveness that is hard to derive from analyzing waiting times and queue length.

For modeling purposes, the balking and reneging behavior is usually converted into some probability distributions or rules to trigger them. To alleviate complexity, simplified approaches have been suggested in the literature.  Each treatment is based on simplifying assumptions and only approximates the behavior of customers. This article addresses some approaches to simulate balking.  Reneging will be covered in future articles.  Scenarios to model balking behavior include:

  1. On arrival, the entity joins the queue only if the queue length is less than a specified number but balks otherwise.
  2. On arrival, the entity joins the queue if the queue length is less than or equal to a specified number. However, if the length of the queue exceeds, the entity joins the queue with probability  and balks with probability  (Bernoulli distribution).
  3. The same as Model 2 but several (Bernoulli) conditional probability distribution is constructed for various queue lengths (see the Example).
  4. On arrival, a maximum tolerable length of queue is determined from a discrete probability distribution for each entity. The maximum number is then compared with the queue length at the moment of arrival to determine whether or not the entity balks.

The first three approaches model the underlying tolerance for waiting implicitly.  Model 4 allows tolerance variation among customers to be modeled explicitly.

A simulation example of Model 3 is presented. The purpose is to demonstrate the structure of the model and not model efficiency and compactness.  The model includes a single server, FCFS discipline, random arrival and service.  The conditional probability distributions of balking behavior are presented in the table. The data must be extracted from the field.  The simulation model is also presented below. After running the models for 10 hours, 55 (10% of) customers balked. Balking information can be very useful in designing or fine-tuning queuing systems in addition to other statistics such as average/maximum waiting time or queue length, etc.

Condition

Conditional Probability Distribution

Probability of Joining the Queue (p) Probability of Balking (1-p)
Queue Length <= 4 1.00 0
5<=Queue Length <= 10 0.7 0.3
Queue Length > 10 0.2 0.8

Prof Mooeini Sim Chart

About Dr. Moeeni:

Dr. Farhad Moeeni is professor of Computer and Information Technology and the Founder of the Laboratory for the Study of Automatic Identification at Arkansas State University. He holds a M.S. degree in industrial engineering and a Ph.D. in operations management and information systems, both from the University of Arizona.

His articles have been published in various scholarly outlets including Decision Sciences Journal, International journal of Production Economics, International Journal of Production Research, International Transactions in Operational Research, Decision Line, and several others. He has also co-authored two book chapters on the subject of automatic identification with applications in cyber logistics and e-supply chain management along with several study books in support of various textbooks. .

He is a frequent guest lecturer on the subject of information systems at the “Centre Franco Americain”, University of Caen, France.

Current research interests are primarily in the design, analysis and implementation of automatic identification for data quality and efficiency, RFID-based real-time location sensing with warehousing applications, and supply chain management. Methodological interests include design of experiments, simulation modeling and analysis, and other operations research techniques. He is one of the pioneers in instructional design and the teaching of automatic identification concepts within MIS programs and also is RFID+ certified.

Dr. Moeeni is currently the principle investigator of a multi-university research project funded by Arkansas Science and Technology Authority, Co-founder of Consortium for Identity Systems Research and Education (CISRE), and on the Editorial Board of the International Journal of RF Technologies: Research and Applications.

Contact Information

moeeni@astate.edu

In the OR with Dale Schroyer

Dale%20Schroyer

Dale Schroyer – Sr. Consultant & Project Manager

I generally find that in healthcare, WHEN something needs to happen is more important than WHAT needs to happen.  It’s a field that is rife with variation, but with simulation, I firmly believe that it can be properly managed.  Patient flow and staffing are always a top concern for hospitals, but it’s important to remember that utilization levels that are too high are just as bad as levels that are too low, and one of the benefits of simulation in healthcare is the ability to staff to demand.

Check out Dale’s work with Robert Wood Johnson University Hospital where they successfully used simulation to manage increased OR patient volume: 

About Dale

Since joining ProModel in 2000, Dale has been developing simulation models used by businesses to perform operational improvement and strategic planning. Prior to joining ProModel Dale spent seven years as a Sr. Corporate Management Engineering Consultant for Baystate Health System in Springfield, MA where he facilitated quality improvement efforts system wide including setting standards and facilitating business re-engineering teams. Earlier he worked as a Project Engineer at the Hamilton Standard Division of United Technologies.

