Tag Archives: capacity planners

Simulating The Impact Of New Laws On Probation Systems

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JCowden Profile Pic

Jennifer Cowden – Sr. Consultant

It was recently announced that the U.S. Justice Department is planning to release 6000 inmates near the end of the month due to new sentencing policies for non-violent drug-offenders.  Most of the prisoners will be placed in half-way houses and drug rehab centers as part of the “largest one-time release of federal prisoners” in U. S History, which begs the question: are these rehabilitation centers going to be ready for this sudden influx?

One state has had a similar law change recently and is rightly concerned about the impact that the new sentencing structure will have on the probation system and ancillary support services.  ProModel consultants have been working with this state’s Administrative Office of Probation to build a series of models around different aspects of the probation system.  The previous phase model studied the movement of youths through the juvenile probation system, while the model discussed in the video below addresses the adult probationer population.

In addition to gaining insight into bottlenecks in the process, the Probation Office was interested in using Predictive Analytics to assess the impact that the new law will have on the probation office workload and the local county jail occupancy rate.  As part of the law change, convicts who are guilty of certain felonies will spend part of their sentence in probation instead of spending all of it in prison.  These felons are at a higher risk level than the current average probationer,  and will likely cause a disproportionate workload increase on the probation officers as well as take up county jail space should custodial sanctions need to be implemented.  The model will be used to help quantify the increased demand so that the appropriate adjustments can be made ahead of time.

The next steps for this model is to combine it with the juvenile model in order to predict more accurately the demand on shared services and resources.

Teaching Systems Analysis and Modeling

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ProModel Guest Blogger: Robert Loomis, Ph.D. Adjunct Professor, Florida Institute of Technology; NASA (Retired)

Loomis

Robert Loomis, Ph.D.

I teach a number of courses for the Florida Institute of Technology, one of which (Systems Analysis and Modeling) is a 17 week graduate level survey course in Systems Analysis, various types of modeling and how the modeling fits into the SA process.  This course is designed to be “a mile wide and an inch deep” in that it introduces several topics that could, by themselves, be the subject of dedicated courses.

One of the challenges in teaching a course such as this (particularly in an MBA environment) is to find tools that are effective and demonstrate the concepts well without becoming bogged down in the mechanics of the tools employed.  It also helps if the students find them engaging to use.  I ended up writing some of my own applications for certain deterministic models in order to meet those requirements and to emphasize the concepts that I felt were important.

I chose ProModel to use as a simulation package for a number of reasons. It has:

  • A graphical User Interface that is attractive, easy to use, and (at least at the level my class uses) easy to learn.
  • Outstanding documentation.
  • An excellent Professor Package.
  • An excellent Student Package. It is modestly-priced and fully-featured (limited only by the size of the model that can be created).
  • A Workstation Simulator (added by ProModel this year) that is extremely useful for instructors and students.

I have also found the ProModel staff to be responsive, courteous, and willing to help with any issues that may arise. I believe ProModel recognizes that offering an excellent value and support in the teaching environment will pay long-term dividends as the students move into their professional environment, and I applaud ProModel for their insight.

About Robert Loomis

Robert Loomis received a BSEE from Michigan State University, and an MS and Ph.D. in Industrial Engineering from Texas A&M University.  For the last 30 years he has worked for NASA and the United Space Alliance (USA) in the space and aerospace environment as a safety and reliability expert. His NASA positons included Chairman of the Kennedy Space Center (KSC) Safety Engineering Review Panel, Chairman of the KSC Ground Risk Review Panel, Manager of Data Systems at NASA Headquarters, Deputy Director of Safety at Dryden Flight Research Center (DFRC), and Head of the Independent Technical Authority at DFRC. He held numerous positions with USA, culminating in Corporate Director of Mission Assurance.  Dr. Loomis’ recognitions include the NASA QASAR Award, the NASA Exceptional Public Service Medal the Astronauts Silver Snoopy Award; the IEEE Millennium Medal; IEEE Reliability Society Lifetime Achievement Award; and Leadership and Teamwork Awards from the United Space Alliance.  He is a Senior Member of the IEEE and a Fellow of the Society of Reliability Engineers. He is an adjunct professor at Florida Tech; and most importantly, a Full-Time Grandfather to the three nicest, smartest, and best-looking grandchildren on the planet.

In the OR with Dale Schroyer

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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

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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

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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!

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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.

 

Flanagan Industries Brings New Facility Online Thanks To ProModel Solution

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Flanagan Industries is a major contract manufacturer of aerospace hardware specializing in highly engineered and high value machined components and assemblies.  Over the years their manufacturing operations had been growing steadily to the point where they absolutely needed additional capacity . The original space was not conducive to a manufacturing environment and had become an impediment to taking on more business and staying competitive in the global economy.  So Flanagan decided to expand by opening a new facility that could house bigger and better machinery, however they needed to ensure that the move to the new location would not disrupt their current operations and customer orders.

In the video below, see how Flanagan used a ProModel Simulation Solution to successfully bring their new facility online:

 

 

 

FREE ProModel Webinar: Predictive vs. Prescriptive Analytics

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Join ProModel’s CTO, Dan Hickman, and Product Manager, Kevin Jacobson (KJ), on Wednesday November 5, 2014 – 2:00 PM EST for an informative webinar on predictive vs. prescriptive analytics. 

