Category Archives: Lean Simulation

ProModel and Autodesk – Partners in Optimal Factory Design

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Autodesk, the creator of AutoCAD® and Inventor® software, and ProModel have teamed up to bring you the best of both worlds of factory design and process optimization.  ProModel’s Process Simulator Autodesk Edition and ProModel Optimization Suite Autodesk Edition connect to Autodesk’s AutoCAD, Inventor, and Factory Design Utilities in order to streamline model building, process optimization and ultimately facility design.  You can get a 30-day evaluation version of both products from the Autodesk App Store

Process Simulator Autodesk Edition Eval

ProModel Optimization Suite Autodesk Edition Eval

Check out these great new Process Simulator Autodesk Edition videos:

Executive Overview – 2 min

 

Detailed Overview – 6 min


 

Univ. Texas Master of Public Health Program Teaches Lean Concepts With Process Simulator

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headshot_prof michael kennedy 10-14-18

Michael H. Kennedy PhD, MHA, FACHE Assoc Prof & Chair Dept of Healthcare Policy, Economics and Management University of Texas Health Science Center at Tyler

I was first exposed to ProModel simulation products as an alternative to GPSS-H when earning my doctorate in Decision Sciences and Engineering Systems at Rensselaer Polytechnic University from 1988 – 1992.  I taught one simulation course as a graduate elective for the U.S. Army-Baylor University Graduate Program in Healthcare Administration soon after graduation.  Since then, I have employed MedModel as a component of several health administration courses, with simulation comprising approximately one-third of the course content.  Simulation modeling is a wonderful tool to reinforce concepts from statistics and probability such as the effects of random variation, practical application of probability distributions, and employment of goodness-of-fit testing.

After I arrived in January 2017 at the University of Texas Health Science Center at Tyler to begin teaching in our Master of Public Health Program, I decided to revise my approach to teaching quality.  Previous iterations of my quality course had generally proceeded as follows:

image_kennedy blog chart 1

Content was taught face-to-face and students used their laptops to create process analysis tools and control charts with me in class.

My decision to include Lean in the quality course provided the opportunity to learn and teach Process Simulator, another ProModel product.  Process Simulator operates as an add-in to Visio.  I couldn’t think of a better way to teach flow than to have students build process flow diagrams in Visio and then to model the flow of entities through the system using Process Simulator.  The revised course proceeded as follows:

image_kennedy blog chart 2

The first process model was built after an introduction to M/M/1 queuing formulas in an exercise to establish the correspondence between queuing and simulation.  I modified a problem provided by Ragsdale (2004) to model Acme Pharmacy which has one pharmacist with the capacity to fill prescriptions from 12 customers per hour.  The pharmacy averages 10 customers per hour seeking to fill prescriptions.  Students used an M/M/1 Excel template to compute and characterize Acme Pharmacy operations and to record queuing formula and simulation results.  The Process Simulator model looked like this:

image_kennedy blog chart 3

The comparison of the first two columns of Table 1 between queuing results and simulation results confused me until I remembered that the M/M/1 queuing formulas represented a steady state solution.  An 8-hour run with 20 replications shown in the third column produced results more closely aligned, but not as close as expected.

image_kennedy blog chart 4

Table 1.  Comparing Queuing and Process Simulator Results                                                        Note – Expected times have been converted from hours to minutes.

An extended 2080-hour run and an 8-hour run after an 8-hour warm-up were also subsequently modeled to determine if those efforts to move the system towards steady state produced results more closely aligned with queuing formula results.  The results in the last two columns showed that to be true.

During the remainder of the course, Lean principles were reinforced with a variety of Process Simulator models.

Reference

Ragsdale, C. T. (2004). Spreadsheet modeling and decision analysis: A practical introduction to business analytics (4th ed.). Mason, OH: Thomson South-Western.

