Just-In-Time for the Holidays!

Kevin Field, Senior Product Manager, ProModel Corporation

Kevin Field, Senior Product Manager, ProModel Corporation

A few weeks back, prior to the Thanksgiving holiday (in the USA), we released Service Pack 1 (SP1) for the 2016 version of our products. For those of you that know the purpose and traditions of this holiday, you also know it is a time to show gratitude as well as a time to feast on turkey, mashed potatoes and gravy, and pumpkin pie. The timing of the release definitely made the holiday more enjoyable for us and enabled stress-free indulging.

However, the release really wasn’t for us but rather for you, our customers! So with more holidays to come this month, I hope you are able to feast on the new features and capabilities we have introduced in the 2016 SP1 release. It is focused around allowing you to capture, collect and extract custom statistics for your process improvement and analysis. Starting lightly with seemingly subtle changes and implementation of logic functions to the real “meat” of a new API for statistics extraction, there really is quite a bit to consume.

(To learn more about the SP1 release of ProModel and MedModel, please see the What’s New and Release Webinar sections below. If you are a Process Simulator user, you can go to the Process Simulator “What’s New?” page on our corporate website and see a detailed description along with an accompanying webinar. )

What’s New in the ProModel / MedModel 2016 SP1 release?

Resource Distance Traveled Statistics

The distance your resources travel over the course of a simulation is now collected and reported in Output Viewer. It is displayed as Total Distance Traveled in Resource Summary reports and tracks the overall distance traveled for individual units.

Identify Captured Resource Units

In addition to determining which resource you’ve captured, you can now find out the specific unit of that resource as well. With the new OwnedResourceUnit() function, you can get the unit number of any resource owned by an entity. This is useful when collecting custom resource statistics at the individual unit level.

In-Process Resource Utilization Statistics

Access the utilization of your resources at any time during simulation. Using the newly modified PercentUtil() function, you can find out the utilization of individual units of a resource or a summary of all units of a given resource type. This allows you to make dynamic logical decisions or write out custom statistics to a CSV or Excel file.

Utilization for a specific resource unit

Utilization of all units for a resource type

Quickly Access Element Definitions

Have you ever been writing or viewing logic, and wanted to quickly see the definition (or details) of a specific subroutine, array, location, etc.? Well, now you can! Simply highlight that model element in logic, press the F12 key, and you will be taken to that element’s specific record in its edit table. For example, if you highlight a subroutine name in logic and press F12, you will be taken to its exact record in the Subroutine table.

Programmatic Export of Statistics

The statistical results of your simulation runs can be programmatically accessed through a new API to Output Viewer. You can get the raw data, down to the individual replication, or have it summarized or grouped (just like in Output Viewer) prior to accessing it. Either way, you can access your results, for example to load into Excel or a database, for analysis outside of Output Viewer.

As an example, this is a Time Plot in Output Viewer where the time series data is averaged over a custom period of 15 minute intervals.

Using the new API, you can have Output Viewer summarize the data into 15 minute intervals prior to exporting it to Excel.

The exported format makes it easy to create a Pivot table and Pivot chart in Excel.

Enhancements

  • When exporting Array data at the end of simulation, the replication number is no longer written out in the Excel Sheet name if “Export after final replication only” is checked.
  • Minitab version 17.3 is now supported

Release Webinar

We even followed up the release with a live webinar showcasing the new capabilities of SP1. So, if after devouring the descriptions above, you still find yourself hungry for additional details, head on over to our YouTube channel or view the webinar directly below.  You can also log into our Solutions Café to have a second or more complete helping of SP1.

And Happy Holidays!

Save

Save

Save

Save

Save

Save

Save

Save

Save

Save

Save

Save

Save

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:

originalityaward

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

 

complexityaward

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

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

all-students

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. modehttp://journals.lww.com/transplantationdirect/toc/2016/07000l

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

.

 

 

 

 

ProModel at the Olympics 2016 and 2002

rio2016

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.  http://www.belge.com.br

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.

simulationgames

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.

Interconnectivity

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.

PACUComparison

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.