I had the great privilege to attend Enterprise 2.0 this week in Boston with our friends from New Paradigm, now part of the nGenera Innovation Network. Perhaps it was the intellectual energy of Cambridge or the diversity and enthusiasm of the notable attendees...but something was "in the air" here that stimulated some great discussions, both on stage and off.

In the spirit of this confluence of social networks, collaboration, technological progress, new business models...I would like to offer some thoughts that came to my head...


1. Analytics and search. Think about web search...the form that comes back from a search expression is a list of links or an equivalent list of images. Rather primitive, don't you think? What if searches were "smart" - ie they had a point of view on how the returned information should be expressed.


My inspiration for this comes from an amazing Mathematica demonstration from Theo Gray, the front end designer at Wolfram Research. He built a periodic table of the elements where each entry is the top image from a search of that element name.



This is a great example of search results cast in a smarter, more contextualized form.


2. A simulation as a member of a social network. We think of a social network as comprised of human members. But how do we interact with those members? We ask them questions and we have information pushed our way. Hmmmm...can't an analytical model do the same thing? Why not go to the Facebook of my favorite model, post a question, such as ..."what is the current forecast for sales in Q4" and let it come at me with an answer? Why not let it receive my posted inputs and that of my colleagues (ex. "I think customers are enthusiastic about the new SuperWidget"). Intelligent but non-human actors on a social network could strengthen the collaboration that already exists.

3. Teaching the Net Generation on their terms. Lots of industries suffer from a demographic bubble - a large percentage of the workforce is within a few years of retirement, and with it goes all of that valuable experience and knowledge. The Net Generation is now entering the workforce and in some cases taking on key leadership positions. How do we make this work? I have suggested for a long time that the process of simulation is ideal for capturing and codifying human knowledge about a given system. The rigor and discipline that model development brings vets theories of the business from the participants in a comprehensive way. But until now I hadn't closed the loop on where this information goes...to the Net Generation! And how does this cohort learn? Through simulation...this is a very natural and comfortable medium for them. So perhaps simulation is an efficient way to transfer all of that great wisdom while allowing the Net Generation to find its own way?

George E. Danner

I was visiting with a client the other day. We were talking about the possibility of building a model to support a small airlines operation. A gentleman in the room, a subject matter expert on aviation operations, turned to me and said, Why do I need a simulation model? I can answer the questions we have by just sitting down and thinking it through.

I was taken aback at first. Try to design a complex set of routes, maintenance bases, schedules and the like in your head? That seemed so foreign to mathematicians like me.

And yet, it was a great question. Why do we need models? And especially why models when we have recognized experts in the field, just down the hall? After pondering this for a while I came to rest on a basic philosophy models dont replace human intuition, knowledge, and experience they codify them. Done properly, models and human judgment are seamlessly interwoven into a single, organized body of knowledge. An institutional artifact that is worthy of preservation and continuous use.

Intuition plays a subtle but key role in all we do in the process of building models. First we start with a hypothesis, which is a problem statement that suggests an answer to be proven or disproven by analysis. How do we come up with the problem statement? The answer, of course, is that humans have grappled with a complex problem and certainly know enough to restate its bounds.

During the course of model building, humans weigh in on how the system works, while the modeler translates that guidance into model-able form.

And finally, when the model is built, experiments are conducted. What happens to the call center response time if we increase the number of resources in Brazil or how does our supply chain performance look when we add that customer in Peoria. These are questions that arise from hunches about how the system at hand can and should perform under a variety of conditions. Again, not generated in a vacuum but coming from human experience.

Perhaps pop culture has given us this false zero sum game between human brainpower and computer simulation. Hollywood certainly has its share of dark man v. machine portrayals. But best practice in simulation is diametrically opposed to this view creating models that leverage humans in a way that simply transforms their knowledge into a fast and convenient format.

Pass the popcorn, please.

Youve probably heard a lot about Business Analytics lately it is a term that is thrown around with other vague notions like added value, ecosystem, and integration. If we keep it vague, firms wont be able to embrace it, wont recognize when theyve got it, and cant compare it to not having it. Yet Business Analytics is one of those things that defy strict definition, even to those of us who build Business Analytics every day.