Dale has a BS in Mechanical Engineering from the University of Michigan and a Masters of Management Science from Lesley University. He is a certified Six Sigma Green Belt and is Lean Bronze certified.

NEW! ProModel’s Patient Flow Solution:

http://patientflowstudio.com/

ProModel Healthcare Solutions:

http://www.promodel.com/Industries/Healthcare

Teaching Simulation to Graduate Students Using ProModel Products and Real-World Problems

ProModel Guest Blogger:  Larry Fulton, Ph.D. & MSStat – Assistant Professor of Health Organization Management at Texas Tech University Rawls College of Business.  After serving 25 years in military medicine, Dr. Fulton began a second career in teaching and research.

Larry Fulton, Ph.D. & MSStat

Larry Fulton, Ph.D. & MSStat

Teaching introductory Monte Carlo, Discrete Event, and Continuous simulation to business graduate students requires at least two components beyond a good set of reference materials:   realistic or real-world problems and an excellent modeling platform allowing for relatively rapid development.  In the case discussed here, the real-world scenarios derived from interests and background of the professor and students (portfolio analysis, sustainability, and military medicine), while ProModel products addressed the platform requirements. Each of the case study  scenarios served to underscore various simulation building elements, while ProModel supported rapid  product development for a 14-week, lab-intensive course that included some  reviews of probability, statistics, queuing, and                                                    stochastic processes.

Scenario 1:  Monte Carlo Simulation (Portfolio Analysis)

Business students generally have an affinity for portfolio analysis, and I do as well. Using ProModel  features, one of the earliest student projects involves fitting univariate distributions to return rates to several funds and simulating results of investment decisions of various time horizons.  Students discuss methods that might account for covariance as well as autoregressive components in these simulations.  While developing the simulations, students also determine sample size requirements to bracket mean return on investment within a specified margin of error and confidence interval and use random numbers seeds.

Scenario 2:  Continuous Simulation using Rainwater Harvesting

Students in this course are generally from a semi-arid region (Central Texas), which has significant water shortages (so much so that desalinization is being considered.)  I rely 100% on rainwater harvesting for my home water supply, so extending this to each student’s particular home location is trivial. The “Tank Submodule” provides an easy mechanism for developing the simulations.  Students develop conceptual models of rainwater mechanism as well as flowcharts.  They gather rainfall data from the National Oceanic and Atmospheric Administration regarding rainfall and evaluate various roof sizes (capture space), demand figures based on occupants, and tank sizes. They also learn about the importance of order statistics (the distribution of the minimum in the tank) versus measures of central tendency that often dominate discussions of simulation. Finally, they incorporate tools and techniques to improve and assess V&V.

­­­Scenario 3:  Discrete Event Simulation using Military Scenarios

While serving as the Chief of Operations Research Branch for the Center for Army Medical Department Strategic Studies, I encouraged the use of MedModel for multiple DES projects.  The team built strategic models (resource constrained and unconstrained) for analyzing medical requirements for strategic operations. These same models serve as a basis for a team-based MedModel student capstone project.  The primary entity for these models was the patient with attributes of severity, injury type, and evacuation type.  The primary processes involved collection, treatment and evacuation. Resources included ground ambulances, air ambulances, medics, intensive care units, and operating rooms.   Locations were geographic locations throughout the entire of Afghanistan.  Evacuation paths were built, and treatment logic (triage, ground evacuation, air evacuation, etc.) provided the flow.

Bottom Line:  The ProModel products are outstanding for use in both teaching and industry.

 Larry Fulton Bio:

http://www.depts.ttu.edu/rawlsbusiness/people/faculty/hom/larry-fulton/

Teaching Process Management Using ProModel

ProModel Guest Blogger:  Scott Metlen, Ph.D. – Business Department Head and Associate Professor at University of Idaho

Scott Metlen, Ph.D.

