Archive for the ‘Applications’ Category

Why Simulation is Important in a Tough Economy

Thursday, October 29th, 2009

Everyone wants to cut costs. No one wants to spend unnecessarily. When budgets are tight, software and software projects are an easy place to cut. Staff positions like Industrial Engineers are sometimes easier to cut or redeploy than production jobs. I suggest that following this reasoning to eliminate simulation projects is often short-sighted and may end up costing much more than it saves. Here are a few reasons why it may make sense to increase your simulation work now.

1) Minimize your spending. Cash is tight. You cannot afford to waste a single dollar. But how do you really know what is a good investment? Simulate to ensure that you really need what you are purchasing. A frequent result of simulations intended to justify purchases is to find that the purchases are NOT justified and in fact the objectives can be met using existing equipment better. A simulation may save hundreds of times its cost with immediate payback.

2) Optimize use of what you have. Could you use a reduction in cost? Would it be useful to improve customer satisfaction? I assume that your answer would always be yes, but even more so in difficult times. But how can you get better, particularly with minimal investment? Simulation is a proven way to find bottlenecks and identify often low-cost opportunities to improve your operation.

3) Control change. In a down economy you are often using your facilities in new and creative ways – perhaps running lean or producing products in new ways or in new places. But how do you know these new and creative endeavors will actually work? How do you know they will not cost you even more than you save? Simulation helps you discover hidden interactions that can cause big problems. Different is not always better. Simulate first to avoid costly mistakes.

4) Retain/improve your talent pool. Some people who might otherwise be laid off may have the skills to be part of a simulation SWAT team. By letting them participate in simulation projects, they will likely achieve enough cost reduction and productivity improvements that they more than pay for themselves. As an added bonus, the team will learn much about your systems, the people, and communication – knowledge which will improve their value and contributions long after the project is complete.

5) Reduce risk. You are often forced to make changes. How do you know they are the right changes? Will a little more, a little less, or a different approach yield better results? How do you measure? A strength of simulation is its ability to objectively assess various approaches and configurations. Substitute objective criteria for a “best guess”, and, in turn, reduce the risk associated with those changes. In a down economy it is more important than ever that you don’t make mistakes.

In summary, rather than thinking of the cost of simulation, you should think of what the investment in simulation today will save you today, tomorrow and every day following. Simulation is not a cost, it is an investment that may return one of the best ROIs available in a tough economy.

Dave Sturrock
VP Products – Simio LLC

Can Simulations Model Chaos?

Sunday, January 11th, 2009

Can chaotic systems be predicted? I guess we first need to agree on exactly what a chaotic system is. defines it as a
“Complex system that shows sensitivity to initial conditions, such as an economy, a stockmarket, or weather. In such systems any uncertainty (no matter how small) in the beginning will produce rapidly escalating and compounding errors in the prediction of the system’s future behavior.”

It is hard to imagine a complex system that does not show sensitivity to initial conditions. If the follow-on statement is true, then there is little point to ever trying to model or predict the behavior of such a system because it is not predictable. But it is not hard to find counter-examples, even to the examples they provided. Meteorologists do a reasonable job predicting the weather; it depends on your standards of accuracy. Certainly they can predict fairly accurately the likelihood of a 90 degree day in January in Canada or anticipating the path of a tropical storm for the next 12 hours.

A less technical but perhaps more useful definition comes from
“A chaotic system is one in which a tiny change can have a huge effect.”
That leads us toward a more practical definition for our purposes.

For the types of systems we normally model, I would propose yet another definition.
A chaotic system is one in which it is likely that seemingly trivial changes in the initial conditions would cause significant changes in the predicted results, over the time frame being considered.

This definition, while not technically rigorous, acknowledges that most of us rarely have the opportunity or the need to deal in absolutes. We live in a world where the majority of decisions are made subjectively (“Joe has 20 years experience and he says…”) or with gross simplification (“Of course I can model that in a spreadsheet…”). In this world, being able to base a decision on a simulation model with better accuracy and objectivity can help realize tremendous savings, even if it is still only an approximation and only useful within specified parameters.

