Discrete Event Simulation (DES) plays a crucial role in healthcare
planning, focusing on the optimization of hospital size. Contrasting
traditional static architectural blueprints with the dynamic, data-driven lens
of simulation.
Strategic
Decision-Making:
Traditional architectural plans provide static blueprints,
analogous to snapshots frozen in time. In contrast, discrete event simulation serves
as a dynamic, data-driven lens that anticipates and navigates the complexities
of operational challenges. The integration of simulation isn't just a strategic
addition; it represents a departure from the rigidity of conventional
approaches, enabling the creation of operational ecosystems that dynamically
evolve and adapt to achieve excellence.
Financial
Responsibility:
Architects bring an artistic touch to hospital design, but
discrete event simulation introduces a financial savvy that goes beyond the aesthetic. This presents health systems with a solution that
goes beyond space design—creating hospitals that are not just visually
appealing but also financially sound and operationally efficient. Simulation
provides the means to precisely match the hospital's size with the community's
needs, achieving budgetary efficiency, and responsible resource allocation.
In the realm of healthcare planning, the unintended
consequences of architectural designs often manifest in hospitals that are
either too large or too small. Here, discrete event simulation becomes the
guiding force. Those well-versed in its dynamic intricacies aren't just
advisors; they serve as navigators, steering away from the risks tied to
ill-fitted hospitals. The goal isn't merely to provide solutions but to
transcend the limitations of conventional architectural finesse, dynamically
adapting to the ever-changing operational landscape. By doing so, it aims to
alleviate the need for endless, costly process improvement initiatives that may
become necessary when the hospital is not initially built to meet the community's
precise needs.
Long-Term Impact and
Feedback:
In the ongoing process of healthcare planning, being well-versed in
discrete event simulation means more than a fleeting role in the early planning
stages. It positions you as a vital player in establishing lasting success. By
introducing the concept of continual evaluation and improvement, you guarantee
that your insights have a lasting impact well beyond the hospital's beginning.
Simulation transcends building design; it is about shaping a legacy of
operational excellence, rooted in an enduring dedication to learning and
adaptability.
Conclusion:
Simulation
transcends conventional building design, becoming a driving force behind a
perpetual commitment to learning and adaptability, ultimately reshaping the
landscape of healthcare infrastructure.
This article was collaboratively written with the help of artificial intelligence, with human oversight and editing to ensure accuracy and coherence.
Often when performing Discrete Event Computer Simulations for clients, it is because they need to make "What if" decisions on their complex operations that could be costly to get wrong.
Frequently, you will need a large complex Computer Simulation, which can be a pricey endeavor (most organizations will hire Simulation Engineers/Consultants), which is still worth it because of a 10:1 ROI very common with Computer Simulation.
BUT
Sometimes all you need is a quick tiny model (in a well-scoped project) to give you answers using some limited data and with some underlying assumptions using simulation, which can be somewhat affordable (even affordable for a small business).
What is a Tiny Model?
I am defining a "Tiny Model" as any Custom Discrete Event Computer Simulation where the entire project timeline can fit under 1 week.
Typically, these models have 5 or less processes associated with them and use Subject Matter Expert assumptions rather than historical data to provide the variability for these models.
However, if historical data is already available, found to be reliable, and analysis of this data can be performed in a few hours, you can use a mix of Subject Matter Expert assumptions AND data.
It's also typical to have someone experienced in Industrial Engineering or Operations Research build these models for you.
If interested in learning more about how I can help, you can reach me at Astrozuggs@gmail.com
Why you need Computer Simulation
Making decisions by "gut" or "feel" is very risky because of the complexity and variability in most operations (it's virtually impossible to get it exactly right) and even if the "gut" decision panned out somewhat…. how do you know it was an optimal decision? How do you know that you didn't leave money on the table?
A great example of how you can create a small Discrete Event Computer Simulation can be found in the book "Healthcare Management Engineering: What Does This Fancy Term Really Mean? The Use of Operations Management Methodology for Quantitative Decision-Making in Healthcare Settings".
It discusses how traditional management approaches to problem-solving and decision-making might not be the best way to go….and often point you to do the wrong thing, which can be costly or can impact revenue.
Instead…why not use Computer Simulation! In the book, there is example 2.3.1 "Flu Clinic: Centralized or Separate Locations Example", which uses a walk-in clinic that provides Flu Vaccinations during flu season, which was receiving complaints from patients about the wait time.
Traditional management approach
A traditional management approach is to have a brainstorming session, at which point, it was decided to split the clinic into 2 smaller clinics and place them in more convenient locations.
