Why is Discrete Event Simulation better than spreadsheets?
One important reason Discrete Event Simulation (DES) is a more powerful tool for analysis than spreadsheets is its resourcing and variability capability, which models real-world scenarios more easily.
What is Simulation? Discrete Event Simulation (DES), often referred to as "Simulation" describes software which is designed to model complex processes for discrete events or rather, distinct processes which take into consideration the dimension of time (I know I’m getting kind of nerdy here, but I don’t know how else to describe it.)
Simulation software also typically includes the capability to create "animations" of what you are simulating. For example, if you are simulating an emergency room in a hospital, you would see an "animation", which may look similar to a video game showing patients, nurses, and doctors walking around. For all of my simulations, I use extend 7 (now known as ExtendSim).
Here are some questions that simulation can reliably help answer:
If my business increases volumes, at what point do I need to purchase more machines, or hire more people?
How much more annual revenue can I expect if I reduce my customers perceived waiting time by 3, 5, or 10 minutes?
How large should we make our waiting room for our urgent care clinic?
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Let’s look at an example, which is similar to an example that we used at my previous employer when we would do simulation for healthcare. I have changed it to a carwash example.
Cars are arriving to a carwash at an average of every ten minutes, using the vacuum for ten minutes, then wash for ten minutes, and finally dry and detail for ten minutes.
In a spreadsheet analysis, you might include averages in cells where cars arrive every ten minutes and each step in the process happens in exactly 10 minute intervals, then the car leaves 30 minutes after it arrived (Figure 1).
In a simulation analysis, however, the vacuum along with the wash bay and dry/detail areas are considered resources that can only be used one at a time, which will allow for queuing of cars
until that resource becomes available again. Now, introducing variability into your processes will cause cars to wait in line. In the example below I programmed variability into the processes by introducing random distributions for each of the car wash bays, still with the same average as before but with plus or minus two minutes (i.e. 8-12 minutes instead of just always every 10 minutes.)
I then programmed customers to leave if they wait longer than 15 minutes (Figure 2), which can represent customers becoming frustrated or even the fact that your carwash may not have the physical space to hold more than a 15 minute line (i.e. customers cannot even pull in to your establishment from the street.)
Results for Spreadsheet:
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Figure 1 spreadsheet analysis
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Using a spreadsheet analysis (Figure 1), you can complete 58 cars in a ten hour shift (i.e. at the end of 10 hours there are still 3 cars in the process, which are not yet vacuumed, washed, or dry and detailed).
Results for Simulation:
Because the simulation allows for waiting in line, only 39 cars were completely finished and 15 customers left after 15 minutes of waiting (Figure 2).
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Figure 2: Extendsim Simulation showing 15 customers leave without having been served. Wait-times for the vacuum are still over 8 minutes
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Figure 3 Extendsim simulation showing average wait times of over 76 minutes when customers do not leave after 15 minutes
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You can see that there exists a notable difference between the spreadsheet analysis and the simulation analysis. In our small car wash example, the spreadsheet analysis suggests that the carwash is capable of handling almost 60 customers a day. If you consider a carwash that can make anywhere from $5.00 to $10.00 per customer then the annual revenue could be as much as $160,000 for our little carwash.
The simulation analysis suggests the carwash can realistically only handle about 40 customers a day with an additional 15 customers leaving or not being satisfied. Considering many customers would be given free carwashes to make up for the wait, we can realistically expect to see at most $95,000 in annual revenue.
This example was very simple with only 3 processes. Simulation becomes even a more powerful tool as complexity increases.
In one instance, I helped build a simulation (along with several other engineers) for a large portion of a hospital where several months after our project was complete, the head engineer received a frantic call from one of the hospital administrators. They were experiencing longer wait times and patients queuing up in several areas of the hospital and they could not determine why. One of the associates of the company grabbed his laptop (with the simulation on it), flew to California to make some observations, then adjusted and performed the simulation. He was able to do a root cause analysis of the simulation results, which also applied to the hospital’s real-world scenario the hospital was experiencing and was able to create a strategy to mitigate the issue.
The simple carwash example illustrates the benefits a properly built simulation can provide.
Additional benefits of DES include (but not limited to):
1. The ability to change process patterns to a granular level (i.e. you can dynamically change the volume of cars entering your system by hour, day, or month of year to capture seasonality)
2. The capability to perform scenarios such as increasing customer demand to determine at what point your business is at capacity (ex. Many healthcare systems use DES to determine when to build additional OR’s, Inpatient rooms, or what the effect of changing one process has on another part of the hospital)
3. The ability to use animations helps others (who may not be technical) clearly understand what the simulation is doing and what the outcomes of the simulation are (as opposed to viewing a presentation with graphs and numbers.)
See a more in depth list of Simulation benefits vs. spreadsheets at ProModel.
Conclusion:
I do not want to imply that spreadsheet analysis is not useful. Spreadsheet analysis can be a powerful tool to provide good answers and is fairly easy to perform (I use spreadsheets all the time.)
However, using simulation software will cause you to think about your processes, how it behaves, and will reveal things about your business that you would have never have seen or even considered.
Simulation does have some drawbacks, which include:
1. Simulation software is expensive. More affordable simulation software can still cost thousands of dollars.
2. You need lots of good data. You need to pull data anyway you can and have the ability to clean it up for analysis prior to including it in your simulation. Often you will be required to observe the process yourself, making note of times, how things work, and documenting anomalies that happen, which will later be built into the simulation.
3. Some knowledge of computer programming and logic is required.
4. Knowledge of probability and statistics are necessary to be sure that you created an accurate model. When reporting results, you need understanding of statistics to be able to determine if your model’s outcomes are statistically significant.
I hope this helped you understand simulation, ways it can be used, and why it can be more powerful than a spreadsheet analysis.
Please contact me at Astrozuggs@gmail.com with any questions you may have about this or any other Continuous Improvement Question.
Also, please see my other Continuousimprovementpal blogs
