Simulation and Strategic Management

Guest article from Marco Ribeiro

Corporations everywhere today face the huge challenge of surviving and growing in an extremely competitive environment. Markets are shaped and reshaped due to constant innovation, customer demands and fierce competition. All these forces demand that corporations continuously reinvent themselves trying to maintain competitive advantages that differentiate them from the competition.

Strategic planning in such an environment is a difficult challenge that corporations must overcome successfully. Corporate strategic planning deals with such complex issues as:

    * Understanding the market and its future trends – understand suppliers, competition, their competitive advantages and market positioning. Know the future trends that will shape the market.
    * Future resource allocation – how the corporation’s resources should be organized in order to maintain an efficient operation.
    * Scope of operations -in which businesses should the corporation operate, which ones should be dropped out
    * Diversification of the corporation’s business – should the corporation focus its operations in a small and related set of businesses or should it look to diversify to heterogeneous businesses
    * Future structure of the company – draw the boundaries of the corporation and determine how these boundaries will affect relationships with suppliers and customers

The strategy defined will address all these issues in detail and determine the future direction of the corporation.

Can we use simulation to support the strategic planning process?
Yes, we can. As Thomas Davenport and Jeanne Harris describe in their book: Competing on Analytics: The new Science of Winning, we will see an increasing demand and use of analytical technologies supporting corporation’s decision-making processes.

Simulation can play an important role by helping managers create models of their markets and processes and “toy” with them in order to get a deeper understanding. We can also use simulation to support such efforts as portfolio analysis and management, helping managers determine how to most effectively manage and configure their product life cycle. We can build models of processes and determine the most efficient configuration. Simulation is a valuable tool to test scenarios and make better business decisions.

Marco Ribeiro
LinkedIn Profile

Industrial Engineers are Happy

I just saw an interesting article written by the Institute of Industrial Engineers (IIE) citing a National Opinion Research Center study at the University of Chicago. According to that study, Industrial Engineering is one of the top ten occupations when rated by job satisfaction and overall happiness. I have long been an IE evangelist, because I feel it is a great career choice, but it never hurts to have some additional evidence.

The study goes on to evaluate compensation for each of those professions and concludes that IE’s are the third highest paid group out of those top ten happiest careers. While I think it is a mistake to choose a career primarily based on financial compensation, it is a nice bonus when a career that makes you happy also pays well.

Here is a short article summarizing the results: Industrial engineering for your mental health?

I’ve always felt that IE was one of the best career choices possible. And I think this is especially true in the field of simulation.

I personally try to visit a few high school classes each year to help students discover our profession and help motivate them to excel in the classes that they need to be successful in engineering. IIE can help you do this.

I urge you to also get involved with your local high schools and help spread the word.

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.

BusinessDictionary.com 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 membrane.com:
“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