Discrete Event Computer Simulation Setup
Well…let's make a quick Computer Simulation to test out this change with some basic information that we already know.
Simulate Patients arriving randomly
It is explained that for the current large clinic there would be the following:
• 28 patients arrive per hour (i.e., On average, a patient every 2 minutes and 8 seconds)
• It takes on average 8 minutes to register and give the flu vaccine to the patient
• There are 4 exam rooms that can be used
• Clinic is open for 12 hours (7a – 7p)
And for the future 2 smaller clinics, it was decided to split everything evenly, specifically,
• 14 patients arrive at each clinic per hour
• It takes on average 8 minutes to register and give the flu vaccine to the patient
• There are now only 2 exam rooms for each clinic
• Clinic is open for 12 hours (7a – 7p)
To simulate 28 patients arriving randomly each hour, the authors use an exponential inter-arrival time of 2.143 minutes between arrivals for the Large Clinic scenario and 14 patients arriving randomly each hour at each small clinic with an inter-arrival time of 4.2857 minutes between arrivals, which I will use as well.
Simulate how long it takes to get a patient vaccinated
To simulate the registration and administration of the flu vaccine, the authors use an exponential distribution with a mean of 8 minutes, which I will use as well.
Results
The results are as follows:
As expected, (because of the patient complaints), with 1 large clinic the average time in the waiting room was 15 minutes with the longest wait up to 61 minutes AND at the end of the day, there were 11 patients still waiting to be seen…. Ouch!!!!
How much more efficient were the 2 smaller clinics?
With 2 smaller clinics, the average time in the waiting room increased to 26 minutes with the longest wait up to 98 minutes AND at the end of the day, there were 39 patients still waiting to be seen.
Wait! Did you catch that? Their decision to make 2 smaller clinics made things worse for their patients!!! How can this be? This doesn't make sense! Why did things get worse?
Here is why:
Basically, if you split up resources in a process whose duration times are random, with no other changes, your performance will decrease.
Because patients arrive randomly, there will be times, for example, at one of the small clinics where both exam rooms are being used and a 3rd patient arrives. This patient will be forced to wait. If this happened at the larger clinic (with 4 exam rooms), they could have been helped in one of the other 2 available rooms.
So, you see, there was an unintended consequence by splitting up the rooms that made things worse.
Could you imagine being the decision-maker for these 2 clinics and spending hundreds of thousands if not millions of dollars to build out these 2 clinics only to realize that you made things worse?
Wouldn't it have been better for you to hire someone to build a computer simulation at a fraction of the cost and get it right?
Run your Simulation multiple times to account for real-world variability
It should be noted that each simulation run will be different than another and as such, you will want to run multiple simulation runs to see the effect of your scenarios over long periods of time.
I happened to run the above-mentioned scenario 100 times to see how different each run was from the other.
Some run's results were not as bad as the first one, and a few runs were somewhat close to each other, however, over time, having 2 smaller clinics made things almost 30% worse.
So, you can see not every Computer Simulation Project needs to be a 3-5 month-long complex project with hundreds of observations and gigabytes worth of data.
Sometimes you can get enormous benefits from building a Computer Simulation around a small, but well-scoped process with a few pieces of information and not "break the bank" with enormous consulting fees or long project run times.
How Many COVID-19 Patients Can My Hospital Handle?
Computer Simulation can tell you!!!
Back in 2020 I did a Discrete Event Computer Simulation for HonorHealth to help them understand at what point each of their 6 Hospitals would "Max Out" of COVID-19 patients, why that was, and tell them when each hospital would have to start transferring patients to other hospitals.
Despite popular belief....ICU's were not always the reason (Although for most Hospitals it is true, but this isn't true for all Hospitals)!!!
This is what makes Discrete Event Computer Simulation so much more powerful than doing an analysis using spreadsheets, or making your best guess.
This Presentation (below) was included in a webinar for Catalysis in March of 2021.
First let me give you a little background as to why I was there: In 2007, Kitchell Corporation (my employer at the time) was involved in the construction of new Receiving and Release facilities for several California Department of Corrections (CDCR) facilities. Back then, the way that each prison received and released (R&R) prisoners were different and not standard. CDCR wanted to standardize the processes of R&R across all state prisons in California to make it more efficient and safer for staff and prisoners (for example, accidently putting northern and southern gang members in the same holding tank is not a good idea.)
Our part was to build computer models of each prison’s processes, to identify the best way to do things with the physical constraints of their new facilities. San Quentin’s new R&R facility was the newest building at San Quentin since 1920.
