What is Simulation Science and SAP SEM-BPS

More details here:

href="http://justinlyonandsimulation.blogspot.com/">http://justinlyonandsimulation.blogspot.com

Simulation Science,

in my mental model, is the application of 'linear science' **AND** 'nonlinear science' to solving problems in the real

world. Companies with investments in SAP-SEM BPS are extremely well-positioned to take advantage of the power of simulation

for radically improving the management of complex supply chains and global enterprises.

'Linear

science' is located within the larger set of 'nonlinear science'.

I hate the phrase 'nonlinear science' as to me

it's like calling Zoology the 'study of nonelephant animals.' (thanks go to the mathematician Stanislaw Ulam for this

simile).

Nor do I like the phrase 'complexity science' as

that makes something easy sound unnecessarily hard. And, 'chaos' is even worse

because it implies to the vast majority of people that the business will become frenzied or wild in appearance, when, in

fact, management scientists are interested mostly in how a '

href="http://chaos.aip.org/chaos/staff.jsp">chaotic

' business system can actually evolve in a way which appears smooth

and ordered.

So, I call it 'Simulation Science' instead. Or, just good science.

Simulation

Science requires the use of computers and the application of nonlinear techniques (and 'linear' techniques when

appropriate) for understanding our complex world so that people can solve real world problems more creatively and

efficiently.

By doing so, a new meta science, Simulation Science, emerged in the 1950's

that emphasizes multidisciplinary collaboration in pursuit of understanding the common themes that arise in natural,

artificial, and social systems. This unique scientific enterprise attempts to uncover the mechanisms that underlie the

href="http://www.randomhouse.com/catalog/display.pperl?isbn=9781400062560">deep simplicity

present in our complex

world.

Techniques used within Simulation Science might come from a variety of fields, e.g., regression analysis, agent

-based modelling, cognitive neuroscience, network dynamics, brand dynamics, discrete-event simulations, system dynamics,

strategy dynamics, fractal mathematics, dynamical systems, chaos, etc. ad nausea depending on the problem being studied.

So, illustratively, System

Dynamics is a discipline within the larger set of Simulation Science. And,

href="http://www.strategydynamics.com/jl">Strategy Dynamics

is a sub-speciality within the discipline of System Dynamics.

Additionaly, I would argue that Biochemistry and Economics are also subsets within the larger Simulation Science set.

That is, people need to understand the real-life problem and then choose the appropriate scientific discipline to

analyze it and develop policy. Maybe it's a System Dynamics model, maybe a discrete-event model or maybe some combination of

a variety of techniques. Just make sure the scientific technique you are using embraces the facts of complex adaptive

systems.

Greater than 90% of all business problems in the world currently being analyzed by

the vast majority of consultants, economists, marketers, etc., are almost exclusively analyzed using techniques that paint

patently false simulacrums of reality. Executives need to demand more from their analysts than just more black box

spreadsheet models that create false security. 

Many of these traditional simulacrums (often in

the form of highly complex spreadsheet models) include assumptions that violate the most basic of physical laws.

They

are more of a hindrance to decision making than a benefit. I argue that analytical reductionism is more often used for

improving perceived certainty and reducing perceived risk (leading to the all to often heard lament, "we did a bunch of

analysis before it all went wrong, so its not our fault, rather something extrinsic to us caused our failure").

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Nothing wrong with doing what you’ve always done other than the technology for applying 'nonlinear science'

is available (after decades of refinement) in the form of fast computers, excellent modelling software, solid management

science, etc. Today, humans can build evermore robust simulacrums for improving their decision making.

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It is cheaper doing simulation science than traditional analysis. And, you get better

results. 

"Do I get my traditional consultants to answer this question or should I retain a

simulation facilitator to help me understand what we need to change about our system to make it a more viable predator in the

cut-throat business world?"

It's a no brainer decision . . . Hire the simulation

facilitators and give your pricey consultants their walking papers.

Like a good

book that has been relentlessly edited over time, simulation models become simpler over time (unlike traditional analysis

which gets more complex over time). Over time, the user interface gets more complex and robust, but the underlying simulation

models (objects) should get simpler.

Or, the more time I have, the simpler I can make the simulation objects.

The complexity of the model arises when you connect a bunch of very simple objects (molecules) together into a larger

simulation.

It was *nearly* impossible *before computers* to truly understand the dynamics of businesses,

because businesses are complex adaptive systems and it is IMPOSSIBLE for any human being, no matter how clever, to solve

high-order, nonlinear, dynamic systems other than in the most 'gut-feel' sort of way by using the most powerful computer on

the planet (our brains).

Nothing wrong with that either -- I just think it's easier for people to follow their

leaders if they make their mental models explicit in a computer simulation instead of requiring faith in the hidden mental

models. Or, even worse, are the consultants who are always forcing us to wade through a morass of complex, boring 

(and most likely completely wrong) spreadsheet models where the true structure remains hidden in the cells.

Simulation

Science insights have diffused slowly over the past fifty years from the natural sciences to the social sciences.

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A 2002 citation search compared 5,400 social science journals against the 100 natural science disciplines covered by

INSPEC (>4,000 journals) and Web of Science ( >5,700 journals) indexes. It shows keywords comput* and simulat* peak at

around 18,500 in natural science, whereas they peak at 250 in economics and around 125 in sociology. For the keyword

nonlinear citations peak at 18,000 in natural science, at roughly 180 in economics, and near 40 in sociology.

In

the words of the researchers who conducted this study, "How can it be that sciences founded on the mathematical linear

determinism of classical physics have moved more quickly toward the use of nonlinear computer models than economics and

sociology—where those doing the science are no different from social actors—who are Brownian Motion?"

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My answer: It takes time.

So, one of the key challenges I'm facing as I promulgate simulation science

insights outside the academic world, is . .

How do I make the CEO realize that the reliability and accuracy of

his decision-making can be improved by using simulation science?

I believe that this involves educating managers

that it is time to set aside their historical (and erroneous) preconceptions and prejudices of how to manage a business in

the 21st century.

Few executives today realize that the complexity of business is genuinely beyond them. And

that's not an easy sell.

Finally, humans can profoundly deepen their understanding of the world they live in .

. . thanks to computers and only with the help of computers. And, that’s an even harder sell.

Don't

agree? Pop down to your local antique store, buy an

href="http://www.ee.ryerson.ca:8080/~elf/abacus/history.html">abacus

and get back to work!

© Justin

Lyon, 2005– Reproduction permitted with reference to the author: Justin Lyon -

href="mailto:justin1028@yahoo.com">justin1028@yahoo.com