Scott Metlen, Ph.D.

Understanding process management, the design, implementation, management and control, and continuous improvement of the enterprise wide set of an organizations processes is the key to well deployed strategies. It was not until Tim Cook made Apple’s total set of processes world class including all supply chain linked processes (Brownlee, 2012) that Apple hit its amazing climb to become the world’s highest valued company; even though the company had cutting edge products before his arrival. Gaining effective understanding of process management is not easy due to the strategic variability inherent in the portfolio of products that companies sell, and in markets they service. This strategic variability (Rajan, 2011) in turn drives variability in many processes that an organization uses to operate. For instance, different markets require different marketing plans supported by different processes.  Order processes often vary by product and target market. Employee skill sets differ by product requiring different hiring and training processes. Different products, whether it be services or goods that have a slight variation require, at the very least, an adjustment to the production process. Adding to, and often caused by the variability just mentioned, are multiple process steps, each with different duration times and human resource skills.  Depending on what product is currently being produced, process steps, process step order and duration time, interdependency between the process steps, and business rules all vary. Where a product is in its life cycle will drive the experience curve, again creating variation across products. In addition, the numerous interfaces with other processes all vary depending on the product being produced. All of these sources of variability can make process management hard to do, teach, and learn. One tool that helps with process management in the face of variance is discrete event simulation and one of the best software suites to use is ProModel. ProModel is a flexible program with excellent product support from the company.

Effective process management is a multi-step process. The first step of process management is to determine the process flow while at the same time determining the value and non-value added process steps. Included in the process flow diagram for each step are the duration times by product and resources needed at each step, and product routes. Also needed at this time are business rules governing the process such as working hours, safety envelopes, quality control, queueing rules, and many others. Capturing this complex interrelated system begins by visiting the process and talking with the process owner and operators. Drawing the diagram and listing other information is a good second step, but actually building and operating the process is when a person truly understands the process and its complexities.  Of course many of the processes we want to improve are already built and are in use. In most cases, students will not be able to do either of these. However, building a verified and validated simulation model is a good proxy for doing the real thing, as the model will never validate against the actual process output unless all of the complexity is included or represented in the model. In the ‘Systems and Simulation’ course at the University of Idaho students first learn fundamentals of process management including lean terms and tools. Then they are given the opportunity to visit a company in the third week of class as a member of a team to conduct a process improvement project. In this visit students meet the process owner and operators. If the process is a production process, they walk the floor and discuss the process and the delta between expected and actual output. If the process is an information flow process, such as much of an order process, the students discuss the process and, again, the delta between expected and realized output. Over the next six weeks students take the preliminary data and begin to build a simulation model of the current state of the process. During this time period students discover that they do not have all the data and information they need to replicate the actual process. In many cases they do not have the data and/or information because the company does not have that information or how the model is operated is not the same as designed. Students then have to contact the process owner and operators throughout the six weeks to determine the actual business rules used and/or make informed assumptions to complete their model.

Once the model has been validated and the students have a deep understanding of the process, students start modeling process changes that will eliminate waste in the system, increase output, and decrease cost. Examples of methods used to improve the process include changing business rules, adding strategically placed buffers and resources, and reallocating resources. To determine the most effective way to improve the process, a cost benefit analysis in the form of an NPV analysis is completed. The students use the distribution of outputs from the original model to generate appropriate output and then compare that output to output pulled from the distributions of each improvement scenario. This comparison is then used to determine a 95% confidence interval for the NPV and the probability of the NPV being zero or less. Finally, several weeks before the semester is finished, students travel to the company to present their findings and recommendations.

Student learning on these projects is multifaceted. Learning how to use ProModel is the level that the students are most aware of during the semester, as it takes much of their time. However, by the end of the semester they talk about improving their ability to manage processes, work in teams, deal with ambiguity, manage multiple projects, present to high level managers, and maintain steady communication with project owners.

Utilizing external projects and discrete event simulation to teach process management has been used in the College of Business and Economics at the University of Idaho for the past six years. As a result, the Production and Operation area has grown from 40 to 150 students and from five to 20 projects per semester. More importantly, students who complete this course are being sought out and hired by firms based on the transformational learning and skill sets students acquired through the program.

