I worked on a simulation study last year with Dr. Holt (University of Washington) and Dr. Srinivasan (University of Tennessee). The results of the study surprised me. It made me start thinking differently about variation and its effect on systems. It might change the way you look at bottlenecks in a resource-constrained system as well.
We modeled a multi-project system, but the results we found can be applied to any system. This multi-project system (think of the projects as engineering projects) required various resources at different stages. We modeled a variety of project structures. The projects we modeled used several common resources. The primary output we studied was the project flow time, or the time it takes a project to complete the system.
It is very difficult to correctly determine the appropriate workload that should be placed upon resources in this environment. There is no question that putting too little work into the system will tend to starve key resources. And while there is pressure to keep resources busy, overloading them usually results in unfavorable outcomes like projects taking too long.
In an ideal setting, work schedules can be developed in advance, so that resources have just the right amount of work allocated to them at various points in time. However, in the project world, demand is highly uncertain, workflow is quite unpredictable, and task durations have significant variability. Even the best-planned schedules become difficult to execute in this environment. And when many different resources are used multiple times in a single project and frequently shared between projects, any unexpected delay in a single task can cause significant ripple effects delaying one or more projects. Even a small delay in a task far away from a key resource can cause chaos in the complicated and interrelated schedules that exist in a project environment, and attempts to tightly schedule projects are soon abandoned.
Our study outlined several steps to dramatically improve the performance of these organizations and I want to talk about two of them here.
1. Determine how resources should be loaded
2. Identify the appropriate level of reserve resources
The first step is not a new concept. This is basically controlling the amount of work in the system, and there are several approaches to implement this. In manufacturing, you might refer to it as CONWIP or Constant Work in Process. In the project-management environment, the term is CONPIP or Constant Projects in Process. We applied a slightly different mechanism, but it had a similar effect as CONWIP or CONPIP. In our system, we monitored the backlog of work for the resources. This backlog would generally not be completely present in the immediate queue of work at that resource. We would only release new work into the system once the bottleneck resource was below a specified threshold.
The chart below shows the first set of results from the resource loading study. The X-Axis shows the resource workload. For example, at 100 percent, the bottleneck resource has enough work in the system to keep it busy for 36 days. At 200 percent, the bottleneck resource has enough work in the system to keep it busy for 72 days.
The blue line shows the effect that an increased workload has on the average flow time. As more work is pushed into the system, the average flow time increases. Increased flow time means it takes longer for projects to complete, so customers are much less happy! Longer flow times can be detrimental to a company. The black line is plotting the throughput. With an increased workload in the system, more projects can be completed, but at a certain point, the increase is negligible.
The red line is something we called the project value index. This is defined as the number of projects completed over a given period divided by the 90-percent probable flow time. The project value index is a value we want to maximize (more projects completed while decreasing flow time). We tend to want to be just a bit to the right of the high point on the project value index. This is a good balance of throughput.
The next issue we studied was the use of additional resources. The results of this study are what really surprised me. The typical thought process for improving a system is to add resources to the bottleneck. Then to keep improving the system, you would find the next bottleneck to add resources to. This feels like the natural progression for improving projects, right? In the system studied, we did have a clear bottleneck. We had nine resources. When the bottleneck resource was at 100 percent utilization, the other resources ranged from 50 percent to 75 percent.
Another strategy we tried was to use an Expert resource. This is a resource that can be used to help any other resource, not just the bottleneck. This would be the most experienced staff member that can do everything. We didn’t want this expert resource working just at the bottleneck resource. The task durations were all random. We let the expert resource help any resource when the task was taking longer than the expected value. These expert resources would ONLY be requested for help after the task duration had exceeded the expected mean value. For example, let’s say the expected task duration was six days. If the task was not complete by day six, then the expert resource would be requested to help complete the task to “shorten the long tail” of the task duration. The expert resource is specifically used to reduce the long tail on the right side of the service time distribution.
In the chart below, we used the Project Value Index to compare the two strategies of a) adding an Expert resource, which helps reduce the long task times and b) adding a resource at the bottleneck. As you can see, using the Expert resource had a significantly better impact! Wow. I did not expect this.
Here is what I learned from this study: When a task consumes a resource for an excessive amount of time, it not only delays this project from completing but it also delays every project in the queue for this resource. So long-tail tasks have an impact on potentially all the projects in the system, not just on the individual project. Focusing on these long-tail tasks, even on non-bottleneck processes, has a bigger impact on the system than just focusing on improving the bottleneck process.
That is something you should noodle on. This concept can of course be applied not only to project-management systems but also to many other resource-constrained systems.
