Predicting Process Variability

November 3rd, 2008

Systems rarely perform exactly as predicted. A person doing a task may take six minutes one time and eight minutes the next. Sometimes variability is due to outside forces, like materials that behave differently based on ambient humidity. Some variability is fairly predictable such as tool that cuts slower as it gets dull with use. Others seem much more random, such as a machine that fails every now and then. Collectively we will refer to these as process variability.

How good are you are predicting the impact of process variability? Most people feel that they are fairly good at it.

For example, if someone asked you what is the probability of rolling a three in one role of a common six-sided die, you could probably correctly answer one in six (17%). Likewise, you could probably answer the likelihood of flipping a coin twice and having it come up heads both times, one in four (25%).

But what about even slightly more complex systems? Say you have a single teller at a bank who always serves customers in exactly 55 seconds and customers come in exactly 60 seconds apart. Can you predict the average customer waiting time? I am always surprised at how many professionals get even this simple prediction wrong. (If you want to check your answer, look to the comment attached to this article.)

But let’s say that those times above are variable as they might be in a more typical system. Assume that they are average processing times (using exponential distributions for simplicity). Does that make a difference? Does that change your answer? Do you think the average customer would wait at all? Would he wait less than a minute? Less than 2 minutes? Less than 5 minutes? Less than 10 minutes? I have posed this problem many times to many groups and in an average group of 40 professionals, it is rare for even one person to answer these questions correctly.

This is not a tough problem. In fact this problem is trivial compared to even the smallest, simplest manufacturing system. And yet those same people will look at a work group or line containing five machines and feel confident that they can predict how a random downtime will impact overall system performance. Now extend that out to a typical system with all its variability in processing times, equipment failures, repair times, material arrivals, and all the other common variability. Can anyone predict its performance? Can anyone predict the impact of a change?

With the help of simulation, you can.

This simple problem can be easily solved with either queuing theory or a simple model in your favorite simulation program. More complex problems will require simulation. After using your intuition to guess the answer, I’d suggest that you determine the correct answer for yourself. If you want to check your answer look at the comment attached to this article.

And the next time you or someone you know is tempted to predict system performance, I hope you will remember how well you did at predicting performance of a trivial system. Then use simulation for an accurate answer.

Dave Sturrock
VP Products – Simio LLC

Simulation in Agriculture

October 19th, 2008

Guest article from Sophie Scotts

Over the past several months you have touched on many fields that simulation would benefit such as healthcare and disaster management. I would like now to recall something you said in your “Simulation Expertise through Tours” blog from September, “Don’t limit yourself to just your area of interest/expertise. Often you can learn even more from tours outside your comfort zone.” I think for many professionals in the simulation industry, applying simulation to the field of agriculture might be out of your expertise or comfort zone, but don’t let this stop you.

Since I work for the United States Department of Agriculture (USDA) I see first hand how beneficial simulation could be to our American farmers. Nowadays farmers must be laborers and savvy business men in order to survive in our current economy. It isn’t just milking old Bessie in the barn anymore; they must consider how each area on the farm affects the bottom line, just like any business. Farmers must look at the efficiency of their livestock and harvesting processes and the possibility of diversification in order to stay in business, and simulation could help in each of these areas.

Any farm that has livestock has 3 main questions they must ask themselves; How do I efficiently get livestock onto my farm? How do I efficiently get food to my livestock? And how do I efficiently use (or dispose of) the waste? If they are a dairy they must also consider the most efficient method to milk the cows. For instance, a poultry facility will house several thousands chickens a year for a few months each. During each cycle the chicks are trucked in, food is trucked in (or harvested from the fields), chickens are provided a specified amount of food and space, then they are trucked out (full grown), and wastes are trucked out so the nutrients can be utilized elsewhere. This process could benefit from simulation to create the most efficient scenario.

It is very common now for farmers to turn to non-traditional methods of bringing income onto the farm. One of these methods is to direct market their goods to the public through farmers markets, community supported agriculture (CSA), or opening stores on-property. They must ask themselves; How do I efficiently transport my products to the farmers market? How do I efficiently package and deliver my products to my customers? Or how do I handle parking and lines in my store? Simulation in each of these processes would allow the farmer to make an informed decision on the best management of his business.