Business Analytics feels different from simple graphs derived from a spreadsheet, even different from a finely tuned executive dashboard using the best available Balanced Scorecard practices. It is different from Six Sigma measures of process performance. But if these arent it, what is it?

For the moment Ill sidestep the challenge of strict definition and try to lend some identifying characteristics to Business Analytics. Think of these as signature features that if present, probably mean that you are somewhere in that space, somewhere near the arena of good practice a place we like to call a Next Generation Enterprise.

Business Analytics has the feel of search. BA is not static, not a bunch of numbers in a pre-ordered formatgood BA starts with nothing but a notion of I know what I want when I see it. Therefore you might start with typing a phrase such as how many blue widgets with the optional thingy did we produce last quarter? Hmmmlower than I thoughtwas it a seasonal thing? Ill now compare that to different quarters across the yearshmmmyepit does appear to be seasonal. I wonder if the red widgets are similarly effected? If such free-flowing navigation is not on par with your favorite search engine, you probably dont possess Business Analytics.

Business Analytics allows for user-driven, rule-based automation. If people are thinking about their jobs, and thinking about their firms, they also should be thinking about what vital information might trigger an important action. Lets say that youve noticed that whenever a competitor adds a new product line, unit prices follow a distinctive curve over two quarters. Any BA system worth its salt should let you take that idea, describe it in a non-programmatic way, and have the system automatically support or refute that hypothesis over time.

Business Analytics measures everything. I know a certain software CEO who has measured every hit to the companys website since 1994. Theyve committed every email archive to a freeform searchable repository. Each year when the Nobel Prizes are awarded, they know within minutes whether the winner owns their software, uses it regularly, and how many interactions theyve shared. Now this is a bit radical, I knowbut the overarching point here is that storing data comes at a near-zero cost, and data is the basic fuel of good analytics. You cant hope to know in advance what data someone might need to do some innovative study -- why not err on the side of too much data than the more frequent stance of collecting just enough data to get by?

So your homework is this: think of one basic, fundamental question that you could ask about your company a question that anyone outside the firm might want to know. Then see how much energy, time, and consternation this question generates.

If you are in a car company, you might ask: How many white XLC pickups did we sell in Nebraska in 1987?

If you are in a drug company, you might ask: how many labor-hours went into the development of our latest cholesterol drug?

If you are in a retailer, you might ask: which store has the best ratio of sales to floor space?

These are fairly simple, straightforward questions, wouldnt you agree? This is data the company should be studying regularly, and therefore should be within someones grasp at a moments notice. I suspect that you will find that more often than not, this data will be surprisingly difficult and expensive to acquire.

Firms talk a big game these days about innovation unleashing the intellectual power of its talent to solve tough problems. But if we havent given our talent access to the most basic ingredient of innovation, such talk is exactly that.

George Danner

In building business simulations, we are often called upon to create systems that mimic what humans do manually. Call them business rules, work processes, whatever you like these are the minute-by-minute decisions and analyses that people perform, often without even thinking very hard about the complexity of their own solutions.

We have recently been working in two projects where the essence of our software simulation is the duplication of a basket of these human cognitive tasks. Our clients were surprised by our seemingly slow and clunky process for creating software code, just to crudely mirror its human counterpart. Why does it take so long for you guys to write code to do X? was our clients frustrated plea.

Making software that competes with human intellect is hard, was our less-than-profound response. But its true the human brain processes an amazing amount of information very fast, and also in parallel. If I showed you, for example, a box of red colored balls with one yellow, the human can spot the yellow in an instant. If I build some code to do the same thing, I effectively have to write a loop that makes sure that it examines each and every ball exactly once, up to the total number of balls. I have to create a logic statement to determine the difference between yellow and red and apply it inside the loop. I have to command it to stop when it finds a yellowyou get the picture.

Of course, once the software is in place, it runs very fast, possibly as fast as or faster than a human. Computers are brawny they execute brute force, single-minded logic very fast once programmed to do so. It is the creation of the logic that is a slow and arduous process that even a child perceives to be primitive.

