For example, imagine that an airport has a problem with long passenger queues through security. A simulation can be built to model all the standard process behaviours and constraints involved in the passenger journey through the airport; typical passenger volumes arriving at the airport, the number of flights, the number of check-in desks, the number of security scanners, available departure gates, and how many employees are on shift.
Then take into account that some people will arrive two hours before they need to, while others arrive 30 minutes ahead of time. A few might even arrive with only minutes to spare. Some passengers will want to shop, others will want to eat or grab a drink in the airport lounges. Someone will lose their passport. A large group of passengers will hold everything up as they try to organise themselves. Perhaps several staff fall ill on the same day, or maybe bad weather has caused a number of incoming flights to be severely delayed. Perhaps a scanner breaks down and there is an hour where it is out of action...
Planning for all of this by referring to averages based on past data is possible, but the margin of error will increase with every contingency. Drawing a flowchart will only get you so far. Digital simulation, however, has moved this planning capability on significantly, opening the door to account for numerous layers of complexities and ‘what if’ scenarios. By modelling all possible outcomes rather than experimenting in real life with different scenarios and then waiting weeks or months to see the outcomes, organisations can fast track their decision to best case scenario with solid, evidence-based data built on a digital replica of real life.
Simulation allows organisations to rapidly pinpoint and test valuable opportunities for improvement - without any of the risk, time and cost of testing in a real-world environment - and then hit the ground running with the implementation.
Through this technology, it is now possible to design and then optimise a new process before implementation of the real system. It is possible to assess the validity and return of capital investment before investing any time and money. For a system that is already in place, it is possible to identify bottlenecks and experiment with changes to resolve them.