Can we accurately predict true chaotic systems? By strict definition clearly not. And even by my definition, there will be some systems that are just too chaotic to allow any predictions to be useful.

But can we provide useful predictions of most common systems, even those with some chaotic aspects? Absolutely yes. Every model is an approximation of a real or intended system. Part of our job as modelers is to ensure that the model is close enough to provide useful insight. A touch of chaos just makes that more interesting. 🙂

Dave Sturrock
VP Products – Simio LLC

Six Sigma and Simulation: Part 2

Tuesday, December 9th, 2008

By Jeff Joines (Associate Professor In Textile Engineering at NCSU)

This is the second of the three part series on Six Sigma, Lean Sigma, and Simulation. The first part explained the Six Sigma methodologies. Recall the goal of the DMAIC continuous improvement methodology is to control/reduce process variability of a current process or product while the Design for Six Sigma process DMADV is used to design a new process or product with minimal variability before creation. Simulation modeling can be employed in almost every phase of either methodology.


Six Sigma practitioners have to estimate the cost savings for each project to be certified or justify the project typically. However, most of these cost forecasts are made on point estimates of key parameters (i.e., raw material cost, customer/product demand, cost of capital, currency rates, etc.). By employing Monte Carlo simulation, variability and/or ranges on these point estimates can be employed to provide a more reliable estimate. Along these lines, several projects have been proposed and simulations can be utilized to help management perform project selection based on resource constraints and objectives.

Analyze and Improve

During the Analysis and Improve phases, Design of Experiments (Full, Fractional, Mixed, etc.) is the most common tool utilized which provides a base line to illustrate improvement when changes are made as well identifying factors of interest to control or change. The normal baseline measure is defined as the process capability (Cpk) which is an indication of the ability of a process to produce consistent results – the ratio between the permissible spread and the actual spread of a process. The Cpk index takes into account off centeredness and defined as the minimum of (USL-Mean)/ 3? or (Mean-LSL)/ 3? where USL and LSL are the upper and lower specification limit. A six sigma process is normally distributed with a Cpk value greater than 1.5.

Using the real system is better in terms of capturing all complexities, interactions, etc. However as simulation practitioners, we recognize when that might be possible or viable. The following lists examples where simulation modeling in terms of Monte Carlo or process simulation can be used.

  • If the product or process does not exist as is the case in a Design for Six Sigma, simulation models can be used to ascertain capability of a new process and product before implementation. For example, tolerance stack up of individual parts or processes can be determined. Take parts or processes which are within tolerance individually (e.g., bearing and a shaft) but the assembly process might not be capability owing to the tolerance stack up problem which occurs in manufacturing, service, and transactional processes.
  • The cost of performing a DOE with replications is too high (e.g., raw material cost, cost of shutting down current process). We have worked with companies in developing process and Monte Carlo simulation models that could be used to determine their capabilities and ascertain the potential improvement in their changes.
  • The time of running the set of experimentation makes it impractical to determine the baseline or ascertain the improvements of a process. While working with a large company and their six sigma process improvement team with a complex global supply chain, one of their projects was to reduce inventories of a series of products with a ten to twelve week lead time. The team had to evaluate six inventory policies, indentify which one of three suppliers was best, etc. The DOE with sufficient replications would have taken years to complete and made the project useless without the simulation model. Also, most of the data driving the model was based on lead-times which are not normally distributed.
  • Think of systems where there are multiple processes that feed one another (e.g., departments, plants, etc.) which contain only five or six factors each. Transfer functions can be generated from a traditional DOE on each individual process but not the entire system. A simulation model can be used to combine each individual transfer function into determining the capability of the whole system as well as testing a wider range of values.
  • There are several environments, where performing a DOE is impractical or impossible. For example, we have trained dozens of people associated with hospital systems from around the country in Six Sigma. Simulation modeling and analysis allows these practitioners to be able ascertain process capability with a model because the real system cannot be used since patient care is at stake. Other environments where we have used simulation modeling instead of the real system is in processes which are transactional like the banking or insurance industries.