About Dr. Michael H. Kennedy

Dr. Michael H. Kennedy, FACHE, arrived at the University of Texas Health Science Center at Tyler on January 23, 2017 to begin service as an Associate Professor in the Department of Healthcare Policy, Economics and Management in the School of Community and Rural Health. He has 42 years’ experience in teaching and health services administration that have been divided between academic positions and operational assignments in the military health system as a human resources manager, equal opportunity advisor, ambulatory care administrator, and other positions of leadership culminating as the Chief Operating Officer of a small military hospital.

In past academic assignments, Dr. Kennedy has served as Director of the Health Services Management Program at East Carolina University, Director of the Doctor of Health Administration Program at Central Michigan University, and Associate Professor in the Health Services Administration Program at Slippery Rock University. Dr. Kennedy was also Deputy Director of the U.S. Army-Baylor University Graduate Program in Healthcare Administration where he was twice selected by the students as Instructor of the Year.

Dr. Kennedy is a Fellow in the American College of Healthcare Executives.  He was appointed as Chair of the Department of Healthcare Policy, Economics and Management on September 1, 2018.

Dr. Linda Ann Riley’s (Univ. New Haven) Innovative Teaching Approach for ProModel and Process Simulator (1 of 2)

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Headshot_Linda Riley UNH

Dr. Linda Ann Riley Ph.D. UNH

I have been teaching ProModel for the past 20 years and most recently in the capstone simulation course in the University of New Haven’s (UNH) M.S. in Engineering and Operations Management (MSEOM) program.  In the course immediately preceding simulation, Six Sigma Quality Planning, I use both Microsoft Visio and Minitab as the software programs for course delivery.  This brings me to the topic of this blog post: my experiences this past Spring introducing Process Simulator into the ProModel /Visio/ Minitab mix.

 

In a traditional semester setting, teaching four different software programs in addition to subject matter content could be achievable. But the University of New Haven’s MSEOM is delivered in an accelerated format with each class meeting six hours on one evening each week over seven weeks. The final two classes in the program are Six Sigma Quality Planning and Simulation Techniques and Applications.  During these final two classes over 14 weeks, students also undertake a technical capstone project that fulfills a graduation requirement.

The UNH MSEOM is directed to individuals with technical undergraduate degrees presently holding middle to upper level management positions.  Almost all of the participants in the cohort work in engineering related jobs at large organizations such as General Dynamics, Amgen, Pratt & Whitney, Lockheed Martin, Pfizer and General Electric to name a few.  Fortunately, I have always had highly motivated students in each cohort I have taught over the past 10 years.

From day one of the Six Sigma class, three programs were introduced, Visio, Minitab and Process Simulator.  I use Lean Six Sigma and Minitab (5th Edition): The Complete Toolbox Guide for Business Improvement by Quentin Brook as the text for the class.  Teaching for six hours per class requires a pedagogical strategy of moving from content delivery to computer exercises and back multiple times during the class.  This strategy has been highly effective over the years and allows for reinforcement of the subject matter and in-class practice and troubleshooting using software.

Since most of the cohort had at least minimal exposure to Visio in the workplace, introducing Process Simulator proved to be rather seamless.  The Quickstart and How To videos were assigned for homework on the first class and by the second class, my expectation was that each student could create a fairly straightforward process flow diagram in Process Simulator as an in-class lab.

PS first model

Process Simulator – First Student Model

The mechanics of creating a process flow chart in the Process Simulator environment presented no challenges for the students.  However, one of my lessons learned involved the information needed to move from the purely Visio environment to the Process Simulator environment.  The level of detail needed with respect to the amount and type of information to accurately define properties for activities, routing rules and arrivals was of the same magnitude as needed for a simulation exercise using ProModel.

PS properties machine center

Process Simulator – Activity Properties Dialogue Box

Even though the students could apply simulation properties to their Process Simulator diagrams, the output results were far from the expected solution at first.

PM first model capacity graph

Output Viewer – Single Capacity Activity States Graph

Consequently, some time was spent troubleshooting the models.  Yet in the process, students developed a much richer understanding of how to use Process Simulator especially within the context of Lean and Six Sigma methodologies.  After a reasonable level of proficiency was developed with Process Simulator, we were able to export data and further analyze results in Minitab.  We will complete Linda’s story in the next post.