Wednesday, May 10, 2017
Thursday, March 30, 2017
The other day, I was talking to a fellow ExtendSim model developer, Aaron Kim from JWA Consulting. Aaron, who is a Lean consultant in the health care industry, uses ExtendSim as part of his Lean toolbox.
Aaron described one of his recent simulation models, and it got me thinking not only about how underutilized simulation is. Why are there not more models built that simply compare concepts at a high level?
Many of you who have built models know how easy it is to A) include too much detail or B) include processes around the fringe of the problem. Doing either requires extra effort to model and can cause delays to an entire project. I already suspected these were two root causes of unsuccessful projects, but could they also be the two main reasons simulation is not used as much as it should be?
When Aaron described his model, I thought it was a perfect example of how valuable a simple simulation model can be. Aaron built a model that compared two scheduling strategies. He stayed out of the weeds, so to speak, and simply looked at the concepts involved.
Aaron was working with a clinic. The clinic classified their patient visits into two basic categories – Short visits and Long visits. A Short visit would take about 20 minutes, while a Long visit would take about 40 minutes. Generally, the Long visits were new patients and accounted for roughly 25 percent of all visits.
The clinic had been scheduling patients according to what they called a “template” schedule. The template schedule method works by setting up a template of appointment times for both patient visit types. When a patient requests a specific time, the clinic gives him or her the closest appointment block designated for that type of visit.
For example, if a Long patient called in and requested an 8:10 a.m. appointment, that slot could be open for a Short visit but not for a Long one. In such cases, the clinic would then give the patient the closest appointment time slotted for a Long patient, which might be an hour or two later. The clinic felt that their open appointment scheduling was better, since it gave patients appointments closer to their desired times.
An executive at the clinic suggested to Aaron that they switch to a “Open” schedule because they thought it seemed more patient-centric.
The open schedule method works by giving patients the available time closest to their desired appointment time. For example, if a patient wants a 9:00 a.m. appointment, and that slot is open, then the clinic gives it to the patient, even if it causes gaps in the schedule.
Aaron felt like the open scheduling method would leave gaps that were too small to see other patients and therefore result in the clinic scheduling fewer patients overall. Because of that, Aaron felt the template method would provide better patient satisfaction, as calculated by averaging the difference between the desired appointment times and the given appointment times.
Aaron decided to build a simple model to compare the two scheduling methods. He didn’t want an elaborate model with all the grueling details but rather something simple, just to compare the two methods, to see which one would give the better performance.
Rather than modeling all the doctors in the clinic, Aaron chose to model just the scheduling of a single room with a single provider. He also did not model how each doctor worked different hours during the week nor how each took his or her lunch break at different times of the day nor how some preferred to come in late on Mondays or golf on Wednesday afternoons or take Friday afternoons off. Those were important details, but Aaron was not trying to model the entire clinic; rather, he simply wanted to see the difference between the two scheduling strategies.
Aaron’s model had a specified number of patients per day wanting to book appointments for times over the following two weeks. Each patient would be booked on both an open schedule and a template schedule. The key performance measure of the system was the average time difference between the desired appointment times and the given appointment times. The results are shown below.
The Patients Per Day was a variable that varied from 16 patients per day to 20. The results showed that the more patients scheduled per day, the better the template schedule outperformed the open schedule.
Because Aaron was just trying to compare two scheduling policies, this turned out to be a quick modeling project. It took less than eight hours to build the model and analyze the results.
The time spent building simple models like this one can pay off immensely. But I hear far too many stories in which models take months to get data and build and far too few in which models are built quickly just to answer simple questions like this one. The challenge for us all is to know the correct level of detail needed to answer the primary question. So the next time you have a problem that a simulation model could be used to answer, don’t be afraid to build the model, but please pay attention to the level of detail required. It will take far less time to build if you can leave out the unnecessary details, and it could make simulation a much more useful tool for you.
Monday, October 26, 2015
Since the late 1990s, ExtendSim has had an embedded database as part of its simulation tool. Now in its second generation, it is so incredibly useful that I can’t imagine building a model without it. In this post, I’ll describe my favorite advantages of using the internal ExtendSim database but for a more comprehensive description of the major features please read ExtendSim Advanced Technology: Integrated Simulation Database (Diamond, Krahl, Nastasi, and Tag 2010).