With over 15 years in the industry, Dan has an uncanny understanding of how important both types of analyses are to the success of your business. KJ, with ProModel for over 11 years, manages the Project and Portfolio Simulation product development group. He works closely with our clients on the development of advanced PPM (Project Portfolio Management) predictive and prescriptive analytic tools. He has the hands-on experience to best illustrate how the tool works and how it can help you with your predictive and prescriptive analytic needs.

Together they will show you how ProModel’s Enterprise Portfolio Simulator with Portfolio Scheduler provides the benefits prescriptive analysis can bring to resource capacity planning and project selection. Gain an understanding of the difference between applying predictive and prescriptive analytics to your PPM data, with specific examples focusing on scenario experimentation and portfolio optimization.  KJ will demo some of the newer features of EPS that provide logical recipes for modeling  and show how these tools can help you represent your unique PPM business rules.  The new business rules capabilities of EPS provide portfolio simulation like never before.

CLICK BELOW TO REGISTER FOR THIS WEBINAR NOW!

https://www150.livemeeting.com/lrs/8002083257/Registration.aspx?pageName=k09m7ldp55z3t048&FromPublicUrl=1

 

 

Demystifying System Complexity

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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

Same Venue, Different Challenges

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Weeds Pic

Rob Wedertz – Director, Navy Programs

Just a few weeks ago, I had the privilege of attending the Tail Hook Association’s annual conference in Reno, Nevada.  It is the first time I attended the conference not as an active duty member of the Naval Aviation community, but as a vendor supporting the enterprise through our role as the software application provider of the Naval Synchronization Toolset.  Surprisingly, other than keeping much different hours and standing on the opposite side of the booth table, the conference felt much like it did every year I have attended in the past.  There were many “so what are you doing these days?” conversations with old friends and the ever-present aura of “Naval Aviation is special because…” throughout the exhibit hall.

In fact, had I not taken the opportunity to attend some of the panels and engage some of our key stakeholders in pointed conversations it would have been extremely difficult to differentiate this year’s conference from any other I had attended over the last 2 decades.  There was a new vernacular that weaved its way into this year’s conference.  Words like “sequestration”, “draw-down”, and “budget constraints” permeated the Rose A ballroom, and for the first time in many years, I sensed a palpable uncertainty among the leadership of Naval Aviation as they extolled the virtues of tail hook aviation’s role in the world theatre against the backdrop of future shoe string budgets and unknown war fighting requirements.  (Ironically, the Air Boss told a poignant story of a “nugget” strike fighter pilot from CVW-8 expertly delivering ordnance in the fight against ISIS the same day the morning news detailed the withdrawal of forces from Afghanistan as “hostilities in the Middle East come to a close”.)

Given the environment we’re in and the abundance of questions marks hovering over the next several years, it should come as no surprise that many attendees, including most of the NAE leadership took a great deal of interest in the “little” ProModel booth nestled among missile mock-ups, Joint Strike Fighter simulators, and high-tech defense hardware displays.  In fact, as one of the very few (if not the only) predictive/prescriptive analytics software vendors in attendance at Hook ’14, we were an anomaly.

Tailhook '14

ProModel’s Keith Vadas and Carl Napoletano speak with VADM Dunaway, Commander, Naval Air Systems Command

 

A common theme emerged during our discussions with visitors and through comments made during the various panel discussions – decisions must be made via actionable data, courses of action must be modeled and validated, and technology-enabled decision support applications must be agile enough to get an answer in short order.  Thus the interest in ProModel.

While the Naval Synchronization Toolset is in its infancy from a relative viewpoint (we achieved initial operating capability just a year ago), ProModel has been delivering enterprise-wide decision support tool capabilities to its customers (both private and DoD) for over 25 years.  As industries have evolved (adopted Lean Six Sigma methodologies, harnessed data collection and aggregation, and leveraged emerging technologies) so has ProModel.  We have learned, alongside our customers, that there is significant “power” in diminishing uncertainties through “what-if” analysis and exploration of alternatives via technology-enabled decision support tools like the NST.  The questions the NAE gets asked have answers and it is discovering that getting there is a matter of adopting a philosophy that centers around modeling the behavior of the system, deciding on dials (variables), and exploring the alternatives.

The NST is that system.  Through our integration efforts with Veracity Forecasting and Analysis, we have delivered a software application that establishes the demand signal (the Master Aviation Plan module), models the behavior of the system (Carrier Strike Group Schedule, Air Wing Schedules, and Squadron Schedules), models the behavior of elements (the Airframe Inventory Management module) the utilization of the FA-18 A-F inventory over time, and provides a “sandbox” environment that facilitates optimal disposition of assets in order to meet the requirements of the NAE over time.

We heard, during our attendance at Hook ’14, that the optimal management of the FA-18 inventory was one of the focal points of the NAE leadership.  And although we’ve been involved in the development efforts of the NST for more than 2 years, it is the first time that the challenges of inventory management have taken center stage at a venue that has long been unchanged and timeless.  We felt privileged to be among the professionals in attendance at Hook ’14 and even more proud to be an integral part of the solution set to Naval Aviation’s challenges going forward.  We’ll be back next year and hope that the NAE is no longer talking about it.