San Quentin is the same age as Alcatraz (1850’s) and in 2006 just updated their phone system. Prior to that, they were still using an operator who would connect phone calls by pulling out and pushing in phone lines. If the operator went to use the restroom…you may be out of luck (no cell phones allowed in the prison) Note: I did not say there were no cell phones in the prison, what I said was, there were no cell phones allowed in the prison .....crazy I know.
Weeks before this visit, I was required to submit several pages of information about myself and fill out many forms in order to get clearance into the prison...I'll mention more about that later.
On our first day, we were met at the gate by a prison staff member and once past the first gate, you are technically in the prison, although it looks more like a college campus with towers that house the snipers. This is where you will find the cafeteria for staff, Wardens office, ammo building, parking lot for staff, etc. Prisoners wearing lime green were allowed to work in this area and are not supervised in any way.
Any prisoner who is not wearing lime green in this area will be shot…there are no warning shots. There are, however, yellow lines throughout the entire prison that are “out of bounds” that prisoners know not to cross.
As we were approaching the second gate, the staff member said, “San Quentin is home to the most sadistic, diabolical, and disgusting people that has ever walked earth, and only some of them are prisoners.” It was this staff member's opinion guards enjoy and take advantage of their position at the prison and are every bit as bad as the prisoners.
As you approach the gate, which is actually a “sally port”, you are greeted by a huge man with a gun. He will check your ID, give you a black light stamp on your hand and then let you into the sally port. Once you pass through this sally port, you are then inside what one would consider the actual prison.
Once inside, the first thing I saw to my surprise, were geese!!! Weird....I know! Evidently some geese had decided to take residence close to the worship center and are allowed to roam wherever. We got a brief tour along the path we took to the R&R center, including memorials for guards that have been killed, churches, murals that prisoners have painted and the exit vents for the gas chamber.
As we walked to R&R center (about a 5 minute walk), I started to notice that we were actually mingling with the general population of the prison, with no guards or any protection. Of course, there were guard towers but for the most part we were walking by, around and through groups of prisoners with no protection.
We finally reached the R&R center, whose entrance is literally in the same yard (a mere 10 feet) where you will find prisoners congregating, playing basketball, doing pull-ups, pushups, etc. Again, with no fence between us and no guards in sight!!! I should state at this point, I was required to sign an acknowledgement that I agreed and understood that I would not be used as a bargaining chip in the event I was to be taken hostage!!!
Once you walk in to the R&R center, you are where the busses pull up with prisoners waiting to be processed into the prison from around the state.
The process is as follows:
Prisoners get off the bus and enter the R&R building one at a time.
Prisoners then remove all of their clothing and stand with their arms extended with their palms up.
They then must open their mouths to show that they have nothing in it.
They must pull down their earlobes to show they don’t have anything behind their ears.
If they have long or bushy hair, they will be required to show that they don’t have anything in their hair.
Then, they must do other various things that I shouldn’t mention to prove that they don’t have any contraband on or in them...and yes....it's exactly what you are thinking!
As this was my first business trip with Kitchell, I was given the honor and privilege of performing time and motion studies on this part of the receiving process…lucky me!
I could tell some of the inmates were thinking “what in the world is this guy doing here with a stopwatch and clipboard???” Funny thing was...I was thinking the same thing!!!
After that, prisoners get issued clothing, go to the photo area, get an eye exam, dental exam, get blood drawn, psychiatric evaluation, etc.
Once the receiving process is complete, prisoners are all put in one of several holding cages where they will wait for their final housing assignments to come in. If by chance, a prisoner needs prescription medication, they will wait in the holding cage until the pharmacy delivers their drugs. The average time it takes for the pharmacy to deliver the prisoner’s drugs is 14 hours!!!
We spent much of our first day and a good part of the second day collecting data, but afterwards we were fortunate enough to take the grand tour. The mural art in the cafeteria was truly amazing! We were taken inside death row, the special needs yard (SNY) which houses inmates that would most likely be killed by other inmates if they were to mix with the general population (this area for me, was the creepiest), and even the old gas chamber (which doubles as the room they do the lethal injections in.)
As we were being escorted by one of the prison psychologists, we were told several firsthand accounts of Richard Ramirez (AKA the night stalker) which I dare not discuss, Scott Peterson, Charles Ng, Tookie Williams (founder of the Crips gang) and others…very interesting!!!
Those death row inmates who are on “good behavior” actually get the top floor to themselves (called “Club dead” as opposed to “Club med”), where they may mingle, play chess, watch TV, breathe fresh air of the bay (on the Roof) and for the most part have a great time.