Sometimes the work that we do is viewed as magic by our clients. Our ability to quickly turn a business process into a working simulation using a sophisticated 3D visualization simply amazes people (even me, at times). However, simulation is really hard work, and not for the reasons most people think most people assume the work is in the technical integration. The mechanics of putting software componentry together are actually fairly straightforward. No, the hard work is in compressing the thousands of simple rules that humans readily apply into a finite number of lines of code in software. And these days it seems that fewer and fewer people write things down anywhere, making our job even more difficult.

I hear you saying - so what? So computers are dumb? This isnt exactly a groundbreaking finding, so what is the big deal for me and my job at at ACME Corp.?

The big deal is this: a corporation survives or dies on its ability to make decisions based upon data. Fundamentally, thats strategy, my friend. And the difference between good strategy and very good strategy is as high as its ever been. There is nothing more important to competitive advantage.

Therefore if decision making is a function of collective institutional knowledge, it serves you well to preserve that knowledge to as high a degree as possible. If that knowledge is hoarded inside of human brains, youve got a big problem, because brains are fallible, inconsistent, and portable.

Heres what to do about it:

1. Encourage people to think deeply about the top 5 key decisions they make every day and commit to writing down (in a picture is best) the structure of that decision and all of the influencing factors.

2. Create an environment where people role-play and game-play with analogs that are appropriate to your industry (if you are in the real estate industry, encourage the game-play of monopoly and then debrief afterward).

3. If youve never used business simulation, try it out on one particular business process. Be observant as to how human subject matter experts (SMEs) express how they do what they do.

Now I have to get back to workits a human thing.

George Danner

I have written about data in a previous post. This one is about capturing data. Usually, we are simply given data for the models we build. Well... it's never THAT simple, but we are rarely given the task of capturing "raw data". In fact, it's been a while since I have personally been involved with physical task of raw data collection that will be used in some analysis. Or at least, I think so... one never knows for sure with the ease at which data is captured.

But I digress. It's really too bad that I've been removed from the process of collecting data. But in recent weeks, I have been involved with two different activities where I am physically capturing data. One is for a client. I have made visits to several sites where, with pencil and clipboard in hand, I follow someone from entrance into the parking the lot through all the "stations". All the while, I am collecting time and process data, as well as making observations. You see, the client has provided us with all kinds of data; we are on the lookout for "important stuff that is not captured by the data capturing systm". Nothing beats an on-the-ground investigation for getting good intelligence. Now... what to do with the info is another matter.

The second activity is for Arbitron. My family has been selected to become a radio ratings survey for this week. We carry a log around and make hand-written entries to capture our radio-listening habits. What station? When? Where did we listen, etc. This is facinating to me, since I often get to see the aggregation of such data collection, but I rarely get to participate as a "citizen". Hopefully, writing about this on this blog doesn't disqualify me.

Both examples activities have made me think a bit more about what is actually reliable about any kind of data that's collected through distributed means. How do you set it up so you minimize the variations across the different data collectors? How do you deal with fatigue, learning, changing habits? Does the act of capturing data actually change behaviors?

Come to think of it, I am probably NOT a good candidate for a Nielson or Arbitron servey.

In his book Blink, Malcom Gladwell talks about how top athletes such as Ted Williams and Andre Agassi describe their techniques. Being able to track the ball as it hits and leaves the bat or using the writst to roll the racket over the ball are what they attribute as their respective secrets.

The author continues by saying that both statements can be proven false. For Williams, the baseball is moving too fast to observe with the human eye ("it's a three-millisecond event"). For Agassi, computer digitized images show that his wrist is moving only an eighth of a degree at impact, and most of the wrist movement happens long after impact.

Gladwell concludes that "we have, as human beings, a storytelling problem." He doesn't deny the athletes' (or any other experts in his book) their abilities. But he does say that in many cases, even experts cannot accurately describe what makes them an expert. I believe that we as human beings like to tell stories. Stories make sense. Stories are easier to remember than technical specifics, disconnected details, and minute (but perhaps important) details. So how about the complex systems we live in? Without a cohesive narrative, many complex systems can seem like a large beast that obeys the will of no one but chaos.

