Data Collection Basics Part 2

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

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

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

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

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