The ability of companies to attain a high level of knowedlge sharing, in effect becoming knowledge generation networks, is well-known in the areas of auto manufacturing, specifically at Toyota (Dyer, Nobeoka, 2000). Simulations such as these show the potential to predict if and when a company will attain this level of process and knowledge sharing performance. Assessing the Pros and Cons of Simulations as Learning Platforms

The primary strength of constraint-based models is their ability to interpret many potentially conflicting constraints and still arrive at an optimal solution for logistics and transportation management initiatives and programs. This simulation also illustrates how the trade-offs inherent in the constraints have a major impact on the overall profitability of a company over time. Another aspect of this simulation showed how a bottleneck at any one point can cost tens of thousands or even millions in lost sales and inventory carrying costs. Finally, the simulation showed very clearly how the logistics and distribution dynamics in a company can either drastically contribute or detract from overall profitability and performance.

One of the greatest advantages of this approach to learning is the ability to continually review concepts by reviewing them and going...

The graphical nature of this simulation also made it possible to quickly capture complex concepts quickly and understand them. The simulation is also exceptionally good at showing how all these concepts related to supply chain optimization and logistics planning work in conjunction with each other.
Despite these advantages, there also several significant disadvantages including the lack of customization for the simulation, lack of flexibility in defining secondary and tertiary workflows and the lack of ability testing advanced concepts. The simulation could also benefit from more strategic groups of options so entire groupings or macros of constraints could be tested in conjunction with each other.