MedModel OR Case Cart Implementation Study Shows How to Improve Patient Throughput by 38%

Situation

Hospitals face increasing pressure to reduce costs while continuing to provide quality care to patients. The operating room, one of the most difficult and expensive wings to manage, must run efficiently in order to avoid unnecessary costs. Hospital managers often implement case cart systems which create a centralized materials management system. Case carts carry medical supplies within an operating room. The case cart system ensures that the staff obtain the necessary materials and instruments in time for their upcoming procedures (1. Making a Case for a Case Cart System).

This study was undertaken to test the impact of implementing a case cart system on the OR process in a client’s newly configured OR Suite. The impact was determined by patient delays in any stage of the OR process that was attributable to case carts.

Objectives

The client wanted a predictive analytic model which would help answer the following key questions:

  • Has the medical center acquired enough carts to satisfy the volume requirements?
  • Are there enough Sterile Processing Department resources to support the case cart process?
  • Will the case carts introduce any new delays in the patient process?
  • How many carts need to be staged prior to morning start to ensure smooth OR Suite flow?

Results

The model outputs suggest that maximum patient throughput could increase by 38% in 6 months with the implementation of a case cart system. The following additional insights were also gained from the study.

  • Determined that 55 small carts and 28 large carts are needed to ensure there are no delays due to case carts.
  • Determined that 6 SPD FTE’s are required to pack the morning case carts and 4-5 SPD FTE’s are required during normal OR operation hours.
  • Realized that cart picking must begin as soon as possible after midnight to ensure there are enough carts ready at the start of the day. To maintain a steady flow, the carts must be available and ready for the first two procedures. The modelers found that maximum case cart use time occurs early for a maximum of 1 hour.
  • The implementation of case carts caused no significant delays in patient flow times.

 

Maximum System Volume

At these higher volumes both POCU and PACU spaces become limiting factors.

SS-HC-Case-Carts-Improve-Patient-Throughput[1]

Solutions

Defining the Process

A spreadsheet defines the “patient flow” process as it relates to patient type, location sequence, staffing utilized and task times. The spreadsheet “Staff” columns work together to schedule the first staff member required for each procedure step. Some procedure steps have the staffing flexibility of allowing an alternate position to “back up” the primary position. Times for each process step are defined in the Process spreadsheet using triangular distributions which account for work time as well as wait time.

Cases Defined by Historical Data

The medical center provided historical data such as original date of surgery, the service which performed the procedure, the surgeon assigned to the case, and the OR assignment.

Block Schedule

Operating room schedules are entered onto a spreadsheet. The model solution places the previously entered cases into schedule blocks and continues through the process until the patient completes the surgical experience.

Staffing

The simulation model uses the data on a worksheet to perform scheduling tasks by staff person, primary or secondary resource group, and times that shifts begin and end.

Sterile Processing Department Input Worksheet

Data entered into the Sterile Processing Department (SPD) worksheet is matched with the procedure from the “Cases” worksheet. The model solution will produce results indicating the turn around time on the carts, and will predict the performance of SPD.

Location Assignments

A worksheet defines the primary and secondary uses of each location in the model.

Procedure Requirements

Three triangular time distributions are used on this model (Min / Mode/ Max) to represent procedure times for all clinic procedures. The first triangular is used for the procedure itself. The second triangular is used for room turnover. The third and last triangular distribution is the set-up time occurring before the next procedure is performed.

Room Restrictions

“Special Restrictions” may apply for up to five ORs. These restrictions define the rooms that may be used

by each service. An entry of “999” indicates that “any” OR may be used.

Services Using Case Carts Chart

Services using carts receive a “1” in corresponding column while services not using case carts remain blank.

References: SS-HC-Case-Carts-Improve-Patient-Throughput[2]“1. Making a Case for a Case Cart System.” Making a Case for a Case Cart System – Research – Herman Miller. Herman Miller Inc., n.d. Web. 14 June 2017.

