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. mode

Click here to view his LinkedIn Profile.

If you are a professor interested in learning more about ProModel’s Academic offerings, please email for more information.  You may also check out the following:






ProModel at the Olympics 2016 and 2002


Alain de Norman, President of Belge, ProModel’s Brazilian based partner was in attendance at the several events during the 2016 Olympics.  He was kind enough to provide us with some great shots he took during several of those events. One shot shows a US basketball player blocking a shot from one of the French players. There is another great shot of a long jumper and it shows the progression of his jump. Check out the montage video below:

Here is a video of some of that same US vs France Basketball game.

Thanks for sharing those great images of the 2016 Olympic Games Alain!  Here is a link to the Belge website for more information about the services they provide in Brazil.

ProModel also has some experience with Olympic venues.  Take a look back at the 2002 Winter Olympics in Salt Lake City and the ProModel Solution that made it all possible! The 2002 Winter Olympics were one of the most complex logistics challenges ever. ProModel products and services were used to design security systems and bus transportation for most of the venues. The predictive technology enabled the Salt Lake Organizing Committee to model and test various scenarios related to security operations, weather, and transportation system design.


Click to read the full story in IIE Solutions Magazine.

If you would like more information about ProModel solutions contact us.






Healthcare IT: Top Trends and Innovations in 2016

This post was originally published by Christine Slocumb on the Clarity Quest Marketing blog and has been re-posted with permission. 

As the President of one of the top healthcare marketing agencies, I’m continually fascinated at the wide array of technologies emerging in the space. Every week we get lead calls from companies with new products or services addressing pressing healthcare technology issues.

Here are some of the trends we hear lots of buzz around in 2016.

Prescriptive Analytics

Health systems are getting more sophisticated at understanding their current state using descriptive analytics of their data, however, knowing what’s going right or wrong is only a small step in fixing the issues. Now companies offer predictive and even prescriptive analytics to forecast the future and to offer corrective suggestions.

One example is FutureFlow Rx by ProModel, which not only predicts patient flow across a health system, but also gives corrective actions and likely outcomes for each.

Prescriptive Analytics versus Predictive Analytics versus Diagnostic Analytics graphic from FutureFlowRx
The move from descriptive to prescriptive analytics. Courtesy: ProModel Corporation

Data Migration Tools

Whether it’s moving DICOM images from one system to another or migrating from one EHR system to another, tools to transfer data are here to stay.


Connecting records and information systems is still in its infancy. We’re seeing more and more demand for tools that provide interfaces, such as eMedApps’ Care Connectivity Platform™, which maintains continuity and uptime while establishing bridges.

eMedApps CareBridge Interoperability Platform Diagram

Niche Practice Systems by Specialty

Epic, Allscripts, and the other big EHR dogs have trouble breaking into specialty areas such as dermatology, ENT and more notably behavioral health. Smaller companies, such as Logik Solutions, which sells billing software for behavioral health, are growing by selling into practices in specialty areas.

Consolidation in Imaging IT

IBM bought Merge; Fuji acquired TeraMedica; and Hitachi left the VNA business. PACS is a tough replacement sale and vendor neutral archives are often seen as a “nice to have” versus a “must have”. Expect to see more shakeout and consolidation in this area.

More Data Integration Between Payers, Providers and Pharmaceutical Companies

Clinicians need a better way to understand which drugs are covered under specific payer plans, at what levels, and if policies and restrictions are attached to a drug. Payers need to keep costs under control. Pharmaceutical companies want to promote their drugs as quickly and efficiently as possible. Expect to see systems such as MMIT’s Mobile Search Formulary App that offer an accurate display of drug coverage to all parties by validating data from multiple payer and pharma sources.

No doubt these are exciting times for health IT. Stay tuned for our next post on this topic after HIMSS 2017.

About the Author:
Chris is the founder and president of Clarity Quest Marketing, where she leads a talented group of marketers and designers helping healthcare and technology companies achieve marketing and business goals. To learn more about Chris’ experiences and qualifications, visit our Meet Our Executive Team page.

Architectural Firm Compares PACU Designs with Ease Using MedModel

A post-anesthesia care unit, PACU, is a vital area within every hospital where patients can recover  from general anesthesiaregional anesthesia, or local anesthesia.

ProModel built a PACU model for an architectural firm to illustrate the difference between two design options.  This was accomplished by placing both designs in one model and having patients follow the exact same patient pattern as they enter the PACUs simultaneously.

The left or A side of the model is a Single Room design and the right or B side is an Open Bay design.  There are the same number of conceptual uses of PACU I beds in each design.  The A side has 27 dual designated pre-op and PACU II rooms.  The B side has 30 rooms, but designates specific rooms for pre-op and specific rooms for PACU II.  Macros were used extensively during the model build to enable rapid changes to the interactions of the patients within the designs.  Real arrival patterns from the hospital were used and entered using an arrival spreadsheet.