About Dr. Linda Ann Riley Contact Information: linda.ann.riley@gmail.com

Linda Ann Riley, Ph.D. presently serves as an Adjunct Professor of Engineering for the University of New Haven’s graduate program in Engineering and Operations Management. She retired from full time teaching and administration in 2015.  Dr. Riley worked for 12 years at Roger Williams University (RWU) where she held the positions of Associate Dean, Engineering Program Coordinator and Professor of Engineering. Prior to RWU, she was a Professor and Program Director at New Mexico State University for 18 years.  Her teaching experience includes both engineering and business courses and she is the recipient of a number of corporate, university and national excellence in teaching awards. Dr. Riley is the author/co-author of over 100 articles, technical and research reports, and book contributions. Her area of scholarly interest involves stochastic system optimization using simulation and evolutionary algorithms.

From Reality to Model

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Adjunct Prof Mark Klee Headshot

Mark Klee; Adjunct Professor – Eastern Kentucky University

I know what you are thinking “From Reality to Model” shouldn’t that be the other way around? As an engineer at Toyota for the past 24 years I often encounter manufacturing processes that have slowly de-optimized. And now, just by walking by the processes on the floor, I can see waste (motion, waiting, over-processing). I know this means that these processes need some work.

Our typical method of improving these processes would be to employ the traditional Toyota Production System tools. We begin with observation and time study. Then we use video for motion analysis making these processes visual on paper with standardized work combination tables, standardized work charts, and production capacity calculations. Through these simple analysis tools, the waste in the process becomes more obvious and begins to generate ideas for improvement.

This is typically done one process or one zone of processes at at time. It is also usually done with paper, pencil, and stopwatch. The methods have proven time and again to be effective for process improvement and an effective method of developing engineers as well as manufacturing floor members in process improvement.

After the waste is discovered and the improvement ideas generated it is time to try some improvement ideas. The process visualization and capacity calculation documents are then modified to simulate the improvement idea. Then it is time to try the modified process on the production floor. The concept is tested in a controlled environment. After success is documented, the process standards are modified the team is trained to the new standard.

Using ProModel works very well with the Toyota Production System and as a method for developing manufacturing engineers, manufacturing floor members and students in manufacturing focused curriculum. In Eastern Kentucky University’s Applied Engineering and Management class, we follow this progression.

  • We first focus on learning process observation and visualization skill using the standard Toyota Production System tools.
  • Next we learn the processes of implementing controlled change in a mass production environment. We learn and practice these skills on the manufacturing floor to gain real world experience.
  • After learning the basics of observation and improvement, we come back to the classroom where we employ ProModel to fine tune our processes and learn if there are any opportunities for optimization that may have been overlooked.
  • With ProModel we can also test scenarios that may be difficult to test on the actual production floor like moving a piece of equipment, modifying a cycle time, changing a conveyor length or changing a delivery frequency.
  • These trials can be done as quickly as you can change the numbers in the model allowing for many more cycles of trial and error or trial and success in a shorter time.

As a result of the course and ProModel, students have deeper understanding of both the theory and application of process improvement allowing them to be an instant contributor to a manufacturing organization upon their graduation.

In the end, deeply understanding the current reality through observation, documentation, and modification of the current process helps us make a more accurate model. The result of the more accurate model is further optimization. This deepens learning and the improvement cycle continues.

Brief Bio:

Mark Klee, BS Eastern Kentucky University 1990, MS Purdue University 1992
Toyota Motor Manufacturing Kentucky 1994-Present
Eastern Kentucky University Adjunct Faculty 2012-Present

State Probation Office Assesses Jail Occupancy Rate with Simulation

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CHALLENGES

The mission of the Office of Probation for any state in the US is to provide seamless services to the victims, communities, offenders, and courts of that state. The administration of probation is a complex and ever-changing process. Recently a state probation organization’s Sr. IT representative contacted ProModel looking for help understanding, analyzing, and improving its probation office processes. Its systems and infrastructure needed to be updated, but before that could begin they needed to understand the “As Is”condition of its processes and all that was involved.