Joint Force Capability Catalog (JFCC)

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Rob Wedertz – VP DoD Programs

The United States Military consists of more than 4 million active and reserve men and women, operates at 800 military bases in over 70 countries and has an annual budget of nearly $600B.  The requirements to manage this force globally and ensure it is adequately equipped, trained, and ready to implement both our National Security Strategy and National Military Strategy are daunting tasks.  For the military planners who must provide the most sound and reasoned advice to military and civilian decision makers who ultimately have the authority to direct the forces to carry out the global strategy, detailed information about these forces must be readily available and current.

The Vice Chairman of the Joint Chiefs of Staff has doctrinally mandated the integration of Enterprise Force Structure data (Army, Air Force, Navy, and Marine Corps forces – capability, readiness, availability and employment) via the Global Force Management – Data Initiative.  This effort will provide the Force Providers (Services), the Combatant Commanders (Force Requirements), and the Joint Staff (Force Allocators) with a technology-enabled Decision Support Platform to carry out the National Military Strategy.

The Joint Staff J35S has been tasked with the technology implementation of this capability, called the Joint Force Capability Catalog (JFCC).  The Joint Staff J35S has chosen ProModel Corporation to design, develop, and implement the JFCC.

JFCC Dashboard

ProModel was chosen as the lead software provider based upon our deep-seeded experience providing Decision Support Tools such as the ARFORGEN Synchronization Toolset (AST), the Lead Materiel Integrator – Decision Support Tool (LMI-DST), and the Naval Synchronization Toolset (NST).

The JFCC is a “sea change” for the Global Force Management community because it is not being developed as a stand-alone platform, but rather as an integrated system with the capability to:

  • Aggregate data from more than 60 disparate systems
  • Present the data in a user-friendly graphical user interface
  • Conduct Course of Action (COA) predictive analysis

The JFCC ultimately provides stakeholders with the ability to do the following:

  • Account for forces and capabilities committed to ongoing operations and changing unit availability
  • Identify the most appropriate and responsive force for capability to meet Combatant Commander requirements
  • Identify risk for the Secretary of Defense associated with sourcing recommendations
  • Improve the Department of Defense’s ability to win multiple overlapping conflicts
  • Improve the Department of Defense’s responsiveness to unforeseen contingencies
  • Provide predictability of the Services’ rotational force requirements
  • Identify forces and capabilities that are unsourced or hard to source

ProModel is proud to have been chosen to provide this much needed capability to the Department of Defense.

Increasing Use of Custom ProModel Integration Yields Big Benefits

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Keith Knudsen ProModel Project Manager

Traditional Modeling Provides Great Benefits, But Can Do More…

Over the past 29 years, ProModel users across a spectrum of industries have demonstrated the value that modeling can bring to an organization. Just a few examples include:

  • A leading manufacturer of oilfield equipment who modeled their existing location to identify ways to optimize their processes and gained a 45% throughput increase
  • Shipbuilding companies that have used our traditional modeling to improve shipyard production and capacity planning
  • Hospitals that have used MedModel for decades to improve patient flow.

Increasingly, however, ProModel customers are looking to extend these benefits by integrating their models with other IT systems to develop web-based decision support tools. These model-based tools utilize a ProModel engine on a server, read in live data and utilize ProModel simulation and optimization capabilities to provide forecasting, automated scenario exploration and prescriptive suggestions.

Custom Integration Amplifies The Value of a Good Model

Whether created by ProModel consultants or in-house analysts, a good model (traditional or integrated) is composed of several key elements as shown below.

Custom Dev Integration Model Architecture

  1. Process Forecasting: A good model simulates an important business process in a concise way, at an appropriate level of abstraction, and provides accurate forecasting to inform key business decision making.
  2. Operational Data: To the extent possible, a good model brings together real-world data (typically from several sources) that has been validated and normalized. Operational data feeds the model, but also is mined for distributions, patterns and trends that improve the model’s predictive and prescriptive fidelity.
  3. Resources & Constraints: Every business process has factors which throttle its throughput – often in non-linear and sometimes unexpected ways. A good model can forecast the impact of changes to resources and other constraints.
  4. Business Priorities: Providing information about business priorities allows a good model to do two things: a) predict and notify users about problems and opportunities, and b) utilize automated scenario creation and optimization to explore alternatives and seek decisions that lead to most optimal outcomes.
  5. Prescriptive Analytics: A good model provides the prescriptive information to key decision makers as early as possible to support effective planning. These improved plans then feed back into the system as operational decisions and changes to resourcing, process and priorities.