There are many other stories I could tell about my visit to San Quinton but it would not be suitable or proper to do in this blog.
As one might expect, it was a very interesting and educational experience.
Have you seen the television show on TruTV titled “Adam ruins everything” where Adam Conover destroys our perception of everything from green
technology to the real origins of Christmas.
Here is my attempt to do just that with statement we all are
led to believe namely,
It is safer to fly
than to drive
You have heard the old saying: “You are more likely to get
killed driving to the airport than flying on the plane” then people will go
on and on about how much safer it is to fly than to drive.
But is this really safer to fly than to drive? Not really!
Fact: You are over 280% more likely to die in a plane crash
than driving in a car.
How can that be…….are
the experts wrong?
I believe it is because experts are using a flawed metric
namely, miles:
When you look at the statistics online, most all articles use
the metric of “miles.”
For example, there are “X” number of deaths per million miles
for a car and there are “Y” number of deaths per million miles for a
commercial flight.
Passenger Mile?
First, the concept of a passenger mile, although used in the travel
industry, can certainly skew the numbers (to the positive) to the extreme. For example, if a flight with 500 passengers
travel from Los Angeles to New York (approximately 2400 miles), then the statisticians would say that flight had 1.2 million
passenger miles. With around 34000
flights a day, that is tens of billions of passenger miles a day. Certainly there are
millions of miles traveled by the big commercial airliners in the U.S.
everyday…but not BILLIONS.
This creates the case where you have a very large
denominator in the equation (# of deaths / passenger mile) and the larger the
denominator, the smaller the overall chances are they say.
Also, due to the jet
stream, distances between destinations on the ground and in the air are different.
For example, a flight from the west coast to the east coast
despite being the same miles apart will be a quicker flight (i.e. reducing the
chance of dying in a plane because you are in it a shorter period of time) That
same exact flight from east to west will be longer (i.e. increasing the chance
of dying in a plane because you are in the plane longer.) In a car, driving 100
miles at 50 miles per hour would take you 2 hours while driving that same 100
miles at 40 miles per hour would take you 2.5 hours. Using their logic, the
risk of getting in an accident are exactly the same, whereas I argue that the
2.5 hour trip would give you an extra half hour for something to go wrong.
Also, someone can certainly die in a car without having traveled
any miles or an airplane without it flying any distance yet.
Most importantly:
Pilots training time (which has a
significant impact on safety) is logged in flight hours…not miles
Airplane
maintenance schedules (which has a significant impact on safety) are in flight
hours…not miles
Why shouldn’t our metric be something we can make most common
to each.
To eliminate as much variation as possible I will use the
most consistent metric regardless of anything else, which is time. The total time in an airplane and total time in a
car….regardless of the actual miles traveled.
Automobile results
There is an average of 220 million cars on the road on any
given day in the United States for an average of 1.5 hours each. Multiplying these two numbers gives us the
total number of “Car Hours” per day in the United States. The actual number is
330 million car hours / day.
Annually
there are about 34000 car deaths, which make the daily average for car deaths
about 93 per day. If you want to determine the number of car deaths per car hour,
you simply divide 93 car deaths by 330 million car hours. The number is 0.00000028 (deaths / car
hour). This also represents the
probability that you will die in a car accident in one hour of driving or the
odds are 3,533,568 to 1.
Please note: This is not the same as deaths per
hour…..it is deaths per car hour.
To determine the number of deaths per hour, you would divide 93 daily
deaths by 24 hours, which is 3.8 deaths per hour somewhere in the United States.
Commercial Airline
results
There is an average of 34,000 commercial flights in the
United States every day with an average flight time of let’s say 2.5 hours….some
are more and some are less but 2.5 hours is a conservative number (that is
about the same as flying from Phoenix to Portland. In actuality there will be years pass with no
major incidences and then, unfortunately, there will be an accident resulting
in many deaths…but if you average up the previous 10 years or so….you will
arrive at a number anywhere from 25 – 35 per year or so, but for our purpose…I
use 25 airline deaths per year to be conservative and skew the numbers to be "safer".
Multiplying these 34,000 commercial flights and 2.5 hours gives
us the total number of “Flight Hours” per day in the United States. The actual
number is 85,000 Flight hours / day.
Annually there are about 25 commercial flight deaths annually, which
make the daily average for commercial flight deaths at about 0.0685 per day. If
you want to determine the number of commercial flight related deaths per flight
hour, you simply divide 0.0685 deaths by 85,000 flight hours. The number is 0.00000081 (Deaths / flight
hour). This also represents the
probability that you will die in a commercial flight in one hour of flying or
the odds are 1,234,566 to 1.