References:

Rajan Suri. Beyond Lean: It’s About Time. 2011 Technical Report, Center for Quick Response Manufacturing, University of Wisconsin-Madison.

Brownlee, John. Apples’s Secret Weapon 06/13/2012. http://www.cnn.com/2012/06/12/opinion/brownlee-apple-secret/index.html?hpt=hp_t2. 12/301/2014.

Scott Metlen Bio:

http://www.uidaho.edu/cbe/business/scottmetlen

 

Happy Holidays!

President & CEO ProModel Corporation

Keith Vadas – President & CEO ProModel Corporation

The ProModel family would like to wish everyone a very joyous holiday season and a prosperous 2015!  We thank you for all your support and business this past year.  As always, our goal is to help you meet or exceed your performance goals.  We hope that our people and solutions were able to assist you in that endeavor this past year.

2014 was a busy year for ProModel filled with exciting new products like Process Simulator Pro, revamped new releases of ProModel, MedModel and Enterprise Portfolio Simulator, and of course our custom solutions designed for a host of clients across all industries. As most of you know, we have an extraordinary team of consultants and software developers always available to help your organization meet the next business challenge. Looking ahead, 2015 is shaping up to be another BIG year here at ProModel as we continue to develop new products including Healthcare solutions and other business improvement tools. 

Thank you, and I wish you and your families a happy holiday and a joyful New Year.

 

Demystifying System Complexity

Charles Harrell, Founder ProModel Corporation

Charles Harrell, Founder ProModel Corporation

One can’t help but be awe struck, and sometimes even a little annoyed, by the complexity of modern society. This complexity spills over into everyday business systems making them extraordinarily challenging to plan and operate. Enter any factory or healthcare facility and you can sense the confusion and lack of coordination that often seems to prevail. Much of what is intended to be a coordinated effort to get a job done ends up being little more than random commotion resulting in chance outcomes. Welcome to the world of complex systems!

A “complex system” is defined as “a functional whole, consisting of interdependent and variable parts.” (Chris Lucas, Quantifying Complexity Theory, 1999, http://www.calresco.org/lucas/quantify.htm) System complexity, therefore, is a function of both the interdependencies and variability in a system. Interdependencies occur when activities depend on other activities or conditions for their execution. For example, an inspection activity can’t occur until the object being inspected is present and the resources needed for the inspection are available. Variability occurs when there is variation in activity times, arrivals, resource interruptions, etc. As shown below, the performance and predictability of a system is inversely proportional to the degree of interdependency and variability in the system.

Untitled-1

Suppose, for example, you are designing a small work cell or outpatient facility that has five sequential stations with variable activity times and limited buffers or waiting capacity in between. Suppose further that the resources needed for this process experience random interruptions. How does one begin to estimate the output capacity of such a system? More importantly, how does one know what improvements to make to best meet performance objectives?

Obviously, the larger the process and greater the complexity, the more difficult it is to predict how a system will perform and what impact design decisions and operating policies will have. The one thing most systems experts agree on, however, is that increasing complexity tends to have an adverse effect on all aspects of system performance including throughput, resource utilization, time in system and product or service quality.

For Charleys new blog

ProModel and Medmodel are powerful analytic tools that are able to account for the complex relationships in a system and eliminate the guesswork in systems planning. Because these simulation tools imitate the actual operation of a system, they provides valuable insights into system behavior with quantitative measures of system performance.

To help introduce process novices to the way interdependencies and variability impact system performance, ProModel has developed a set of training exercises using an Excel interface to either ProModel or MedModel. Each exercise exposes the initiate to increasingly greater system complexity and how system performance is affected. Additionally, these exercises demonstrate the fundamental ways system complexity can be mitigated and effectively managed.

ProModel is offering these exercises to students and practitioners who are seeking an introduction to simulation and systems dynamics.

 

For more information please contact ProModel Academic

Sandra Petty, Academic Coordinator  spetty@promodel.com