During the project, the US Justice Department planned to release about 6,000 inmates early from prison—the largest one-time release of federal prisoners — in an effort to reduce overcrowding and provide relief to non-violent drug offenders who received harsh sentences over the past three decades. The inmates from federal prisons nationwide were set free between Oct. 30 and Nov. 2 of the same year. This followed action by the U.S. Sentencing Commission—that reduced the potential punishment for future drug offenders from the previous year and then made that change retroactive. The panel estimated that its change in sentencing guidelines eventually could result in 46,000 of the nation’s approximately 100,000 drug offenders in federal prison qualifying for early release.

It became important to also determine the impact of this action on this state’s probation services and local jail systems.

OBJECTIVES

The Probation Office was interested in using Predictive Analytics to analyze the as-is condition of its processes:

• Where might there be any bottlenecks or constraints?

• Assess the impact of the new law on the probation office workload and the local county jail occupancy rate.

• Where could other improvements be made?

SOLUTION

In order to model and simulate the current processes, they needed to be fully understood and documented. ProModel’s resident lean expert was brought in to work with Office of Probation personnel to create a quick high-level Process Simulator Model of the voucher process. Together in a room with four or five probation team employees, ProModel documented in Microsoft Visio, the ins and outs of the voucher system. When this model was built and simulated, the results so closely resembled the realities of the current process and resource utilization of certain team members, that the go-ahead was given to proceed to a complete model of the voucher process.

The entire probation process was modeled and simulated by several experienced members of the ProModel consulting team, along with Office of Probation personnel. The following processes models were completed:

1. Voucher process

2. Juvenile probation process

3. Adult probation process

4. Problem-solving court process

Probation Voucher Process_Image_5

One Part of the Overall Model

VALUE PROVIDED

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.

This simulation model clearly communicated that the current processes and resources available were not adequate to handle the predicted increases in probation candidates. Several areas of the process were evaluated for improvements and the model was used to validate several proposed IT enablers and Lean modifications.

 

Oilfield Equipment Manufacturer Optimizes New Facility Design

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CHALLENGES

A leader in the design, manufacture, and supply of oilfield equipment had recently purchased land to build a world class manufacturing facility.  The new location would be designed to capture future growth but needed to be sized correctly; not a wasteful over-construction yet not too small at the same time.

The senior executive team thought simulation modeling would allow them to analyze their manufacturing processes, identify bottlenecks, capture productivity improvements, and properly size the new facility.  After a lengthy vendor sourcing exercise, ProModel Corporation was selected as the best provider to answer this modeling challenge.

OBJECTIVES

  • Model the existing manufacturing processes
  • Identify current process constraints using various customer demand scenarios
  • Simulate maximum throughput potential with the current processes and equipment layout
  • Using LEAN process improvement skills, simulate a more productive manufacturing process and scale that upward to capture growth
  • Simulate the new manufacturing facility and validate the desired growth rates. Upon completion of this step, the layout would be given to the architects for structural design.

VALUE PROVIDED

  • Immediate identification of a critical bottleneck that once resolved, increased cell throughput by 53% and overall production by 19%
  • Throughput has grown 45% since the launch of the initiative due to a much better understanding of their manufacturing methods and related constraints
  • Manufacturing standards used by the production planning team were far from accurate thus creating a workflow imbalance
  • Equipment previously slated for purchase was determined to add no throughput benefit thus saving several hundred thousand in capital expenditures
  • Numerous future state layouts were modeled thus allowing the team to ultimately select the most productive equipment arrangements
  • The simulation model became a powerful sales tool with customers; understanding the flow in the facility and how it could absorb their incremental orders
  • Even during a severe industry downturn, the company continued to capture market share due to improved manufacturing methods.