Custom integrated model(s) take all of the above, integrate it with live data and makes its power available in a live, web-based format so that tactical decision-makers at all levels of the organization can utilize it. In a live integrated environment, prescriptive analytics can be provided daily or even hourly in support of near real-time decision-making.

A Growing Portfolio of Proven Custom Integration Success Stories

ProModel has now developed about half a dozen custom predictive prescriptive platforms with direct integration of the ProModel server into the customer’s operational IT environment.  Examples include:

  • Shipyard Manufacturing Capacity Planning: AREAS
  • Supply Chain Planning: DST
  • Personnel Readiness: AST
  • Hospital Patient Flow Optimization: FutureFlow Rx

AREAS capacity planning capability was featured in the Signal Magazine on page 2. The article states that Ingalls Shipbuilding estimated a potential annual cost savings of just under $1M from the use of this tool.

Benefits of Integrating Models With Live Data Systems

Benefits of a custom integration of ProModel include:

  • Pulls operational data to support strategic planning on an ongoing basis
  • Automatically projects “strategic what-ifs” (changes to resourcing, facilities or sales) to show true impact on “daily tactical decisions”
  • Ties strategic targets to operational decisions, and continuously explores alternatives to provide early warning of opportunities and risks.

For More Information…

Contact saleshelp@promodel.com if you are interested in learning more about custom model integration.

ProModel AutoCAD App for Warehouses and Distribution Centers

Steve-Courtney-100-x100

Steve Courtney, ProModel Sr. Consultant

I have several years of experience in supply chain and logistics modeling helping clients who have large warehouses and distribution centers.  These models are often very large (thousands or tens of thousands of locations), which can be very time consuming to model.  I’ve found the old adage to be very true: “Necessity is the Mother of Invention”, so I developed a ProModel App that is used from within AutoCAD which enables us to quickly build the graphical portions of the model using OLE automation.  This capability is also very useful when experimenting with several different layouts.

The types of Warehouse / DC modeling questions that can be answered include:

  • Slotting questions – where should my SKUs go?
  • Racking questions – which type of racking is best (flow rack, bin shelving, single pallet deep, double pallet deep, drive-in racking, etc.)?
  • How high should our racking go 5 levels, 7 levels, etc?
  • Which material handling devices are best – narrow aisle, forklifts, single/double/triple pallet jack, reach trucks, side loaders, clamp trucks, electric/propane/natural gas, etc.?
  • Staffing questions – how many of each type and when?

I recently gave a webinar on this topic which you can view here

The requirements for using the app include:

  • Current AutoCAD drawing
  • AutoCAD not AutoCAD Light
  • Know where each location is physically on the drawing
  • Location levels 2-X should be mapped to the level 1 location
  • Build indexed location file in the order you plan to add to the drawing
  • Know which material handling device accesses each location

If you would like to discuss this further, or have other ideas that can help us all improve warehouse and distribution center modeling, please comment below.  Thanks and Happy Modeling!

Thanks, Steve Courtney

 

Ancillary Tools Helpful for a Successful ProModel Discrete Event Simulation Project

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Prof. Scott Metlen Univ. of Idaho

Introduction

Using ProModel to teach process management inadvertently necessitates that students become more proficient with many tools centered on data and working with people. Of course, students learn many aspects of ProModel such as the need to understand parts of a process; these include locations, entities, arrival rates, process logic (LEAP), variables, and attributes.

They also learn about graphics, Statfit, batch/group, create, order, wait until, logic statements that operationalize business rules, and many other commands that help to model a process. However, when conducting a successful large process improvement project using discrete event simulation for an organization, students need to become proficient with many other tools to best utilize ProModel over the course of semester long projects.

Project Management Methodology

Understanding and being able to set up a project using project management methodology is critical to having a successful ProModel project. As in any project the scope and expected outcomes need to be delineated. To design the work break down structure for the project it is also critical to understand what tasks need to be accomplished to produce the final output, and when those tasks need to be completed.

