WHY SHOULD A FIRM SIMULATE POTENTIAL BUSINESS PROCESSES?

Simulation becomes valuable when there is variability of outcomes in the business
processing or when parameters and rules are changed. Under these conditions,
it is not easy to predict the outcomes, particularly when these factors change
over time. For example, the famous �beer distribution game� represents a simple
supply chain consisting of a retailer, wholesaler, and factory. The retailer orders
cases of beer from the wholesaler, which in turn orders beer from the factory.
There is a delay between placing an order and receiving the cases of beer. The
game demonstrates that a small perturbation in the number of cases of beer sold
by the retailer can cause large shifts in the quantity of cases stored and produced
by the wholesaler and factory, respectively. Such a system is subject to dynamic
complexity � like the majority of contemporary supply chains.
Of course, many operating systems are subject to both variability and dynamic
complexity. Indeed, the variability of one component interacts with the
variability of another to create dynamic complexity.The level of customer
service is simple to predict, since there is no variability
in the system and because there is no interaction between components.
The lack of interaction is a result of the service time being exactly the same
for each customer, which means that there is no queuing or blocking. The
Most activities, however, do not take exactly the same time every time.
Assume that the times given above are averages, so customers arrive on average
every five minutes and it takes on average five minutes to serve a customer.
What is the average time a customer spends in the queue waiting to be served?
This is not an easy question to answer, especially when subsequent steps are
also considered, as there is variability in both customer arrivals and service
times. Queues would develop between the steps and create consequential effects
on performance. Most people, when asked for a specific time, tend to underestimate
the likely queuing time. Of course, the actual queuing time depends
on many things even in the one-step system, such as:
Variability in arrivals
Variability in regular service time
Variability in service time that might be affected by type of customer
or time of day
Discrete event simulation provides the technique for evaluating such systems
effectively, modeling the process from the �bottom up,� starting at the
required level of detail. Changing the process inherent in the model provides
the ability to evaluate the effect of such a change. Operations that involve
multiple processes and how they interact can also be modeled, by effectively
linking multiple models together.
The benefits of simulation include:
Risk reduction
Greater understanding of process conditions and interactions1.