Here is a list of some of the major features of the ExtendSim database:
- Rapid data access
- Connectivity to other data sources
- Parent \ child relationships
- Support for easy component scaling
- Multiple databases in one model
- Database aware modeling components
- Database address attributes
- Embedded distributions
- Excel Add-in
- Data linking
- Link alerts
The first thing you should know about the ExtendSim database is that speed consideration was (and still is) a very high priority in its design. I have seen cases where a modeler used Excel or Access as the primary simulation data repository in such a way that the model interacted with it constantly during the model run. Having that constant interaction with Excel or Access during a model run tends to really slow the model down. Interacting with the internal ExtendSim database during the model run is comparable to the speed of interacting with arrays, which is really fast.
Using the internal database does not prevent you from connecting to other data repositories like Excel or Access or ODBC or ADO; however, the best practice when using another data repository is to import the input data into the ExtendSim database ONCE at the beginning of the model run, interact with the ExtendSim database during the run and then export the results to the external data repository ONCE at the end of the model run. This gives you the ability to use an external data repository to store your input and output data while using the ExtendSim database for speed during the model run.
The next major benefit of using the ExtendSim database is its visibility and the separation of model and data. I once had a discussion with someone who had just completed building a model – and he didn’t use a database! His model had roughly 300 processes in it. He used best guesses for the process times, because he didn’t have the actual data at the time he was building the model. When I spoke to him, he was beginning to get real data, and he wanted to start testing the sensitivity of the model. He was having a difficult time with the task. All of his process data was hidden in the block dialogs, which were spread out across the model. It was difficult for him to see the data he was currently using without going into every single process and looking for it!
Understandably, he was frustrated. He asked me if there was an easier way, and I suggested that he update his model using the ExtendSim database. The database helps make all of your data visible in organized table structures. This enables you to use best guesses as you are building the model, and afterwards, you can easily find and make modifications when you get real data. Using the database, the original best guess data will not get lost and forgotten.
The embedded ExtendSim database also allows the user to create parent \ child relationships between tables. This has a number of advantages. First and foremost it helps endure data integrity but it also helps with making the data readable so the user does not have to maintain separate lists of indexes.
The embedded ExtendSim database can also be used to help scale a model. Often, models have many processes that are similar, if not identical, except for their data. In ExtendSim, constructs like this can be encapsulated in a hierarchical block (h-block). Encapsulating the construct into an h-block makes duplication of these similar processes much easier, and it helps organize the model. In order to make maintenance of the encapsulated construct easier, the h-block can be placed in a library so that if changes are needed within the constructs, modifications may be made in one h-block and those same modifications can be automatically replicated to other identical h-blocks. The difficulty comes when the constructs have slightly different input data. This can be handled easily though by using the database to store the input data instead of storing it in the block dialogs.
Let me show you what I mean. In the illustration below, the station construct is stored in an h-block in a library. Each station has a different process time and capacity. The process time is stored in a database table in which each station looks at a different record. Each station construct is reading the database for that process time, so each process time can be unique, even though the h-block construct for the four stations are identical. The only difference really is that they read from different records for their process time and capacity.
In summary, using the internal ExtendSim database can help make your simulation data visible and easy to find. It can speed up the run time, compared to constantly interacting with an external data repository during the simulation run. It is much easier to work with the data when the data repository is native to the simulation tool, and it can be used to help scale your model as it grows. Keep in mind that this is just a short list of the key benefits; there are many others.
If you have not already started using the ExtendSim database, I would highly encourage you to check it out. I also teach a week long class on the ExtendSim database. We spend about two days covering the mechanics of using the database and about three days on the techniques of how to integrate it throughout a model. I cover almost all of the tricks I know. So if you have the time, then I encourage you to come and learn how to effectively use the ExtendSim database.
I also have included a 30 minute overview of the ExtendSim Database on youtube. Check that out when you have a chance.
I have included some references below for further reading. The first one, ExtendSim Advanced Technology: Integrated Simulation Database, is a more in-depth look at the advantages of the ExtendSim database. The next two references are some good examples of users taking full advantage of the ExtendSim database by not just creating a model but creating their own application within ExtendSim.
Diamond, B., Krahl, D., Nastasi, A., Tag, P., 2010. ExtendSim Advanced Technology: Integrated Simulation Database. In Proceedings of the 2010 Winter Simulation Conference, eds. B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yucesan, 32-39. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
Saylor, S., Dailey, J., 2010. Advanced Logistics Analysis Capabilities Environment. In Proceedings of the 2010 Winter Simulation Conference, eds. B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yucesan, 2138-2149. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
Akiya, N., Bury, S., 2011. Generic Framework for Simulating Networks using Rule-Based Queue and Resource-Task. In Proceedings of the 2011 Winter Simulation Conference, eds. S. Jain, R. R. Creasey, J. Himmelspach, K. P. White, and M. Fu, 2194-2205. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
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