Scheduling in the Industry 4.0

Today started badly.

As soon as I hopped into my car, the GPS system was flashing red to show queues of stationary traffic on my regular route to the office. Thankfully, the alternative offered allowed me to arrive on time and keep my scheduled appointments.

In the same way as a GPS combines live traffic data with an accurate map of the city, Simio Software connects real time data sources with a modeled production situation. Just like a GPS, Simio can also impose rules, make decisions, schedule and reschedule.

The major difference is in the scale.

Simio Simulation and Scheduling Software can model entire factories, holding huge quantities of detailed data about each resource, component and material. It leverages big data analysis to run thousands of permutations of scenarios, finding the optimum outcomes for specific circumstances. Lightning fast, it can detect and respond to changes with suggestions that will keep everything flowing in the best possible way.

Thank goodness for Simio, because Industry 4.0 is here.

Smart Factories employ fully integrated and connected equipment and people, each providing real time feedback about their state. Data is constantly collected on each product component, for process monitoring and control. Every aspect of the entire operation is managed through its associated specifications and status data. This large, constant stream of information coming from a known factory configuration can be received, stored, processed and reported upon by the powerful Simio software.

With Industry 4.0, nothing is left to chance. Everything is monitored and optimized, and performance is predicted, measured, improved and adapted on an ongoing basis. Management of so many interconnected components requires a scheduling system that is specifically designed to operate in this dynamic data environment. Simio Production Scheduling Software can be relied upon to provide the integrated solution for enabling technology in the Smart Factories of the future.

We are already seeing a rise in robotics and the increasing digitalization of the manufacturing industry under the effects of Industry 4.0. Soon all components of the factory model will be interconnected, just like my future driverless car that will communicate directly with my GPS to take the best route using current traffic information.

All I will have to do is sit back and enjoy the ride.

How Much Data Do I Need?

I have discussed data issues in several previous articles. People are often confused about how much data they really need. In particular, I frequently hear the refrain “Simulation requires so much data, but I don’t have enough data to feed it.” So let’s examine a situation where you have, say 40% of the data you would like to have in order to make a sound decision and let’s examine the choices.

1) You can possibly defer the decision. In many cases no decision is a decision in itself because the decision will get made by the situation or by others involved. But if you truly do have the opportunity to wait and collect more data before making the decision, then you must measure the cost of waiting against the potential better decision that you might make with better data. But either way, after waiting you still have all of the following options available.

2) Use “seat of the pants” judgment and just decide based on what you know. This approach compounds the lack of data by also ignoring problem complexity and ignoring any analytic approach. (Ironically enough this approach often ignores the data you do have.) You make a totally subjective call, often heavily biased by politics. There is no doubt that some highly experienced people can make judgment calls that are fairly good. But it is also true that many judgment calls turn out to be poor and could have benefited greatly from a more analytical and objective approach.

3) Use a spreadsheet or other analytical approach that doesn’t require so much data. On the surface this sounds like a good idea and in fact, there is a set of problems for which spreadsheets are certainly the best (or at least an appropriate) choice. But for the modeling problems we typically come across, spreadsheets have two very significant limitations: they cannot deal with system complexity and they cannot adequately deal with system variability. With this approach you are simply “wishing away” the need for the missing data. You are not only making the decision without that data, but you are pretending that the missing data is not important to your decision. An oversimplified model that doesn’t consider variability or system complexity and ignores the missing data … doesn’t sound like the makings of a good decision.

3) Simulate with the data you have. No model is ever perfect. Your intent is generally to build a model to meet your project objectives to the best of your ability given the time, resources, and data available. We can probably all agree that better and more complete data results in a more accurate, complete, and robust model. But model value is not true false (valuable or worthless) but rather it is a graduated scale of increasing value. Referencing back to that variability problem, it is much better to model with estimates of variability than to just use a constant. Likewise a model based on 40% data won’t provide near the results of one with all of the desired data, but it will still outperform the analytical techniques that are not only missing that same data, but are also missing the system complexity and variability.

And unlike the other approaches, simulation does not ignore the missing data, but can also help you identify the impact and prioritize the opportunities to collect more data. For example some products have features that will help you assess the impact of guesses on your key outputs (KPIs). They also have features that can help assess where you should put your data collection efforts to expand sample or small data sets to most improve your model accuracy. And all simulations provide what-if capability you can use to evaluate best and worst case possibilities.

Perfection is the enemy of success. You can’t stop making decisions while you wait for perfect data. But you can use tools that are resilient enough to provide value with limited data. Especially if those same tools will help you better understand the value of both the existing and the missing data.

Happy modeling!

Dave Sturrock
VP Operations – Simio LLC

Simulation Stakeholder Bill of Rights

The people who request, pay for, consume, or are affected by a simulation project and its results are often referred to as its stakeholders. For any simulation project the stakeholders should have reasonable expectations from the people actually doing the work.

Here I propose some basic stakeholder rights that should be assured.