So you can see that simulation can have a place in even the most unlikely fields (literally). American farms are a business and thus need to consider the efficiency of processes they undertake in order to meet the bottom line, and simulation can help. So don’t be afraid to think outside of the box and your area of expertise.

Sophie Scotts
United States Department of Agriculture

Help Wanted

October 11th, 2008

Yes, it looks like hard economic times may be coming. But no, this has nothing to do with that.

This blog is a community service. To continue to be effective, we need community participation. That means you.

There are many ways you can participate.

1) Comment – At the end of each article is a link. Click it and add to the discussion. Agree. Disagree. Add new information or a different viewpoint. All civil discussion is welcome.
2) Suggest Topics – Contact me with any ideas you have about future content or ideas for making the blog more useful.
3) Write an Article – It doesn’t have to be rocket science. Nor does it have to be long or formal. Everyone has something to share. The main rule is to keep it unbiased and non-commercial. I am happy to edit it if you like and even publish it under a pen name if you are publicity shy (although I strongly prefer using your real name).
4) Become a Guest Author – I would like nothing better than to “share the limelight” with others. You can write one article or regular articles. Choose your own topics and frequency.

It’s all about sharing to help the simulation community. This is a simple way to give back. Anyone can do it. For any of the above or other ideas, you can contact me using dsturrock at Simio dot biz (name slightly obscured to slow down spammers).

Thanks for your help.

Dave Sturrock
VP Products – Simio LLC

Simulation in Healthcare

October 5th, 2008

Over the years, I have had several occasions to use medical facilities for myself and my family. Some visits were routine, such as for a diagnostic tests or images. Others were for much more critical visits to an emergency department. As my visits spanned many facilities and many time periods, I observed a dramatic difference in the service provided. In the case of bad service I just had to wonder “Didn’t anyone ever study this operation? Did anyone ever simulate it?”

Simulation can bring significant benefits to healthcare, just as it does in other types of systems. Some of those benefits come from the simulation’s ability to:
• Account for variability in human behavior
• Account for variability in demand
• Capture complexities and interdependencies
• Capture system performance over a period of time
• Support continuous process improvement and evaluation of new scenarios
• Provide an objective basis for evaluating policies and strategies

Here are a few possible applications to illustrate how simulation is often used in the healthcare industry:

New Facility Design – Evaluate design to assure that present and future objectives will be met. Reduce capital costs by “running” the facility under various scenarios and identifying excess capacity . Reduce operating costs by supporting lean and six sigma analyses. Increase throughput through process flow optimization and identification of bottlenecks and capacity constraints.

Emergency Department (ED) – Decrease LOS (Length of Stay) and LWBS (Leave Without Being Seen) yielding higher patient satisfaction. Improve staff efficiency and improve room and resource utilization resulting in lower costs.

Outpatient Lab and Surgery – Determine optimal staff and resource allocation. Balance scheduled demand with the often-critical unscheduled demand. Decrease lab and diagnostic turn-around time. Identify non-value-added and redundant processes.

Ambulance Service – Evaluate operational scenarios for both road and air-based vehicles. Evaluate new technology to determine their effect on the entire system. Pre-plan dynamic utilization-based response guidelines to optimize performance during major ED demand periods.

Vaccine Distribution – Evaluate regional material stocking strategies, distribution strategies, and staffing.

Often the benefits from these studies are reported in the millions of dollars so they are well worth the undertaking.

One source of additional information is the Society for Simulation in Healthcare which is having their annual conference in January. Another source is the Society for Health Systems which offers the latest in process analytics, tools, techniques and methodologies for performance improvement.

Dave Sturrock
VP Products – Simio LLC

Data Collection Basics Part 2

September 28th, 2008

Last week in Data Collection Basics (Part 1) I discussed data collection, introducing the topics of identifying required data and then locating or creating that data. Once you have some data, you typically need to do some analysis on it before you can effectively use that data.

