Tag Archives: Process Consulting

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.

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

Simulation Ensures Patient Safety During Hospital Move

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Northwest Community Hospital is an acute care hospital in Arlington Heights Illinois, right outside of Chicago.  The staff at NCH had the very complex and delicate task of arranging and accomplishing the move of 150 patients over to a newly constructed facility on campus.  This is a welcome but difficult situation that many healthcare organizations find themselves in today as technology improvements and rising patient populations demand growth.

See how NCH achieved a flawless transition through predictive analytics and simulation:

Power of Predictive Analytics for Healthcare System Improvement and Patient Flow

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Hospitals are currently under intense pressure to simultaneously improve the effectiveness and efficiency of healthcare delivery in an environment where operating costs are being reduced, downsizing and consolidation is the norm, and cost for care is increasing while revenue is decreasing.  At the same time the systemic effects of peak census and varying demand on patient LOS are creating capacity issues and unacceptable patient wait times…leading to a major decline in patient satisfaction.

The amount of proposals to enhance a hospitals quality care are as numerous as the healthcare professionals dedicated to the cause.  What hospitals need however is the ability to quickly and accurately evaluate the impact of those various operational proposals and to experiment with system behavior without disrupting the actual system – and ProModel’s simulation technology is allowing them to do just that.

The predictive analytic capability of ProModel simulation will allow healthcare professionals to test assumptions and answer those patient flow “what if” questions in a matter of minutes and days, not weeks and months.  Simply put, it’s providing a decision support system to assist healthcare leaders in making critical decisions quickly with a higher degree of accuracy and confidence.

Simulation will also help healthcare staff quickly identify room availability and recognize high risk patient flow bottlenecks before extreme problems occur.  This invaluable knowledge will then lead to reductions in patient wait times and LOS, avoid unnecessary re-admissions and costly expansions, and most importantly – increase the overall quality of service and patient satisfaction.

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

 

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.

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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

Designing Better Care For Your OR

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JCowden Profile Pic

Jennifer Cowden – Sr. Consultant

Earlier this year, my family and I took a vacation to a certain kid-friendly theme park.  As we wandered from ride to ride, we couldn’t help but note that, even at the peak times on the more popular rides, you rarely saw crowds standing outside waiting. The long lines were all contained within a succession of fairly climate-controlled rooms that obviously took some thought to plan. This particular company is big into predictive analytics, so I would hazard to say that they didn’t just guess at the maximum size of the line at peak time; they are probably not going to go live with a new attraction or other big change unless they simulate it first.  An interesting dynamic that we observed was that when a wait time for an attraction was lowered on their new mobile app, we could literally see the “flash mob” of patrons converge on that ride, causing the line to go from a 10-minute wait to a 30-minute wait in the blink of an eye.  I turned to my husband, who is also an engineer and a geek, and said “I wondered if their model predicted that.”

Theme parks obviously need to be concerned about a positive overall  visitor experience; after all, they are always competing for discretionary funds with other sources of entertainment.  Now, more and more hospitals are developing that same mindset: being cognizant of the overall patient experience to the point of modeling new spaces before they go live.  How many OR rooms should they outfit for opening day, and how many can wait?  How can they make the best use of the spare rooms?    Is there enough space in the corridors that the patients won’t feel too crowded?  Is there enough space in the waiting areas for the families of the outpatients?  How many staff members do they need for each department to minimize patient wait time?  Are there any unforeseen bottlenecks due to sudden dynamic shifts?  These are just a few of the questions that simulation can answer.

Check out Jennifer’s Ambulatory Care/OR Suite Model:

About Jennifer

Before joining ProModel in 2013, Jennifer spent 15 years in the automation industry working for a custom turnkey integrator. As an Applications Engineer she built simulation models (primarily using ProModel) to demonstrate throughput capacity of proposed equipment solutions for a variety of customers. Jennifer’s experience covers a wide range of industrial solutions – from power-and-free conveyor systems to overhead gantries and robotic storage and retrieval systems. She has also created applications in the pharmaceutical, medical device, automotive, and consumer appliance industries.

Jennifer has a BS in Mechanical Engineering and a Master of Science in Mechanical Engineering from the Georgia Institute of Technology.

Busy Season at ProModel

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Keith Vadas

Keith Vadas – ProModel President & CEO

I am pleased to report ProModel’s second quarter was very positive.  Like many businesses in the US we find ourselves on a serious upswing this Summer of 2014.  Our consultants are working on several projects in a variety of industries, including ship building, power management, retail, manufacturing, food processing, and government contracting.  In all of these projects our experienced team of consultants is working to improve efficiency, save money, and make better decisions for their clients.

ProModel’s DOD projects continue to thrive.  It is hard to believe it has been eight years since we started working with FORSCOM (US Army Forces Command)   on AST (ARFORGEN SYNCHRONIZATION TOOL).  LMI-DST (Lead Materiel Integrator – Decision Support Tool) with the LOGSA Team (US Army Logistics Support Activity) is also going strong.  Our agile team of software developers keeps improving the development process within ProModel and it shows. Just recently the NST Airframe Inventory Management Module was Granted Full Accreditation by the Commander, Naval Air Systems Command.

The time is also ripe for opportunities in Healthcare.  Our patient flow optimization capabilities are perfect for helping hospitals and outpatient clinics improve efficiencies.  Now that the Affordable Care Act has been around for a couple of years, its impact is being felt by healthcare organizations around the country.  The expanded insured-base, and the need for improved processes and different care models is making it absolutely necessary to consider the value of modeling and simulation.  ProModel continues to work with several facilities including Presbyterian Homes and Services, and Array Architects who enhance the flow in Healthcare Facilities design by using MedModel simulation in their design processes.

To better support our base of existing customers, we just released ProModel/MedModel 2014 in July and PCS Pro 2014 at the end of Q1.  EPS 2014 (Enterprise Portfolio Simulator) was released in Q2  and includes a new easy to use, web-based rapid scenario planning tool – Portfolio Scheduler.  You can check this tool out online at – http://portfoliostud.io/#.

There continue to be lots of exciting things happening at ProModel.  We have an outstanding team of consultants and software developers-designers just looking for an opportunity to PARTNER with you to help you meet the next business challenge, or solve the next unexpected problem.