Simulation can also be used as a process control aid as the process is being implemented to determine potential problems.

Hopefully it is apparent that simulation experts already posses the skills that can greatly help Six Sigma projects. These types of projects are not unique but just general simulation models we are know how to build. They only require us to learn the Six Sigma language as well as the need to calculate Cpk statistics. I find it easier to work with Six Sigma people because of their statistical training for understanding input and output analysis even though they typically have only used the Normal distribution. In Six Sigma and Simulation: Part 3, the use of simulation in the Lean Sigma world will be addressed.

Six Sigma and Simulation: Part 1

Sunday, November 30th, 2008

By Jeff Joines (Associate Professor In Textile Engineering at NCSU)

This is a three part series on Six Sigma, Lean Sigma, and Simulation. The first blog will explain the Six Sigma methodology and the bridge to simulation analysis and modeling while the second and third parts will describe the uses of simulation in each of the Six Sigma phases and Lean Sigma (i.e., Lean Manufacturing) respectively.

“Systems rarely perform exactly as predicted” was the starting line for the blog Predicting Process Variability and is the driving force behind most improvement projects. As stated, variability is inherent in all processes whether these processes are concerned with manufacturing a product within a plant, producing product via an entire supply chain complex or providing a service in the retail, banking, entertainment or hospital environment. If one could predict or eliminate the variability of a process or product, then there would be no waste (or Muda in the Lean World which will discussed in a third part) associated with a process, no overtime to finish an order, no lost sales owing to having the wrong inventory or lengthy lead-times, no deaths owing to errors in health care, shorter lead times, etc. which ultimately leads to reduced costs. For any organization (manufacturing or service), reducing costs, lead-times, etc. is or should be a priority in order to compete in the global world. Reducing, controlling and/or eliminating the variability in a process is key in minimizing costs.

Six Sigma is a business philosophy focusing on continuous improvement to reduce and eliminate variability. In a service or manufacturing environment, a Six Sigma (6?) process would be virtually defect free (i.e., only allowing 3.4 defects out of a million operations of a process). However, most companies operate at four sigma which allows 6,000 defects per million. Six Sigma began in the 1980s when Motorola set out to reduce the number of defects in its own products. Motorola identified ways to cut waste, improve quality, reduce production time and costs, and focus on how the products were designed and made. Six Sigma grew from this proactive initiative of using exact measurements to anticipate problem areas. In 1988, Motorola was selected as the first large manufacturing company to win the Malcolm Baldrige National Quality Award. As a result, Motorola’s methodologies were launched and soon their suppliers were encouraged to adopt the 6? practices. Today, companies who use the Six Sigma methodology achieve significant cost reductions.

Six Sigma evolved from other quality initiatives, such as ISO, Total Quantity Management (TQM) and Baldrige, to become a quality standardization process based on hard data and not hunches or gut feelings, hence the mathematical term, Six Sigma. Six Sigma utilizes a host of traditional statistical tools but encompasses them within a process improvement framework. These tools include affinity diagrams, cause & effects, failure modes and effective analysis (FMEA), Poka Yoke (mistake proofing), survey analysis (voice of customer), design of experiments (DOE), capability analysis, measurement system analysis, statistical process control charts and plans, etc.

There are two basic Six Sigma processes (i.e., DMAIC and DMADV) and they both utilize data intensive solution approaches and eliminate the use of your gut or intuition in making decisions and improvements. The Six Sigma method based on the DMAIC process and is utilized when the product or process already exists but it is not meeting the specifications or performing adequately is described as follows.

    Define, identify, prioritize, and select the right projects. Once selected to define the project goals and deliverables.
    Measure the key product characteristics and process parameters to create a base line.
    Analyze and identify the key process determinants or root causes of the variability.
    Improve and optimize performance by eliminating defects.
    Control the current gains and future process performances.

