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490 CHAPTER 12 SIMULATION

Simulation is probably one of the most widely used quantitative approaches to
business decision making. Simulation is typically applied in decision-making situa-
tions where it may not be possible or desirable to search for an optimum solution.
Instead, a simulation model provides the decision maker with the opportunity to
experiment with certain parts of a decision problem and analyze the likely conse-
quences of alternative decisions. Consider the following situation. You are the
manager of a local supermarket. Customers have recently been complaining that
they have had to wait too long at the checkouts for service and you are concerned
that this will affect customer satisfaction and loyalty and future sales and profit-
ability. So, you have decided to try to identify the appropriate number of checkouts
to open at specific times of the day to prevent large queues forming. Clearly, given
the appropriate information you could try to develop an appropriate queuing model
(Chapter 11). However, as manager you have concerns that simply using an average
for the service rate and an average for the arrival rate is too simplistic for the
situation you face. You know that service rates and arrival rates are highly variable
and to some extent unpredictable and that you need to take this complexity into
account in terms of trying to decide how many checkouts to open. With simulation,
we build an appropriate mathematical model to represent the situation and specif-
ically incorporate the variability and uncertainty we face. The model then allows us
to assess what ifs. What if we add an extra checkout? How will this affect queuing
times? What about two extra checkouts? What if service times at the checkout are
slower than usual? What if we have more arrivals than usual? The what-if capability
of simulation modelling is one of the features that has made it so popular and
successful.
Simulation is commonly applied in a variety of business situations with the
following examples being typical.

New product development An organization is trying to decide whether to develop
a new product or service and typically may simulate the probability that the new
product/service will be profitable or successful. Typical aspects of the problem that
will be uncertain include: levels of demand for the new product/service; precise
production costs; competitor response. A simulation model allows for different
levels of these variables to be factored into the model.
Reservation systems Airlines are faced with a typical situation in their passenger
reservation systems. They know that a particular flight has a given passenger
capacity. They also know that typically not all passengers booked on to a flight will
show up. So, they have the opportunity to overbook a flight – selling more tickets
than they have seats. The problem they face is: how many extra tickets to sell given
that the number of no-show passengers is uncertain? Too many and they run the risk
of an overbooked flight. Too few and they lose potential revenue. A simulation
model allows the airline to try out different overbooking strategies to assess the
likely consequences. Clearly, the same situation faces any organization that has a
reservation system: hotels; car rental companies; service and repair companies;
healthcare providers.

Inventory systems As we saw in Chapter 10, having the right inventory strategy in
place is critical both for customer service and for cost control. Simulation allows the
decision maker to assess the effects of changing elements of the existing inventory
system on costs and service levels.

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