Please note:
This is not the same as deaths per hour…..it is deaths per flight
hour. To determine the number of deaths
per hour, you would divide .068 daily deaths by 24 hours, which are 0.0028
deaths per hour.
Do you see it…..the odds of dying in a car hour is 3,533,568 to 1
while the odds of dying in a flight hour are 1,234,566 to 1!
The chances of being killed in any one hour on a plane are 286%
greater than that in a car.
The perception that planes are safer, in my opinion, stem from the
fact that there are far fewer airplanes flying than there are cars driving.
With a whopping 220 million cars on the road daily with only 34,000 commercial
flights it’s no surprise there are more car related accidents…
But what would it look like if the number of flights hours equaled
the number of car hours per day…that is to say, what if there were 361,644
flights a day in the United States….
There would be over 97,000 commercial airline deaths a year…that
is about 260 deaths each day…..basically a commercial jetliner crashing almost
daily.
It’s all about how you present it….rhetoric if you will, to
persuade others to believe something or infer that because one scenario is
safer…all scenarios are safer.
For example, if you said the following:
”You are more likely to die in a
car crash while driving from Los Angeles to New York than flying from Los
Angeles to New York.”
Then yes, that would be correct……WHY…..because it takes
almost 800% more time to drive than to fly to New York from Los Angeles, therefore
increasing your chance of dying in a car crash.
In reality both driving and flying are extremely safe. The actual odds of dying in a 2.5 hour plane
ride are still very small (about 1:500,000 in my example.)
To give you some perspective on how remote this is....the chances of a person being struck by lightning in their lifetime is about 1:3000
You could spend your entire life riding on an airplane (and never getting
off) and still not ever crash…..but to say that flying is safer than
driving is not accurate.
Still don’t believe it. Take a look at any top ten most “Dangerous Jobs” list and see that airline pilots always rate more dangerous than professional drivers.
All of my referenced statistics were obtained from legitimate
sources found on the internet.
I encourage you to play around with my numbers.
Be more conservative….or less conservative with my averages and see that this
is the case.
We have all seen the I love Lucy episode titled “Job Switching”, where Lucy and Ethel obtain a job at a chocolate factory while Ricky and Fred play homemaker. There is a scene where Lucy and Ethel have been given the job of wrapping chocolates…except too many chocolates come out and hilarity ensues.
The main reason this video is funny is quite simple:
Their cycle time is greater than the TAKT time…Now that is funny stuff!!!
OK….that doesn’t sound funny….but it is. Click below to watch.
This clip has time and time again been used by Management gurus and Lean practitioners to illustrate many different concepts including:
• Management methods
• Lack of visibility (by Management)
• Flow
• Push vs. Pull processes
• Work stress
• Waste
• Quality
• Variability in processes
When presenting Lean, I myself usually show this video as an example of push processes and the waste that is created by this extreme and visual example.
No Scientific analysis exists!!!
One thing that I haven’t seen is a scientific evaluation around this clip. Concepts such as TAKT time, cycle time, and other key performance indicators that can be determined have only been briefly mentioned in other articles. No real serious deep dive exists.
Question’s such as:
“What was the TAKT time”
“What were Lucy and Ethel’s cycle time to wrap chocolates”
“How many workers would it take to be successful”
“How many chocolates would have passed without being wrapped.”
All of these questions I hope to answer. Step by step on how I did this. 1st step: Study video to determine Lucy and Ethel’s process.
To accurately determine their processes, I observed the video and then created a process map of what I observed. First, I determined the very first step, which was “chocolates arrive via the conveyer belt.” I then determined the last step in the process, namely “Chocolates exit via the conveyer belt.”
Now knowing the very first thing and the very last thing, I was able to fill in the gaps (See process map below.)
2nd Step: Time Study.
How fast were chocolates coming in?
Approximately 6 chocolates came out during the 1st 10 seconds and then started increasing until it reached about 14 chocolates every 10 seconds. Determining this information allowed me to determine TAKT times (What is TAKT time?) for these periods by using the calculation “Seconds/Number of chocolates”, which were every 1.67 seconds/chocolate and increasing to .71 seconds/chocolate respectively. It is also interesting to note that the conveyer belt moved chocolates down the entire line in about 14 seconds to begin with but then sped up to about 7 seconds. This will be important later.
Precise times taken were taken of both Lucy and Ethel’s cycle time to wrap chocolates.