We work with many folks who are the Williams or the Agassi's of their world. They are experts, often seeing things before the rest of us. Or perhaps they can execute excellence in a way that seems impossible. They, like the rest of us, like to tell stories. Their stories are sometimes very complex and may at times have built-in paradoxes.

And that seems to be a good space for us. We are model-builders, and our models seem to tell the story of our clients' complexities. Like all good stories, our clients strive to capture the imagination of their audience, to leave an impression long after the telling of the story.

Science is our profession here. Every day we navigate the fields of economics, mathematics, finance, statistics, physicsit is easy for us to get wrapped up in all of the high minded work that we do. Indeed, many of the brightest minds in research today are laser-focused in their fields, collaborating with very few peers in thin vertical slices of study.

Too bad we arent more like the ancient intellectuals. Up until the Industrial Age, it was popular for great scientists to traverse a variety of subjects, each time carrying over the tools of reasoned, systematic thinking.

The contrast between science then and science now got me thinkingwhat is science, really? Is it pure discovery, or is it something else? Discovery is fundamental, of course every serious researcher has some guiding hypothesis that he or she hopes to answer through the hard work of experimentation and analysis. But Ive got to believe that a scientists mission is more than that it is also the sharing of that discovery to an audience well outside of the field, even perhaps to the public at large. The subtle presentation of a complex subject telling the story of the process, the result, and its meaning now that is what separates the good from the great. It is the art of science that I think is both necessary and yet slowly disappearing from common practice today.

Just recently I attended a seminar by Edward Tufte, Yale University professor and author of the acclaimed Visual Display of Quantitative Information (first in a series of three works). Dr. Tuftes insights on how to present data accurately and in highly compressed visual form are noteworthy. He uses modern day and centuries-old examples of good and bad practice to show us what is in essence a whole other science of telling complex stories using properly structured graphics.

So my reference to art in the title is literal: telling the story of a great piece of research is often just that: a picture, a graph lines and forms on a page using just the right blend of colors and shapes and scale.

Visualization of complexity is one of the most profound yet under-applied tools in business and science. Here at Industrial Science, we often put as much time into the visual display of a simulation than in the underlying logic, and that is as it should be. For all the work that goes into making a great piece of analysis is useless if it is cloaked in a language that cannot be expressed to all.

It happens every time I go to the doctor. For some odd reason I want to impress him by telling him how Ive been exercising, how well Ive done with my dietit is as though Im looking for some kind of reward.

We know what the rewards are. If we do all these good things, we will, on average, live longer and feel better. We do this to set the stage to function better. Exercise and proper diet arent home runs in our lives, rather they are odds-enhancers.

This last point is a crucial one when we think about business. Intuitively we pay our vendors on time because its good hygieneto not do so is to risk a disease welling up in an important part of our corporate body. So lets play doctor for a momentwhat is a valuable regimen for the average firm to increase the odds that it can perform when called upon by random events? Heres my own listI certainly would love to hear extensions to this from our readers.

1. Set aside dedicated think time for yourself and all of the critical players in your company. Measure the outcome.

2. Model the company. A simple abstract is finejust lift yourself out of the noise and conceptualize what the company is and does. It is amazing what insights can come from a simple picture. Many of our greatest products and organizations started from a few quickly assembled pictures.

2. Data, data, data. You cant do any kind of meaningful action without data to prime the pump. There is no such thing as too much data.

3. Can you describe you company to an eighth grader?

4. Be a seeker of analogies the best ones are far afield of your industry.

There you have it. Now bend overtrust me, you will only feel a moment of discomfort

In every project we do, there is the "data" phase. In order to do our analysis, build/test/run the model, show results, before any of that is possible, we need data.

Many of you know what this REALLY means. Data is never innocent nor clean. It always comes with some story, or hidden skeletons from many parts of the organization. We're always relieved when we hear that data is available; actually getting it and understanding what it really means is another story altogether.