The simulation revealed that the A side showed significant time savings.  Were the ORs to be kept open the same length of time, more patients could be seen on the A side.  The A side also reduced the time spent waiting, after initial arrival for a pre-op bed.  For example, the average wait for pre-op bed on the A side was 3.9 minutes.  On the B side this average was 52.8 minutes.  That’s a pretty significant difference!

These and many other solution videos are available on our YouTube Channel.

If you would like more information about ProModel solutions contact us.

A True Cowboy in Our Midst

4-7-2016 9-01-42 PM

ProModel’s Western Regional Sales Director, Mike Townsend, recently won multiple amateur horseback riding (cutting) competitions. After a 10-year hiatus from showing, Mike made his presence felt after tying for first in the 2016 NCHA Super Stakes Amateur final in Fort Worth, TX.




Check out the video of Mike on the winning ride, working his horse hard to keep those cows in line!

Not familiar with Cutting, neither were we.  Check out the Wikipedia explanation:


Is Patient Care a Repeatable Process and Can It Benefit from System Improvements?

headshots-daleIn my eight years at ProModel, I have come to appreciate the serious talent of our consultants.  I think they are one of our greatest assets and bring tremendous value to our customers.  When I really want to get the scoop on a project, I turn to one of them and they explain the very complex nature of our projects to me in a way I understand and appreciate.  One of these talented consultants is Dale Schroyer.

Dale is a first time grandfather, which in itself is a new challenge. As he said “Its old, but its new.  In his work as a Promodel Consultant Dale travels a great deal, however he does not really get to see or enjoy the places to which he travels.  So he and his wife have decided to start traveling and just this year they took their first vacation to Italy and thoroughly enjoyed themselves. Next on their bucket list is another trip.  They are deciding between Alaska or the British Isles.

When I last spoke with Dale he was attending the NPSF Patient Safety Congress, in Scottsdale, Arizona one of those may places he visits but doesn’t really get to see. He was happy to be in 80-degree sunshine after weeks in cool, cloudy Massachusetts. One of the programs Dale attended at the NPSF conference was an emersion workshop on RCA or Root Cause Analysis.

This program looked at what hospitals do when an adverse event occurs.  Usually such events occur because of system faults or failures, not necessarily human error.  The challenge is determining what the faults in the system are, how they can be fixed and instituting actions to fix them and measure those fixes.  Dale found it a fascinating topic because of its similarities to what is done in the Aerospace industry in which he started his career.  The instructors were Dr. James P. Bagian and Mr. Joseph M. DeRosier, one of whom is from the Aerospace industry.  Both teach at the University of Michigan which is Dale’s alma mater.  Dale spoke with them about simulation as a tool to determine hospital system shortfalls.  They mentioned that the barriers to simulation are many and often the learning curve is long and cumbersome.  Dale discussed using ProModel’s Process Simulator which can be an easier way around those barriers, since it is a simpler, Visio based tool.

As most of the attendees at the conference were nurses, doctors and an eclectic mix of engineers, what Dale observed in talking and listening to many of them is that healthcare does not consider itself a process or system industry. At this year’s conference, conversations were being started around this very issue.  The fact that doctors and nurses were having the conversation is a considerable step in the right direction.  Many in attendance wanted to know what techniques would best serve them in convincing their coworkers back home that the system approach is a good and necessary one for the healthcare industry that can benefit patients, hospitals, nurses and physicians.

Dale has over 20 years as an improvement consultant in the healthcare field at ProModel and Baystates Healthcare. One of his most significant consulting engagements for ProModel has been at Robert Wood Johnson.  In this multi-year engagement, ProModel and Dale served as a trusted advisor.  It was a project that did not just cover one unit of the hospital, but dealt with the whole evolution of the OR Suite.  It was not just the building of a single model, but a collaborative work with positive and rewarding results.

Part of what makes Dales so good at his job is the fact that he loves tackling new challenges.  Working for ProModel guarantees that each day will be very different from the last.  He will meet new people in a new environment and tackle a new problem.  The first step he generally takes when starting a new project is to spend a lot of time listening to those with whom he will be working.  He needs to understand their environment and what he must do as a ProModel expert to yield them tremendous value.

Dale just earned a Data Scientist certification. The program he completed was from Johns Hopkins and required the completion of 9 courses along with a capstone project. His capstone focused on natural language processing and it brought all of the elements of the other 9 courses together and applied them in a new and fascinating way.

As Dale and I closed our conversation, we were both wondering how others in the Healthcare Community felt about his notion that Healthcare is not a process or system industry.  We, of course, disagree.  What do you think?

We would be happy to hear your opinion about this notion.  Comment below or email me at  To recommend whether Dales should visit Alaska or the British Isles, email him at

Interested in learning more about ProModel consulting, check out:, or