Select Distribution. Typically input data to a simulation model is specified as a distribution. If you have estimated data you must select the most appropriate distribution (for example a minimum time, typical time, and maximum time may be represented as a Triangular distribution). If you have actual data, then you will need to run a statistical analysis on it. Many software products (some generic and some simulation-specific) are available to help you with selecting (fitting) a distribution and its shape parameters, and even with cleaning the data to eliminate bad observations.

Analyze Sensitivity. Once you have some data you can build it into your model and start making trial runs. Particularly if you have relied on an estimate, you might want to run your model with values above and below the estimated values to determine system sensitivity to that parameter. If you find that the system is sensitive to an estimated value (e.g. the results change significantly with a change to the input parameter), then you can determine if it is worth a greater investment to obtain a more reliable value. This is one potential solution to the problems of bias and inaccuracy discussed in the initial article. But more than that, it is also a good way to iteratively determine how much time to spend on your input data.

Adjust Detail. Sometimes the quality of the available data can help you determine the appropriate level of detail for a model. If the data you intend to use is not very good, then there is little point to building a highly detailed model. This is not to imply that such a model is of no value, after all every model is just a representation or estimate of reality – no model will be perfect. But it is important to represent to your stakeholders the relative accuracy of the model and its underlying data.

This was a quick overview of some steps to data collection. Whole textbook chapters have been written about each of these, so be sure to look for greater detail when you are ready.

Dave Sturrock
VP Products – Simio LLC

Data Collection Basics

September 21st, 2008

Even though the people responsible for building models are often the “data collection people”, I know very few associates who think this is a particularly enjoyable part of their job. But data collection is a necessary part of most simulation projects. An early task in each simulation project should be to identify what data will be needed and how that data will be obtained.

Identify Data. There are many different types of data that you will potentially need. Like other aspects of simulation, the identifying required data is best done iteratively. Start by looking at the major areas of your model: arrival sections, processing sections, storage areas, departure areas, internal movement and similar aspects. For each area, then consider the key parameters necessary to describe it. For example, in an arrival area: What is arriving? Are there many different types of entities? Do they each have descriptive attributes that are important? Do you expect the arrivals to follow some type of a time-based pattern? Considering questions such as these will also help you define the model and modeling approach and iteratively, more detail on the exact data required.

Locate Data. With the current level of automation and electronic tracking, the availability of data has become more prevalent. If it’s an existing system, there may already be data that is routinely collected. If it is a new system, the vendor may have access to data collected on similar systems. In either case, the existence of data does not necessarily make your job easy. For example, perhaps you are interested in a processing time on an operation, and that processing time is automatically captured. But what may not be obvious is exactly what that number represents. Does it (sometimes) include time when the process was failed (perhaps short failures are imbedded but long failures are not)? Does it (sometimes) include time when an operator went on break and forgot to properly log out? Detecting and cleaning such situations can be a tedious and frustrating part of using existing data.

Create Data. If the data you need does not exist or cannot be appropriately cleaned, you must often create it. On an existing system, the most accurate method is to electronically capture the data or have manual studies done to determine it. Either of these can be very expensive. An alternate approach is to get estimates from people who know – people running or managing the operation. Although fast and inexpensive, this may introduce bias and inaccuracy. Likewise on a system that does not yet exist, you may need to rely on specifications provided by a vendor, again possibly introducing bias and inaccuracy. More on dealing with this situation later.

This was a quick overview of some initial steps to consider in data collection. Next week I will discuss some additional steps on what to do next with that data. Until then, Happy Modeling!

Dave Sturrock
VP Products – Simio LLC

Simulation and Disaster Management

September 13th, 2008

While the last couple months have been pretty dry where I live here in the Northeastern part of the U.S., in the Southeastern part several severe hurricanes have already hit and it looks like more are coming. While every severe storm can have serious consequences, often the major difference between a severe storm and an outright disaster is the level of preparation.

Of course weather is just one of many potential causes of disasters. We have all seen floods, fires, earthquakes, and other disasters around the world that have been made much worse through inadequate planning and poor execution. Simulation can play a major role in preparing communities to avoid or at least reduce the impact of such disasters.

More accurate weather prediction is due in part to simulation. Combining advanced detection technology with sophisticated simulations has allowed us to become much better at predicting storm paths and severity. This allows for improved warnings and appropriate responses.