For Lucy, during the 1st 10 seconds, the amount of time that it took from when she grabbed a piece of chocolate, wrap the chocolate, place it back on the conveyer belt and grab the next piece of chocolate was minimum 3 seconds with a maximum of 4 seconds. For Ethel, it was a bit slower minimum 3 seconds with a maximum of 5 seconds.
There were indeed examples of faster cycle times, but those examples were wrought with waste and quality issues as you saw in the video. One major assumption of my analysis was that a (minimum of 3 seconds, most likely 4 seconds, maximum of 5 seconds) was a good pace for Lucy and Ethel for an 8 hour shift. 3rd step: Develop Discrete Computer Simulation of the “I Love Lucy Chocolate Scene”:
I set up simulations for the slow pace, faster pace and the fastest pace at which chocolates were arriving to Lucy and Ethel to see what would have happened if they were allowed to work an 8 hour shift. Original scenario set up in Simulation (Lucy and Ethel at the slower pace) Think you would like me to do simulation for you? Contact me at Astrozuggs@gmail.com
Using Extend simulation software…To begin, I programmed Chocolates to arrive exponentially distributed with an average of 1.67 seconds (about 36 a minute), then the chocolates get grabbed by either Lucy or Ethel where I gave it a triangular distribution of minimum 3 seconds, most likely 4 seconds, and maximum 5 seconds to wrap the chocolates.
I also programmed the simulation the “reneg” a chocolate out of the system if it has been on a conveyer belt for more than 14 seconds, which represents a chocolate making it past both Lucy and Ethel without being wrapped. The simulation runtime was set for an 8 hour shift. Analysis of Original scenario (Lucy and Ethel at the slower pace)
One surprising realization after running this first model is that even at the original “slow” pace, Lucy and Ethel would not be able to keep up for very long.
This is because the simulation results reveal there were 17,087 chocolates that went down the line and Lucy and Ethel’s only managed to successfully wrap 14,207 of them!!!
This means that they missed about 2,875 chocolates! Utilization* (i.e. the time they were actually physically working)
Utilization was hovering right at 98%. This is an extremely high and unrealistic utilization number. Typically, from my experience, utilization should be anywhere from 80% - 85%.
Once you start pushing over 85% consistently, workers get stressed, mistakes happen, and so forth.
Conversely, the lower you get from 80% the more idle time your workers will experience and may not be an effective use of their time. This is all variable and there are exceptions depending on your industry but let’s stick with around 80% to 85% being what we would like to shoot for as far as utilization.
In healthcare, we often talk about utilization when we speak to Daily Patient Census, Operating Room Utilization, Nurse or Tech productivity numbers (which are calculated based off of patient volumes and work time)….we are really just trying to understand how our workers, machines, and rooms are being utilized. Original scenario set up in Simulation (Lucy and Ethel at the faster pace) It should be noted that I used Camtasia studio software to record the simulation, which slowed down the video significantly. All videos appear very slow.
I programmed Chocolates to arrive exponentially distributed with an average of .71 seconds (about 85 a minute), then the chocolates get grabbed by either Lucy or Ethel where again, I gave it a triangular distribution of minimum 3 seconds, most likely 4 seconds, and maximum 5 seconds to wrap the chocolates.
It is interesting to note that the speed of the conveyer belt in this faster original scenario increased. It now takes only 7 seconds for a chocolate to travel across the room. Because of this, I programmed the simulation to “reneg” a chocolate out of the system if it has been on a conveyer belt for more than 7 seconds, which represents a chocolate making it past both Lucy and Ethel without being wrapped.
Again, the simulation runtime was set for an 8 hour shift. Analysis of Original scenario (Lucy and Ethel at the faster pace)
The simulation results indicate that there were 40,697 chocolates that went down the line and Lucy and Ethel’s only managed to successfully wrap 14,414 of them!
Yikes…..this means that they missed about 26,273 chocolates!!! Utilization
Utilization was over 99%, which again…you couldn’t possibly do real world. Original scenario set up in Simulation (Lucy and Ethel at the fastest pace)
Toward the end of the scene, the manager comes out and say’s “Speed it up a little!!!”
If you thought it was going fast before, think again!!! Chocolates were to arrive exponentially distributed with an average of .167 seconds (about 360 chocolates a minute!!!) At this point, it was actually pointless to run the simulation (but I did) because it looks almost identical (with respect to Lucy and Ethel’s utilization percentage, and the number they actually wrapped) to the previous simulation except with much more missed chocolates than actually being wrapped. I sure would have liked to peek behind the wall to see what was going on behind the scenes!!! What to do with this information?