Tasks include developing the scope and expected outcomes working with the project sponsor, analyzing and preparing data for entry into ProModel, base model construction, verification and validation of the base model, determining what treated models to build, statistical analysis of the outputs from each model relative to the base model, cost/benefit analysis, and a report delineating findings and recommendations. Each team in the class I guide has to complete a Project Execution Plan (PEP) and then discuss in their final paper how well they met their time gates, why they did or did not meet those dates, and what they did to catch up if they did not meet those time lines. There are times in the project where they learn the lesson of not utilizing the ‘student syndrome’.

Relationship Management

To do a good job of all the tasks mentioned above, students have to become accomplished at relationship management. They have to visit with their sponsor not only about the scope and expected outcomes, but what data and information is needed to complete the project. There will be missing data, acronyms that need to be explained, assumptions that have to be made and supported due to the missing data and information, uncertainty about the proper rule to guide the logic, and many other items to discuss on at least a weekly bases with the sponsor. Oftentimes it is being uncomfortable talking to a sponsor that leads to procrastination and missed time gates.

Data Sets and Simulation

Of course there is the ever present need to be able to make sense out of large sets of data and be able to convert them to information that ProModel can utilize. When dealing with nearly 12,000 different types of entities for one process, being processed through a job shop with 1400 unique process centers, the data sets become large. The route array that informs ProModel which machine which entity goes to when can become 12,000 rows and 200 columns, and the duration array can become too large and have to be split into four arrays, each with 3000 rows and 1400 columns.

There are many Excel tools that help the students explore their data sets. These tools include but are not limited to: filters, pivot tables, different types of lookup commands, find and replace, if statements, count statements, the ‘and’ function to build many lines of logic quickly, and different types of conditional formatting.

Once the base model and treated models are created and have generated 30 replications the students determine if the treatments actually made a difference by conducting a hypotheses test, if the null (the means of the samples have a high probability of being drawn from the same population) is rejected they proceed to the cost/benefit analysis. If the null is accepted, they try other treatments. If the treatment was successful, Statfit is utilized to determine the distribution of the output, at which point a Monte Carlo simulation is utilized to generate a larger sample of deltas between the base and a treated model to determine the distribution of deltas used to generate the net benefit.  That benefit could be number of extra units built, decrease in throughput time, time in system, net present value, or some other form of benefit.

Report Out

While the models are being built, the team is also working on their presentations and written reports. Thus, as they are discovering assumptions that they need to make, they are putting them into the oral and written report, thereby learning the value of parallel processing. By the time the last statistical analysis is completed, the presentation and paper are completed and ready for presentation for the teams that do a good job of following their PEP.

Conclusion

As demonstrated above there are many tools that ProModel users need to be proficient with when conducting a successful discrete event simulation using ProModel. However, perhaps it is not only ProModel and the ancillary tools that need to be taught and modeled when teaching a discrete event class, but the willingness to say, “I do not know how to do that, lets do some research and discover how”. That is the most important trait that modelers need to have, the willingness and perseverance to learn new tools and apply them in unique ways to capture unique opportunities.

Meet Professor Scott Metlen, Ph.D.

Dr. Scott Metlen earned his Ph.D. in Business Administration at the University of Utah in 2002 and is currently an associated professor of Production Operations Management at the University of Idaho. Dr. Metlen teaches Quality Management and Systems and Simulation, both are aspects of  Process Management. Prior to his academic carrier, Dr. Metlen spent 20 years managing products and processes in agriculture and food processing. Through a gift from the Micron Foundation, he has the resources to oversee at least twenty process improvement projects for various organizations per year through the classes he teaches. These projects provide meaningful experiential learning for the 40 to 80 students involved.

 

Retail Has Transformed – Has Your Distribution Center?

It seems everything changes so rapidly these days with technology leading the way.  What is interesting, though, is how technological changes create a ripple (tsunami) effect in other industries.  Take retail as an example, there is no doubt online purchases have increased and the negative impact that has had on certain retailers is evident.  After all, when is the last time you went to the mall?  Chances are if you are over 18, you can’t remember.