Simulation use in evacuation planning has a very high potential, but is not used as much as it could be. Communities should be able to examine various scenarios and evaluate the best ways to move people to safety, well before a dangerous situation actually occurs.

First-responder rescue efforts can also be pre-planned and evaluated. Where should various types of equipment be stored? How can it be moved? Who will staff it? What procedures should be used for various types of disasters?

As for relief scenarios, they too could be planned ahead of time with the assistance of simulation. What equipment and supplies should be stockpiled and where? How can it be quickly relocated? Who will staff it? The logistics of a large scale disaster-relief effort, including health care provisions, security at all levels, and even communications, (all of which often involve multi-organization coordination) is a great opportunity to showcase the true benefits of using simulation.

Large corporations and other organizations can also do their own simulation-based planning. Contingency plans for various scenarios can minimize the impact of a local or regional event and help ensure that a single event does not cripple the entire organization.

Louisiana State University has a relatively new center for disaster management and has organized a conference November 16-18 dealing with some of these issues.

Be Prepared” is a motto that anyone planning for a disaster should live by; Simulation helps make that a bit easier.

Dave Sturrock
VP Products – Simio LLC

Simulation Expertise through Tours

September 7th, 2008

This past weekend I had the good fortune to be invited to a tour of Ernst Conservation Seeds sponsored by the Soil and Water Conservation Society (SWCS). Ernst is a small company that raises and sells specialty seeds used primarily in seeding conservation areas like wetlands. But more on that in a minute…

One key to success in simulation is your ability to understand the systems being modeled. Education and experience both play an important role in this, but there is something else you can do that expands your knowledge base and is interesting – facility tours.

Facility tours (plant tours) offer a rich hands-on environment. In my experience, most are conducted by a domain expert (often an Industrial Engineer or equivalent) who knows both the facility and how to “speak your language”. Most will take you through the “behind the scenes” parts of their facility. I usually find guides to be both willing and able to explain how things work and discuss both their successes and their remaining challenges. These tours can be an incredible way to experience new things and get great new insights.

Where can you find tour opportunities?

The easiest way to get involved with these types of events and continue to enhance your understanding of systems is to participate in professional societies. The local chapters of groups like the Institute of Industrial Engineers (IIE) and the Society of Manufacturing Engineers (SME) are known for frequent facility tours. But don’t stop there. There are many other professional, industry, and technology groups like banking, healthcare, and plastics that offer tours.

Major conferences often have facility tours available as well. IIE usually has several tours available at their annual conference. Likewise some user groups and educational gatherings from major companies often include facility tours.

Ask your associates working in other companies if they could possibly arrange a personal tour where they work. If you are interviewing for a job, sometimes it may be appropriate to ask for a tour of their facility. And sometimes you can even find public tours like a beer or candy manufacturer (don’t forget the free samples :-)). Or simply get a few people together and organize a tour of your own to explore a topic you are interested in.

Don’t limit yourself to just your area of interest/expertise. Often you can learn even more from tours outside your comfort zone. You might question if I could learn things pertinent to my job by touring a small seed company like Ernst. Not only was it generally interesting, I learned quite a bit about their system as I toured their preparation, sorting and processing. I was particularly interested in their innovative work making biomass into a more effective fuel source (like a process to turn fast-growing native switch grasses into efficient fuel briquettes).

I take every possible opportunity to tour a facility. I encourage you to add frequent facility tours as part of your own continuing education and success in simulation.

Dave Sturrock
VP Products – Simio LLC

Keep Simulation Projects Simple Too

September 1st, 2008

We all have stories about company decisions that make us shake our head. If you have ever worked for a large organization, it may have seemed that some of their decisions were, shall we say, sub-optimal.

For example, one particular organization was using a “home grown” time reporting system that was simple, efficient, and worked well. However upper management felt the need to buy a more sophisticated “name brand” system. Unfortunately it was poorly designed and overly complicated. Rollout required extensive training and retraining to learn the simplest tasks. It was so difficult to use that many employees simply stopped using it in favor of informal arrangements with their managers (who also found it difficult to use). As a result, the company spent a lot of money and wasted a lot of employee time, and in the end they had a system that produced inferior results.