Let’s try to determine what appropriate staffing at the slower pace.
I will do both a brief calculation using averages of TAKT and Cycle times to arrive at the appropriate staffing.
Using the TAKT time and Cycle times by dividing (4 seconds per chocolate per person to wrap / 1.67 seconds per chocolate coming in) = 2.4 workers to meet the TAKT time but does this tell you how many chocolates will be missed or what the staff utilization will be?
Scenario: Adding one more worker:
Using the simulation, I ran a scenario where everything remained the same except I added one more worker.
The results indicated that 17,084 chocolates created, our 3 workers utilization was at 80% with only 13 chocolates not getting wrapped…not bad. I would be pretty comfortable with this result and hire and train only 1 additional person.
Let’s try to determine what the appropriate staffing should be at the faster pace.
It may prove more difficult to accurately determine staffing levels at the faster pace…let’s give it a shot.
Again, a calculation of TAKT and Cycle times reveal (4 seconds per chocolate per person to wrap / .71 seconds per chocolate coming in)= 5.6 workers to meet the TAKT time.
Let’s go ahead and round up and say the number of workers we need is 6. Let’s then throw this number of workers into the simulation.
Using the simulation, I ran a scenario where chocolates were arriving every .71 seconds with 6 workers and the conveyer belt was moving chocolates through in 7 seconds.
The results indicated 40,155 chocolates were created, our 6 workers utilization was at 91% (significantly high) and 729 chocolates not getting wrapped. This is pretty bad. Basically almost 2 out of every 100 chocolates were not wrapped.
Let’s try it with 7 workers
The results indicated that 40,767 chocolates were created, our 7 workers utilization was at 80% and 38 chocolates not getting wrapped. This is much better. I would be comfortable with this result and hire and train 7 workers.
In this case the calculation results of 5.6 vs the simulation answer of 7 workers was off by 1.4 workers, which is about a 20% difference. This illustrates how useful even a simple simulation can be. What if instead of 7 workers, we were deciding to hire hundreds of workers!!!
Let’s try to determine what the appropriate staffing should be at the super-fast pace.
A calculation of TAKT and Cycle times reveal (4 seconds per chocolate per person to wrap / .167 seconds per chocolate coming in) = 23.95 workers to meet the TAKT time.
So hopefully, you have started to realize that we need to bump up our estimates from what our calculation gives us. Let’s go ahead and make the number of workers we need to 25. Let’s then throw this number of workers into the simulation.
Using the simulation, I ran a scenario where chocolates were arriving every .167 seconds (recall this is about 360 a minute) with 25 workers and the conveyer belt moving chocolates through in 7 seconds.
The results indicated that 172,742 chocolates were created, our 25 workers utilization was at 96% (unrealistically high) and 126 chocolates not getting wrapped. I would be very concerned that the utilization is too high.
After a few more staffing scenarios, I landed on 28 workers with a utilization of 87%.
Do you see another problem here?......Hopefully you realized that you couldn’t possibly fit 28 workers in that area….Time to build out a new space or get a new facility? This is a topic for a different day.
In summary, I hoped to put some real scientific analysis around this clip and answer some important questions in a fun way. I also hoped to illustrate using TAKT time calculations is a good starting point but please do not go through real-time PDSA cycles with respect to the number of resources (machines and workers) you should have…it is costly and painful. Why not use simulation to do further analysis to reduce risk. It only took me about an hour to observe the video and no more than 15 minutes to build this simulation from start to finish.
In this example, I used Discrete Event Computer Simulation. Depending on your circumstances you can certainly leverage prior knowledge or experience to help guide your decisions or perhaps building a “mock-up” of your current operations and simulating it that way. Performing a table top exercise may be appropriate in certain instances. Whatever you do….give it some thought!!!
Think your organization or business needs help? Contact me at Astrozuggs@gmail.com *For our purposes…utilization (in the simulation) is defined as the actual working time with nothing else included. For example if you are working for an hour and every 2 minutes you take 3 seconds to wipe the sweat from your forehead then you are not “working” for 1.5 minutes out of that hour and are being utilized 97.5% of the time.
The annual ACT Kids Health Fair serves at-risk children who are eligible for metropolitan Phoenix Head Start programs, but lack appropriate medical clearances. This all-volunteer event addresses the full spectrum of health requirements: transportation To and from the children's neighborhoods, all appropriate medical screenings and immunizations, Establishing and updating Medical records, and arranging emergency or continuing care as needed. Over 20,000 children have been screened to date.¹
2013 was the first year that the ACT Children Health Fair used technology to track patients and families as they flowed through the fair. Learning from using this technology, how to implement it, and the information it gives will help the navigation of families through future ACT Kids Health Fairs more efficient.