There have been incredible advances in recent years that have made "getting the data" much easier. Thanks to zip files and the "power" of XL, we can query, sort, pivot table, and provide all kinds of data. If we go beyond the magical 65,536 lines in XL, we can split up the data in different tabs/worksheets, or go with Access or MySQL.

We also have these wonderful "thumb drives" or USB drives. And we can now burn CDs. Simply amazing! No more "sneaker-net"-ting the 2MB disks around, or trying to line up the infra-red ports on laptops. Thanks to all the $ weve spent on ERPs, we have LOTS of data. And thanks to ftp sites, servers, shared directories, USB ports, VPNs, it's easier than ever to get data from here to there.

Volume is nice, and thanks to our ever-increasing HD space, we can store more and more stuff. So what? Have we gotten better at understanding what our organizations? How they work? The needles may not have increased, but the haystacks have certainly gotten larger. In fact, we often find that we have to limit the data gathering effort so we can get on with the rest of the work. It sounds crazy sometimes, yes, there is more data available, but we have to STOP gathering the data.

Visualization helps, but even here, the trick is to LIMIT what you try to show. Technology is nice, but it no longer is the constraint to what we're trying to do with data. It's often our ability to grasp, understand, and communicate. And here, sometimes, less is more.

The title is a phrase used by one of my former professors at the Harvard Business School, a noted game theorist. He distinguishes average firms from outperforming firms by the latters focus on building company structures that dont just increase performance, but accelerate it.

This is a subtle but extraordinarily important distinction, brought home by the following word picture: imagine that I hold a piece of paper. I fold the paper in half, then I fold it again in half, and then again. After I have folded the paper another 30 times, how tall is that stack of paper? Answer: over 135 miles. This remarkable outcome is achieved by doubling the previous fold, in essence using 100% of the past to drive performance in the future, keeping the rate of output constant. Businesses that grasp this, and subsequently put the ideas into action have an outsized opportunity for growth especially given that these reinforcing effects can be harnessed at both sides of the P/L statement simultaneously. Ford Motor Company achieved this in the 1920s by creating factories (and a monolithic product line) that took advantage of learning curve effects. Google did this by first creating a technology (search methods) and an infrastructure (Linux clusters) that could be rapidly deployed across a range of service channels.

It is trendy these days to write about smart companies, best practices, and so on. That is all well and good but what about dumb, or at least blind companies that learn at high rates? A company that is engaged in a product line or service with no precedent, yet that builds processes for capturing and exploiting learning is just as high on the best practice scale as the companies that we deem smart at a particular point in time. Thats good news for all firms, no matter how well you perform today.

Now understand that closing the loop from outcome to learning to adjusted action is not trivial. Most companies have immune responses to such things, because learning is painful and public; learning is thoughtful work. Most of the firms Ive observed in my career that have pulled off the better and better dynamic have done so at the willful behest of a committed group of employees or a brave, progressive leadership team. Models can help by their very nature, business models demand attention, spur debate, and shed the light of analysis on dark corners of the landscape of a firm, where long held beliefs and business rules have rarely been questioned systematically. But those teams pushing the envelope of what is possible frankly need some air cover from the top as a license to overturn entrenched institutional structures.

Good luck, business leaders...this stuff aint easy. And we may not be the smartest two guys in the business world, but weve done some things with some bright, progressive companies - specifically under the better and better theme - that we would love to share with you. You know where to find us...were on top of a very tall stack of paper.

George Danner

Last week, George and I were meeting with an associate. At the end of the meeting, he said, "let's 'rapid prototype', to make sure we're all on the right track". Ah, another $5 business word. But what does it mean?

I actually use the term frequently, and George and I are big proponents of the rapid prototype approach. It works well in our business of building models. The idea is to quickly put something together that may NOT be anywhere near the final and actual end "thing", but a "something" nevertheless that allows us to argue, agree, comment, destroy, communicate, and inspire. Without it, we may be agreeing to "words only" and realize much later that we've been visualizing very different things.

So much of today's business requires the imagining of things that have never exactly been built before. From simple documents to complicated business models, sometimes, there's very little concrete precedence that allows us to say, "yeah, it's exactly like that thing over there".