If the process or product does not exist and needs to be developed, the Design for Six Sigma (DFSS) process (DMADV) has to be employed. Processes or products designed with the DMADV process typically reach market sooner; have less rework; decreased costs, etc. Even though, the DMADV is similar to DMAIC method and start with the same three steps, they are quite different as defined below.

    Define, identify, prioritize, and select the right projects. Once selected to define the project goals and deliverables.
    Measure and determine customer needs and specifications through voice of the customer.
    Analyze and identify the process options necessary to meet the customer needs.
    Design a detailed process or product to meet the customer needs.
    Verify the design performance and ability to meet the customer needs where the customer maybe internal or external to the organization.

Both processes use continuous improvement from one stage back to the beginning. For example, if during the analyze phase you determine a key input is not being measured, new metrics have to be defined or new projects can be defined once the control phase is reached.

Now that we have defined six sigma, you may be wondering what is the bridge to computer simulation and modeling. Simulation modeling and analysis is just another tool in the Six Sigma toolbox. Many of the statistical tools (e.g., DOE) try to describe the dependent variables (Y’s) in terms of the independent variables (X’s) in order to improve it. Also, most of the statistical tools are parametric methods (i.e., they rely on the data being normally distributed or utilize our friend the central limit theorem to make the data appear normally distributed). Many of the traditional tools might produce sub-optimal results or cannot be used at all. For example, if one is designing a new process or product, the system does not exist so determining current capability or future performance cannot be done. The complexity and uncertainty of certain processes cannot be determined or analyzed using traditional methods. Simulation modeling and analysis makes none of these assumptions and can yield a more realistic range of results especially where the independent variables (X’s) can be described as a distribution of values. In Six Sigma and Simulation: Part 2, a more detailed look at how simulation is used in the two six sigma processes (DMAIC and DMADV) will be discussed.

Simulation Applications in Assembly

Sunday, November 9th, 2008

Assembly processes are a common part of manufacturing and can be found in applications as diverse as apparel, electronics, automotive, aerospace, and even food processing. Assembly operations share many common simulation applications with general manufacturing, but also have many unique characteristics and problems which can often be assisted using simulation.

Material handling and other automated equipment are prevalent in most assembly operations. Simulation can help both in the initial design as well as analyzing to get improved efficiency.

I have found that most people think they can predict process variability fairly well, but when pressed to predict the behavior of even the simplest system, they fail miserably. This is a dangerous combination. Process variability can make the performance of typical systems hard to predict and overconfidence can lead you to incorrect decisions. Fortunately, simulation can provide extensive analysis to project performance, demystify variability, and reduce risk.

Often assemblies are made following a Bill of Material (BOM). Some simulation software has built-in BOM modeling features to make this easy. Whether your supply chain for the assembly involves only other departments in the building or involves off shore companies, simulation can help you assess the supply chain risk and design a system to meet corporate objectives.

For both manual and highly automated systems, line balancing can be a difficult task in assembly. Getting it wrong, even by a small amount, can result in an expensive loss of efficiency. Simulation can help not only tweaking a system for optimal efficiency, but also evaluating major changes in a safe, inexpensive, off-line environment.

Assembly operations can be capital or labor-intensive. Effective allocation of capital and labor is often a need that simulation can fulfill. Simulation can help identify bottlenecks and underutilized resources so that you can gain insight into your operations and get more out of your resources.

Markets change. Technology changes. It sometimes seems like the sole job of Marketing is to make your job miserable by introducing new productivity-damaging products. Simulation can help you respond to change requests with objective data about the cost and other impacts to your system.

It is well known that simulation technology is very effective at creating work schedules while taking into account the complexities of the facility. A few simulation products offer features to enable this application. You can even use the model built for optimizing design as the basis for a plant scheduling model.

In summary, simulation applied to assembly like in other applications, can help streamline designs, reduce risk, improve throughput, and increase your bottom-line profitability.

Dave Sturrock
VP Products – Simio LLC

Predicting Process Variability

Monday, 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

Sunday, 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

Simulation in Healthcare

Sunday, 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

Simulation and Disaster Management

Saturday, 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