Anonymous wrist bands to track flow
An anonymous wrist band with a unique ID was given to fair participants as they entered the fair. A hand-held scanner (used by volunteers) was then used to scan the wristband as they entered the fair. Scanning also took place at various points inside the fair and then one last time as they exited the fair. This scan documented a “time-stamp” of the exact time the wristband was scanned. The time stamp information was then put into a database with the ID number being the unique key.
I performed time studies and observations during the ACT Kids Health Fair on September 28, 2013 and analysis of the time stamp data.
Analysis of Time-Stamp data
Figure #1
An analysis (Figure 1) of the white-band time stamps, which inlcuded Children and Parents revealed that wrist band scanning was inconsistent (as evidenced by missing Check-in and Check-out times.) For example, you can see (Figure 1) that only 63% of white bands had an associated check-in time documented. For stations inside of the fair with very low percentage of scanning (i.e. Dental, Hearing, etc) it is also likely not every child visited every station.
Data that connects a wristband to any particular screening or a simple count of how many children visited each station could be used to confirm this.
Figure 2
The number of Patients and Family arriving by hour (Figure 2) was determined by using the “check-in” time stamp to determine the distribution pattern of the number of people that checked in by hour.Due to the fact that we now know many check-in times did not get captured, I used this pattern of “check-ins” to distribute all of the 2083 white bands into their respective hour. It was later determined that the wristband tracking had some glitches in the beginning of the day. Despite the fact that patients and family were participating in the fair prior to 9:00am, wristband tracking data starts at 9:00am. It is interesting to note that there exists a steady decrease in families arriving until about 2:00pm (one hour before the fair ends) where it increases again. Leaders of the fair in previous years suspected this was the case and can now confirm this increase at the end of the day every year and have already incorporated this into their resource planning for the day of the event. This also served to help further demonstrate that my analysis of the data was indeed valid.
Figure 3
How long patients and families stayed in the fair (Figure 3) was determined by subtracting the check-out time from the check-in time of white bands. The sample only included white bands that had both check-in and check-out data (Sample size was 734, which gives approx +/- 3%.)
Figure 4
Using the wristband data to determine volumes of patients (Figure 4) that arrived in each hour and how long patients stayed in the fair,5 discrete event computer simulations were performed to determine the approximate number of patients and family members physically in the fair in 15 minute intervals.This graph represents the averages of the 5 simulation runs.The Maximum number of people during any of the simulation runs was 753 people. Recall: Patients were in the fair before 9:00am, however, the tracking did not start until 9:00. This would have the affect of elongating the shape of figure 4 to the left and slightly reducing the number of people in the fair at any one time (because the number of people in the fair can now be spread out over more time) without dramatically changing the overall distribution of the graph.
Direct Observations
I also made random observations of every area in the ACT Kids Health Fair over the course of the day where I documented (69 observations) how long each station took to see a child (also known as cycle time.) Most stations had 2 or more observations (with different Clinicians being observed) except for Lead Screening which only had 1 observation. Also, families were observed: Approx 56.25% of white bands were children. Using this assumption: 56.25% of the 2083 white bands (1172) were children participating in the ACT Kids Health Fair.
TAKT Time and work balance was determined.
Figure 5
TAKT time is the time a patient needs to spend at any one station (includes the area’s check-in and check-out stations for areas such as dental, vision, etc) for the fair to complete 1172 patients in 7 ½ hours*. 420 minutes to see 1172 children is approx a child every 23 seconds (27000 seconds / 1172 people) ~ 23 seconds. Figure 5 shows each station on the horizontal axis with the dark bars demonstrating how long each station takes to process one patient. The red line, which is at 23 seconds, shows how fast each process must be to keep the TAKT time. The idea is that you can add more stations until you reach a point where someone in your area is leaving every 23 seconds.
Knowing the TAKT time can help determine how many stations we may need to meet demand. For example: If we want all 1172 Children to get dental screenings ( a dental screen takes approx 250 seconds) in a 7 ½ hour period. We know from our TAKT time that a child must be screened every 23 seconds. To accommodate this demand, we would need approx 11 dental screeners to meet this demand (i.e. 11 screeners x 23 seconds = 253 seconds)
I put this together with information that I thought may be interesting and helpful. It was a pleasure to be able to have the opportunity to help make future ACT Kids Health Fair Events more successful.