Sometimes I use the phrase "creative destruction". Not that I'm an artist, but many sculptors will build versions of their masterpieces on paper or in small scale. Sometimes exhibits will show the pre-incarnations of the final work so that you can see the thought process or the "here's what DIDN'T work". The term "creative destruction" also has an added meaning (for me, at least), in that I believe that in some cases, the actual destroying of a prototype can be creative itself.

A more common term I have seen is "strawman". I usually don't like this term as much, since it seems to present the prototype as a "target for criticisms". The enemy may strike at any time, send a strawman to peek over the walls to see what arrows it may take. There are, of course, times when such an analogy is very appropriate. (hmmm... how is this different from "creative destruction"?)

I go back to "rapid prototype". It implies working with gusto, and in good spirit to "suggest" a version of something that will last. I imagine a bunch of scientists huddling around the prototype as it gets poked around. Ideas abound, creativity ensues, and we get closer to the final "end thing".

You cant pick up a newspaper or business magazine these days without some talking head describing the next big thing that will revolutionize business, and perhaps life as we know it. The hype was more or less true about the Internet it just took a bit longer than the breathless prose about it had predicted. The Internet has changed life as we know it and is still changing it as we speak.

Sadly, there are far more examples of technologies that did not even come close to living up to their promise. It has created an unfortunate level of cynicism about any new technology. How do we know what is real and what is not?

As you might imagine, in our business, we get exposed to lots of new technologies that of our clients, our partners, and with the groups that we interact with in the course of the research side of the business. No, I dont consider myself an authority of the evaluation of technology but I am perhaps just a bit more informed than the average pedestrian. And from this position I can tell you that there is one technology that is quietly reshaping the world of information technology: grid computing.

What? Grid computing isnt new! Whats so revolutionary about linking together a bunch of computers on a network? Arent we already there with the Internet anyway?

Fair points. But lets really get down to what we are talking about here. Grid computing is not merely linking a bunch of computers together rather, it is creating a single environment where computers work cooperatively on a single problem. You order a book from your favorite online seller the system breaks different pieces of the transaction up (debiting your credit card, updating your purchase history, placing an order at the distribution center) and processes them on different devices instead of handling each instruction sequentially.

Ok, finesounds efficient but whats the big deal? Not much in the above example. But for those of us who build sophisticated models that make use of parallel processing, this is a HUGE deal. Supercomputers have always worked this waythe difference today is that we can build the near equivalent of a supercomputer with armies of inexpensive components, like PCs running the Linux operating system precisely how Google is architected.

So many problems that we are beginning to see have a parallel feature to them. Agent-based models, Monte Carlo simulations, landscape search problems all are highly suitable to a parallel processing environment. Companies have spent decades building elaborate IT systems that are housing terabytes of data with grid computing and its parallel processing cousin we now have the means to tackle business problems of enormous scope imagine a retailer developing a sales forecast based upon a demographic profile and shopping pattern of every one of its 3 million customers. Imagine an energy firm assessing its reserve position on every one of its 60,000 producing wells worldwide

Grid computing is real. Believe it. Now, get back to [work, work, work, work].

I love chess. It always amazes me how such simple rules for piece movements can give rise to matches of such extraordinary complexity and drama. Often I see corporate strategy analogized as a chess game. On the surface, that seems right managers adeptly try to outthink their opponents in a rich interplay of competing strategies and asset positions.

However, the cold light of critical analysis casts grave doubt on this analogy. CEOs (or anyone in senior leadership) rarely have the ability to command a direct reaction to a competitive attack in the market, or even to oversee core business transactions (if this was the case we wouldnt need Sarbanes-Oxley). Economists call this the Principal-Agent Problem. What senior leaders really do is set the stage for an enterprise to do its job in the marketplace. In effect, CxOs create structures by which organizations generate their behavior. Going back to our chess analogy, it would work as if our hypothetical CxO would say, OK, lets let Joe handle the Knights and the Rooks. Sally will take care of the Bishops. I want Joe and Sally to huddle every move, especially in cases where their pieces are on the attack. Then Ill have Jim working the pawns meta-chess, if you will.