If this was an isolated case, it could be easily forgiven. But I expect most people working for large organizations could cite similar situations. Large organizations often tend to replace simplicity with complexity.

Last week in Keep Simulation Simple I talked about KIS; the Keep It Simple concept of doing just enough to do it well and no more!

I discussed how KIS could be applied to model building, but you can also extend the KIS principle to many other aspects of simulation, especially the tools you routinely use.

For time tracking, you can buy expensive highly integrated software systems like the organization above, or for desk- bound employees you can buy software that will sense or periodically ask and record what you are working on. But the cheaper, simpler and more effective solution is simply using a spreadsheet or paper form and having the employee take two minutes at the end of each day and record time against their tasks for that day. Sure there can sometimes be reason for other methods, but for the majority of us the spread sheet solution is superior.

For project management, choose the simplest tool that will meet your needs. Some projects are complex enough that they need project management software like Microsoft Project or something even better. But in many cases, such software results in a waste of time when a simple spreadsheet could meet your needs. In my experience project management software is often overkill for the types of projects we usually encounter.

In simulation software there is some inclination to buy the most comprehensive software that you can afford. But it is often better just to buy the simplest software that is likely to meet your short to intermediate-term needs. An important caution here – make sure that your software has an adequate upgrade path so that as your needs evolve you can migrate into more feature-rich software without losing your initial investment in software, training, and models.

Stay vigilant for time wasters – they often come disguised as “cool technology” and “time savers”.
Keep It Simple.

Dave Sturrock
VP Products – Simio LLC

Keep Simulation Simple

August 25th, 2008

I mentioned a while back that I am a Boy Scout. OK, maybe my boyhood days are long gone, but I still consider myself to be a Scout. I learned many lessons as a Scout; lessons that continue to serve me well today. One of those is KIS or Keep It Simple.

I remember learning primitive camping skills. Many novice campers would bring too much gear, requiring hauling and storing it, and just in general complicating camp life. The simple (KIS) approach is to bring only what you absolutely need. Many novice campers would also select poor camp sites and then spend time dealing with dampness, bugs, discomfort, safety issues and more. The simple approach is to avoid those issues by selecting a good camp site. Then in both cases, you spend all that saved time enjoying the camp and doing what you came to do. KIS pays off.

KIS applies equally well to many aspects of simulation. When things go wrong, it can often be traced back to too much complexity.

  • How many people are subjected to overly complex management procedures?
  • Are the procedures used for planning and tracking your work making the most effective use of everyone’s time?
  • Is every aspect of your work done effectively?
  • The basic concept of KIS is to do just enough to do it well and no more! Does this mean you should not do your best? No. But it does mean that you should segment your work into small phases and KIS on each phase.

    In model-building for example, let’s say a stakeholder expresses desire for a detailed model for the 10 areas of his system. One common approach is to go off and create exactly what the stakeholder asked for. Unfortunately, this will probably be wrong. A better approach is to pick one representative area, and do a very high-level model of that one area. Then review that model and results with the stakeholder. In most cases, you will both learn a lot and you may jointly decide on a different approach. Then perhaps do a detailed model of that same area or perhaps extend that high-level model to a few more areas. Again you will probably learn something that will change your approach or objectives. For each phase, you want to do the simplest thing (KIS) that will meet the objectives for that phase. In this way, you will minimize any wasted effort and come much more quickly to exactly what the stakeholder needs.

    Let’s consider model-building at a much more detailed level. A common mistake by novices is to build a large section of a model (perhaps even an entire system) all at once. And then you hit “Go” and it does not work. Why doesn’t it work? There are perhaps a thousand possible reasons to investigate. Even worse, there are most likely dozens of small or large problems, each potentially obscuring the others. Verifying and validating such a model is a daunting task. A much better approach is to start by selecting a very small (KIS) portion of the model to build and verify that it works. Then repeat. When a problem is discovered in any new section, it is generally easy to find it because you know it is a result of that latest section just added. Again, “Keep It Simple”.

    Remember, Keep It Simple. Work effectively and exceed your stakeholder expectations one simple step at a time.

    Dave Sturrock
    VP Products – Simio LLC