1. Partnership – The modeler will do more than provide information on request. The modeler will assume some ownership of helping stakeholders determine the right problems and identify and evaluate proposed solutions.
2. Functional Specification – A specification will be created at the beginning of the project to help define clear project objectives, deadlines, data, responsibilities, reporting needs, and other project aspects. This specification will be used as a guide throughout the project, especially when tradeoffs must be considered.
3. Prototype – All but the simplest projects will have a prototype to help stakeholders and the modeler communicate and visualize the project scope, approach, and outcomes. The prototype is often done as part of the functional specification.
4. Level of Detail – The model will be created at an appropriate level of detail to address the stated objectives. Too much or too little detail could lead to an incomplete, misunderstood, or even useless model.
5. Phased Approach – The project will be divided into phases and the interim results should be shared with stakeholders. This allows problems in approach, detail, data, timeliness, or other areas to be discovered and addressed early and reduces the chance of an unfortunate surprise at the end of a project.
6. Timeliness – If a decision-making date has been clearly identified, usable results will be provided by that date. If project completion has been delayed, regardless of reason or fault, the model will be re-scoped so that the existing work can provide value and contribute to effective decision-making.
7. Agility – Modeling is a discovery process and often new directions will evolve over the course of the project. While observing the limitations of level of detail, timeliness, and other aspects of the functional specification, a modeler will attempt to adjust project direction appropriately to meet evolving needs.
8. Validated and Verified – The modeler will certify that the model conforms to the design in the functional specification and that the model appropriately represents the actual operation. If there is inadequate time for accuracy, there is inadequate time for the modeling effort.
9. Animation – Every model deserves at least simple animation to aid in verification and communication with stakeholders.
10. Clear Accurate Results – The project results will be summarized and expressed in a form and terminology useful to stakeholders. Since simulation results are an estimate, proper analysis will be done so that the stakeholders are informed of the accuracy of the results.
11. Documentation – The model will be adequately documented both internally and externally to support both immediate objectives and long term model viability.
12. Integrity – The results and recommendations are based only on facts and analysis and are not influenced by politics, effort, or other inappropriate factors.

Note that every set of rights comes with responsibilities. The associated stakeholder responsibilities are discussed as part of the Simulationist Bill of Rights.

Give these expectations careful consideration to improve the effectiveness and success of your next project.

Dave Sturrock
VP Products – Simio LLC

Why Simulation is Important in a Tough Economy

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?

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

Professional Development

The annual Winter Simulation Conference (WSC) starts two weeks from today. Initially as a practitioner and then later as a vendor I have attended over 20 of these conferences in addition to dozens of other similar events. WSC is just one of many events that you could choose to attend. But why should you attend any of them?

All such events are not identical, but here are a few attributes of WSC that are often found in other events as well:

Basic tutorials – If you are new to simulation, this is a good place to learn the basics from experienced people.

Advanced tutorials – If you already have some experience, these sessions can extend your skills into new areas.

Practitioner papers – There is no better way to find out how simulation can be applied to your applications than to explore a case study in your industry and talk to someone who may have already faced the problems you might face.

Research – Catch up on state-of-the-art research through presentations by faculty and graduate students on what they have recently accomplished.

Networking – The chance to meet with your peers and make contacts is invaluable.

Software exhibits and tutorials – If you have not yet selected a product or you want to explore new options, it is extremely convenient to have many major vendors in one place, many of whom also provide scheduled product tutorials.

Supplemental sessions – Some half and full day sessions are offered before and after the conference to enhance your skill set in a particular area.

Proceedings – A quick way to preview a session, or explore a session that you could not attend. This serves as valuable reference material that you may find yourself reaching for throughout the year.

I think every professional involved in simulation should attend WSC or an equivalent conference at least once early in your career, and then periodically every 2-3 years, perhaps rotating between other similar conferences. If you want to be successful you have to keep your skills and knowledge up to date. And in today’s economy, a strong personal network can be valuable when you least expect it.

I hope to see you at WSC in Miami!

Dave Sturrock
VP Products – Simio LLC

Read My Project Report!

I read a lot, both for business and pleasure. But it seems I never have enough time. So when I sit down with a magazine, for example, most articles probably get less than a couple seconds of attention. Unless an article immediately captures my attention, I quickly move on to the next one. I know that I occasionally miss out on good content, but it is a way to cope with the volume of information that I need to process each day. Consider the implications when you are writing a project report for others to read…

We are all busy. When we are presented with information to read or review, we often don’t have time to wade through the details to see if the content merits our time.

Tell me the most important thing first! Give me the summary! How many times have you asked (or wished) for that?

At one point, it was common to give presentations by starting with an introduction, building the content, and ending with the conclusion – “the big finish”. While this is appropriate for some audiences, many people don’t want to take the time to follow such a presentation. Instead, they want to be presented with a quick overview and a concise summary first. They will then decide to read on if the overview has captured their interest and they need more information.

Think about your own experiences. When you have a document to read and you are not sure it is worth your time, what do you do? If you are like most people you will probably consider most, if not all of the following:
• Does the title look interesting?
• Do you know/respect the author?
• Scan the major headings or callouts for content of interest.
• Scan any pictures/diagrams for content of interest.
• Evaluate the summary or abstract.
While the order and details might differ slightly, at each stage of the above process if you are not convinced of the value of continuing, you will put the document aside. Only after the document has passed this gauntlet of tests, will you spend the time to seriously read the content.

What can we learn from this?

Content is not enough. The best content in the world is of little value unless it is read.

When you are preparing a project report, try to get inside the head of your target audience. If you expect that they will also have a process something like the above, spend adequate time on those parts. Take an extra minute to create an interesting title. Add major headings and callouts to help focus the reader’s attention. Add some figures to help convey and support your message. Have a good abstract and/or summary that is easy to find to help your audience quickly get the point of your report.

Write each report so everyone, including your busy stakeholders, will take the time to read it. Keeping these simple suggestions in mind will help you succeed at getting your message across.

Dave Sturrock
VP Products – Simio LLC

Predicting Process Variability

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 Healthcare

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

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