Jay Forrester, the creator of the discipline of System Dynamics once described the CEO as an organization designer. That is a much better way to describe the role than that of a chess player. Simple rules are the core of what it takes to make an organization work semi-autonomously.

So what are those magic simple rules that will cause an organization to double its stock price? The answer lies in understanding the underlying physics of a given organization different for every firm. Simulation models are an important tool in this regard. If one could abstract the organization into a model, one might test a wide range of simple rules, acted upon by agents, to determine the implications of structure (sets of rules) on aggregate organizational performance.

Its your move.

The other day Howard and I were driving back from a client meeting. This client had a software system for managing business transactions and continuously referred to it as a model. It seemed odd at first, but it got us thinkingwhat really is the definition of a model?

Unfortunately for us, the word is so overused that it has lost its real meaning. We have to describe what we do to every client who has a different interpretation of our introductory phrase: we build simulation models for a living.

There are mental models images in someones head about how a system works. There are models that describe a process or formulation Joe has a certain model for doing this, ACME Corps business model is . There are models of real things that are abstractions, because the real thing is too hard to deal with geographic maps or equations that describe planetary motion.

What we do here at industrial science is business simulation modeling. Our clients are confronted with a bewildering array of complex problems, and our standard response is to create a simulation replica of the organization at hand. With such a replica, one can experiment in ways that would be problematic and risky to do in real life (what would happen if we tripled our price for widgets?). We use models as tools for the discovery of meaningful insight into the behavior of complex systems like companies and markets.

Some of you may want to learn more about simulation modeling. Let me suggest the following reading list:

1. For a good overview of how models and simulations are used in a corporate setting, read Serious Play by Michael Schrage.

2. For an excellent and entertaining primer on agent-based models, read Turtles, Termites, and Traffic Jams by Mitchell Resnick.

3. Stephen Wolfram spent 10 years of his life conducting exhaustive research and model building for his 2002 book A New Kind of Science. If you dont have time to read all 1200 pages, I suggest you read at least the first 250. An extraordinary body of science.

4. The bible of the discipline of System Dynamics is Business Dynamics by John Sterman. Dr. Sterman was my professor at MIT and is quite possibly the smartest man on the planet Earth (Sterman and Wolfram are too close to call).

Once you have tackled this library, I suggest you read the newspaper. If you truly have a modeling mindset, youll begin to see structure in every article. This kind of thinking is a good test of your progress as a practitioner.

So what is the process for building a model? Are there good models and bad models? What problems are appropriate to a modeling approach? Stay tuned, readers we will be covering these and more as we describe our experiences working in this amazing field.

The current BusinessWeek boldly claims that "Bogs Will Change Your Business". The "blogsphere" is filled with all kinds of idle chatter, political ramblings, musings of people you may have nothing in common with. Then there are so-called "corporate blogs". What are such crazy notions? Why would a company want to establish a blog? Will the lawyers allow us to do such a thing?

The BW article is a good read and has some good "Blog 101" information (including RSS, software, podcasting, links to blogs of note, etc.). To make it even more authentic, the article is written in the form of a blog, with sentence fragments, abbreviations, date/time stamps, and of course, links. The only thing it lacks are comments from readers (more on that, in a second). Linking to the article will place you squarely in the middle of the blog phenomenon. As if to prove a point, the authors end the article by "birthing" a real blog (with the ability to post comments) called "Blogspotting", as in Trainspotting. Yes... a blog about blogs (a meta-blog). A blog you can link to from an on-line version of a print article on blogs. It can be very dizzying.

Back to the Industrial Science Blog. What are we trying to do here? We are constantly looking for applications of the right technology to address business and management issues. So whether it's mathematic/scientific tools such as agent-based modeling or Monte Carlo simulation, or on-line tools such as IM or the use of ftp sites to get around coporate email attachment policies, we are always solving problems. We experiment and try new things to promote and elevate the use of scientific methods and tools for addressing business issues.