OnLine Purchases Rising

Online purchases are on the rise as demonstrated in the chart above. The impact on some retailers has been bankruptcy filings and the closing of retail brick & mortar locations around the country; Aéropostale, JC Penney, and Sears just to name a few. http://time.com/money/4386499/retail-stores-closing-locations/

What’s the Difference?

Because so many purchases are now online, retailers are facing shipping smaller quantities of goods more frequently.  These shipments will go either direct to stores or direct to customers. Retailers must make accommodations for these changes and adjust their strategies in order to remain successful.  Those retailers who make the appropriate adjustments will have a higher chance to succeed.

So just how do you adjust your existing distribution centers to accommodate these changes? Shipping individual orders to customers or retail stores requires greater speed and accuracy. Distribution operations managers have realized that in order to achieve these greater speeds with more accuracy they must add high-speed conveyors, high-speed sortation systems, robotic palletizers, different picking & packing solutions, etc.

As distribution operations managers make these adjustments and choose the right equipment for their facilities, it also becomes very important to optimize the use of the chosen equipment.  Often, it is not until a state-of-the-art facility is up and running until management really understands how it works and how all of the complex parts and pieces come together.

What Can You Do About It?

This is where predictive modeling can be valuable.  As with any complex system, it is difficult to see and understand all the interdependent cause and effect relationships and overall system behavior.  For example, the tote size and number can affect high-speed conveyor performance, which in turn can affect the packing and shipping processes.

Enter predictive modeling.  A predictive simulation model of your DC can help you understand many aspects of system behavior including:

Order Mix:

  • What percent of orders use full pallets or full cartons?
  • What is the percent of single unit shipments?
  • What is the typical order size?
  • How many line numbers in each order?

Service Level Performance:

  • Do we need to offer overtime or use seasonal staffing to handle seasonal volume?
  • How do we balance the shipping docks to evenly load the work for the stores we service?
  • How do we balance the stores that each distribution center in the network services?
  • Do we have enough people or equipment to complete the day’s work?
  • Do we have items slotted correctly so that the fastest moving products are closest to the shipping doors?

Additionally, a predictive model can help you identify areas for improvement:

Picking strategy:

  • Single order picking or multi-order picking?
  • Order consolidation?
  • Should you use pick waves?

Which in turn will help you determine the design of your pick line. 

  • Straight line
  • Branch or pick zone
  • Serpentine line
  • Pick to conveyor
  • Pick to light
  • Automated conveyors or carousels

Wrapping Up

This shift in retail shopping behavior and delivery expectations is not likely to end anytime soon.  If anything, it will continue to become even more individualized and immediate.  Has anyone had a drone drop off a package yet?   It will be hard for retailers to keep with us overly demanding customers. Maximizing the performance of your DC’s, warehouses and delivery network will likely have to be part of the equation.

Happy Holidays to You and Your Families From the ProModel Family!

Keith Vadas, President & CEO ProModel Corporation

Keith Vadas, President & CEO ProModel Corporation

The ProModel family would like to wish you and your families a very healthy and happy holiday season!  We thank you for all your support and business this past year and hope we have helped you meet or exceed your performance goals for 2016!

Some of the 2016 highlights include:

  • Awarded a contract for the Joint Staff Operations Directorate requirements associated with GFMDI (Global Force Management Data Initiative)
  • Army LMI-DST (Logistics Materiel Integrator – Decision Support Tool) achieved the major milestone of being listed in the Army Regulation (AR) 710-1 report as “the authoritative source to synchronize the distribution and redistribution of materiel in accordance with Army priorities and directives”
  • Awarded option year for AST which will expand the application to the unclassified network
  • Launch of FutureFlow Rx™ a brand new Patient Flow Analysis solution
  • Significant enhancement of our ship building capacity planning tool
  • Collaboration with the Orlando VA to improve healthcare access for our Vets

As many of you know, we have an extremely dedicated team of customer account representatives, consultants, software developers, and technical support engineers always available to help your organization meet the next business challenge. Looking ahead to 2017, we anticipate another exciting year of launching new products and enhancing current products and services.

Please let me know if you have ideas for products or services that would help you improve your business processes in the comments section below. Thank you, and I wish you and your families a happy holiday and a Prosperous new year.

Best Regards,

 

Keith Vadas

President & CEO
ProModel Corporation

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