SOLUTION

A ProModel senior consultant worked with the engineering staff to build dynamic models of their current production facility and planned future construction.

First, a dynamic flexible model of the existing facility was created and validated.  That model was used to define the true capacity of the existing facility, analyze current constraints, evaluate capital improvement options, and test new LEAN concepts that were under consideration for the current and future facility.

A major challenge to creating the model was accommodating the tremendous variety of products manufactured.  A user friendly interface for running the model was developed to provide the ability to run any variation of mix/demand against several operational configurations.

The key learnings from the existing facility model were then applied to the new facility design.  Alternate facility layouts and new material handling concepts were evaluated to ensure the plant of the future would meet all capacity targets.

3D Animation of a Portion of the Plant

3D Animation of a Portion of the Plant

 

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

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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 ezohil@promodel.com.  To recommend whether Dales should visit Alaska or the British Isles, email him at dschroyer@promodel.com.

Interested in learning more about ProModel consulting, check out: http://www.promodel.com/Services/Consulting, or consulting@promodel.com.

 

 

One Way Automotive Manufacturers Can Meet the Challenges of a Rapidly Changing Market

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The automotive industry is likely to change more in the next 10 years than it has in the previous 50. It seems like in so many industries today including technology, entertainment, and consumer products, change at a very rapid pace.  The auto industry is by far no exception.  There are many new entrants into car making, add to that self-driving vehicles, electric cars, and car sharing just to name a few.  All these factors are providing increased competition.  Not to mention the rapidly fluctuating price of gasoline.  With instability in the Middle-East and increased oil production in the US and other parts of the world, who knows how that may change in the next 6 months.  There is no doubt that reacting quickly and strategically to these rapid demand shifts will be an absolute priority for auto leaders in 2016.

Simulation is a tool that can help automakers accommodate these rapid changes and develop scenarios for planning for the uncertainties that may occur.

Consider that a US plant reduced its work force by 20% in 2010 during the recession.  Not only that, but floor space has been re-arranged to accommodate those reductions.  Now in this post-recession period the demand for vehicles from this plant is increasing rapidly.  How do you meet that demand with the existing workforce? Can you build the number of vehicles necessary without moving lines or cells around again and hiring more workers?  If you do hire, which positions, how many, and on what shifts do you need more FTEs?  Simulation can help you make these decisions more confidently.  Here are some ways in which it has already been done.

The Rim Assembly Model

A large automotive component manufacturer experienced difficulties reaching a desired line speed.  The operation involved mating a set of tires with rims for multiple manufacturers.  The line was consistently under producing and management wanted the problem solved now!  Given the interactions between the various parts of the line, it was difficult to assess which component was the actual bottleneck. Only a limited number of things could be changed, so the objective was to find what modification to the line was possible to achieve improved speed in a short period of time with as little capital investment as possible.  The following modifications were tested:

  • Sequence the tires to the lean cells. The baseline was for tires one and two to go to lean cell one and tires three and four to go to lean cell two.
  • Shorten the load time between rims by staffing and laying out load position differently
  • Use only one lean cell
  • Eliminate the use of “switch-outs” where a failed mating between rim and tire at the lean cell required that the lean cell be stopped
  • Adjust the tire feed spur lengths

The largest gain in line rate required three changes: the time between rim arrivals was reduced from 23 seconds to 16 seconds, the elimination of switch-outs and the lengthening of tire feed spur lengths.

These modifications allowed the client to get to the desired line rate and the model was developed and results were submitted within 5 days. View the video for a quick sample of the model.

Check out one of our success stories about another auto manufacturer: Tofus-FIAT Realizes 48% Reduction in WIP with ProModel Simulation. This solution story is available among many from our online library. Many solution and model videos are also available on our YouTube Channel. If you would like to learn more about ProModel solutions contact us.

Other References:
http://www.weforum.org/agenda/2016/01/the-next-revolution-in-the-car-industry
http://www.mckinsey.com/industries/automotive-and-assembly/our-insights/a-road-map-to-the-future-for-the-auto-industry

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