So we invite you to visit often. Welcome to our clients, friends, mentors, associates, competitors, corporate lawyers, conrtibutors, and members of modeling/simulations community. As for the title of this post? So maybe you started your blog in 2004 or are already part of the phenomenon (if so, you are way ahead!). 2005 will be the year that you will start reading blogs on a regular basis. Perhaps you will start your own, or make contribution on this or other blogs.

The journey continues.

Earlier this evening, I spent a little time with my two-year old playing ball. I threw long arcs with a very soft ball and although he couldn't catch it, he was starting to track the ball in the air.

This reminded me of something I read that discussed how smart we must be to be able to compute 2-dimensional physics equations in our head. Somehow, we know where the ball will land and place ourselves there well before the ball actually arrives. Of course, this is done through experience (trial & error), since most of us will not take measurements, count steps and do s = ut + 1/2 at² in our head. As I was throwing this green soft ball in large arcs, I thought that in reality the problem is actually much easier and at the same time much more difficult.

The problem is made more difficult than our Physics 101 problem since we have to deal with things like wind and terminal velocity. Also, we have height (y<>0), and our eyesight is not connected to the glove itself. In order to do the actual math, we would have to take all these (and a few more) factors into account. But we have a secret weapon. It's more powerful than the equations and ability to accurately measure things on the "fly".

It's called feedback. We can make a good guess (from experience), and make adjustments as the ball is in the air. There's a reason why Coach told us to "keep our eye on the ball". We don't have to recalculate -- we don't even have to do the initial calculation. We can guess, and make smart adjustments.

Now, this is not earthshattering news. This is Baseball 101, Chapter 3: "how to play outfield". Feedback is built into our human nature. It's amazing how well we learn and solve how to do things.

Unfortunately, many models ignore such feedback mechanisms. Think of pricing models that ignore competitor's responses; tax models without the ability of people giving up their US citizenship to avoid taxes. Feedback is an important part of many systems. The world is not static -- the agents in a system are not static either. They react, they learn, they try different things, they fail, they succeed.

I am often asked by clients, colleagues (and even family), "can your models really predict the future?".

It's enticing to think that it's possible to know, with absolute certainty, how the future will play out--in a business dealing, in world politics, in your stock portfolio, tommorow's game, or tonight's dinner. If we're "smart", and we make our models "smart", couldn't we use it to tell us what will happen? In fact, the early days of computing was marked by an attempt to better predict (and perhaps control!) weather. We know today that our weather reports can be wrong.

"We're not in the business of predicting the future. Instead our models help you prepare and, if you're bold enough, help you shape your future." is the sort of reponse I give. In fact, we find that in many organizations there are varying ideas of what the future is and why it's important. Even when confronted with lots of data (and perhaps because there is too much data) it's difficult to put it all together into a coherent viewpoint.

Sometimes its best to think about the range of possible futures to see what's possible. Think of the three ghosts who visit Scrooge--there is but one future (if we ignore the parallel universe argument), but I'll show you what COULD happen. Why is this important? Becuase it causes Scrooge to change behavior NOW. Dickens ends the story here without fast-forwarding to the actual future. He doesn't have to. We the readers "get it".

We are entering yet another exciting chapter in the history of Industrial Science. Founded just under three years ago, we have witnessed an amazing number of complex business organizations and the problems that they face. We have learned a great deal, and this forum is ideal for us to share our experiences with you, as well as to get your reactions and thoughts.

Our mission here at Industrial Science is to help our clients navigate the extraordinarily complex world of engaging durable strategies in a constantly changing climate. The answer, in a word, is science. Yet there also exists a great deal of "art" in the application of appropriate science to the particular problem at hand. Moreover, the process of building computer simulations (our primary tool) is as valuable as the numerical result. What we are privileged to see on a daily basis is astounding - and too important to keep just to ourselves.

In this blog, we will be writing about a whole range of subjects under the broad umbrella of the application of science to business strategy and operations. We will be inviting guest authors whom we consider to be undiscovered thought leaders, to weigh in as well. And finally, we invite you to come along with us, by granting us the favor of your reply to our posts.

Let the journey begin.

George E. Danner
President
Industrial Science, LLC